OXIESEC PANEL
- Current Dir:
/
/
var
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www
/
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/
inc
/
simplepie
/
library
/
SimplePie
/
Cache
Server IP: 139.59.38.164
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Name
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-
07/27/2024 04:04:53 PM
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00075c93132acf7a6e46e48d2291ce41.spc
5.69 KB
08/08/2022 06:41:41 AM
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0102169e52b6a27a410e7b237202fe84.spc
140.81 KB
06/20/2024 08:52:22 AM
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027d4dde1e82475da3d9afe4844afb1d.spc
2.63 KB
08/04/2022 02:47:12 PM
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03036edfece701eaa1537fea4014dd44.spc
56.35 KB
08/28/2024 10:14:14 AM
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0446f65691fba260d3eabbd1377240f8.spc
5.75 KB
08/03/2021 02:55:43 AM
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04d0c6cc2bf146b1318b78f84416b912.spc
124.45 KB
06/20/2024 08:52:14 AM
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0582678c8cfff117f770f9368b70c2b5.spc
19.33 KB
10/06/2021 12:58:29 AM
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0601d608f5e2ea8e198130b17fe6ef01.spc
157 bytes
04/20/2023 03:33:59 PM
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061ad7f2b0116c570fdc35c36824c7c6.spc
42.24 KB
05/14/2024 04:53:51 AM
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06e0c598a46c483b6b9d775e1ba1ecd4.spc
124.09 KB
07/03/2024 11:17:48 AM
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0802b12194f292de0e9d9617ac014785.spc
290.02 KB
07/09/2022 04:43:03 PM
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083aed319a0b5c8691e31d9150d8005e.spc
19.84 KB
04/02/2024 02:41:54 AM
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0a3bf48c84477cd58dbc2036a0331134.spc
70.63 KB
03/03/2023 03:29:10 AM
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0b5e5f226006af7e46d02ba8ce216a45.spc
54.71 KB
06/20/2024 08:52:24 AM
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0b73d04c6bba0acaf2f9a569f388313a.spc
33.59 KB
07/12/2023 02:13:33 PM
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0b8a46fca237497cfc90498f9eb909ab.spc
686.66 KB
02/14/2024 01:18:37 AM
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0ce2bdd7061489c6136e7614d421b874.spc
47.7 KB
03/23/2023 06:13:09 AM
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0de8a2204854bb5dd311607494c671e4.spc
828.58 KB
04/27/2022 03:38:58 AM
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0e15494dca4aeb24ea769582482c5162.spc
150.58 KB
04/20/2023 03:33:43 PM
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0eaec40cfb584fcb55fcdfb5d76684b9.spc
16.95 KB
03/30/2023 03:18:33 AM
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0ed254d4d9db6e3afe193b00bc6471bb.spc
89.85 KB
05/21/2024 04:51:10 AM
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0f079d9bb09fef940c38ee73b52b91d4.spc
34.42 KB
10/05/2021 09:26:01 AM
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0f5e21d9d8354d10ea23d99101259ba2.spc
42.06 KB
07/17/2024 02:56:55 AM
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0ffc1fa29a6bad7fb49e55940c374610.spc
75.61 KB
05/23/2024 12:21:08 PM
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1071b4a15b6c2fe6f7a96f194d0ba524.spc
196 bytes
08/28/2024 10:14:23 AM
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10ae571a6266a8e21b0fbb15f552a1cb.spc
13.15 KB
07/17/2024 02:56:58 AM
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118c129ff99a905e4e9325e388b841fe.spc
45.34 KB
10/06/2021 01:12:01 AM
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131a4ad07dda46888cbbc1cb4c710a91.spc
59.6 KB
06/20/2024 08:52:24 AM
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100.76 KB
10/06/2021 12:25:04 AM
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142d8795402a4e8a520be8ebea6f54f3.spc
22.7 KB
06/20/2024 08:52:34 AM
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1469d584e9747d132077c9df3cda6c97.spc
121.15 KB
07/17/2024 02:56:55 AM
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95.45 KB
06/20/2024 08:52:43 AM
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26.74 KB
10/13/2021 06:46:54 AM
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19f3a21c36072f501f634db8e658bc9f.spc
16.6 KB
05/02/2024 07:13:38 AM
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1b8954ae7aab6fd9784cbcc827133f80.spc
186 bytes
08/12/2021 10:27:02 AM
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1c0bbac8beea30e555f26fd02994e7a5.spc
19.96 KB
04/02/2024 02:41:54 AM
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1c1a63fc25720b7c22c9c28fa2aa9379.spc
236.54 KB
08/12/2021 10:27:08 AM
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1f1672e0ecc5e7a6d278c930015520ab.spc
166 bytes
03/23/2023 06:13:09 AM
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1f4cf3ae9ba91935f556711c1cfc34d4.spc
88.33 KB
06/20/2024 08:52:38 AM
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1f5e96e3f1a01f95ab611ec1458fe470.spc
169.16 KB
07/17/2024 02:57:03 AM
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20a75b688975a2d5d342eae9f4c33411.spc
1.22 MB
04/27/2022 03:32:10 AM
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225d97aca36305a8b407ea6d8d5b187e.spc
55.08 KB
06/20/2024 08:52:44 AM
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242d3dabf79d13154fcc384ff8b2d25e.spc
113.19 KB
07/17/2024 02:56:54 AM
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25512b0d18ae6e4d20d027abbc467365.spc
31.2 KB
06/19/2021 12:29:12 PM
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25948504a82cd8da1985fddd4500c1c7.spc
153.7 KB
04/27/2022 03:38:55 AM
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26e0c631724f3653c10c3123546ab5e2.spc
110.09 KB
02/21/2022 03:01:10 PM
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2704664dff0e40e19de087fe00892bc2.spc
24.51 KB
08/12/2021 10:27:02 AM
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274ae07ff50cfde2bda57a71703b62f4.spc
2.54 KB
05/04/2024 06:41:03 AM
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2799184659106c88b5072a3e3f763a4d.spc
2.54 KB
03/12/2024 05:50:14 AM
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154.53 KB
11/05/2021 11:40:13 AM
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28099e26c5c9a06acb85a41ccd789efc.spc
500.36 KB
07/03/2024 11:17:48 AM
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2aabe0323264e3f60916621039be0e76.spc
42.37 KB
02/18/2022 06:14:46 AM
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2abcd685295b4a261ad2e866188e5e11.spc
125.3 KB
06/20/2024 08:52:21 AM
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151 bytes
01/05/2023 02:13:14 PM
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2b2654a64e8b0f5d9cf497e0883b2042.spc
96.1 KB
03/28/2023 10:36:23 AM
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87.25 KB
11/05/2021 11:40:07 AM
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2db16a36af8daf383cb739dd57a44d90.spc
147.19 KB
07/17/2024 02:57:01 AM
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2de250597c053bd81359233c14c51db4.spc
286.38 KB
06/06/2021 03:15:58 PM
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2fb670ecdcda7db936aa7d2f018a79e4.spc
23.75 KB
06/20/2024 08:52:22 AM
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30d5af6cd4c10ea02520bcaba31f3d1c.spc
141.02 KB
07/26/2024 07:37:50 AM
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31591159e55bceb27be71ce43cd1517e.spc
443.64 KB
03/16/2022 05:35:22 PM
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31f817c15425941589a9819216265501.spc
68.33 KB
08/28/2024 10:14:14 AM
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20.99 KB
08/28/2024 10:14:17 AM
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3567037b5acd1842946ba40397edead4.spc
84.5 KB
06/20/2024 08:52:39 AM
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181 bytes
05/21/2024 04:14:40 PM
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389ae768f4ecb350b56b92da3b04c1ac.spc
180.5 KB
08/28/2024 10:14:21 AM
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142.35 KB
09/23/2022 10:34:29 AM
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3ca755a78dd04c91695e5fcee845991f.spc
42.02 KB
04/05/2023 07:09:34 AM
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46.18 KB
09/09/2024 03:09:14 PM
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614.85 KB
08/13/2024 02:27:32 PM
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3e804b49f84699d48348b3bee312090d.spc
25.24 KB
03/23/2023 06:57:07 AM
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407.55 KB
08/01/2023 07:53:29 AM
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307.02 KB
08/03/2021 02:55:41 AM
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19.87 KB
04/17/2023 02:08:43 PM
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163 bytes
11/21/2023 07:57:07 AM
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39.14 KB
05/23/2024 12:21:14 PM
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3.18 KB
10/05/2021 09:26:02 AM
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103.66 KB
08/24/2022 06:38:59 AM
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67.86 KB
08/28/2024 10:14:14 AM
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07/11/2024 09:08:36 AM
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155 bytes
06/18/2024 08:58:50 AM
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22.31 KB
11/05/2021 11:40:08 AM
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88.77 KB
08/04/2022 02:46:49 PM
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31.56 KB
04/20/2023 03:34:22 PM
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06/18/2024 08:58:27 AM
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134 bytes
04/20/2023 03:34:27 PM
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02/26/2023 07:17:42 AM
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06/21/2022 09:28:32 AM
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This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals.Methods:An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values.Results:Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones.Conclusions:These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1671:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. 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A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones.Conclusions:These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241252819";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-17T12:37:44Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:13:{i:0;a:5:{s:4:"data";s:28:"Muhammad Rafaqat Ali Qureshi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:20:"Stephen Charles Bain";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Stephen Luzio";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Consuelo Handy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:16:"Daniel J. 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";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241250355?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:87:"Day-to-Day Glycemic Variability Using Continuous Glucose Monitors in Endurance Athletes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241250355?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1851:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objectives:The application of continuous glucose monitors (CGMs) to measure interstitial glucose in athletic populations is limited by the lack of accepted athlete-specific reference values. The aim of this study was to develop athlete-specific reference ranges for glycemic variability under standardized diet and exercise conditions.Methods:A total of 12 elite racewalkers (n = 7 men, 22.4 ยฑ 3.5 years, VO2max 61.6 ยฑ 7.3 mL kgโ1 minโ1) completed two 4-d trials separated by 4-d. Athletes were provided a high-energy, high-carbohydrate diet (225 ยฑ 1.6 kJ kgโ1 dayโ1, 8.4 ยฑ 0.3 g kgโ1 dayโ1 carbohydrate) and completed standardized daily exercise. The timing of food consumed and exercise undertaken were matched each day across the 4-d trials. Interstitial glucose data were collected via Freestyle Libre 2 CGMs. Glycemic variability was calculated as the mean amplitude of glycemic excursions (MAGEs), mean of daily differences (MODD), and standard deviation (SD).Results:Twenty-four hour MODD, MAGE, and SD for interstitial glucose were 12.6 ยฑ 1.8 mg/dL (0.7 ยฑ 0.1 mmol/L), 36.0 ยฑ 5.4 mg/dL (2.0 ยฑ 0.3 mmol/L), and 16.2 ยฑ 1.8 mg/dL (0.9 ยฑ 0.1 mmol/L), respectively. Twenty-four hour mean glucose (MG; 102.6 ยฑ 5.4 mg/dL [5.7 ยฑ 0.3 mmol/L]) was higher than overnight (91.8 ยฑ 5.4 mg/dL [5.1 ยฑ 0.3 mmol/L]; P < .0001) and was lower in women than men (99.0 ยฑ 3.6 mg/dL [5.5 ยฑ 0.2 mmol/L] vs 104.4 ยฑ 3.6 mg/dL [5.8 ยฑ 0.2 mmol/L]; P = .059, d = 1.4).Conclusions:This study provides reference indices under standardized diet and exercise conditions for glycemic variability derived from CGMs in endurance athletes which are similar than previously reported for healthy individuals, despite strenuous daily training and a high daily energy and carbohydrate diet.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1854:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objectives:The application of continuous glucose monitors (CGMs) to measure interstitial glucose in athletic populations is limited by the lack of accepted athlete-specific reference values. The aim of this study was to develop athlete-specific reference ranges for glycemic variability under standardized diet and exercise conditions.Methods:A total of 12 elite racewalkers (n = 7 men, 22.4 ยฑ 3.5 years, VO2max 61.6 ยฑ 7.3 mL kgโ1 minโ1) completed two 4-d trials separated by 4-d. Athletes were provided a high-energy, high-carbohydrate diet (225 ยฑ 1.6 kJ kgโ1 dayโ1, 8.4 ยฑ 0.3 g kgโ1 dayโ1 carbohydrate) and completed standardized daily exercise. The timing of food consumed and exercise undertaken were matched each day across the 4-d trials. Interstitial glucose data were collected via Freestyle Libre 2 CGMs. Glycemic variability was calculated as the mean amplitude of glycemic excursions (MAGEs), mean of daily differences (MODD), and standard deviation (SD).Results:Twenty-four hour MODD, MAGE, and SD for interstitial glucose were 12.6 ยฑ 1.8 mg/dL (0.7 ยฑ 0.1 mmol/L), 36.0 ยฑ 5.4 mg/dL (2.0 ยฑ 0.3 mmol/L), and 16.2 ยฑ 1.8 mg/dL (0.9 ยฑ 0.1 mmol/L), respectively. Twenty-four hour mean glucose (MG; 102.6 ยฑ 5.4 mg/dL [5.7 ยฑ 0.3 mmol/L]) was higher than overnight (91.8 ยฑ 5.4 mg/dL [5.1 ยฑ 0.3 mmol/L]; P < .0001) and was lower in women than men (99.0 ยฑ 3.6 mg/dL [5.5 ยฑ 0.2 mmol/L] vs 104.4 ยฑ 3.6 mg/dL [5.8 ยฑ 0.2 mmol/L]; P = .059, d = 1.4).Conclusions:This study provides reference indices under standardized diet and exercise conditions for glycemic variability derived from CGMs in endurance athletes which are similar than previously reported for healthy individuals, despite strenuous daily training and a high daily energy and carbohydrate diet.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:87:"Day-to-Day Glycemic Variability Using Continuous Glucose Monitors in Endurance Athletes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241250355";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-10T08:53:39Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:17:"Amy-Lee M. 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Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1871:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>The older population is increasing worldwide and up to 30% of older adults have diabetes. Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Glucose Targets Using Continuous Glucose Monitoring Metrics in Older Adults With Diabetes: Are We There Yet?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247568";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-08T04:49:53Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:12:"Elena Toschi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"David OโNeal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Medha Munshi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Alicia Jenkins";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:108:"Glucose Targets Using Continuous Glucose Monitoring Metrics in Older Adults With Diabetes: Are We There Yet?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247568";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247568?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:5;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247219?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247219?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1738:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures.Method:Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration โฅ1 year; โฅ50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively.Results:In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia.Conclusions:In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1738:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The glycemia risk index (GRI) is a composite metric developed and used to estimate quality of glycemia in adults with diabetes who use continuous glucose monitor (CGM) devices. In a cohort of youth with type 1 diabetes (T1D), we examined the utility of the GRI for evaluating quality of glycemia between clinic visits by analyzing correlations between the GRI and longitudinal glycated hemoglobin A1c (HbA1c) measures.Method:Using electronic health records and CGM data, we conducted a retrospective cohort study to analyze the relationship between the GRI and longitudinal HbA1c measures in youth (T1D duration โฅ1 year; โฅ50% CGM wear time) receiving care from a Midwest pediatric diabetes clinic network (March 2016 to May 2022). Furthermore, we analyzed correlations between HbA1c and the GRI high and low components, which reflect time spent with high/very high and low/very low glucose, respectively.Results:In this cohort of 719 youth (aged = 2.5-18.0 years [median = 13.4; interquartile range [IQR] = 5.2]; 50.5% male; 83.7% non-Hispanic White; 68.0% commercial insurance), baseline GRI scores positively correlated with HbA1c measures at baseline and 3, 6, 9, and 12 months later (r = 0.68, 0.65, 0.60, 0.57, and 0.52, respectively). At all time points, strong positive correlations existed between HbA1c and time spent in hyperglycemia. Substantially weaker, negative correlations existed between HbA1c and time spent in hypoglycemia.Conclusions:In youth with T1D, the GRI may be useful for evaluating quality of glycemia between scheduled clinic visits. Additional CGM-derived metrics are needed to quantify risk for hypoglycemia in this population.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247219";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-08T04:48:14Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:13:"Kelsey Panfil";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Jacob M. Redel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:21:"Craig A. Vandervelden";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Brent Lockee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:16:"Anna R. Kahkoska";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"Erin M. Tallon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:17:"David D. Williams";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"Mark A. Clements";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:125:"Correlation Between the Glycemia Risk Index and Longitudinal Hemoglobin A1c in Children and Young Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247219";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247219?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:6;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241249970?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:179:"Infrared Thermography Shows That a Temperature Difference of 2.2ยฐC (4ยฐF) or Greater Between Corresponding Sites of Neuropathic Feet Does Not Always Lead to a Diabetic Foot Ulcer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241249970?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1758:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:There is emerging interest in the application of foot temperature monitoring as means of diabetic foot ulcer (DFU) prevention. However, the variability in temperature readings of neuropathic feet remains unknown. The aim of this study was to analyze the long-term consistency of foot thermograms of diabetic feet at the risk of DFU.Methods:A post-hoc analysis of thermal images of 15 participants who remained ulcer-free during a 12-month follow-up were unblinded at the end of the trial. Skin foot temperatures of 12 plantar, 15 dorsal, 3 lateral, and 3 medial regions of interests (ROIs) were derived on monthly thermograms. The temperature differences (โTs) of corresponding ROIs of both feet were calculated.Results:Over the 12-month study period, out of the total 2026 plantar data points, 20.3% ROIs were rated as abnormal (absolute โT โฅ 2.2ยฐC). There was a significant between-visit variability in the proportion of plantar ROIs with โT โฅ 2.2ยฐC (range 7.6%-30.8%, chi-square test, P = .001). The proportion of patients presenting with hotspots (ROIs with โT โฅ 2.2ยฐC), abnormal plantar foot temperature (mean โT of 12 plantar ROIs โฅ 2.2ยฐC), and abnormal whole foot temperature (mean โT of 33 ROIs โฅ 2.2ยฐC) varied between visits and showed no pattern (P > .05 for all comparisons). This variability was not related to the season of assessment.Conclusions:Despite the high rate of hotspots on monthly thermograms, all feet remained intact. This study underscores a significant between-visit inconsistency in thermal images of neuropathic feet which should be considered when planning DFU-prevention programs for self-testing and behavior modification.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1761:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:There is emerging interest in the application of foot temperature monitoring as means of diabetic foot ulcer (DFU) prevention. However, the variability in temperature readings of neuropathic feet remains unknown. The aim of this study was to analyze the long-term consistency of foot thermograms of diabetic feet at the risk of DFU.Methods:A post-hoc analysis of thermal images of 15 participants who remained ulcer-free during a 12-month follow-up were unblinded at the end of the trial. Skin foot temperatures of 12 plantar, 15 dorsal, 3 lateral, and 3 medial regions of interests (ROIs) were derived on monthly thermograms. The temperature differences (โTs) of corresponding ROIs of both feet were calculated.Results:Over the 12-month study period, out of the total 2026 plantar data points, 20.3% ROIs were rated as abnormal (absolute โT โฅ 2.2ยฐC). There was a significant between-visit variability in the proportion of plantar ROIs with โT โฅ 2.2ยฐC (range 7.6%-30.8%, chi-square test, P = .001). The proportion of patients presenting with hotspots (ROIs with โT โฅ 2.2ยฐC), abnormal plantar foot temperature (mean โT of 12 plantar ROIs โฅ 2.2ยฐC), and abnormal whole foot temperature (mean โT of 33 ROIs โฅ 2.2ยฐC) varied between visits and showed no pattern (P > .05 for all comparisons). This variability was not related to the season of assessment.Conclusions:Despite the high rate of hotspots on monthly thermograms, all feet remained intact. This study underscores a significant between-visit inconsistency in thermal images of neuropathic feet which should be considered when planning DFU-prevention programs for self-testing and behavior modification.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:179:"Infrared Thermography Shows That a Temperature Difference of 2.2ยฐC (4ยฐF) or Greater Between Corresponding Sites of Neuropathic Feet Does Not Always Lead to a Diabetic Foot Ulcer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241249970";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-06T08:38:34Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:12:"Huiling Liew";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:10:"Wegin Tang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Peter Plassmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Graham Machin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Robert Simpson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:18:"Michael E. Edmonds";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:15:"Nina L. 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Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youthsโ engagement in habits associated with glycemic outcomes.Method:We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ยฑseven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual.Results:The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia.Conclusions:In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1690:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The Glycemia Risk Index (GRI) was developed in adults with diabetes and is a validated metric of quality of glycemia. Little is known about the relationship between GRI and type 1 diabetes (T1D) self-management habits, a validated assessment of youthsโ engagement in habits associated with glycemic outcomes.Method:We retrospectively examined the relationship between GRI and T1D self-management habits in youth with T1D who received care from a Midwest pediatric diabetes clinic network. The GRI was calculated using seven days of continuous glucose monitor (CGM) data, and T1D self-management habits were assessed ยฑseven days from the GRI score. A mixed-effects Poisson regression model was used to evaluate the total number of habits youth engaged in with GRI, glycated hemoglobin A1c (HbA1c), age, race, ethnicity, and insurance type as fixed effects and participant ID as a random effect to account for multiple clinic visits per individual.Results:The cohort included 1182 youth aged 2.5 to 18.0 years (mean = 13.8, SD = 3.5) comprising 50.8% male, 84.6% non-Hispanic White, and 64.8% commercial insurance users across a total of 6029 clinic visits. Glycemia Risk Index scores decreased as total number of habits performed increased, suggesting youth who performed more self-management habits achieved a higher quality of glycemia.Conclusions:In youth using CGMs, GRI may serve as an easily obtainable metric to help identify youth with above target glycemia, and engagement/disengagement in the T1D self-management habits may inform clinicians with suitable interventions for improving glycemic outcomes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"The Glycemia Risk Index Predicts Performance of Diabetes Self-Management Habits in Youth With Type 1 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247215";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-06T08:36:26Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:13:"Kelsey Panfil";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:21:"Craig A. Vandervelden";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Brent Lockee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Erin M. Tallon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"David D. Williams";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Joyce M. 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Integrated CGM (iCGM) FDA-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing). Studies have assessed accuracy and safety data of CGMs that were eventually cleared for iCGM by the FDA and that informed the recommendation for their nonadjunctive use. Subsequent robust clinical trials and real-world studies demonstrated clinical effectiveness with improvements in a range of patient outcomes. In recent years, a number of non-iCGM-approved CGM devices have entered the market outside the United States worldwide. Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive. Moreover, in many non-iCGM-approved CGM devices, accuracy studies published in peer-reviewed journals are scarce or have many limitations. Consequently, non-iCGM-approved CGM devices cannot be automatically perceived as having the same performance or quality standards than those approved for iCGM by the FDA. As a result, although these devices tend to cost less than iCGMs that carry FDA clearance and could therefore be attractive from the point of view of a health care payer, it must be emphasized that evaluation of costs should not be limited to the device (such as the usability preference that patients have for nonadjunctive sensors compared to adjunctive sensors) but to the wider value of the total benefit that the product provides to the patient.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1798:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Continuous glucose monitoring (CGM) has been shown to improve glycemic control and self-monitoring, as well as to reduce the risk of hypoglycemia. Integrated CGM (iCGM) FDA-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing). Studies have assessed accuracy and safety data of CGMs that were eventually cleared for iCGM by the FDA and that informed the recommendation for their nonadjunctive use. Subsequent robust clinical trials and real-world studies demonstrated clinical effectiveness with improvements in a range of patient outcomes. In recent years, a number of non-iCGM-approved CGM devices have entered the market outside the United States worldwide. Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive. Moreover, in many non-iCGM-approved CGM devices, accuracy studies published in peer-reviewed journals are scarce or have many limitations. Consequently, non-iCGM-approved CGM devices cannot be automatically perceived as having the same performance or quality standards than those approved for iCGM by the FDA. As a result, although these devices tend to cost less than iCGMs that carry FDA clearance and could therefore be attractive from the point of view of a health care payer, it must be emphasized that evaluation of costs should not be limited to the device (such as the usability preference that patients have for nonadjunctive sensors compared to adjunctive sensors) but to the wider value of the total benefit that the product provides to the patient.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241250357";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-05-03T10:07:28Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:16:"David C. Klonoff";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Monica Gabbay";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Sun Joon Moon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Emma G. Wilmot";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:108:"Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241250357";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241250357?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:9;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241248402?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241248402?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2138:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Automated insulin delivery (AID) systems, permit improved treatment of type 1 diabetes (T1D). Unfortunately, malfunctioning in the insulin pump or in the infusion set can prevent insulin from being administered, reducing the AID efficacy and posing the patient at risk. Different data-driven methods available in the literature can be used to deal with the problem of automatically detecting complete insulin suspension in real-time. This article investigates both supervised and unsupervised strategies and proposes a fair comparison under either population or personalized settings.Methods:Several algorithms are compared using data generated through the UVA/Padova T1D simulator, a computer simulator widely used to test control strategies in silico and accepted by the Food and Drugs Administration (FDA) as a substitute to animal pre-clinical trials. Two synthetic data sets, each consisting of 100 virtual subjects monitored for 1 month, were generated. Occasional faults of the insulin pump are simulated as complete occlusions by suspending the therapy administration. Personalized algorithms are investigated with unsupervised approaches only, since personalized labels are hardly available.Results:In the population scenario, the supervised approach outperforms the unsupervised strategy. In particular, logistic regression and random forest achieves a recall of 72% and 82%, with 0.12 and 0.21 false positives (FP) per day, respectively. In the personalized setting scenario, the unsupervised algorithms are tailored on each patient and outperform the population ones, in particular isolation forest achieves a recall 80% and 0.06 FPs per day.Conclusions:This article suggests that unsupervised personalized approach, by addressing the large variability in glucose response among individuals with T1D, is superior to other one-fits-all approaches in detecting insulin suspensions caused by malfunctioning. Population methodologies can be effectively used while waiting to collect sufficient patient data, when the system is installed on a new patient.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2138:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Automated insulin delivery (AID) systems, permit improved treatment of type 1 diabetes (T1D). Unfortunately, malfunctioning in the insulin pump or in the infusion set can prevent insulin from being administered, reducing the AID efficacy and posing the patient at risk. Different data-driven methods available in the literature can be used to deal with the problem of automatically detecting complete insulin suspension in real-time. This article investigates both supervised and unsupervised strategies and proposes a fair comparison under either population or personalized settings.Methods:Several algorithms are compared using data generated through the UVA/Padova T1D simulator, a computer simulator widely used to test control strategies in silico and accepted by the Food and Drugs Administration (FDA) as a substitute to animal pre-clinical trials. Two synthetic data sets, each consisting of 100 virtual subjects monitored for 1 month, were generated. Occasional faults of the insulin pump are simulated as complete occlusions by suspending the therapy administration. Personalized algorithms are investigated with unsupervised approaches only, since personalized labels are hardly available.Results:In the population scenario, the supervised approach outperforms the unsupervised strategy. In particular, logistic regression and random forest achieves a recall of 72% and 82%, with 0.12 and 0.21 false positives (FP) per day, respectively. In the personalized setting scenario, the unsupervised algorithms are tailored on each patient and outperform the population ones, in particular isolation forest achieves a recall 80% and 0.06 FPs per day.Conclusions:This article suggests that unsupervised personalized approach, by addressing the large variability in glucose response among individuals with T1D, is superior to other one-fits-all approaches in detecting insulin suspensions caused by malfunctioning. Population methodologies can be effectively used while waiting to collect sufficient patient data, when the system is installed on a new patient.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241248402";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-29T11:51:01Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:9:"Elena Idi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Andrea Facchinetti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Giovanni Sparacino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Simone Del Favero";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:121:"Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241248402";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241248402?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:10;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241248606?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:166:"Accuracy and Feasibility of Using a Smartphone Application for Carbohydrate Counting Versus Traditional Carbohydrate Counting for Adults With Insulin-Treated Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241248606?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1937:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Patients with insulin-treated diabetes struggle with performing accurate carbohydrate counting for proper blood glucose control. Little is known about the comparative accuracy and feasibility of carbohydrate counting methods.Purpose:The purpose of this study was to determine whether carbohydrate counting using a smartphone application is more accurate and feasible than a traditional method.Theoretical/conceptual framework:Based on a conceptual model derived from the Technology Acceptance Model, feasibility was defined as usefulness, ease of use, and behavioral intention to use each method.Methods:A standardized meal was presented to 20 adults with insulin-treated diabetes who counted carbohydrates using traditional and smartphone methods. Accuracy was measured by comparing carbohydrate counting estimates with the standardized meal values. Perceived feasibility (usefulness, ease of use, behavioral intention) was measured using rating forms derived from the Technology Acceptance Model.Results:The number of training and estimation minutes were significantly higher for the traditional method than the smartphone method (Z = โ3.83, P < .05; Z = โ2.30, P < .05). The traditional method took an additional 1.4 minutes for estimation and 12.5 minutes for training. There were no significant differences in accuracy between traditional and smartphone methods for carbohydrate counting (Wilcoxon signed-rank test, Z = โ1.10, P = .28). There were no significant differences between traditional and smartphone methods for feasibility (usefulness, Z = โ.10, P = .95; ease of use, Z = โ.36, P = .72; or behavioral intention, Z = โ.94, P = .35).Conclusion:While both traditional and smartphone methods were found to be similar in terms of accuracy and feasibility, the smartphone method took less time for training and for carbohydrate estimation.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1943:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Patients with insulin-treated diabetes struggle with performing accurate carbohydrate counting for proper blood glucose control. Little is known about the comparative accuracy and feasibility of carbohydrate counting methods.Purpose:The purpose of this study was to determine whether carbohydrate counting using a smartphone application is more accurate and feasible than a traditional method.Theoretical/conceptual framework:Based on a conceptual model derived from the Technology Acceptance Model, feasibility was defined as usefulness, ease of use, and behavioral intention to use each method.Methods:A standardized meal was presented to 20 adults with insulin-treated diabetes who counted carbohydrates using traditional and smartphone methods. Accuracy was measured by comparing carbohydrate counting estimates with the standardized meal values. Perceived feasibility (usefulness, ease of use, behavioral intention) was measured using rating forms derived from the Technology Acceptance Model.Results:The number of training and estimation minutes were significantly higher for the traditional method than the smartphone method (Z = โ3.83, P < .05; Z = โ2.30, P < .05). The traditional method took an additional 1.4 minutes for estimation and 12.5 minutes for training. There were no significant differences in accuracy between traditional and smartphone methods for carbohydrate counting (Wilcoxon signed-rank test, Z = โ1.10, P = .28). There were no significant differences between traditional and smartphone methods for feasibility (usefulness, Z = โ.10, P = .95; ease of use, Z = โ.36, P = .72; or behavioral intention, Z = โ.94, P = .35).Conclusion:While both traditional and smartphone methods were found to be similar in terms of accuracy and feasibility, the smartphone method took less time for training and for carbohydrate estimation.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:166:"Accuracy and Feasibility of Using a Smartphone Application for Carbohydrate Counting Versus Traditional Carbohydrate Counting for Adults With Insulin-Treated Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241248606";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-29T10:18:40Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:15:"Mohammad Shehab";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Robert M. Cohen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Bonnie Brehm";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Tamilyn Bakas";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:166:"Accuracy and Feasibility of Using a Smartphone Application for Carbohydrate Counting Versus Traditional Carbohydrate Counting for Adults With Insulin-Treated Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241248606";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241248606?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:11;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247559?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:194:"A Nutrition-Focused Approach During Continuous Glucose Monitoring Initiation in People With Type 2 Diabetes: Using a Theoretical Framework to Unite Continuous Glucose Monitoring and Food Choices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247559?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:945:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Continuous glucose monitoring (CGM) has transformed diabetes care, yet opportunities for further innovations still exist. Some research suggests CGM could be an ideal tool to guide food choices and other healthy lifestyle behaviors, especially for people with type 2 diabetes (T2D). Behavior change theories can be used to understand and describe how CGM users make food-related decisions, which could ultimately lead to the design of more tailored and effective interventions. In this commentary, we describe what it looks like to use the behavior change wheelโa theory-based intervention development frameworkโto design an intervention for people with T2D who will use CGM data to guide food choices aligned with evidence-based nutrition recommendations. Such frameworks may be beneficial when designing or evaluating future technology-focused behavior change interventions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:945:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Continuous glucose monitoring (CGM) has transformed diabetes care, yet opportunities for further innovations still exist. Some research suggests CGM could be an ideal tool to guide food choices and other healthy lifestyle behaviors, especially for people with type 2 diabetes (T2D). Behavior change theories can be used to understand and describe how CGM users make food-related decisions, which could ultimately lead to the design of more tailored and effective interventions. In this commentary, we describe what it looks like to use the behavior change wheelโa theory-based intervention development frameworkโto design an intervention for people with T2D who will use CGM data to guide food choices aligned with evidence-based nutrition recommendations. Such frameworks may be beneficial when designing or evaluating future technology-focused behavior change interventions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:194:"A Nutrition-Focused Approach During Continuous Glucose Monitoring Initiation in People With Type 2 Diabetes: Using a Theoretical Framework to Unite Continuous Glucose Monitoring and Food Choices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247559";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-26T11:35:57Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:15:"Holly J. Willis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Elizabeth Johnson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Meghan JaKa";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:194:"A Nutrition-Focused Approach During Continuous Glucose Monitoring Initiation in People With Type 2 Diabetes: Using a Theoretical Framework to Unite Continuous Glucose Monitoring and Food Choices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247559";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247559?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:12;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247530?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"Development of a Real-time Force-based Algorithm for Infusion Failure Detection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247530?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1850:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous subcutaneous insulin infusion (CSII) is a common treatment option for people with diabetes (PWD), but insulin infusion failures pose a significant challenge, leading to hyperglycemia, diabetes burnout, and increased hospitalizations. Current CSII pumpsโ occlusion alarm systems are limited in detecting infusion failures; therefore, a more effective detection method is needed.Methods:We conducted five preclinical animal studies to collect data on infusion failures, utilizing both insulin and non-insulin boluses. Data were captured using in-line pressure and flow rate sensors, with additional force data from CSII pumpsโ onboard sensors in one study. A novel classifier model was developed using this dataset, aimed at detecting different types of infusion failures through direct utilization of force sensor data. Performance was compared against various occlusion alarm thresholds from commercially available CSII pumps.Results:The testing dataset included 251 boluses. The Bagging classifier model showed the highest performance metrics among the models tested, exhibiting high accuracy (96%), sensitivity (94%), and specificity (98%), with lower false-positive and false-negative rate compared with traditional occlusion alarm pressure thresholds.Conclusions:Our study developed a novel non-threshold classifier that outperforms current occlusion alarm systems in CSII pumps in detecting infusion failures. This advancement has the potential to reduce the risk of hyperglycemia and hospitalizations due to undetected infusion failures, offering a more reliable and effective CSII therapy for PWD. Further studies involving human participants are recommended to validate these findings and assess the classifierโs performance in a real-world setting.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1850:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous subcutaneous insulin infusion (CSII) is a common treatment option for people with diabetes (PWD), but insulin infusion failures pose a significant challenge, leading to hyperglycemia, diabetes burnout, and increased hospitalizations. Current CSII pumpsโ occlusion alarm systems are limited in detecting infusion failures; therefore, a more effective detection method is needed.Methods:We conducted five preclinical animal studies to collect data on infusion failures, utilizing both insulin and non-insulin boluses. Data were captured using in-line pressure and flow rate sensors, with additional force data from CSII pumpsโ onboard sensors in one study. A novel classifier model was developed using this dataset, aimed at detecting different types of infusion failures through direct utilization of force sensor data. Performance was compared against various occlusion alarm thresholds from commercially available CSII pumps.Results:The testing dataset included 251 boluses. The Bagging classifier model showed the highest performance metrics among the models tested, exhibiting high accuracy (96%), sensitivity (94%), and specificity (98%), with lower false-positive and false-negative rate compared with traditional occlusion alarm pressure thresholds.Conclusions:Our study developed a novel non-threshold classifier that outperforms current occlusion alarm systems in CSII pumps in detecting infusion failures. This advancement has the potential to reduce the risk of hyperglycemia and hospitalizations due to undetected infusion failures, offering a more reliable and effective CSII therapy for PWD. Further studies involving human participants are recommended to validate these findings and assess the classifierโs performance in a real-world setting.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"Development of a Real-time Force-based Algorithm for Infusion Failure Detection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247530";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-24T05:04:01Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:14:"Luis E. Blanco";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"John H. Wilcox";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Michael S. Hughes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Rayhan A. Lal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:79:"Development of a Real-time Force-based Algorithm for Infusion Failure Detection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241247530";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241247530?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:13;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245930?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245930?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1621:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN.Method:To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process.Results:The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia.Conclusions:Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1621:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN.Method:To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process.Results:The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia.Conclusions:Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245930";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-22T08:51:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:12:"Jacopo Bonet";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Roberto Visentin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Chiara Dalla Man";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:113:"Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245930";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245930?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:14;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241246209?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Q-Score Complements the Time in Range in the Evaluation of Short-Term Glycemic Control";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241246209?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1809:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background and aims:The Q-Score is a single-number composite metric that is constructed based on the following components: central glycemic tendency, hyperglycemia, hypoglycemia, and intra- and interday variability. Herein, we refined the Q-Score for the screening and analysis of short-term glycemic control using continuous glucose monitoring (CGM) profiles.Methods:Continuous glucose monitoring profiles were obtained from noninterventional, retrospective cross-sectional studies. The upper limit of the Q-Score component hyperglycemiaโ that is, the time above target range (TAR), was adjusted from 8.9 to 10 mmol/L (n = 1562 three-day-sensor profiles). A total of 302 people with diabetes mellitus treated with intermittent CGM for โฅ14 days were enrolled. The time to stability was determined via correlation-based analysis.Results:There was a strong correlation between the Q-Scores of the two TARs, that is, 8.9 and 10 mmol/L (Q-ScoreTAR10 = โ0.03 + 1.00 Q-ScoreTAR8.9, r = .997, p < .001). The times to stability of the Q-Score and TIR were 10 and 12 days, respectively. The Q-Score was correlated with fructosamine concentrations, the glucose management indicator (GMI), the time in range (TIR), and the glycemic risk index (GRI) (r = .698, .887, โ.874, and .941), respectively. The number of Q-Score components above the target increased as the TIR decreased, from two (1.7 ยฑ 0.9) in CGM profiles with a TIR between 70% and 80% to four (3.9 ยฑ 0.5) in the majority of the CGM profiles with a TIR below 50%. A conversion matrix between the Q-Score and glycemic indices was developed.Conclusions:The Q-Score is a tool for assessing short-term glycemic control. The Q-Score can be translated into clinician opinion using the GRI.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1812:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background and aims:The Q-Score is a single-number composite metric that is constructed based on the following components: central glycemic tendency, hyperglycemia, hypoglycemia, and intra- and interday variability. Herein, we refined the Q-Score for the screening and analysis of short-term glycemic control using continuous glucose monitoring (CGM) profiles.Methods:Continuous glucose monitoring profiles were obtained from noninterventional, retrospective cross-sectional studies. The upper limit of the Q-Score component hyperglycemiaโ that is, the time above target range (TAR), was adjusted from 8.9 to 10 mmol/L (n = 1562 three-day-sensor profiles). A total of 302 people with diabetes mellitus treated with intermittent CGM for โฅ14 days were enrolled. The time to stability was determined via correlation-based analysis.Results:There was a strong correlation between the Q-Scores of the two TARs, that is, 8.9 and 10 mmol/L (Q-ScoreTAR10 = โ0.03 + 1.00 Q-ScoreTAR8.9, r = .997, p < .001). The times to stability of the Q-Score and TIR were 10 and 12 days, respectively. The Q-Score was correlated with fructosamine concentrations, the glucose management indicator (GMI), the time in range (TIR), and the glycemic risk index (GRI) (r = .698, .887, โ.874, and .941), respectively. The number of Q-Score components above the target increased as the TIR decreased, from two (1.7 ยฑ 0.9) in CGM profiles with a TIR between 70% and 80% to four (3.9 ยฑ 0.5) in the majority of the CGM profiles with a TIR below 50%. A conversion matrix between the Q-Score and glycemic indices was developed.Conclusions:The Q-Score is a tool for assessing short-term glycemic control. The Q-Score can be translated into clinician opinion using the GRI.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Q-Score Complements the Time in Range in the Evaluation of Short-Term Glycemic Control";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241246209";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-20T09:03:05Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:14:"Petra Augstein";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Peter Heinke";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Alexandra Nowak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Eckhard Salzsieder";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Wolfgang Kerner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:86:"Q-Score Complements the Time in Range in the Evaluation of Short-Term Glycemic Control";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241246209";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241246209?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:15;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245654?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245654?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1835:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management.Methods:We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study.Results:The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders.Conclusion:We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1838:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management.Methods:We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study.Results:The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders.Conclusion:We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245654";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-20T08:58:44Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:14:"Joseph Sartini";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Michael Fang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Mary R. Rooney";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Elizabeth Selvin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Josef Coresh";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Scott Zeger";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:103:"Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245654";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245654?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:16;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241246458?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:219:"Assessment of Glycemia Risk Index and Standard Continuous Glucose Monitoring Metrics in a Real-World Setting of Exercise in Adults With Type 1 Diabetes: A Post-Hoc Analysis of the Type 1 Diabetes and Exercise Initiative";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241246458?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1838:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Standardized reporting of continuous glucose monitoring (CGM) metrics does not provide extra weighting for very high or very low glucose, despite their distinct clinical significance, and thus may underestimate glycemic risk in people with type 1 diabetes (T1D) during exercise. Glycemia Risk Index (GRI) is a novel composite metric incorporating clinician-validated extra weighting for glycemic extremes, which may provide a novel summary index of glycemia risk around exercise.Methods:Adults (โฅ18 years) in the T1D EXercise Initiative study wore CGM and activity trackers for four weeks. For this analysis, exercise days were defined as 24 hours following โฅ20 minutes of exercise, with no other exercise in the 24-hour period. Sedentary days were defined as any 24 hours with no recorded exercise within that period or the preceding 24 hours. Linear mixed-effects regression was used to evaluate exercise effects on GRI and CGM metrics within 24 hours postexercise.Results:In 408 adults with T1D with >70% CGM and activity data, GRI on exercise (N = 3790) versus sedentary days (N = 1865) was significantly lower (mean [SD]: 29.9 [24.0] vs 34.0 [26.1], respectively, absolute mean difference โ1.70 [โ2.73, โ0.67], P < .001), a ~5% reduction in glycemic risk. Percent time in range (TIR; 70-180 mg/dL) increased on exercise days (absolute mean difference 2.67 [1.83, 3.50], P < .001), as did time below range (TBR; relative mean difference 1.17 [1.12, 1.22], P < .001), while time above range (TAR) decreased (relative mean difference 0.84 [0.79, 0.88], P < .001).Conclusions:Glycemia Risk Index improved on exercise versus sedentary days, despite increased TBR, which is weighted most heavily in the GRI calculation, due to a robust reduction in TAR.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1853:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Standardized reporting of continuous glucose monitoring (CGM) metrics does not provide extra weighting for very high or very low glucose, despite their distinct clinical significance, and thus may underestimate glycemic risk in people with type 1 diabetes (T1D) during exercise. Glycemia Risk Index (GRI) is a novel composite metric incorporating clinician-validated extra weighting for glycemic extremes, which may provide a novel summary index of glycemia risk around exercise.Methods:Adults (โฅ18 years) in the T1D EXercise Initiative study wore CGM and activity trackers for four weeks. For this analysis, exercise days were defined as 24 hours following โฅ20 minutes of exercise, with no other exercise in the 24-hour period. Sedentary days were defined as any 24 hours with no recorded exercise within that period or the preceding 24 hours. Linear mixed-effects regression was used to evaluate exercise effects on GRI and CGM metrics within 24 hours postexercise.Results:In 408 adults with T1D with >70% CGM and activity data, GRI on exercise (N = 3790) versus sedentary days (N = 1865) was significantly lower (mean [SD]: 29.9 [24.0] vs 34.0 [26.1], respectively, absolute mean difference โ1.70 [โ2.73, โ0.67], P < .001), a ~5% reduction in glycemic risk. Percent time in range (TIR; 70-180 mg/dL) increased on exercise days (absolute mean difference 2.67 [1.83, 3.50], P < .001), as did time below range (TBR; relative mean difference 1.17 [1.12, 1.22], P < .001), while time above range (TAR) decreased (relative mean difference 0.84 [0.79, 0.88], P < .001).Conclusions:Glycemia Risk Index improved on exercise versus sedentary days, despite increased TBR, which is weighted most heavily in the GRI calculation, due to a robust reduction in TAR.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:219:"Assessment of Glycemia Risk Index and Standard Continuous Glucose Monitoring Metrics in a Real-World Setting of Exercise in Adults With Type 1 Diabetes: A Post-Hoc Analysis of the Type 1 Diabetes and Exercise Initiative";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241246458";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-17T01:12:00Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:13:"Dale Morrison";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Sara Vogrin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Dessi P. Zaharieva";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:219:"Assessment of Glycemia Risk Index and Standard Continuous Glucose Monitoring Metrics in a Real-World Setting of Exercise in Adults With Type 1 Diabetes: A Post-Hoc Analysis of the Type 1 Diabetes and Exercise Initiative";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241246458";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241246458?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:17;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245680?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Reduced Efficacy of Glucagon-Like Peptide-1 Receptor Agonists Therapy in People With Type 1 Diabetes and Genetic Forms of Obesity";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245680?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1677:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Once weekly Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RA) have been shown to improve glycemic outcomes and cause significant weight loss. However, 9% to 27% of individuals have little or no response to these drugs. In this article, we investigated the efficacy of GLP-1 RA therapy among adults with type 1 diabetes and obesity likely related to genetic mutations compared with obesity likely unrelated to genetic mutations.Methods:In this retrospective study, we compared body weight and glycated hemoglobin (HbA1c) change with the use of GLP-1 RA therapy (including a dual agonist, Tirzepatide) over six months among adults with type 1 diabetes and obesity likely (n = 11, median age 39.5 years with a median BMI of 43.0 kg/m2) versus unlikely related to genetic mutation(s) (n = 15, median age 45.8 years with a median BMI of 38.7 kg/m2).Results:Six months of GLP-1 RA treatment resulted in a numerically lower reduction of weight (โ5.75 ยฑ 9.46 kg vs โ8.65 ยฑ 9.36 kg, P = .44) and HbA1c (โ0.28 ยฑ 0.96% vs โ0.43 ยฑ 0.57%, P = .64) among individuals with obesity likely versus unlikely related to a genetic mutation(s), respectively. Fewer individuals with genetic obesity met goal weight loss โฅ5% or HbA1c decrease โฅ0.4% than did individuals with obesity unlikely related to a genetic cause (36.4% vs 80.0%, P = .04).Conclusions:The weight loss and glycemic lowering effects of GLP-1 RA therapy may be decreased in people with type 1 diabetes and obesity likely related to genetic causes. Further research is needed to understand GLP-1 RA mechanisms via energy regulating genes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1677:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Once weekly Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RA) have been shown to improve glycemic outcomes and cause significant weight loss. However, 9% to 27% of individuals have little or no response to these drugs. In this article, we investigated the efficacy of GLP-1 RA therapy among adults with type 1 diabetes and obesity likely related to genetic mutations compared with obesity likely unrelated to genetic mutations.Methods:In this retrospective study, we compared body weight and glycated hemoglobin (HbA1c) change with the use of GLP-1 RA therapy (including a dual agonist, Tirzepatide) over six months among adults with type 1 diabetes and obesity likely (n = 11, median age 39.5 years with a median BMI of 43.0 kg/m2) versus unlikely related to genetic mutation(s) (n = 15, median age 45.8 years with a median BMI of 38.7 kg/m2).Results:Six months of GLP-1 RA treatment resulted in a numerically lower reduction of weight (โ5.75 ยฑ 9.46 kg vs โ8.65 ยฑ 9.36 kg, P = .44) and HbA1c (โ0.28 ยฑ 0.96% vs โ0.43 ยฑ 0.57%, P = .64) among individuals with obesity likely versus unlikely related to a genetic mutation(s), respectively. Fewer individuals with genetic obesity met goal weight loss โฅ5% or HbA1c decrease โฅ0.4% than did individuals with obesity unlikely related to a genetic cause (36.4% vs 80.0%, P = .04).Conclusions:The weight loss and glycemic lowering effects of GLP-1 RA therapy may be decreased in people with type 1 diabetes and obesity likely related to genetic causes. Further research is needed to understand GLP-1 RA mechanisms via energy regulating genes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"Reduced Efficacy of Glucagon-Like Peptide-1 Receptor Agonists Therapy in People With Type 1 Diabetes and Genetic Forms of Obesity";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245680";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-17T01:11:00Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:16:"Matthew P. Klein";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Halis Kaan Akturk";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:22:"Janet K. Snell-Bergeon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Viral N. 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The aim was to evaluate the effects of the AHCL systems, Tandemยฎ t: slim X2โข with Control IQโข, and MiniMedโข 780G, on glucose control, safety, treatment satisfaction, and practical barriers for individuals with type 1 diabetes.Method:One hundred forty-two randomly selected adults with type 1 diabetes at six diabetes outpatient clinics in Sweden at any time treated with either the Tandem Control IQ (TCIQ) or the MiniMed 780G system were included. Glycated hemoglobin A1c (HbA1c) and glucose metrics were evaluated. Treatment satisfaction and practical barriers were examined via questionnaires.Results:Mean age was 42 years, median follow-up was 1.7 years, 58 (40.8%) were females, 65% used the TCIQ system. Glycated hemoglobin A1c was reduced by 0.6% (6.8 mmol/mol; 95% confidence interval [CI] = 0.5-0.8% [5.3-8.2 mmol/mol]; P < .001), from 7.3% to 6.7% (57-50 mmol/mol). Time in range (TIR) increased with 14.5% from 57.0% to 71.5% (95% CI = 12.2%-16.9%; P < .001). Time below range (TBR) (<70 mg/dL, <3.9 mmol/L) decreased from 3.8% to 1.6% (P < .001). The standard deviation of glucose values was reduced from 61 to 51 mg/dL (3.4-2.9 mmol/L, P < .001) and the coefficient of variation from 35% to 33% (P < .001). Treatment satisfaction increased, score 14.8 on the Diabetes Treatment Satisfaction Questionnaire (DTSQ) (change version ranging from โ18 to 18, P < .001). Four severe hypoglycemia events were detected and no cases of ketoacidosis. Skin problems were experienced by 32.4% of the study population.Conclusions:Advanced hybrid closed-loop systems improve glucose control with a reasonable safety profile and high treatment satisfaction. Skin problems are common adverse events.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1932:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:There are few studies providing a more comprehensive picture of advanced hybrid closed-loop (AHCL) systems in clinical practice. The aim was to evaluate the effects of the AHCL systems, Tandemยฎ t: slim X2โข with Control IQโข, and MiniMedโข 780G, on glucose control, safety, treatment satisfaction, and practical barriers for individuals with type 1 diabetes.Method:One hundred forty-two randomly selected adults with type 1 diabetes at six diabetes outpatient clinics in Sweden at any time treated with either the Tandem Control IQ (TCIQ) or the MiniMed 780G system were included. Glycated hemoglobin A1c (HbA1c) and glucose metrics were evaluated. Treatment satisfaction and practical barriers were examined via questionnaires.Results:Mean age was 42 years, median follow-up was 1.7 years, 58 (40.8%) were females, 65% used the TCIQ system. Glycated hemoglobin A1c was reduced by 0.6% (6.8 mmol/mol; 95% confidence interval [CI] = 0.5-0.8% [5.3-8.2 mmol/mol]; P < .001), from 7.3% to 6.7% (57-50 mmol/mol). Time in range (TIR) increased with 14.5% from 57.0% to 71.5% (95% CI = 12.2%-16.9%; P < .001). Time below range (TBR) (<70 mg/dL, <3.9 mmol/L) decreased from 3.8% to 1.6% (P < .001). The standard deviation of glucose values was reduced from 61 to 51 mg/dL (3.4-2.9 mmol/L, P < .001) and the coefficient of variation from 35% to 33% (P < .001). Treatment satisfaction increased, score 14.8 on the Diabetes Treatment Satisfaction Questionnaire (DTSQ) (change version ranging from โ18 to 18, P < .001). Four severe hypoglycemia events were detected and no cases of ketoacidosis. Skin problems were experienced by 32.4% of the study population.Conclusions:Advanced hybrid closed-loop systems improve glucose control with a reasonable safety profile and high treatment satisfaction. Skin problems are common adverse events.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:142:"Effects, Safety, and Treatment Experience of Advanced Hybrid Closed-Loop Systems in Clinical Practice Among Adults Living With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241242386";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-17T01:10:41Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:14:"Ramanjit Singh";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Henrik Imberg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Shilan Seyed Ahmadi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Sara Hallstrรถm";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Johan Jendle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:19:"Bengt-Olov Tengmark";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:11:"Anna Folino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Ekstrรถm Marie";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:11:"Marcus Lind";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:142:"Effects, Safety, and Treatment Experience of Advanced Hybrid Closed-Loop Systems in Clinical Practice Among Adults Living With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241242386";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241242386?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:19;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241242487?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:73:"Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241242487?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1274:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous glucose monitoring (CGM) has transformed the care of type 1 and type 2 diabetes, and there is potential for CGM to also become influential in prediabetes identification and management. However, to date, we do not have any consensus guidelines or high-quality evidence to guide CGM goals and metrics for use in prediabetes.Methods:We searched PubMed for all English-language articles on CGM use in nonpregnant adults with prediabetes published by November 1, 2023. We excluded any articles that included subjects with type 1 diabetes or who were known to be at risk for type 1 diabetes due to positive islet autoantibodies.Results:Based on the limited data available, we suggest possible CGM metrics to be used for individuals with prediabetes. We also explore the role that glycemic variability (GV) plays in the transition from normoglycemia to prediabetes.Conclusions:Glycemic variability indices beyond the standard deviation and coefficient of variation are emerging as prominent identifiers of early dysglycemia. One GV index in particular, the mean amplitude of glycemic excursion (MAGE), may play a key future role in CGM metrics for prediabetes and is highlighted in this review.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1274:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous glucose monitoring (CGM) has transformed the care of type 1 and type 2 diabetes, and there is potential for CGM to also become influential in prediabetes identification and management. However, to date, we do not have any consensus guidelines or high-quality evidence to guide CGM goals and metrics for use in prediabetes.Methods:We searched PubMed for all English-language articles on CGM use in nonpregnant adults with prediabetes published by November 1, 2023. We excluded any articles that included subjects with type 1 diabetes or who were known to be at risk for type 1 diabetes due to positive islet autoantibodies.Results:Based on the limited data available, we suggest possible CGM metrics to be used for individuals with prediabetes. We also explore the role that glycemic variability (GV) plays in the transition from normoglycemia to prediabetes.Conclusions:Glycemic variability indices beyond the standard deviation and coefficient of variation are emerging as prominent identifiers of early dysglycemia. One GV index in particular, the mean amplitude of glycemic excursion (MAGE), may play a key future role in CGM metrics for prediabetes and is highlighted in this review.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:73:"Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241242487";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-17T12:31:13Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:16:"Salwa J. Zahalka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Rodolfo J. Galindo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Viral N. Shah";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:19:"Cecilia C. Low Wang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:73:"Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241242487";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241242487?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:20;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245923?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"First Accuracy and User-Experience Evaluation of New Continuous Glucose Monitoring System for Hypoglycemia Due to Hyperinsulinism";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245923?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1638:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Patients with congenital hyperinsulinism (HI) require constant glucose monitoring to detect and treat recurrent and severe hypoglycemia. Historically, this has been achieved with intermittent self-monitoring blood glucose (SMBG), but patients are increasingly using continuous glucose monitoring (CGM). Given the rapidity of CGM device development, and increasing calls for CGM use from HI families, it is vital that new devices are evaluated early.Methods:We provided two months of supplies for the new Dexcom G7 CGM device to 10 patients with HI who had recently finished using the Dexcom G6. Self-monitoring blood glucose was performed concurrently with paired readings providing accuracy calculations. Patients and families completed questionnaires about device use at the end of the two-month study period.Results:Compared to the G6, the G7 showed a significant reduction in mean absolute relative difference (25%-18%, P < .001) and in the over-read error (Bland Altman +1.96 SD; 3.54 mmol/L to 2.95 mmol/L). This resulted in an improvement in hypoglycemia detection from 42% to 62% (P < .001). Families reported an overall preference for the G7 but highlighted concerns about high sensor failure rates.Discussion:The reduction in mean absolute relative difference and over-read error and the improvement in hypoglycemia detection implies that the G7 is a safer and more useful device in the management of hypoglycemia for patients with HI. Accuracy, while improved from previous devices, remains suboptimal with 40% of hypoglycemia episodes not detected.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1644:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Patients with congenital hyperinsulinism (HI) require constant glucose monitoring to detect and treat recurrent and severe hypoglycemia. Historically, this has been achieved with intermittent self-monitoring blood glucose (SMBG), but patients are increasingly using continuous glucose monitoring (CGM). Given the rapidity of CGM device development, and increasing calls for CGM use from HI families, it is vital that new devices are evaluated early.Methods:We provided two months of supplies for the new Dexcom G7 CGM device to 10 patients with HI who had recently finished using the Dexcom G6. Self-monitoring blood glucose was performed concurrently with paired readings providing accuracy calculations. Patients and families completed questionnaires about device use at the end of the two-month study period.Results:Compared to the G6, the G7 showed a significant reduction in mean absolute relative difference (25%-18%, P < .001) and in the over-read error (Bland Altman +1.96 SD; 3.54 mmol/L to 2.95 mmol/L). This resulted in an improvement in hypoglycemia detection from 42% to 62% (P < .001). Families reported an overall preference for the G7 but highlighted concerns about high sensor failure rates.Discussion:The reduction in mean absolute relative difference and over-read error and the improvement in hypoglycemia detection implies that the G7 is a safer and more useful device in the management of hypoglycemia for patients with HI. Accuracy, while improved from previous devices, remains suboptimal with 40% of hypoglycemia episodes not detected.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:129:"First Accuracy and User-Experience Evaluation of New Continuous Glucose Monitoring System for Hypoglycemia Due to Hyperinsulinism";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245923";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-15T06:23:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:11:"Chris Worth";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Sarah Worthington";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Sameera Auckburally";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Elaine OโShea";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Sumera Ahmad";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:18:"Catherine Fullwood";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:23:"Maria Salomon-Estebanez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:18:"Indraneel Banerjee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:129:"First Accuracy and User-Experience Evaluation of New Continuous Glucose Monitoring System for Hypoglycemia Due to Hyperinsulinism";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245923";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245923?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:21;a:6:{s:4:"data";s:193:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245627?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Hybrid Closed Loop in Adults With Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245627?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1883:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Benefits of hybrid closed-loop (HCL) systems in a high-risk group with type 1 diabetes and impaired awareness of hypoglycemia (IAH) have not been well-explored.Methods:Adults with Edmonton HYPO scores โฅ1047 were randomized to 26-weeks HCL (MiniMedโข 670G) vs standard therapy (multiple daily injections or insulin pump) without continuous glucose monitoring (CGM) (control). Primary outcome was percentage CGM time-in-range (TIR; 70-180 mg/dL) at 23 to 26 weeks post-randomization. Major secondary endpoints included magnitude of change in counter-regulatory hormones and autonomic symptom responses to hypoglycemia at 26-weeks post-randomization. A post hoc analysis evaluated glycemia risk index (GRI) comparing HCL with control groups at 26 weeks post-randomization.Results:Nine participants (median [interquartile range (IQR)] age 51 [41, 59] years; 44% male; enrolment HYPO score 1183 [1058, 1308]; Clarke score 6 [6, 6]; n = 5 [HCL]; n = 4 [control]) completed the study. Time-in-range was higher using HCL vs control (70% [68, 74%] vs 48% [44, 50%], P = .014). Time <70 mg/dL did not differ (HCL 3.8% [2.7, 3.9] vs control 6.5% [4.3, 8.6], P = .14) although hypoglycemia episode duration was shorter (30 vs 50 minutes, P < .001) with HCL. Glycemia risk index was lower with HCL vs control (38.1 [30.0, 39.2] vs 70.8 [58.5, 72.4], P = .014). Following 6 months of HCL use, greater dopamine (24.0 [12.3, 27.6] vs โ18.5 [โ36.5, โ4.8], P = .014), and growth hormone (6.3 [4.6, 16.8] vs 0.5 [โ0.8, 3.0], P = .050) responses to hypoglycemia were observed.Conclusions:Six months of HCL use in high-risk adults with severe IAH increased glucose TIR and improved GRI without increased hypoglycemia, and partially restored counter-regulatory responses.Clinical trial registration:ACTRN12617000520336";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1889:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Benefits of hybrid closed-loop (HCL) systems in a high-risk group with type 1 diabetes and impaired awareness of hypoglycemia (IAH) have not been well-explored.Methods:Adults with Edmonton HYPO scores โฅ1047 were randomized to 26-weeks HCL (MiniMedโข 670G) vs standard therapy (multiple daily injections or insulin pump) without continuous glucose monitoring (CGM) (control). Primary outcome was percentage CGM time-in-range (TIR; 70-180 mg/dL) at 23 to 26 weeks post-randomization. Major secondary endpoints included magnitude of change in counter-regulatory hormones and autonomic symptom responses to hypoglycemia at 26-weeks post-randomization. A post hoc analysis evaluated glycemia risk index (GRI) comparing HCL with control groups at 26 weeks post-randomization.Results:Nine participants (median [interquartile range (IQR)] age 51 [41, 59] years; 44% male; enrolment HYPO score 1183 [1058, 1308]; Clarke score 6 [6, 6]; n = 5 [HCL]; n = 4 [control]) completed the study. Time-in-range was higher using HCL vs control (70% [68, 74%] vs 48% [44, 50%], P = .014). Time <70 mg/dL did not differ (HCL 3.8% [2.7, 3.9] vs control 6.5% [4.3, 8.6], P = .14) although hypoglycemia episode duration was shorter (30 vs 50 minutes, P < .001) with HCL. Glycemia risk index was lower with HCL vs control (38.1 [30.0, 39.2] vs 70.8 [58.5, 72.4], P = .014). Following 6 months of HCL use, greater dopamine (24.0 [12.3, 27.6] vs โ18.5 [โ36.5, โ4.8], P = .014), and growth hormone (6.3 [4.6, 16.8] vs 0.5 [โ0.8, 3.0], P = .050) responses to hypoglycemia were observed.Conclusions:Six months of HCL use in high-risk adults with severe IAH increased glucose TIR and improved GRI without increased hypoglycemia, and partially restored counter-regulatory responses.Clinical trial registration:ACTRN12617000520336";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:94:"Hybrid Closed Loop in Adults With Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245627";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-13T07:29:41Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:14:{i:0;a:5:{s:4:"data";s:14:"Melissa H. Lee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Judith Gooley";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Varuni Obeyesekere";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:7:"Jean Lu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Barbora Paldus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:19:"Christel Hendrieckx";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:19:"Richard J. MacIsaac";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"Sybil A. McAuley";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:12:"Jane Speight";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:11:"Sara Vogrin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:17:"Alicia J. Jenkins";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:21:"D. Jane Holmes-Walker";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:17:"David N. OโNeal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:13;a:5:{s:4:"data";s:13:"Glenn M. Ward";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:94:"Hybrid Closed Loop in Adults With Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241245627";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241245627?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:22;a:6:{s:4:"data";s:179:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241242803?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:158:"Hypoglycemia and Hyperglycemia According to Type of Diabetes: Observations During Fully Closed-Loop Insulin Delivery in Adults With Type 1 and Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241242803?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1494:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:CamAPS HX fully closed-loop (FCL) system, with no user input required at mealtimes, has been shown to be safe and effective in adults with type 1 and type 2 diabetes. We assessed whether time spent in hypoglycemia and hyperglycemia during FCL insulin delivery in adults varied by type of diabetes over the 24-hour period.Methods:We retrospectively analyzed eight weeks of data from 52 participants (adults with type 1 diabetes and adults with insulin-treated type 2 diabetes) recruited to two single-center randomized controlled studies using FCL insulin delivery during unrestricted-living conditions. Key outcomes were time spent in hypoglycemia <70 mg/dL and marked hyperglycemia >300 mg/dL by type of diabetes.Results:The median percentage of time spent in hypoglycemia <70 mg/dL over the 24-hour period was lower for those with type 2 diabetes than for those with type 1 diabetes (median [interquartile range (IQR)] 0.43% [0.20-0.77] vs 0.86%, [0.54-1.46]; mean difference 0.46 percentage points [95% CI 0.23-0.70]; P < .001). Median percentage time in marked hyperglycemia >300 mg/dL was lower for those with type 2 diabetes than for those with type 1 diabetes (median [IQR] 1.8% [0.6-3.5] vs 9.3% [6.9-11.8]; mean difference 7.8 percentage points [95% CI 5.5-10.0]; P < .001).Conclusions:Using the FCL system, hypoglycemia and marked hyperglycemia exposure were lower in type 2 diabetes than in type 1 diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1512:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:CamAPS HX fully closed-loop (FCL) system, with no user input required at mealtimes, has been shown to be safe and effective in adults with type 1 and type 2 diabetes. We assessed whether time spent in hypoglycemia and hyperglycemia during FCL insulin delivery in adults varied by type of diabetes over the 24-hour period.Methods:We retrospectively analyzed eight weeks of data from 52 participants (adults with type 1 diabetes and adults with insulin-treated type 2 diabetes) recruited to two single-center randomized controlled studies using FCL insulin delivery during unrestricted-living conditions. Key outcomes were time spent in hypoglycemia <70 mg/dL and marked hyperglycemia >300 mg/dL by type of diabetes.Results:The median percentage of time spent in hypoglycemia <70 mg/dL over the 24-hour period was lower for those with type 2 diabetes than for those with type 1 diabetes (median [interquartile range (IQR)] 0.43% [0.20-0.77] vs 0.86%, [0.54-1.46]; mean difference 0.46 percentage points [95% CI 0.23-0.70]; P < .001). Median percentage time in marked hyperglycemia >300 mg/dL was lower for those with type 2 diabetes than for those with type 1 diabetes (median [IQR] 1.8% [0.6-3.5] vs 9.3% [6.9-11.8]; mean difference 7.8 percentage points [95% CI 5.5-10.0]; P < .001).Conclusions:Using the FCL system, hypoglycemia and marked hyperglycemia exposure were lower in type 2 diabetes than in type 1 diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:158:"Hypoglycemia and Hyperglycemia According to Type of Diabetes: Observations During Fully Closed-Loop Insulin Delivery in Adults With Type 1 and Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241242803";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-13T07:25:33Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:12:{i:0;a:5:{s:4:"data";s:15:"Nithya Kadiyala";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Malgorzata E. 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The seminal randomized controlled trial (RCT) evaluating CGM use added to standard care in pregnancy in T1D demonstrated significant improvements in maternal glycemia and neonatal health outcomes. Current clinical guidance recommends targets for percentage time in range (TIR), time above range (TAR), and time below range (TBR) during pregnancy complicated by T1D that are widely used in clinical practice. However, the superiority of CGM over blood glucose monitoring (BGM) is still questioned in both T2D and GDM, and whether glucose targets should be different than in T1D is unknown. Questions requiring additional research include which CGM metrics are superior in predicting clinical outcomes, how should pregnancy-specific CGM targets be defined, whether CGM targets should differ according to gestational age, and if CGM metrics during pregnancy should be similar across all types of diabetes. Limiting the potential for CGM to improve pregnancy outcomes may be our inability to maintain TIR > 70% throughout gestation, a goal achieved in the minority of patients studied. Adverse pregnancy outcomes remain high in women with T1D and T2D in pregnancy despite CGM technology, and this review explores the potential reasons and questions yet to be investigated.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1583:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Ascertaining the utility of continuous glucose monitoring (CGM) in pregnancy complicated by diabetes is a rapidly evolving area, as the prevalence of type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM) escalates. The seminal randomized controlled trial (RCT) evaluating CGM use added to standard care in pregnancy in T1D demonstrated significant improvements in maternal glycemia and neonatal health outcomes. Current clinical guidance recommends targets for percentage time in range (TIR), time above range (TAR), and time below range (TBR) during pregnancy complicated by T1D that are widely used in clinical practice. However, the superiority of CGM over blood glucose monitoring (BGM) is still questioned in both T2D and GDM, and whether glucose targets should be different than in T1D is unknown. Questions requiring additional research include which CGM metrics are superior in predicting clinical outcomes, how should pregnancy-specific CGM targets be defined, whether CGM targets should differ according to gestational age, and if CGM metrics during pregnancy should be similar across all types of diabetes. Limiting the potential for CGM to improve pregnancy outcomes may be our inability to maintain TIR > 70% throughout gestation, a goal achieved in the minority of patients studied. Adverse pregnancy outcomes remain high in women with T1D and T2D in pregnancy despite CGM technology, and this review explores the potential reasons and questions yet to be investigated.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Continuous Glucose Monitoring Metrics for Pregnancies Complicated by Diabetes: Critical Appraisal of Current Evidence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241239341";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-12T11:43:16Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:20:"Emily D. 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However, data on those who struggle with suboptimal glycemic levels despite insulin pump and continuous glucose monitoring (CGM) are limited. We conducted a randomized controlled trial to assess the effects of an AID system in this population.Methods:Participants with hemoglobin A1c (HbA1c) โฅ 58 mmol/mol (7.5%) were allocated 1:1 to 14 weeks of treatment with the MiniMed 780G system (AID) or continuation of usual care (UC). The primary endpoint was change in time in range (TIR: 3ยท9-10ยท0 mmol/L) from baseline to week 14. After this trial period, the UC group switched to AID treatment while the AID group continued using the system. Both groups were monitored for a total of 28 weeks.Results:Forty adults (mean ยฑ SD: age 52 ยฑ 11 years, HbA1c 67 ยฑ 7 mmol/mol [8.3% ยฑ 0.6%], diabetes duration 29 ยฑ13 years) were included. After 14 weeks, TIR increased by 18.7% (95% confidence interval [CI] = 14.5, 22.9%) in the AID group and remained unchanged in the UC group (P < .0001). Hemoglobin A1c decreased by 10.0 mmol/mol (95% CI = 7.0, 13.0 mmol/mol) (0.9% [95% CI = 0.6%, 1.2%]) in the AID group but remained unchanged in the UC group (P < .0001). The glycemic benefits of AID treatment were reproduced after the 14-week extension phase. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study.Conclusions:For adults with type 1 diabetes not meeting glycemic targets despite use of insulin pump and CGM, transitioning to an AID system confers considerable glycemic benefits.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1721:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Automated insulin delivery (AID) systems offer promise in improving glycemic outcomes for individuals with type 1 diabetes. However, data on those who struggle with suboptimal glycemic levels despite insulin pump and continuous glucose monitoring (CGM) are limited. We conducted a randomized controlled trial to assess the effects of an AID system in this population.Methods:Participants with hemoglobin A1c (HbA1c) โฅ 58 mmol/mol (7.5%) were allocated 1:1 to 14 weeks of treatment with the MiniMed 780G system (AID) or continuation of usual care (UC). The primary endpoint was change in time in range (TIR: 3ยท9-10ยท0 mmol/L) from baseline to week 14. After this trial period, the UC group switched to AID treatment while the AID group continued using the system. Both groups were monitored for a total of 28 weeks.Results:Forty adults (mean ยฑ SD: age 52 ยฑ 11 years, HbA1c 67 ยฑ 7 mmol/mol [8.3% ยฑ 0.6%], diabetes duration 29 ยฑ13 years) were included. After 14 weeks, TIR increased by 18.7% (95% confidence interval [CI] = 14.5, 22.9%) in the AID group and remained unchanged in the UC group (P < .0001). Hemoglobin A1c decreased by 10.0 mmol/mol (95% CI = 7.0, 13.0 mmol/mol) (0.9% [95% CI = 0.6%, 1.2%]) in the AID group but remained unchanged in the UC group (P < .0001). The glycemic benefits of AID treatment were reproduced after the 14-week extension phase. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study.Conclusions:For adults with type 1 diabetes not meeting glycemic targets despite use of insulin pump and CGM, transitioning to an AID system confers considerable glycemic benefits.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:176:"Automated Insulin Delivery in Adults With Type 1 Diabetes and Suboptimal HbA1c During Prior Use of Insulin Pump and Continuous Glucose Monitoring: A Randomized Controlled Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241242399";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-11T07:06:33Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:21:"Merete B. 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The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days.Result:Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was โ0.1 (โ0.8, 0.3), โ0.6 (โ1.1, โ0.15), and โ0.55 (โ0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR.Conclusion:There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1629:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting.Method:This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days.Result:Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was โ0.1 (โ0.8, 0.3), โ0.6 (โ1.1, โ0.15), and โ0.55 (โ0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR.Conclusion:There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"Comparative Effectiveness of Hybrid Closed-Loop Automated Insulin Delivery Systems Among Patients with Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234948";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-04-01T11:30:59Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:9:"Sara Folk";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Janet Zappe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Kathleen Wyne";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Kathleen M. 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Key eligibility criteria were a diagnosis of type 1, type 2, or gestational diabetes and regular self-monitoring of blood glucose. Participants were randomly assigned (2:1 ratio) to either use the digital diabetes logbook (mySugr PRO), or to the control group without app use. The primary outcome was the reduction in diabetes distress at the 12-week follow-up. All analyses were based on the intention-to-treat population with all randomized participants. The trial was registered at the German Register for Clinical Studies (DRKS00022923).Results:Between February 11, 2021, and June 24, 2022, 424 participants (50% female, 50% male) were included, with 282 being randomized to the intervention group (66.5%) and 142 to the control group (33.5%). A total of 397 participants completed the trial (drop-out rate: 6.4%). The median reduction in diabetes distress was 2.41 (interquartile range [IQR]: โ2.50 to 8.11) in the intervention group and 1.25 (IQR: โ5.00 to 7.50) in the control group. The model-based adjusted between-group difference was significant (โ2.20, IQR: โ4.02 to โ0.38, P = .0182) favoring the intervention group. There were 27 adverse events, 17 (6.0%) in the intervention group, and 10 (7.0%) in the control group.Conclusions:The efficacy of the digital diabetes logbook was demonstrated regarding improvements in mental health in people with type 1, type 2, and gestational diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1731:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:In a randomized controlled trial, the efficacy of a digital diabetes diary regarding a reduction of diabetes distress was evaluated.Methods:A randomized controlled trial with a 12-week follow-up was conducted in 41 study sites across Germany. Key eligibility criteria were a diagnosis of type 1, type 2, or gestational diabetes and regular self-monitoring of blood glucose. Participants were randomly assigned (2:1 ratio) to either use the digital diabetes logbook (mySugr PRO), or to the control group without app use. The primary outcome was the reduction in diabetes distress at the 12-week follow-up. All analyses were based on the intention-to-treat population with all randomized participants. The trial was registered at the German Register for Clinical Studies (DRKS00022923).Results:Between February 11, 2021, and June 24, 2022, 424 participants (50% female, 50% male) were included, with 282 being randomized to the intervention group (66.5%) and 142 to the control group (33.5%). A total of 397 participants completed the trial (drop-out rate: 6.4%). The median reduction in diabetes distress was 2.41 (interquartile range [IQR]: โ2.50 to 8.11) in the intervention group and 1.25 (IQR: โ5.00 to 7.50) in the control group. The model-based adjusted between-group difference was significant (โ2.20, IQR: โ4.02 to โ0.38, P = .0182) favoring the intervention group. There were 27 adverse events, 17 (6.0%) in the intervention group, and 10 (7.0%) in the control group.Conclusions:The efficacy of the digital diabetes logbook was demonstrated regarding improvements in mental health in people with type 1, type 2, and gestational diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:180:"Efficacy of a Digital Diabetes Logbook for People With Type 1, Type 2, and Gestational Diabetes: Results From a Multicenter, Open-Label, Parallel-Group, Randomized Controlled 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A geriatric assessment was performed, and body composition was determined to investigate its association with achieving time below range (TBR) <70 mg/dL goals.Results:The study included 59 patients (47.5% of men, mean age of 67.6 years, glycated hemoglobin [HbA1c] of 7.5 ยฑ 0.6%, time in range (TIR) 77.8 ยฑ 9.9%). Time below range <70 and <54 mg/dL were 2.2 ยฑ 2.3% and 0.4 ยฑ 0.81%, respectively. Patients with elevated TBR <70 mg/dL (>1%) had higher HbA1c levels, lower TIR, elevated time above range (TAR), and high glycemic variability. Regarding body composition, greater muscle mass, grip strength, and visceral fat were associated with a lower TBR <70 mg/dL. These factors were independent of the type of technology used, but TIR was higher when using AHCL systems compared with SAPT-PLGM and HCL systems.Conclusions:In elderly patients treated with AID systems with good functional status, lower lean mass, lower grip strength, and lower visceral fat percentage were associated with TBR greater than 1%, regardless of the device used. A similar finding along was found with CGM indicators such as higher HbA1c levels, lower TIR, higher TAR, and higher CV. Geriatric assessment is crucial for personalizing patient management.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1794:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This study investigated the characteristics associated with an increased risk of hypoglycemia, in elderly patients with type 1 diabetes mellitus (T1D) using automated insulin delivery (AID) systems.Methods:Cross-sectional observational study including patients >60 years, using sensor-augmented insulin pump therapy with predictive low-glucose management (SAPT-PLGM), hybrid closed-loop (HCL), and advanced hybrid closed-loop (AHCL), for more than three months. A geriatric assessment was performed, and body composition was determined to investigate its association with achieving time below range (TBR) <70 mg/dL goals.Results:The study included 59 patients (47.5% of men, mean age of 67.6 years, glycated hemoglobin [HbA1c] of 7.5 ยฑ 0.6%, time in range (TIR) 77.8 ยฑ 9.9%). Time below range <70 and <54 mg/dL were 2.2 ยฑ 2.3% and 0.4 ยฑ 0.81%, respectively. Patients with elevated TBR <70 mg/dL (>1%) had higher HbA1c levels, lower TIR, elevated time above range (TAR), and high glycemic variability. Regarding body composition, greater muscle mass, grip strength, and visceral fat were associated with a lower TBR <70 mg/dL. These factors were independent of the type of technology used, but TIR was higher when using AHCL systems compared with SAPT-PLGM and HCL systems.Conclusions:In elderly patients treated with AID systems with good functional status, lower lean mass, lower grip strength, and lower visceral fat percentage were associated with TBR greater than 1%, regardless of the device used. A similar finding along was found with CGM indicators such as higher HbA1c levels, lower TIR, higher TAR, and higher CV. Geriatric assessment is crucial for personalizing patient management.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:141:"Characteristics Associated With Elevated Time Below Range in Elderly Patients With Type 1 Diabetes Using an Automated Insulin Delivery System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232659";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-20T12:26:27Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:13:{i:0;a:5:{s:4:"data";s:24:"Ana Marรญa Gรณmez Medina";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Darรญo A. Parra Prieto";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:29:"Diana Cristina Henao Carrillo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:21:"Claudia Milena Gรณmez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:30:"Oscar Mauricio Muรฑoz Velandia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"Sandra Caicedo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:33:"Alfonso Luis Kerguelen Villadiego";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:30:"Luis Miguel Rodrรญguez Hortรบa";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:26:"Oscar David Lucero Pantoja";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:23:"Mauricio Uribe Valencia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:30:"Marรญa Margarita Garcรญa Guete";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:20:"Sofia Robledo Gรณmez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:25:"Martin Rondรณn Sepรบlveda";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:141:"Characteristics Associated With Elevated Time Below Range in Elderly Patients With Type 1 Diabetes Using an Automated Insulin Delivery System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232659";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232659?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:32;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236456?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Time in Range Analysis in Automated Insulin Delivery Era: Should Day and Nighttime Targets be the Same?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236456?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1910:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Hybrid closed-loop systems (HCLS) use has shown that time in range (TIR) tends to improve more during the nighttime than during the day. This study aims to compare the conventional TIR, currently accepted as 70 to 180 mg/dL, with a proposed recalculated time in range (RTIR) considering a tighter glucose target of 70 to 140 mg/dL for the nighttime fasting period in T1DM patients under HCLS.Methods:We conducted a retrospective study that included adults patients receiving treatment with Tandem t:slim X2 Control-IQ. Daytime TIR was characterized as glucose values between 70 and 180 mg/dL during the 07:01 to 23:59 time frame. Nighttime fasting TIR was specified as glucose values from 70 to 140 mg/dL between 00:00 and 07:00. The combination of the daytime and nighttime fasting glucose targets results in an RTIR, which was compared with the conventional TIR for each patient. The 14 days Dexcom G6 CGM data were downloaded from Tidepool platform and analyzed.Results:We included 22 patients with a mean age of 49.7 years and diabetes duration of 24.7 years, who had been using automatic insulin delivery (AID) HCLS for a median of 305.3 days. We verified a mean conventional TIR of 68.7% vs a mean RTIR of 60.3%, with a mean percentage difference between these two metrics of โ8.4%. A significant decrease in conventional TIR was verified when tighter glucose targets were considered during the nighttime period. No significant correlation was found between the percentage difference values and RTIR, even among the group of patients with the lowest conventional TIR.Conclusions:Currently, meeting the conventional TIR metrics may fall short of achieving an ideal level of glycemic control. An individualized strategy should be adopted until further data become available for a precise definition of optimal glucose targets.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1910:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Hybrid closed-loop systems (HCLS) use has shown that time in range (TIR) tends to improve more during the nighttime than during the day. This study aims to compare the conventional TIR, currently accepted as 70 to 180 mg/dL, with a proposed recalculated time in range (RTIR) considering a tighter glucose target of 70 to 140 mg/dL for the nighttime fasting period in T1DM patients under HCLS.Methods:We conducted a retrospective study that included adults patients receiving treatment with Tandem t:slim X2 Control-IQ. Daytime TIR was characterized as glucose values between 70 and 180 mg/dL during the 07:01 to 23:59 time frame. Nighttime fasting TIR was specified as glucose values from 70 to 140 mg/dL between 00:00 and 07:00. The combination of the daytime and nighttime fasting glucose targets results in an RTIR, which was compared with the conventional TIR for each patient. The 14 days Dexcom G6 CGM data were downloaded from Tidepool platform and analyzed.Results:We included 22 patients with a mean age of 49.7 years and diabetes duration of 24.7 years, who had been using automatic insulin delivery (AID) HCLS for a median of 305.3 days. We verified a mean conventional TIR of 68.7% vs a mean RTIR of 60.3%, with a mean percentage difference between these two metrics of โ8.4%. A significant decrease in conventional TIR was verified when tighter glucose targets were considered during the nighttime period. No significant correlation was found between the percentage difference values and RTIR, even among the group of patients with the lowest conventional TIR.Conclusions:Currently, meeting the conventional TIR metrics may fall short of achieving an ideal level of glycemic control. An individualized strategy should be adopted until further data become available for a precise definition of optimal glucose targets.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"Time in Range Analysis in Automated Insulin Delivery Era: Should Day and Nighttime Targets be the Same?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236456";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-19T10:54:26Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:11:"Ariana Maia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:20:"David Subias Andujar";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Cristina Yuste";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Lara Albert";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Joana Vilaverde";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:20:"Maria Helena Cardoso";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Mercedes Rigla";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:103:"Time in Range Analysis in Automated Insulin Delivery Era: Should Day and Nighttime Targets be the Same?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236456";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236456?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:33;a:6:{s:4:"data";s:256:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231950?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:199:"Cost-Effectiveness of Closed-Loop Automated Insulin Delivery Using the Cambridge Hybrid Algorithm in Children and Adolescents with Type 1 Diabetes: Results from a Multicenter 6-Month Randomized Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231950?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2202:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background/Objective:The main objective of this study is to evaluate the incremental cost-effectiveness (ICER) of the Cambridge hybrid closed-loop automated insulin delivery (AID) algorithm versus usual care for children and adolescents with type 1 diabetes (T1D).Methods:This multicenter, binational, parallel-controlled trial randomized 133 insulin pump using participants aged 6 to 18 years to either AID (n = 65) or usual care (n = 68) for 6 months. Both within-trial and lifetime cost-effectiveness were analyzed. Analysis focused on the treatment subgroup (n = 21) who received the much more reliable CamAPS FX hardware iteration and their contemporaneous control group (n = 24). Lifetime complications and costs were simulated via an updated Sheffield T1D policy model.Results:Within-trial, both groups had indistinguishable and statistically unchanged health-related quality of life, and statistically similar hypoglycemia, severe hypoglycemia, and diabetic ketoacidosis (DKA) event rates. Total health care utilization was higher in the treatment group. Both the overall treatment group and CamAPS FX subgroup exhibited improved HbA1C (โ0.32%, 95% CI: โ0.59 to โ0.04; P = .02, and โ1.05%, 95% CI: โ1.43 to โ0.67; P < .001, respectively). Modeling projected increased expected lifespan of 5.36 years and discounted quality-adjusted life years (QALYs) of 1.16 (U.K. tariffs) and 1.52 (U.S. tariffs) in the CamAPS FX subgroup. Estimated ICERs for the subgroup were ยฃ19โ324/QALY (United Kingdom) and โ$3917/QALY (United States). For subgroup patients already using continuous glucose monitors (CGM), ICERs were ยฃ10โ096/QALY (United Kingdom) and โ$33โ616/QALY (United States). Probabilistic sensitivity analysis generated mean ICERs of ยฃ19โ342/QALY (95% CI: ยฃ15โ903/QALY to ยฃ22โ929/QALY) (United Kingdom) and โ$28โ283/QALY (95% CI: โ$59โ607/QALY to $1858/QALY) (United States).Conclusions:For children and adolescents with T1D on insulin pump therapy, AID using the Cambridge algorithm appears cost-effective below a ยฃ20โ000/QALY threshold (United Kingdom) and cost saving (United States).";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2205:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background/Objective:The main objective of this study is to evaluate the incremental cost-effectiveness (ICER) of the Cambridge hybrid closed-loop automated insulin delivery (AID) algorithm versus usual care for children and adolescents with type 1 diabetes (T1D).Methods:This multicenter, binational, parallel-controlled trial randomized 133 insulin pump using participants aged 6 to 18 years to either AID (n = 65) or usual care (n = 68) for 6 months. Both within-trial and lifetime cost-effectiveness were analyzed. Analysis focused on the treatment subgroup (n = 21) who received the much more reliable CamAPS FX hardware iteration and their contemporaneous control group (n = 24). Lifetime complications and costs were simulated via an updated Sheffield T1D policy model.Results:Within-trial, both groups had indistinguishable and statistically unchanged health-related quality of life, and statistically similar hypoglycemia, severe hypoglycemia, and diabetic ketoacidosis (DKA) event rates. Total health care utilization was higher in the treatment group. Both the overall treatment group and CamAPS FX subgroup exhibited improved HbA1C (โ0.32%, 95% CI: โ0.59 to โ0.04; P = .02, and โ1.05%, 95% CI: โ1.43 to โ0.67; P < .001, respectively). Modeling projected increased expected lifespan of 5.36 years and discounted quality-adjusted life years (QALYs) of 1.16 (U.K. tariffs) and 1.52 (U.S. tariffs) in the CamAPS FX subgroup. Estimated ICERs for the subgroup were ยฃ19โ324/QALY (United Kingdom) and โ$3917/QALY (United States). For subgroup patients already using continuous glucose monitors (CGM), ICERs were ยฃ10โ096/QALY (United Kingdom) and โ$33โ616/QALY (United States). Probabilistic sensitivity analysis generated mean ICERs of ยฃ19โ342/QALY (95% CI: ยฃ15โ903/QALY to ยฃ22โ929/QALY) (United Kingdom) and โ$28โ283/QALY (95% CI: โ$59โ607/QALY to $1858/QALY) (United States).Conclusions:For children and adolescents with T1D on insulin pump therapy, AID using the Cambridge algorithm appears cost-effective below a ยฃ20โ000/QALY threshold (United Kingdom) and cost saving (United States).";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:199:"Cost-Effectiveness of Closed-Loop Automated Insulin Delivery Using the Cambridge Hybrid Algorithm in Children and Adolescents with Type 1 Diabetes: Results from a Multicenter 6-Month Randomized Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241231950";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-18T06:53:09Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:23:{i:0;a:5:{s:4:"data";s:13:"D. 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Allen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:21:"Malgorzata E Wilinska";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:17:"Martin Tauschmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Louise Denvir";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:15:"Ajay Thankamony";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:14:"Fiona Campbell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:13:"R. Paul Wadwa";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:19:"Bruce A. 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Weinzimer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:17;a:5:{s:4:"data";s:14:"Lauren Kanapka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:18;a:5:{s:4:"data";s:13:"Craig Kollman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:19;a:5:{s:4:"data";s:12:"Judy Sibayan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:20;a:5:{s:4:"data";s:11:"Roy W. Beck";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:21;a:5:{s:4:"data";s:13:"Korey K. Hood";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:22;a:5:{s:4:"data";s:13:"Roman Hovorka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:199:"Cost-Effectiveness of Closed-Loop Automated Insulin Delivery Using the Cambridge Hybrid Algorithm in Children and Adolescents with Type 1 Diabetes: Results from a Multicenter 6-Month Randomized Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241231950";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231950?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:34;a:6:{s:4:"data";s:214:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236771?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Combining an Electrochemical Continuous Glucose Sensor With an Insulin Delivery Cannula: A Feasibility Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236771?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1807:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Combining a continuous glucose monitor with an insulin delivery cannula (CGM-IS) could benefit clinical outcomes. We evaluated the feasibility of a single-needle insertion electrochemical investigational CGM-IS (Pacific Diabetes Technologies, Portland, Oregon) in type 1 diabetes adults.Methods:Following 48 hours run-in using a Medtronic 780G in manual mode with a commercial insulin set, 12 participants commenced insulin delivery using the CGM-IS. A standardized test meal was eaten on the mornings of days 1 and 4. Venous samples were collected every 10 minutes one hour prior to and 15 minutes post-meal for four hours. CGM-IS glucose measurements were post-processed with a single capillary blood calibration during warm-up and benchmarked against YSI. A Dexcom G6 sensor was worn post-consent to study end.Results:Mean absolute relative difference (MARD) for the CGM-IS glucose measurements was 9.2% (484 paired data points). Consensus error grid revealed 88.6% within zone A and 100% in A + B. Mean (SD) % bias was โ3.5 (11.7) %. There were 35 paired YSI readings <100 mg/dL cutoff and 449 โฅ100 mg/dL with 81.4% within ยฑ15 mg/dL or ยฑ15%, and 89.9% within ยฑ20 mg/dL or ยฑ20%. Two cannula occlusions required discontinuation of insulin delivery: one at 70 hours post insertion and another during the day 4 meal test. Mean (SD) Dexcom glucose measurements during run-in and between meal tests was respectively 161.3 ยฑ 27.3 mg/dL versus 158.0 ยฑ 25.6 mg/dL; P = .39 and corresponding mean total daily insulin delivered by the pump was 58.0 ยฑ 25.4 Units versus 57.1 ยฑ 28.8 Units; P = .47.Conclusions:Insulin delivery and glucose sensing with the investigational CGM-IS was feasible. Longer duration studies are needed.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1810:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Combining a continuous glucose monitor with an insulin delivery cannula (CGM-IS) could benefit clinical outcomes. We evaluated the feasibility of a single-needle insertion electrochemical investigational CGM-IS (Pacific Diabetes Technologies, Portland, Oregon) in type 1 diabetes adults.Methods:Following 48 hours run-in using a Medtronic 780G in manual mode with a commercial insulin set, 12 participants commenced insulin delivery using the CGM-IS. A standardized test meal was eaten on the mornings of days 1 and 4. Venous samples were collected every 10 minutes one hour prior to and 15 minutes post-meal for four hours. CGM-IS glucose measurements were post-processed with a single capillary blood calibration during warm-up and benchmarked against YSI. A Dexcom G6 sensor was worn post-consent to study end.Results:Mean absolute relative difference (MARD) for the CGM-IS glucose measurements was 9.2% (484 paired data points). Consensus error grid revealed 88.6% within zone A and 100% in A + B. Mean (SD) % bias was โ3.5 (11.7) %. There were 35 paired YSI readings <100 mg/dL cutoff and 449 โฅ100 mg/dL with 81.4% within ยฑ15 mg/dL or ยฑ15%, and 89.9% within ยฑ20 mg/dL or ยฑ20%. Two cannula occlusions required discontinuation of insulin delivery: one at 70 hours post insertion and another during the day 4 meal test. Mean (SD) Dexcom glucose measurements during run-in and between meal tests was respectively 161.3 ยฑ 27.3 mg/dL versus 158.0 ยฑ 25.6 mg/dL; P = .39 and corresponding mean total daily insulin delivered by the pump was 58.0 ยฑ 25.4 Units versus 57.1 ยฑ 28.8 Units; P = .47.Conclusions:Insulin delivery and glucose sensing with the investigational CGM-IS was feasible. Longer duration studies are needed.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:108:"Combining an Electrochemical Continuous Glucose Sensor With an Insulin Delivery Cannula: A Feasibility Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236771";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-16T07:34:02Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:17:{i:0;a:5:{s:4:"data";s:13:"Cheng Yi Yuan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Bella Halim";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Yee W. Kong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:7:"Jean Lu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:22:"Ralph Dutt-Ballerstadt";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Peter Eckenberg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:10:"Ken Hillen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:9:"Anh Koski";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:15:"Vlad Milenkowic";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:11:"Emma Netzer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:18:"Varuni Obeyesekere";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:12:"Solomon Reid";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:13:"Catriona Sims";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:13;a:5:{s:4:"data";s:11:"Sara Vogrin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:14;a:5:{s:4:"data";s:12:"Huan-Ping Wu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:15;a:5:{s:4:"data";s:12:"Thomas Seidl";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:16;a:5:{s:4:"data";s:17:"David N. 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Its implementation is mandatory for manufacturers to obtain regulatory approval for the European market. The aim of this evaluation was assessing the role of usability testing in the development process. For this purpose, a continuous glucose monitoring (CGM) device under development was investigated to determine whether it could be used safely and effectively by the intended users.Methods:Conduct of the usability testing was based on the international standard IEC 62366-1. Medical device use of CGM-experienced and non-experienced users (n = 15 each) was observed without initial training in use scenarios containing 18 tasks. The success rate of task completion was determined and the System Usability Scale (SUS) score was calculated from a questionnaire. A prototype of the FiberSense CGM System (EyeSense GmbH, Groรostheim, Germany), comprising of a single-use sensor and a reusable detector, was investigated.Results:Most use errors made by both user groups were related to ease of handling of the reusable detectors. The SUS scores achieved in this study were below the pre-defined SUS score acceptance criterion of โฅ68. The most frequently mentioned reason for use errors was an incomprehensible and non-chronological instructions for use (IFU).Conclusions:The evaluation provides valuable insights on how to improve usability of the prototype device and demonstrates the value of conducting structured usability testing prior to product finalization. The results reflected areas for improvement of the user interface, mainly by restructuring the IFU, provision of an additional leaflet, and device training prior to use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1801:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Usability engineering analyzes the interaction between the intended users and a device. Its implementation is mandatory for manufacturers to obtain regulatory approval for the European market. The aim of this evaluation was assessing the role of usability testing in the development process. For this purpose, a continuous glucose monitoring (CGM) device under development was investigated to determine whether it could be used safely and effectively by the intended users.Methods:Conduct of the usability testing was based on the international standard IEC 62366-1. Medical device use of CGM-experienced and non-experienced users (n = 15 each) was observed without initial training in use scenarios containing 18 tasks. The success rate of task completion was determined and the System Usability Scale (SUS) score was calculated from a questionnaire. A prototype of the FiberSense CGM System (EyeSense GmbH, Groรostheim, Germany), comprising of a single-use sensor and a reusable detector, was investigated.Results:Most use errors made by both user groups were related to ease of handling of the reusable detectors. The SUS scores achieved in this study were below the pre-defined SUS score acceptance criterion of โฅ68. The most frequently mentioned reason for use errors was an incomprehensible and non-chronological instructions for use (IFU).Conclusions:The evaluation provides valuable insights on how to improve usability of the prototype device and demonstrates the value of conducting structured usability testing prior to product finalization. The results reflected areas for improvement of the user interface, mainly by restructuring the IFU, provision of an additional leaflet, and device training prior to use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:154:"Benefits of Usability Evaluation in the Development Process of Diabetes Technologies Using the Example of a Continuous Glucose Monitoring System Prototype";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241238146";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-13T10:46:31Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:12:"Anne Beltzer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Julia Kรถlle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Marta Gil Mirรณ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Stefan Pleus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Collin Krauss";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Cornelia Haug";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:12:"Elvis Safary";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:15:"Beatrice Vetter";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:15:"Guido Freckmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:154:"Benefits of Usability Evaluation in the Development Process of Diabetes Technologies Using the Example of a Continuous Glucose Monitoring System Prototype";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241238146";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241238146?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:36;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236504?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236504?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1881:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the userโs attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Instituteโs POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1881:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the userโs attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Instituteโs POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236504";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-13T10:43:58Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:12:"Stefan Pleus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Manuel Eichenlaub";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Delia Waldenmaier";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Guido Freckmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:109:"A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236504";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236504?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:37;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231565?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:148:"Should We Stop Glucagon-Like Peptide-1 Receptor Agonists Before Surgical or Endoscopic Procedures? Balancing Limited Evidence With Clinical Judgment";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231565?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:947:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>The American Society of Anesthesiologists (ASA) Task Force recently recommended discontinuing glucagon-like peptide-1 receptor agonist (GLP-1 RA) agents before surgery because of the potential risk of pulmonary aspiration. However, there is limited scientific evidence to support this recommendation, and holding GLP-1 RA treatment may worsen glycemic control in patients with diabetes. As we await further safety data to manage GLP-1 RA in the perioperative period, we suggest an alternative multidisciplinary approach to manage patients undergoing elective surgery. Well-conducted observational and prospective studies are needed to determine the risk of pulmonary aspiration in persons receiving GLP-1 RA for the treatment of diabetes and obesity, as well as the short-term impact of discontinuing GLP-1 RA on glycemic control before elective procedures in persons with diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:947:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>The American Society of Anesthesiologists (ASA) Task Force recently recommended discontinuing glucagon-like peptide-1 receptor agonist (GLP-1 RA) agents before surgery because of the potential risk of pulmonary aspiration. However, there is limited scientific evidence to support this recommendation, and holding GLP-1 RA treatment may worsen glycemic control in patients with diabetes. As we await further safety data to manage GLP-1 RA in the perioperative period, we suggest an alternative multidisciplinary approach to manage patients undergoing elective surgery. Well-conducted observational and prospective studies are needed to determine the risk of pulmonary aspiration in persons receiving GLP-1 RA for the treatment of diabetes and obesity, as well as the short-term impact of discontinuing GLP-1 RA on glycemic control before elective procedures in persons with diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:148:"Should We Stop Glucagon-Like Peptide-1 Receptor Agonists Before Surgical or Endoscopic Procedures? Balancing Limited Evidence With Clinical Judgment";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241231565";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-11T11:48:01Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:19:"Guillermo Umpierrez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:20:"Francisco J. Pasquel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Elizabeth Duggan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Rodolfo J. Galindo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:148:"Should We Stop Glucagon-Like Peptide-1 Receptor Agonists Before Surgical or Endoscopic Procedures? 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The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated.Results:Data from 476 adults with type 1 diabetes were analyzed. A participantโs change in glucose during exercise was reproducible within 15 mg/dL of the participantโs other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose.Conclusions:Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participantโs overall glycemic control than other modifiable factors.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1507:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aims:To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise.Methods:Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated.Results:Data from 476 adults with type 1 diabetes were analyzed. A participantโs change in glucose during exercise was reproducible within 15 mg/dL of the participantโs other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose.Conclusions:Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participantโs overall glycemic control than other modifiable factors.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234687";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-08T11:43:56Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:13:{i:0;a:5:{s:4:"data";s:7:"Zoey Li";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Peter Calhoun";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Michael R. Rickels";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Robin L. Gal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Roy W. Beck";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Peter G. Jacobs";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Mark A. Clements";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"Susana R. Patton";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:17:"Jessica R. Castle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:15:"Corby K. Martin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:21:"Melanie B. Gillingham";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:16:"Francis J. Doyle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:18:"Michael C. Riddell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:128:"Factors Affecting Reproducibility of Change in Glucose During Exercise: Results From the Type 1 Diabetes and EXercise Initiative";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234687";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234687?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:39;a:6:{s:4:"data";s:165:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234055?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:182:"A Three-Arm Randomized Controlled Study Comparing Patient-Reported Outcomes in People With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234055?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1805:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The aim of this study was to compare patient-reported outcomes (PROs) in people with type 1 diabetes using either continuous subcutaneous insulin infusion (CSII) with two different insulin patch pumps or multiple daily injections (MDIs).Materials and methods:In this randomized three-arm study, people with type 1 diabetes on MDI therapy were included and used either MDI, the Accu-Chek Solo micropump system (Solo) or Omnipod for 26 weeks. From weeks 26 to 39, all participants used CSII with Solo. Patient-reported outcomes were assessed using the diabetes technology questionnaire (DTQ); in addition, HbA1c values were measured.Results:Overall, 181 participants were randomized (61 MDI arm, 62 Solo arm, 58 Omnipod arm) and 142 completed the study. After 26 weeks in the study, the DTQ โchangeโ score in the Solo group (105.9 [100.6-111.2]; baseline-adjusted mean [95% confidence interval]) was significantly higher than in the MDI group (94.8 [89.6-100.0]) (P = .001). The comparison between the Solo group (105.1 [99.1-111.1]) and the Omnipod group (108.7 [103.1-114.4]) showed no significant differences (P = .382). HbA1c increased by 0.2% ยฑ 0.7% in the MDI group and decreased in both pump groups (Solo group โ0.2% ยฑ 0.8% and Omnipod group โ0.1% ยฑ 0.8%). Differences in HbA1c between the Solo group and the MDI group were significant (P = .009), but not between the Solo group and the Omnipod group (P = .896).Conclusions:This study showed that switching from MDI to CSII improves both psychosocial well-being and physiological outcomes. Furthermore, there were no substantial differences between the established and the recently released patch pump. Trial registration at www.clinicaltrials.gov is NCT03478969.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1805:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The aim of this study was to compare patient-reported outcomes (PROs) in people with type 1 diabetes using either continuous subcutaneous insulin infusion (CSII) with two different insulin patch pumps or multiple daily injections (MDIs).Materials and methods:In this randomized three-arm study, people with type 1 diabetes on MDI therapy were included and used either MDI, the Accu-Chek Solo micropump system (Solo) or Omnipod for 26 weeks. From weeks 26 to 39, all participants used CSII with Solo. Patient-reported outcomes were assessed using the diabetes technology questionnaire (DTQ); in addition, HbA1c values were measured.Results:Overall, 181 participants were randomized (61 MDI arm, 62 Solo arm, 58 Omnipod arm) and 142 completed the study. After 26 weeks in the study, the DTQ โchangeโ score in the Solo group (105.9 [100.6-111.2]; baseline-adjusted mean [95% confidence interval]) was significantly higher than in the MDI group (94.8 [89.6-100.0]) (P = .001). The comparison between the Solo group (105.1 [99.1-111.1]) and the Omnipod group (108.7 [103.1-114.4]) showed no significant differences (P = .382). HbA1c increased by 0.2% ยฑ 0.7% in the MDI group and decreased in both pump groups (Solo group โ0.2% ยฑ 0.8% and Omnipod group โ0.1% ยฑ 0.8%). Differences in HbA1c between the Solo group and the MDI group were significant (P = .009), but not between the Solo group and the Omnipod group (P = .896).Conclusions:This study showed that switching from MDI to CSII improves both psychosocial well-being and physiological outcomes. Furthermore, there were no substantial differences between the established and the recently released patch pump. Trial registration at www.clinicaltrials.gov is NCT03478969.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:182:"A Three-Arm Randomized Controlled Study Comparing Patient-Reported Outcomes in People With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234055";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-08T11:37:45Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:10:{i:0;a:5:{s:4:"data";s:23:"Katharine Barnard-Kelly";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Florian Thienel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Julia K. Mader";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Nick Oliver";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Edward Franek";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Iris Vesper";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Nicole Dagenbach";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:12:"Gerhard Vogt";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:12:"Tobias Etter";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:16:"Thomas Kรผnsting";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:182:"A Three-Arm Randomized Controlled Study Comparing Patient-Reported Outcomes in People With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion or Multiple Daily Injections";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234055";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234055?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:40;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236208?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236208?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1728:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D) care based on retrospective continuous glucose monitoring (CGM) data. Few methods are available to estimate the likelihood of a patient experiencing clinically significant hypoglycemia within one week.Methods:We developed a machine learning model to estimate the probability that a patient will experience a clinically significant hypoglycemic event, defined as CGM readings below 54 mg/dL for at least 15 consecutive minutes, within one week. The model takes as input the patientโs CGM time series over a given week, and outputs the predicted probability of a clinically significant hypoglycemic event the following week. We used 10-fold cross-validation and external validation (testing on cohorts different from the training cohort) to evaluate performance. We used CGM data from three different cohorts of patients with T1D: REPLACE-BG (226 patients), Juvenile Diabetes Research Foundation (JDRF; 355 patients) and Tidepool (120 patients).Results:In 10-fold cross-validation, the average area under the receiver operating characteristic curve (ROC-AUC) was 0.77 (standard deviation [SD]: 0.0233) on the REPLACE-BG cohort, 0.74 (SD: 0.0188) on the JDRF cohort, and 0.76 (SD: 0.02) on the Tidepool cohort. In external validation, the average ROC-AUC across the three cohorts was 0.74 (SD: 0.0262).Conclusions:We developed a machine learning algorithm to estimate the probability of a clinically significant hypoglycemic event within one week. Predictive algorithms may provide diabetes care providers using RPM with additional context when prioritizing T1D patients for review.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1728:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D) care based on retrospective continuous glucose monitoring (CGM) data. Few methods are available to estimate the likelihood of a patient experiencing clinically significant hypoglycemia within one week.Methods:We developed a machine learning model to estimate the probability that a patient will experience a clinically significant hypoglycemic event, defined as CGM readings below 54 mg/dL for at least 15 consecutive minutes, within one week. The model takes as input the patientโs CGM time series over a given week, and outputs the predicted probability of a clinically significant hypoglycemic event the following week. We used 10-fold cross-validation and external validation (testing on cohorts different from the training cohort) to evaluate performance. We used CGM data from three different cohorts of patients with T1D: REPLACE-BG (226 patients), Juvenile Diabetes Research Foundation (JDRF; 355 patients) and Tidepool (120 patients).Results:In 10-fold cross-validation, the average area under the receiver operating characteristic curve (ROC-AUC) was 0.77 (standard deviation [SD]: 0.0233) on the REPLACE-BG cohort, 0.74 (SD: 0.0188) on the JDRF cohort, and 0.76 (SD: 0.02) on the Tidepool cohort. In external validation, the average ROC-AUC across the three cohorts was 0.74 (SD: 0.0262).Conclusions:We developed a machine learning algorithm to estimate the probability of a clinically significant hypoglycemic event within one week. Predictive algorithms may provide diabetes care providers using RPM with additional context when prioritizing T1D patients for review.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:103:"A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236208";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-06T12:48:53Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:17:"Flavia Giammarino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Ransalu Senanayake";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Priya Prahalad";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"David M. Maahs";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"David Scheinker";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:103:"A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236208";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236208?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:41;a:6:{s:4:"data";s:179:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234072?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234072?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1764:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study.Methods:Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A1c (HbA1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose.Results:All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A1c decreased from 7.69% at baseline to 6.87% at 13 weeks (โ0.82%, P < .001).Conclusions:Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1779:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study.Methods:Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A1c (HbA1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose.Results:All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A1c decreased from 7.69% at baseline to 6.87% at 13 weeks (โ0.82%, P < .001).Conclusions:Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234072";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-05T08:00:05Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:12:{i:0;a:5:{s:4:"data";s:17:"Anders L. Carlson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Timothy E. Graham";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Halis K. Akturk";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:20:"David R. Liljenquist";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:21:"Richard M. Bergenstal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Becky Sulik";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Viral N. Shah";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:10:"Mark Sulik";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:10:"Peter Zhao";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:12:"Peter Briggs";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:23:"Ravid Sassan-Katchalski";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:17:"Jordan E. Pinsker";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:117:"Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241234072";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241234072?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:42;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231279?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"A Scoping Review of Wearable Technologies for Use in Individuals With Intellectual Disabilities and Diabetic Peripheral Neuropathy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231279?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1730:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Individuals with intellectual disabilities (IDs) are at risk of diabetes mellitus (DM) and diabetic peripheral neuropathy (DPN), which can lead to foot ulcers and lower-extremity amputations. However, cognitive differences and communication barriers may impede some methods for screening and prevention of DPN. Wearable and mobile technologiesโsuch as smartphone apps and pressure-sensitive insolesโcould help to offset these barriers, yet little is known about the effectiveness of these technologies among individuals with ID.Methods:We conducted a scoping review of the databases Embase, PubMed, and Web of Science using search terms for DM, DPN, ID, and technology to diagnose or monitor DPN. Finding a lack of research in this area, we broadened our search terms to include any literature on technology to diagnose or monitor DPN and then applied these findings within the context of ID.Results:We identified 88 articles; 43 of 88 (48.9%) articles were concerned with gait mechanics or foot pressures. No articles explicitly included individuals with ID as the target population, although three articles involved individuals with other cognitive impairments (two among patients with a history of stroke, one among patients with hemodialysis-related cognitive changes).Conclusions:Individuals with ID are not represented in studies using technology to diagnose or monitor DPN. This is a concern given the risk of DM complications among patients with ID and the potential for added benefit of such technologies to reduce barriers to screening and prevention. More studies should investigate how wearable devices can be used among patients with ID.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1730:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Individuals with intellectual disabilities (IDs) are at risk of diabetes mellitus (DM) and diabetic peripheral neuropathy (DPN), which can lead to foot ulcers and lower-extremity amputations. However, cognitive differences and communication barriers may impede some methods for screening and prevention of DPN. Wearable and mobile technologiesโsuch as smartphone apps and pressure-sensitive insolesโcould help to offset these barriers, yet little is known about the effectiveness of these technologies among individuals with ID.Methods:We conducted a scoping review of the databases Embase, PubMed, and Web of Science using search terms for DM, DPN, ID, and technology to diagnose or monitor DPN. Finding a lack of research in this area, we broadened our search terms to include any literature on technology to diagnose or monitor DPN and then applied these findings within the context of ID.Results:We identified 88 articles; 43 of 88 (48.9%) articles were concerned with gait mechanics or foot pressures. No articles explicitly included individuals with ID as the target population, although three articles involved individuals with other cognitive impairments (two among patients with a history of stroke, one among patients with hemodialysis-related cognitive changes).Conclusions:Individuals with ID are not represented in studies using technology to diagnose or monitor DPN. This is a concern given the risk of DM complications among patients with ID and the potential for added benefit of such technologies to reduce barriers to screening and prevention. More studies should investigate how wearable devices can be used among patients with ID.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"A Scoping Review of Wearable Technologies for Use in Individuals With Intellectual Disabilities and Diabetic Peripheral Neuropathy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241231279";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-05T07:14:11Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:15:"Ercole Barsotti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Bailey Goodman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Riley Samuelson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Martha L. Carvour";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:130:"A Scoping Review of Wearable Technologies for Use in Individuals With Intellectual Disabilities and Diabetic Peripheral Neuropathy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241231279";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231279?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:43;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236458?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:62:"An Augmented Vision of Our Medical and Surgical Future, Today?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236458?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1006:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Incorporating consumer electronics into the operating room, we evaluated the Apple Vision Pro (AVP) during limb preservation surgeries, just as we evaluated Google Glass and FaceTime more than a decade ago. Although AVPโs real-time mixed-reality data overlay and controls offer potential enhancements to surgical precision and team communication, our assessment recognized limitations in adapting consumer technology to clinical environments. The initial use facilitated intraoperative decision-making and educational interactions with trainees. The current mixed-reality pass-through resolution allows for input but not for highly dexterous surgical interactions. These early observations indicate that while AVP may soon improve aspects of surgical performance and education, further iteration, evaluation, and experience are needed to fully understand its impact on patient outcomes and to refine its integration into clinical practice.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1006:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Incorporating consumer electronics into the operating room, we evaluated the Apple Vision Pro (AVP) during limb preservation surgeries, just as we evaluated Google Glass and FaceTime more than a decade ago. Although AVPโs real-time mixed-reality data overlay and controls offer potential enhancements to surgical precision and team communication, our assessment recognized limitations in adapting consumer technology to clinical environments. The initial use facilitated intraoperative decision-making and educational interactions with trainees. The current mixed-reality pass-through resolution allows for input but not for highly dexterous surgical interactions. These early observations indicate that while AVP may soon improve aspects of surgical performance and education, further iteration, evaluation, and experience are needed to fully understand its impact on patient outcomes and to refine its integration into clinical practice.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:62:"An Augmented Vision of Our Medical and Surgical Future, Today?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236458";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-05T07:13:33Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:18:"David G. Armstrong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Sebouh Bazikian";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:23:"Alexandria A. Armstrong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Giacomo Clerici";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Andrea Casini";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Anand Pillai";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:62:"An Augmented Vision of Our Medical and Surgical Future, Today?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241236458";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241236458?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:44;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232686?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Intermittently Scanned Continuous Glucose Monitoring Performance in Patients With Liver Cirrhosis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232686?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1711:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:To evaluate the use of intermittently scanned continuous glucose monitoring (isCGM) in patients with liver cirrhosis (LC).Methods:Observational study including 30 outpatients with LC (Child-Pugh B/C): 10 without diabetes (DM) (G1), 10 with newly diagnosed DM by oral glucose tolerance test (G2), and 10 with a previous DM diagnosis (G3). isCGM (FreeStyle Libre Pro) was used for 56 days (four sensors/patient). Blood tests were performed at baseline and after 28 and 56 days.Results:No differences were found in the baseline characteristics, except for higher age in G3. There were significant differences between G1, G2 and G3 in glucose management indicator (GMI) (5.28 ยฑ 0.17, 6.03 ยฑ 0.59, 6.86 ยฑ 1.08%, P < .001), HbA1c (4.82 ยฑ 0.39, 5.34 ยฑ 1.26, 6.97 ยฑ 1.47%, P < .001), average glucose (82.79 ยฑ 7.06, 113.39 ยฑ 24.32, 149.14 ยฑ 45.31mg/dL, P < .001), time in range (TIR) (70.89 ยฑ 9.76, 80.2 ยฑ 13.55, 57.96 ยฑ 17.96%, P = .006), and glucose variability (26.1 ยฑ 5.0, 28.21 ยฑ 5.39, 35.31 ยฑ 6.85%, P = .004). There was discordance between GMI and HbA1c when all groups were considered together, with a mean difference of 0.35% (95% SD 0.17, 0.63). In G1, the mean difference was 0.46% (95% SD 0.19, 0.73) and in G2 0.69% (95% SD 0.45, 1.33). GMI and HbA1c were concordant in G3, with a mean difference of โ0.10 % (95% SD [โ0.59, 0.38]).Conclusion:Disagreements were found between the GMI and HbA1c levels in patients with LC. isCGM was able to detect abnormalities in glycemic control that would not be detected by monitoring with HbA1c, suggesting that isCGM can be useful in assessing glycemic control in patients with LC.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1720:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:To evaluate the use of intermittently scanned continuous glucose monitoring (isCGM) in patients with liver cirrhosis (LC).Methods:Observational study including 30 outpatients with LC (Child-Pugh B/C): 10 without diabetes (DM) (G1), 10 with newly diagnosed DM by oral glucose tolerance test (G2), and 10 with a previous DM diagnosis (G3). isCGM (FreeStyle Libre Pro) was used for 56 days (four sensors/patient). Blood tests were performed at baseline and after 28 and 56 days.Results:No differences were found in the baseline characteristics, except for higher age in G3. There were significant differences between G1, G2 and G3 in glucose management indicator (GMI) (5.28 ยฑ 0.17, 6.03 ยฑ 0.59, 6.86 ยฑ 1.08%, P < .001), HbA1c (4.82 ยฑ 0.39, 5.34 ยฑ 1.26, 6.97 ยฑ 1.47%, P < .001), average glucose (82.79 ยฑ 7.06, 113.39 ยฑ 24.32, 149.14 ยฑ 45.31mg/dL, P < .001), time in range (TIR) (70.89 ยฑ 9.76, 80.2 ยฑ 13.55, 57.96 ยฑ 17.96%, P = .006), and glucose variability (26.1 ยฑ 5.0, 28.21 ยฑ 5.39, 35.31 ยฑ 6.85%, P = .004). There was discordance between GMI and HbA1c when all groups were considered together, with a mean difference of 0.35% (95% SD 0.17, 0.63). In G1, the mean difference was 0.46% (95% SD 0.19, 0.73) and in G2 0.69% (95% SD 0.45, 1.33). GMI and HbA1c were concordant in G3, with a mean difference of โ0.10 % (95% SD [โ0.59, 0.38]).Conclusion:Disagreements were found between the GMI and HbA1c levels in patients with LC. isCGM was able to detect abnormalities in glycemic control that would not be detected by monitoring with HbA1c, suggesting that isCGM can be useful in assessing glycemic control in patients with LC.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:97:"Intermittently Scanned Continuous Glucose Monitoring Performance in Patients With Liver Cirrhosis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232686";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-03-05T07:13:12Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:24:"Fernanda Augustini Rigon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:24:"Marcelo Fernando Ronsoni";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Alexandre Hohl";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:27:"Andrรฉ Gustavo Daher Vianna";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:23:"Simone van de Sande-Lee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:26:"Leonardo de Lucca Schiavon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:97:"Intermittently Scanned Continuous Glucose Monitoring Performance in Patients With Liver Cirrhosis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232686";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232686?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:45;a:6:{s:4:"data";s:186:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232709?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:186:"Safety and Efficacy of Switching SAR341402 Insulin Aspart and Originator Insulin Aspart vs Continuous Use of Originator Insulin Aspart in Adults With Type 1 Diabetes: The GEMELLI X Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232709?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2116:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:SAR341402 insulin aspart (SAR-Asp) is a rapid-acting insulin analog developed as an interchangeable biosimilar to the marketed insulin aspart reference product (NovoLog; NN-Asp). GEMELLI X was a randomized controlled trial to assess outcomes with a biosimilar in line with the US Food and Drug Administration requirements for designation as an interchangeable biosimilar. This report assessed whether multiple switches between SAR-Asp and NN-Asp lead to equivalent safety and efficacy compared with continuous use of NN-Asp in adults with type 1 diabetes (T1D) treated with multiple daily injections, using once-daily insulin glargine U100 (Lantus) as the basal insulin.Methods:This open-label randomized (1:1), parallel-group, phase 3 trial compared four ร four weeks of alternating use of individually titrated SAR-Asp and NN-Asp (NN-Asp for first four weeks, SAR-Asp in last four weeks; switching group) vs 16 weeks of continuous use of NN-Asp (nonswitching group). End points included pharmacokinetics, immunogenicity, adverse events, hypoglycemia, insulin dose, and change in efficacy parameters.Results:Of the 210 patients randomized, 200 (95.5%) completed the trial. Patients assigned to switching group (n = 104) and nonswitching group (n = 106) showed similar safety and tolerability, including anti-insulin aspart antibody responses, adverse events, and hypoglycemia. At week 16, there was no relevant difference between switching vs nonswitching groups in the change from baseline in glycated hemoglobin (least square [LS] mean difference = 0.05% [95% confidence interval [CI] = โ0.13, 0.22]; 0.50 mmol/mol [โ1.40, 2.39]), fasting plasma glucose (LS mean difference = 0.23 mmol/L [95% CI = โ1.08, 1.53]; 4.12 mg/dL [โ19.38, 27.62]), and changes in insulin dosages.Conclusions:Alternating doses of SAR-Asp and NN-Asp compared with continuous use of NN-Asp showed similar safety, immunogenicity, and clinical efficacy in adults with T1D. This study supports interchangeability between SAR-Asp and NN-Asp in T1D management.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2116:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:SAR341402 insulin aspart (SAR-Asp) is a rapid-acting insulin analog developed as an interchangeable biosimilar to the marketed insulin aspart reference product (NovoLog; NN-Asp). GEMELLI X was a randomized controlled trial to assess outcomes with a biosimilar in line with the US Food and Drug Administration requirements for designation as an interchangeable biosimilar. This report assessed whether multiple switches between SAR-Asp and NN-Asp lead to equivalent safety and efficacy compared with continuous use of NN-Asp in adults with type 1 diabetes (T1D) treated with multiple daily injections, using once-daily insulin glargine U100 (Lantus) as the basal insulin.Methods:This open-label randomized (1:1), parallel-group, phase 3 trial compared four ร four weeks of alternating use of individually titrated SAR-Asp and NN-Asp (NN-Asp for first four weeks, SAR-Asp in last four weeks; switching group) vs 16 weeks of continuous use of NN-Asp (nonswitching group). End points included pharmacokinetics, immunogenicity, adverse events, hypoglycemia, insulin dose, and change in efficacy parameters.Results:Of the 210 patients randomized, 200 (95.5%) completed the trial. Patients assigned to switching group (n = 104) and nonswitching group (n = 106) showed similar safety and tolerability, including anti-insulin aspart antibody responses, adverse events, and hypoglycemia. At week 16, there was no relevant difference between switching vs nonswitching groups in the change from baseline in glycated hemoglobin (least square [LS] mean difference = 0.05% [95% confidence interval [CI] = โ0.13, 0.22]; 0.50 mmol/mol [โ1.40, 2.39]), fasting plasma glucose (LS mean difference = 0.23 mmol/L [95% CI = โ1.08, 1.53]; 4.12 mg/dL [โ19.38, 27.62]), and changes in insulin dosages.Conclusions:Alternating doses of SAR-Asp and NN-Asp compared with continuous use of NN-Asp showed similar safety, immunogenicity, and clinical efficacy in adults with T1D. This study supports interchangeability between SAR-Asp and NN-Asp in T1D management.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:186:"Safety and Efficacy of Switching SAR341402 Insulin Aspart and Originator Insulin Aspart vs Continuous Use of Originator Insulin Aspart in Adults With Type 1 Diabetes: The GEMELLI X Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232709";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-29T12:36:38Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:13:{i:0;a:5:{s:4:"data";s:13:"Viral N. 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As such, only few approaches that examine the trend accuracy have been put forward. In this article, we review existing approaches and propose the clinical trend concurrence analysis (CTCA) which is an adaptation of the conventional trend concurrence analysis. The CTCA is intended to directly evaluate the trend arrows displayed by the CGM systems by characterizing their agreement to suitably categorized comparator RoCs. Here, we call on manufactures of CGM systems to provide the displayed trend arrows for retrospective analysis. The CTCA classifies any deviations between the CGM trend and comparator RoC according to their risk for an adverse clinical event arising from a possibly erroneous treatment decision. For that, the existing rate error grid analysis and a specific set of trend arrow-based insulin dosing recommendations were used. The results of the CTCA are presented in an accessible graphical display and exemplified on data from three CGM systems. 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It focuses on shield size and applied force, parameters that could potentially lead to inadvertent intramuscular (IM) injections due to tissue compression.Method:A blinded ex-vivo study was performed to assess the impact of shield size and applied force on injection depth. Shields of 15, 20, and 30 mm diameters and forces from 2 to 10 N were investigated. The study involved 55 injections in three Landrace, Yorkshire, and Duroc (LYD) pigs, with injection depths measured with computed tomography (CT). An in-vivo study, involving 20 injections in three LYD pigs, controlled the findings, using fluoroscopy (FS) videos for depth measurement.Results:The CT study revealed that smaller shield sizes significantly increased injection depth. With a 15 mm diameter shield, 10 N applied force, and 5 mm needle protrusion, the injection depth exceeded the needle length by over 3 mm. Injection depth increased with higher applied forces until a plateau was reached around 8 N. Both applied force and size were significant factors for injection depth (analysis of variance [ANOVA], P < .05) in the CT study. The FS study confirmed the ex-vivo findings in an in-vivo setting.Conclusions:The study demonstrates that shield size has a greater impact on injection depth than the applied force. While conducted in porcine tissue, the study provides useful insights into the relative effects of shield size and applied force. Further investigations in humans are needed to confirm the predicted injection depths for AIs.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1705:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This study examines how shield-triggered autoinjectors (AIs), for subcutaneous drug delivery, affect injection depth. It focuses on shield size and applied force, parameters that could potentially lead to inadvertent intramuscular (IM) injections due to tissue compression.Method:A blinded ex-vivo study was performed to assess the impact of shield size and applied force on injection depth. Shields of 15, 20, and 30 mm diameters and forces from 2 to 10 N were investigated. The study involved 55 injections in three Landrace, Yorkshire, and Duroc (LYD) pigs, with injection depths measured with computed tomography (CT). An in-vivo study, involving 20 injections in three LYD pigs, controlled the findings, using fluoroscopy (FS) videos for depth measurement.Results:The CT study revealed that smaller shield sizes significantly increased injection depth. With a 15 mm diameter shield, 10 N applied force, and 5 mm needle protrusion, the injection depth exceeded the needle length by over 3 mm. Injection depth increased with higher applied forces until a plateau was reached around 8 N. 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Patterns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232378?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1732:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.Methods:We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called โCGM events.โ We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.Results:The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.Conclusions:Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1732:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.Methods:We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called โCGM events.โ We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.Results:The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.Conclusions:Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:134:"The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232378";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-19T11:53:54Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:14:"Mansur Shomali";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Shiping Liu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Abhimanyu Kumbara";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"Anand Iyer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:20:"Guodong (Gordon) Gao";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:134:"The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241232378";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241232378?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:51;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231307?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:131:"Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241231307?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1716:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited.Methods:A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]).Results:GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference โ22.8 [โ27.2, โ18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms (r = โ.954; P = .001), and positively with TAR >250 mg/dL (r = .901; P = .001), TBR < 54 mg/dL (r = .416; P = .001), and glycemic variability (SD [r = .916] and CV [r = .732]; P = .001 for both).Conclusions:Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1722:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited.Methods:A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]).Results:GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference โ22.8 [โ27.2, โ18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms (r = โ.954; P = .001), and positively with TAR >250 mg/dL (r = .901; P = .001), TBR < 54 mg/dL (r = .416; P = .001), and glycemic variability (SD [r = .916] and CV [r = .732]; P = .001 for both).Conclusions:Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:131:"Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241231307";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-19T11:51:35Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:14:"Melissa H. 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At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.Materials and Methods:A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.Results:The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).Conclusion:In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1759:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.Materials and Methods:A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.Results:The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).Conclusion:In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:148:"Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241230106";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-17T07:10:28Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:12:"Anna Schรผtz";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Birgit Rami-Merhar";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:23:"Ingrid Schรผtz-Fuhrmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:20:"Nicole Blauensteiner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Petra Baumann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Tina Pรถttler";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Julia K. 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Among other factors, endogenous and exogenous substances present in blood samples can impact the measurement results. To ensure and prove that blood glucose monitoring systems (BGMSs) are robust in terms of potential interferents, manufacturers have to perform extensive evaluations.Method:An interference screening test was performed for three reagent system lots of a POCT system and of a BGMS for self-monitoring of BG. A paired-difference approach based on ISO 15197:2013 and CLSI guideline EP07 was used with venous whole blood samples at two different glucose concentrations. Seventy potential interferents expected to be common in people with diabetes were evaluated.Results:The interference effects were determined as normalized biases between test samples and corresponding control samples. For 69 of the 70 investigated potential interferents, both systems met the predefined acceptance criteria, with the normalized biases falling within ยฑ10 mg/dL or ยฑ10% at glucose concentrations โค100 mg/dL or >100 mg/dL, respectively, for each of the three evaluated reagent system lots.Conclusions:The BGMS investigated in this study were found to be robust with respect to the 70 evaluated potential interferents. Interference effects were observed only for N-Acetyl-L-cysteine. Extensive evaluations of potential interfering factors can make an important contribution to ensure reliability of BGMS.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1724:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Reliable blood glucose (BG) measurements are important for people with diabetes to manage their therapy as well as in point-of-care testing (POCT) performed by health care professionals to monitor BG of patients or even to diagnose diabetes. Among other factors, endogenous and exogenous substances present in blood samples can impact the measurement results. To ensure and prove that blood glucose monitoring systems (BGMSs) are robust in terms of potential interferents, manufacturers have to perform extensive evaluations.Method:An interference screening test was performed for three reagent system lots of a POCT system and of a BGMS for self-monitoring of BG. A paired-difference approach based on ISO 15197:2013 and CLSI guideline EP07 was used with venous whole blood samples at two different glucose concentrations. Seventy potential interferents expected to be common in people with diabetes were evaluated.Results:The interference effects were determined as normalized biases between test samples and corresponding control samples. For 69 of the 70 investigated potential interferents, both systems met the predefined acceptance criteria, with the normalized biases falling within ยฑ10 mg/dL or ยฑ10% at glucose concentrations โค100 mg/dL or >100 mg/dL, respectively, for each of the three evaluated reagent system lots.Conclusions:The BGMS investigated in this study were found to be robust with respect to the 70 evaluated potential interferents. Interference effects were observed only for N-Acetyl-L-cysteine. Extensive evaluations of potential interfering factors can make an important contribution to ensure reliability of BGMS.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Investigation of the Effect of 70 Potential Interferents on Measurement Results of Two Blood Glucose Monitoring 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";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241230337?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Comparability Evaluation of Three Benchtop Glucose Analyzers With the Recently Withdrawn YSI 2300 Stat Plus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241230337?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:995:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:We compared the performance of three currently available laboratory benchtop glucose analyzers with the outgoing YSI 2300 Stat Plus.Methods:Plasma samples (100), across a wide glucose concentration range were analysed on the YSI 2500, Randox daytona+ (glucose oxidase) and EKF Biosen in a single laboratory and compared to the YSI 2300 Stat Plus.Results:All three analyzers showed good agreement with the YSI 2300 Stat Plus, and only a small bias (โค1% YSI 2500 and Randox daytona+, 4.6% EKF Biosen) was observed for each analyzer. None of the three comparator analyzers were affected by either proportional or constant bias, thus no significant differences between the YSI 2300 Stat Plus and the comparator methods were identified.Conclusions:The results from this study suggest all could be considered as suitable reference laboratory glucose analyzers and replacements for the recently withdrawn YSI 2300 Stat Plus.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:995:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:We compared the performance of three currently available laboratory benchtop glucose analyzers with the outgoing YSI 2300 Stat Plus.Methods:Plasma samples (100), across a wide glucose concentration range were analysed on the YSI 2500, Randox daytona+ (glucose oxidase) and EKF Biosen in a single laboratory and compared to the YSI 2300 Stat Plus.Results:All three analyzers showed good agreement with the YSI 2300 Stat Plus, and only a small bias (โค1% YSI 2500 and Randox daytona+, 4.6% EKF Biosen) was observed for each analyzer. None of the three comparator analyzers were affected by either proportional or constant bias, thus no significant differences between the YSI 2300 Stat Plus and the comparator methods were identified.Conclusions:The results from this study suggest all could be considered as suitable reference laboratory glucose analyzers and replacements for the recently withdrawn YSI 2300 Stat Plus.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Comparability Evaluation of Three Benchtop Glucose Analyzers With the Recently Withdrawn YSI 2300 Stat Plus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241230337";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-08T09:57:37Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:18:"Gareth J. Dunseath";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:21:"Iulius-Dumitru Vatavu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Stephen D. Luzio";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:107:"Comparability Evaluation of Three Benchtop Glucose Analyzers With the Recently Withdrawn YSI 2300 Stat Plus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241230337";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241230337?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:55;a:6:{s:4:"data";s:200:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241229074?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Safety and Feasibility Evaluation of Automated User Profile Settings Initialization and Adaptation With Control-IQ Technology";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241229074?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1806:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Optimization of automated insulin delivery (AID) settings is required to achieve desirable glycemic outcomes. We evaluated safety and efficacy of a computerized system to initialize and adjust insulin delivery settings for the t:slim X2 insulin pump with Control-IQ technology in adults with type 1 diabetes (T1D).Methods:After a 2-week continuous glucose monitoring (CGM) run-in period, adults with T1D using multiple daily injections (MDI) (N = 33, mean age 36.1 years, 57.6% female, diabetes duration 19.7 years) were transitioned to 13 weeks of Control-IQ technology usage. A computerized algorithm generated recommendations for initial pump settings (basal rate, insulin-to-carbohydrate ratio, and correction factor) and weekly follow-up settings to optimize glycemic outcomes. Physicians could override the automated settings changes for safety concerns.Results:Time in range 70 to 180 mg/dL improved from 45.7% during run-in to 69.1% during the last 30 days of Control-IQ use, a median improvement of 18.8% (95% confidence interval [CI]: 13.6-23.9, P < .001). This improvement was evident early in the study and was sustained over 13 weeks. Time <70 mg/dL showed a gradual decreasing trend over time. Percentage of participants achieving HbA1c <7% went from zero at baseline to 55% at study end (P < .001). Only six of the 318 automated settings adaptations (1.9%) were overridden by study investigators.Conclusions:Computerized initiation and adaptation of Control-IQ technology settings from baseline MDI therapy was safe in adults with T1D. The use of this simplified system for onboarding and optimizing Control-IQ technology may be useful to increase uptake of AID and reduce staff and patient burden in clinical care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1818:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Optimization of automated insulin delivery (AID) settings is required to achieve desirable glycemic outcomes. We evaluated safety and efficacy of a computerized system to initialize and adjust insulin delivery settings for the t:slim X2 insulin pump with Control-IQ technology in adults with type 1 diabetes (T1D).Methods:After a 2-week continuous glucose monitoring (CGM) run-in period, adults with T1D using multiple daily injections (MDI) (N = 33, mean age 36.1 years, 57.6% female, diabetes duration 19.7 years) were transitioned to 13 weeks of Control-IQ technology usage. A computerized algorithm generated recommendations for initial pump settings (basal rate, insulin-to-carbohydrate ratio, and correction factor) and weekly follow-up settings to optimize glycemic outcomes. Physicians could override the automated settings changes for safety concerns.Results:Time in range 70 to 180 mg/dL improved from 45.7% during run-in to 69.1% during the last 30 days of Control-IQ use, a median improvement of 18.8% (95% confidence interval [CI]: 13.6-23.9, P < .001). This improvement was evident early in the study and was sustained over 13 weeks. Time <70 mg/dL showed a gradual decreasing trend over time. Percentage of participants achieving HbA1c <7% went from zero at baseline to 55% at study end (P < .001). Only six of the 318 automated settings adaptations (1.9%) were overridden by study investigators.Conclusions:Computerized initiation and adaptation of Control-IQ technology settings from baseline MDI therapy was safe in adults with T1D. The use of this simplified system for onboarding and optimizing Control-IQ technology may be useful to increase uptake of AID and reduce staff and patient burden in clinical care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"Safety and Feasibility Evaluation of Automated User Profile Settings Initialization and Adaptation With Control-IQ Technology";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241229074";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-07T09:24:55Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:15:{i:0;a:5:{s:4:"data";s:13:"Viral N. Shah";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Halis K. Akturk";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Alex Trahan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Nicole Piquette";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Alex Wheatcroft";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Elain Schertz";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Karen Carmello";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:12:"Lars Mueller";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:13:"Kirstin White";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:8:"Larry Fu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:23:"Ravid Sassan-Katchalski";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:16:"Laurel H. Messer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:11:"Steph Habif";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:13;a:5:{s:4:"data";s:15:"Alex Constantin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:14;a:5:{s:4:"data";s:17:"Jordan E. Pinsker";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:125:"Safety and Feasibility Evaluation of Automated User Profile Settings Initialization and Adaptation With Control-IQ Technology";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241229074";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241229074?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:56;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231223991?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Efficacy and Safety of Tirzepatide in Adults With Type 1 Diabetes: A Proof of Concept Observational Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231223991?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1653:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Tirzepatide is approved by the United States Food and Drug Administration (FDA) for the management of type 2 diabetes. The efficacy and safety of this drug have not been studied in people with type 1 diabetes (T1D).Methods:In this single-center, retrospective, observational study, hemoglobin A1C (HbA1c), weight, body mass index (BMI), and continuous glucose monitoring (CGM) data were collected from electronic health records of adults with T1D at initiation of tirzepatide and at subsequent clinic visits over 8 months. Primary outcomes were reduction in HbA1c and percent change in body weight and secondary outcomes were change in CGM metrics and BMI over 8 months from baseline.Results:The mean (ยฑSD) age of the 26 adults (54% female) with T1D was 42 ยฑ 8 years with a mean BMI of 36.7 ยฑ 5.3 kg/m2. There was significant reduction in HbA1c by 0.45% at 3 months and 0.59% at 8 months, and a significant reduction in body weight by 3.4%, 10.5%, and 10.1% at 3, 6, and 8 months after starting tirzepatide. Time in target range (TIR = 70-180 mg/dL) and time in tight target range (TITR = 70-140 mg/dL) increased (+12.6%, P = .002; +10.7%, P = .0016, respectively) and time above range (TAR >180 mg/dL) decreased (โ12.6%, P = .002) at 3 months, and these changes were sustained over 8 months. The drug was relatively safe and well tolerated with only 2 patients discontinuing the medication.Conclusions:Tirzepatide significantly reduced HbA1c and body weight in adults with T1D. A randomized controlled trial is needed to establish efficacy and safety of this drug in T1D.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1656:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Tirzepatide is approved by the United States Food and Drug Administration (FDA) for the management of type 2 diabetes. The efficacy and safety of this drug have not been studied in people with type 1 diabetes (T1D).Methods:In this single-center, retrospective, observational study, hemoglobin A1C (HbA1c), weight, body mass index (BMI), and continuous glucose monitoring (CGM) data were collected from electronic health records of adults with T1D at initiation of tirzepatide and at subsequent clinic visits over 8 months. Primary outcomes were reduction in HbA1c and percent change in body weight and secondary outcomes were change in CGM metrics and BMI over 8 months from baseline.Results:The mean (ยฑSD) age of the 26 adults (54% female) with T1D was 42 ยฑ 8 years with a mean BMI of 36.7 ยฑ 5.3 kg/m2. There was significant reduction in HbA1c by 0.45% at 3 months and 0.59% at 8 months, and a significant reduction in body weight by 3.4%, 10.5%, and 10.1% at 3, 6, and 8 months after starting tirzepatide. Time in target range (TIR = 70-180 mg/dL) and time in tight target range (TITR = 70-140 mg/dL) increased (+12.6%, P = .002; +10.7%, P = .0016, respectively) and time above range (TAR >180 mg/dL) decreased (โ12.6%, P = .002) at 3 months, and these changes were sustained over 8 months. The drug was relatively safe and well tolerated with only 2 patients discontinuing the medication.Conclusions:Tirzepatide significantly reduced HbA1c and body weight in adults with T1D. A randomized controlled trial is needed to establish efficacy and safety of this drug in T1D.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Efficacy and Safety of Tirzepatide in Adults With Type 1 Diabetes: A Proof of Concept Observational Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231223991";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-02-06T06:13:55Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:17:"Halis Kaan Akturk";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:9:"Fran Dong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:22:"Janet K. 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Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients.Methods:This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability.Results:Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event.Conclusions:Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1926:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients.Methods:This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability.Results:Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event.Conclusions:Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:106:"Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241228606";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-01-30T10:52:56Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:13:"Chien Wei Oei";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Yam Meng Chan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Xiaojin Zhang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Kee Hao Leo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Enming Yong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:16:"Rhan Chaen Chong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:12:"Qiantai Hong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:8:"Li Zhang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:8:"Ying Pan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:19:"Glenn Wei Leong Tan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:19:"Malcolm Han Wen Mak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:106:"Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968241228606";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241228606?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:58;a:6:{s:4:"data";s:179:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241228555?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:150:"Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968241228555?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2145:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes.Objectives:To develop and validate natural language processing (NLP) algorithms to identify diabetes-related complications in the free-text medical records of patients who have LHS membership.Methods:The study data included 2.3 million records of 41โ469 patients with diabetes aged 35 or older between the years 2012 and 2017. The diabetes related complications included cardiovascular disease, diabetic neuropathy, nephropathy, retinopathy, diabetic foot, cognitive impairments, mood disorders and hypoglycemia. A vocabulary list of terms was determined and adjudicated by two physicians who are experienced in diabetes care board certified diabetes specialist in endocrinology or family medicine. Two independent registered nurses with PhDs reviewed the free-text medical records. Both rule-based and machine learning techniques were used for the NLP algorithm development. Precision, recall, and F-score were calculated to compare the performance of (1) the NLP algorithm with the reviewersโ comments and (2) the ICD codes with the reviewersโ comments for each complication.Results:The NLP algorithm versus the reviewers (gold standard) achieved an overall good performance with a mean F-score of 86%. This was better than the ICD codes which achieved a mean F-score of only 51%.Conclusion:NLP algorithms and machine learning processes may enable more accurate identification of diabetes complications in EMR data.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2145:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes.Objectives:To develop and validate natural language processing (NLP) algorithms to identify diabetes-related complications in the free-text medical records of patients who have LHS membership.Methods:The study data included 2.3 million records of 41โ469 patients with diabetes aged 35 or older between the years 2012 and 2017. The diabetes related complications included cardiovascular disease, diabetic neuropathy, nephropathy, retinopathy, diabetic foot, cognitive impairments, mood disorders and hypoglycemia. A vocabulary list of terms was determined and adjudicated by two physicians who are experienced in diabetes care board certified diabetes specialist in endocrinology or family medicine. Two independent registered nurses with PhDs reviewed the free-text medical records. Both rule-based and machine learning techniques were used for the NLP algorithm development. Precision, recall, and F-score were calculated to compare the performance of (1) the NLP algorithm with the reviewersโ comments and (2) the ICD codes with the reviewersโ comments for each complication.Results:The NLP algorithm versus the reviewers (gold standard) achieved an overall good performance with a mean F-score of 86%. This was better than the ICD codes which achieved a mean F-score of only 51%.Conclusion:NLP algorithms and machine learning processes may enable more accurate identification of diabetes complications in EMR data.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:150:"Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing 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Outside of pregnancy, AID has led to a paradigm shift in the management of people with type 1 diabetes (T1D), leading to improvements in glycemic control with lower risk for hypoglycemia and improved quality of life. As the use of AID in clinical practice is increasing, the number of women of reproductive age becoming pregnant while using AID is also expected to increase. The requirement for lower glucose targets than outside of pregnancy and for frequent adjustments of insulin doses during pregnancy may impact the effectiveness and safety of AID when using algorithms for non-pregnant populations with T1D. Currently, the CamAPSยฎ FX is the only AID approved for use in pregnancy. A recent randomized controlled trial (RCT) with CamAPSยฎ FX demonstrated a 10% increase in time in range in a pregnant population with T1D and a baseline glycated hemoglobin (HbA1c) โฅ 48 mmol/mol (6.5%). Off-label use of AID not approved for pregnancy are currently also being evaluated in ongoing RCTs. More evidence is needed on the impact of AID on maternal and neonatal outcomes. We review the current evidence on the use of AID in pregnancy and provide an overview of the completed and ongoing RCTs evaluating AID in pregnancy. In addition, we discuss the advantages and challenges of the use of current AID in pregnancy and future directions for research.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Automated Insulin Delivery for Pregnant Women With Type 1 Diabetes: Where do we stand?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231223934";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-01-10T11:32:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:17:"Katrien Benhalima";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Johan Jendle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Kaat Beunen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Lene Ringholm";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:86:"Automated Insulin Delivery for Pregnant Women With Type 1 Diabetes: Where do we stand?";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231223934";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231223934?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:62;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222271?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:170:"Improvement in Protective Sensation: Clinical Evidence From a Randomized Controlled Trial for Treatment of Painful Diabetic Neuropathy With 10 kHz Spinal Cord Stimulation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222271?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1848:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Painful diabetic neuropathy (PDN) can result in the loss of protective sensation, in which people are at twice the likelihood of foot ulceration and three times the risk of lower extremity amputation. Here, we evaluated the long-term effects of high-frequency (10 kHz) paresthesia-independent spinal cord stimulation (SCS) on protective sensation in the feet and the associated risk of foot ulceration for individuals with PDN.Methods:The SENZA-PDN clinical study was a randomized, controlled trial in which 216 participants with PDN were randomized to receive either conventional medical management (CMM) alone or 10 kHz SCS plus CMM, with optional treatment crossover after 6 months. At study visits (baseline through 24 months), 10-g monofilament sensory assessments were conducted at 10 locations per foot. Two published methods were used to evaluate protective sensation via classifying risk of foot ulceration.Results:Participants in the 10 kHz SCS group reported increased numbers of sensate locations as compared to CMM alone (P < .001) and to preimplantation (P < .01) and were significantly more likely to be at low risk of foot ulceration using both classification methods. The proportion of low-risk participants approximately doubled from preimplantation to 3 months postimplantation and remained stable through 24 months (P โค .01).Conclusions:Significant improvements were observed in protective sensation from preimplantation to 24 months postimplantation for the 10 kHz SCS group. With this unique, disease-modifying improvement in sensory function, 10 kHz SCS provides the potential to reduce ulceration, amputation, and other severe sequelae of PDN.Trial Registration:The SENZA-PDN study is registered on ClinicalTrials.gov with identifier NCT03228420.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1854:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Painful diabetic neuropathy (PDN) can result in the loss of protective sensation, in which people are at twice the likelihood of foot ulceration and three times the risk of lower extremity amputation. Here, we evaluated the long-term effects of high-frequency (10 kHz) paresthesia-independent spinal cord stimulation (SCS) on protective sensation in the feet and the associated risk of foot ulceration for individuals with PDN.Methods:The SENZA-PDN clinical study was a randomized, controlled trial in which 216 participants with PDN were randomized to receive either conventional medical management (CMM) alone or 10 kHz SCS plus CMM, with optional treatment crossover after 6 months. At study visits (baseline through 24 months), 10-g monofilament sensory assessments were conducted at 10 locations per foot. Two published methods were used to evaluate protective sensation via classifying risk of foot ulceration.Results:Participants in the 10 kHz SCS group reported increased numbers of sensate locations as compared to CMM alone (P < .001) and to preimplantation (P < .01) and were significantly more likely to be at low risk of foot ulceration using both classification methods. The proportion of low-risk participants approximately doubled from preimplantation to 3 months postimplantation and remained stable through 24 months (P โค .01).Conclusions:Significant improvements were observed in protective sensation from preimplantation to 24 months postimplantation for the 10 kHz SCS group. With this unique, disease-modifying improvement in sensory function, 10 kHz SCS provides the potential to reduce ulceration, amputation, and other severe sequelae of PDN.Trial Registration:The SENZA-PDN study is registered on ClinicalTrials.gov with identifier NCT03228420.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:170:"Improvement in Protective Sensation: Clinical Evidence From a Randomized Controlled Trial for Treatment of Painful Diabetic Neuropathy With 10 kHz Spinal Cord Stimulation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231222271";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-01-09T11:43:25Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:17:"Charles E. Argoff";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"David G. Armstrong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Zachary B. Kagan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Michael J. Jaasma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Manish Bharara";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Kerry Bradley";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"David L. Caraway";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:17:"Erika A. Petersen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:170:"Improvement in Protective Sensation: Clinical Evidence From a Randomized Controlled Trial for Treatment of Painful Diabetic Neuropathy With 10 kHz Spinal Cord Stimulation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231222271";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222271?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:63;a:6:{s:4:"data";s:102:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222022?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:30:"Virtual Reality Meets Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222022?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1648:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This article provides a detailed summary of virtual reality (VR) and augmented reality (AR) applications in diabetes. The purpose of this comparative review is to identify application areas, direction and provide foundation for future virtual reality tools in diabetes.Method:Features and benefits of each VR diabetes application are compared and discussed, following a thorough review of literature on virtual reality for diabetes using multiple databases. The weaknesses of existing VR applications are discussed and their strengths identified so that these can be carried forward. A novel virtual reality diabetes tool prototype is also developed and presented.Results:This research identifies three major categories where VR is being used in diabetes: education, prevention and treatment. Within diabetes education, there are three target groups: clinicians, adults with diabetes and children with diabetes. Both VR and AR have shown benefits in areas of Type 1 and Type 2 diabetes.Conclusions:Virtual reality and augmented reality in diabetes have demonstrated potential to enhance training of diabetologists and enhance education, prevention and treatment for adults and children with Type 1 or Type 2 diabetes. Future research can continually build on virtual and augmented reality diabetes applications by integrating wide stakeholder inputs and diverse digital platforms. Several areas of VR diabetes are in early stages, with advantages and opportunities. Further VR diabetes innovations are encouraging to enhance training, management and treatment of diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1648:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This article provides a detailed summary of virtual reality (VR) and augmented reality (AR) applications in diabetes. The purpose of this comparative review is to identify application areas, direction and provide foundation for future virtual reality tools in diabetes.Method:Features and benefits of each VR diabetes application are compared and discussed, following a thorough review of literature on virtual reality for diabetes using multiple databases. The weaknesses of existing VR applications are discussed and their strengths identified so that these can be carried forward. A novel virtual reality diabetes tool prototype is also developed and presented.Results:This research identifies three major categories where VR is being used in diabetes: education, prevention and treatment. Within diabetes education, there are three target groups: clinicians, adults with diabetes and children with diabetes. Both VR and AR have shown benefits in areas of Type 1 and Type 2 diabetes.Conclusions:Virtual reality and augmented reality in diabetes have demonstrated potential to enhance training of diabetologists and enhance education, prevention and treatment for adults and children with Type 1 or Type 2 diabetes. Future research can continually build on virtual and augmented reality diabetes applications by integrating wide stakeholder inputs and diverse digital platforms. Several areas of VR diabetes are in early stages, with advantages and opportunities. Further VR diabetes innovations are encouraging to enhance training, management and treatment of diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:30:"Virtual Reality Meets Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231222022";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-01-09T11:41:47Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:12:"Neil Vaughan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:30:"Virtual Reality Meets Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231222022";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222022?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:64;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231221803?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231221803?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1780:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.Methods:A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).Results:A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.Conclusion:This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1780:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.Methods:A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).Results:A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.Conclusion:This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:111:"Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231221803";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2024-01-05T12:30:24Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:17:"Mikkel Thor Olsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:24:"Carina Kirstine Klarskov";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Arnold Matovu Dungu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:20:"Katrine Bagge Hansen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:24:"Ulrik Pedersen-Bjergaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:23:"Peter Lommer Kristensen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:111:"Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231221803";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231221803?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2024 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:65;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222007?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"A Three-Step Data-Driven Methodology to Assess Adherence to Basal Insulin Therapy in Patients With Insulin-Treated Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222007?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1533:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence.Method:Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged โฅ18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach.Results:We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases.Conclusion:The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1533:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence.Method:Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged โฅ18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach.Results:We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases.Conclusion:The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"A Three-Step Data-Driven Methodology to Assess Adherence to Basal Insulin Therapy in Patients With Insulin-Treated Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231222007";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-12-30T04:30:28Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:29:"Jannie Toft Damsgaard Nรธrlev";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Thomas Kronborg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:26:"Morten Hasselstrรธm Jensen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Peter Vestergaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Ole Hejlesen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"Stine Hangaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:130:"A Three-Step Data-Driven Methodology to Assess Adherence to Basal Insulin Therapy in Patients With Insulin-Treated Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231222007";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231222007?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:66;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231221768?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"drCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231221768?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2072:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization.Method:drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the โdynamic riskโ (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics.Results:drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy.Conclusions:The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature methods.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2072:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization.Method:drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the โdynamic riskโ (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics.Results:drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy.Conclusions:The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature 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in efficient data collection and participant monitoring. To address these issues, we developed the IMPACT platform, comprising a real-time mobile application for data collection and a web-based dashboard for remote monitoring and management.Methods:This article presents the design, development, and usability assessment of the IMPACT platform customized for patients with post-bariatric surgery hypoglycemia (PBH). We focus on adapting key IMPACT components, including continuous glucose monitoring (CGM), symptom tracking, and meal logging, as crucial elements for user-friendly and efficient PBH monitoring.Results:The adapted IMPACT platform demonstrated effectiveness in data collection and remote participant monitoring. The mobile application allowed patients to easily track their data, while the clinician dashboard provided a comprehensive overview of enrolled patients, featuring filtering options and alert mechanisms for identifying data collection issues. The platform incorporated various visual representations, including time plots and category-based visualizations, which greatly facilitated data interpretation and analysis. The System Usability Scale questionnaire results indicated a high level of usability for the web dashboard, with an average score of 86.3 out of 100. The active involvement of clinicians throughout the development process ensured that the platform allowed for the collection and visualization of clinically meaningful data.Conclusions:By leveraging IMPACTโs existing features and infrastructure, the adapted version streamlined data collection, analysis, and trial customization for PBH research. The platformโs high usability underscores its alignment with the requirements for conducting research using continuous real-world data in PBH patients and other populations of interest.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1944:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Clinical trials often face challenges in efficient data collection and participant monitoring. To address these issues, we developed the IMPACT platform, comprising a real-time mobile application for data collection and a web-based dashboard for remote monitoring and management.Methods:This article presents the design, development, and usability assessment of the IMPACT platform customized for patients with post-bariatric surgery hypoglycemia (PBH). We focus on adapting key IMPACT components, including continuous glucose monitoring (CGM), symptom tracking, and meal logging, as crucial elements for user-friendly and efficient PBH monitoring.Results:The adapted IMPACT platform demonstrated effectiveness in data collection and remote participant monitoring. The mobile application allowed patients to easily track their data, while the clinician dashboard provided a comprehensive overview of enrolled patients, featuring filtering options and alert mechanisms for identifying data collection issues. The platform incorporated various visual representations, including time plots and category-based visualizations, which greatly facilitated data interpretation and analysis. The System Usability Scale questionnaire results indicated a high level of usability for the web dashboard, with an average score of 86.3 out of 100. The active involvement of clinicians throughout the development process ensured that the platform allowed for the collection and visualization of clinically meaningful data.Conclusions:By leveraging IMPACTโs existing features and infrastructure, the adapted version streamlined data collection, analysis, and trial customization for PBH research. The platformโs high usability underscores its alignment with the requirements for conducting research using continuous real-world data in PBH patients and other populations of interest.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:161:"Design and Usability Assessment of a User-Centered, Modular Platform for Real-World Data Acquisition in Clinical Trials involving Post-bariatric Surgery Patients";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231220061";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-12-24T10:58:00Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:10:"Luca Cossu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Giacomo Cappon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Olivia Streicher";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"David Herzig";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:9:"Lia Bally";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:18:"Andrea Facchinetti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:161:"Design and Usability Assessment of a User-Centered, Modular Platform for Real-World Data Acquisition in Clinical Trials involving Post-bariatric Surgery Patients";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231220061";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231220061?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:69;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231212219?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Prevalence and Predictors of Subclinical Cardiomyopathy in Patients With Type 2 Diabetes in a Health System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231212219?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1948:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Diabetic cardiomyopathy (DbCM) is characterized by subclinical abnormalities in cardiac structure/function and is associated with a higher risk of overt heart failure (HF). However, there are limited data on optimal strategies to identify individuals with DbCM in contemporary health systems. The aim of this study was to evaluate the prevalence of DbCM in a health system using existing data from the electronic health record (EHR).Methods:Adult patients with type 2 diabetes mellitus free of cardiovascular disease (CVD) with available data on HF risk in a single-center EHR were included. The presence of DbCM was defined using different definitions: (1) least restrictive: โฅ1 echocardiographic abnormality (left atrial enlargement, left ventricle hypertrophy, diastolic dysfunction); (2) intermediate restrictive: โฅ2 echocardiographic abnormalities; (3) most restrictive: 3 echocardiographic abnormalities. DbCM prevalence was compared across age, sex, race, and ethnicity-based subgroups, with differences assessed using the chi-squared test. Adjusted logistic regression models were constructed to evaluate significant predictors of DbCM.Results:Among 1921 individuals with type 2 diabetes mellitus, the prevalence of DbCM in the overall cohort was 8.7% and 64.4% in the most and least restrictive definitions, respectively. Across all definitions, older age and Hispanic ethnicity were associated with a higher proportion of DbCM. Females had a higher prevalence than males only in the most restrictive definition. In multivariable-adjusted logistic regression, higher systolic blood pressure, higher creatinine, and longer QRS duration were associated with a higher risk of DbCM across all definitions.Conclusions:In this single-center, EHR cohort, the prevalence of DbCM varies from 9% to 64%, with a higher prevalence with older age and Hispanic ethnicity.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1948:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Diabetic cardiomyopathy (DbCM) is characterized by subclinical abnormalities in cardiac structure/function and is associated with a higher risk of overt heart failure (HF). However, there are limited data on optimal strategies to identify individuals with DbCM in contemporary health systems. The aim of this study was to evaluate the prevalence of DbCM in a health system using existing data from the electronic health record (EHR).Methods:Adult patients with type 2 diabetes mellitus free of cardiovascular disease (CVD) with available data on HF risk in a single-center EHR were included. The presence of DbCM was defined using different definitions: (1) least restrictive: โฅ1 echocardiographic abnormality (left atrial enlargement, left ventricle hypertrophy, diastolic dysfunction); (2) intermediate restrictive: โฅ2 echocardiographic abnormalities; (3) most restrictive: 3 echocardiographic abnormalities. DbCM prevalence was compared across age, sex, race, and ethnicity-based subgroups, with differences assessed using the chi-squared test. Adjusted logistic regression models were constructed to evaluate significant predictors of DbCM.Results:Among 1921 individuals with type 2 diabetes mellitus, the prevalence of DbCM in the overall cohort was 8.7% and 64.4% in the most and least restrictive definitions, respectively. Across all definitions, older age and Hispanic ethnicity were associated with a higher proportion of DbCM. Females had a higher prevalence than males only in the most restrictive definition. In multivariable-adjusted logistic regression, higher systolic blood pressure, higher creatinine, and longer QRS duration were associated with a higher risk of DbCM across all definitions.Conclusions:In this single-center, EHR cohort, the prevalence of DbCM varies from 9% to 64%, with a higher prevalence with older age and Hispanic ethnicity.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Prevalence and Predictors of Subclinical Cardiomyopathy in Patients With Type 2 Diabetes in a Health System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231212219";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-12-08T12:23:14Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:13:"Aditya Nagori";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Matthew W. Segar";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Neil Keshvani";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Lajjaben Patel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:16:"Kershaw V. 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Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia.Methods:Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set.Results:The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%.Conclusions:The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1484:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia.Methods:Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set.Results:The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%.Conclusions:The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:193:"Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231215324";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-28T09:57:11Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:17:"Helene B. Thomsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Mike M. Jakobsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:22:"Nikolaj Hecht-Pedersen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:26:"Morten Hasselstrรธm Jensen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Thomas Kronborg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:193:"Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231215324";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231215324?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:71;a:6:{s:4:"data";s:158:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231213095?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"Sensor-Assisted Wound Therapy in Plantar Diabetic Foot Ulcer Treatment: A Randomized Clinical Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231213095?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1775:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Offloading is the cornerstone of treatment of plantar diabetic foot ulcers. It limits mobility with consequent psychological and cardiovascular side effects, and if devices are removed, healing is delayed.Methods:We developed three non-removable techniques with increasing offloading potential (multilayer felt sole, felt-fiberglass sole, or total contact casts with ventral windows) and sensors built within. Smartwatch and web apps displayed pressure, temperature, humidity, and steps. They alerted patients, staff, and a telemedicine center when pressure limits (125 kPa) were exceeded. Patients were advised to walk as much as they had done before the ulcer episode. To evaluate the potential of this intervention, we enrolled 20 ambulatory patients in a randomized clinical trial. The control group used the same offloading and monitoring system, but neither patients nor therapists received any information or warnings.Results:Three patients withdrew consent. The median time to healing of ulcers was significantly shorter in the intervention group compared with controls, 40.5 (95% confidence interval [CI] = 28-not applicable [NA]) versus 266.0 (95% CI = 179-NA) days (P = .037), and increasing ulcer area was observed less frequently during study visits (7.9% vs 29.7%, P = .033). A reduction of wound area by 50% was reached at a median of 10.2 (95% CI = 7.25-NA) versus 19.1 (95% CI = 13.36-NA) days (P = .2). Participants walked an average of 1875 (SD = 1590) steps per day in intervention group and 1806 (SD = 1391) in the control group.Conclusions:Sensor-assisted wound therapy may allow rapid closure of plantar foot ulcers while maintaining patientโs mobility during ulcer therapy.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1775:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Offloading is the cornerstone of treatment of plantar diabetic foot ulcers. It limits mobility with consequent psychological and cardiovascular side effects, and if devices are removed, healing is delayed.Methods:We developed three non-removable techniques with increasing offloading potential (multilayer felt sole, felt-fiberglass sole, or total contact casts with ventral windows) and sensors built within. Smartwatch and web apps displayed pressure, temperature, humidity, and steps. They alerted patients, staff, and a telemedicine center when pressure limits (125 kPa) were exceeded. Patients were advised to walk as much as they had done before the ulcer episode. To evaluate the potential of this intervention, we enrolled 20 ambulatory patients in a randomized clinical trial. The control group used the same offloading and monitoring system, but neither patients nor therapists received any information or warnings.Results:Three patients withdrew consent. The median time to healing of ulcers was significantly shorter in the intervention group compared with controls, 40.5 (95% confidence interval [CI] = 28-not applicable [NA]) versus 266.0 (95% CI = 179-NA) days (P = .037), and increasing ulcer area was observed less frequently during study visits (7.9% vs 29.7%, P = .033). A reduction of wound area by 50% was reached at a median of 10.2 (95% CI = 7.25-NA) versus 19.1 (95% CI = 13.36-NA) days (P = .2). Participants walked an average of 1875 (SD = 1590) steps per day in intervention group and 1806 (SD = 1391) in the control group.Conclusions:Sensor-assisted wound therapy may allow rapid closure of plantar foot ulcers while maintaining patientโs mobility during ulcer therapy.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"Sensor-Assisted Wound Therapy in Plantar Diabetic Foot Ulcer Treatment: A Randomized Clinical Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231213095";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-25T08:47:13Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:15:"Dirk Hochlenert";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Can Bogoclu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Kevin Cremanns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Lars Gierschner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Dominik Ludmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Mira Mertens";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:10:"Timo Tromp";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:13:"Annika Weggen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:12:"Hubert Otten";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:99:"Sensor-Assisted Wound Therapy in Plantar Diabetic Foot Ulcer Treatment: A Randomized Clinical Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231213095";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231213095?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:72;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231214271?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"Counting the Minutes: Perceived Diabetes Mental Load and its Associations With Technology Use and Mental Disorders";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231214271?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:897:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Little is known about mental load in people with diabetes and associations with demographic, clinical, and treatment characteristics, such as the use of diabetes technologies. To explore perceived mental load, 503 adults with diabetes answered the one-item survey โHow much time (in minutes) would you spontaneously estimate that you spend each day thinking about your diabetes?โ Mental load estimations varied widely within the sample and between subgroups. Perceived mental load was higher in type 1 diabetes than in type 2 diabetes, higher in women than in men and increased with treatment intensity (ie, insulin therapy, technology use) and the number of mental disorders. Further research may explore associations with diabetes-related distress and determine whether (perceived) mental load has relevance in technology use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:897:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Little is known about mental load in people with diabetes and associations with demographic, clinical, and treatment characteristics, such as the use of diabetes technologies. To explore perceived mental load, 503 adults with diabetes answered the one-item survey โHow much time (in minutes) would you spontaneously estimate that you spend each day thinking about your diabetes?โ Mental load estimations varied widely within the sample and between subgroups. Perceived mental load was higher in type 1 diabetes than in type 2 diabetes, higher in women than in men and increased with treatment intensity (ie, insulin therapy, technology use) and the number of mental disorders. Further research may explore associations with diabetes-related distress and determine whether (perceived) mental load has relevance in technology use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"Counting the Minutes: Perceived Diabetes Mental Load and its Associations With Technology Use and Mental Disorders";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231214271";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-20T06:03:55Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:25:"Lilli-Sophie Priesterroth";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Norbert Hermanns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Bernhard Kulzer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Thomas Haak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Dominic Ehrmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:114:"Counting the Minutes: Perceived Diabetes Mental Load and its Associations With Technology Use and Mental Disorders";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231214271";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231214271?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:73;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231210548?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:70:"Privacy Concerns Related to Data Sharing for European Diabetes Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231210548?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1991:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Individuals with diabetes rely on medical equipment (eg, continuous glucose monitoring (CGM), hybrid closed-loop systems) and mobile applications to manage their condition, providing valuable data to health care providers. Data sharing from this equipment is regulated via Terms of Service (ToS) and Privacy Policy documents. The introduction of the Medical Devices Regulation (MDR) and In Vitro Diagnostic Medical Devices Regulation (IVDR) in the European Union has established updated rules for medical devices, including software.Objective:This study examines how data sharing is regulated by the ToS and Privacy Policy documents of approved diabetes medical equipment and associated software. It focuses on the equipment approved by the Norwegian Regional Health Authorities.Methods:A document analysis was conducted on the ToS and Privacy Policy documents of diabetes medical equipment and software applications approved in Norway.Results:The analysis identified 11 medical equipment and 12 software applications used for diabetes data transfer and analysis in Norway. Only 3 medical equipment (OmniPod Dash, Accu-Chek Insight, and Accu-Chek Solo) were registered in the European Database on Medical Devices (EUDAMED) database, whereas none of their respective software applications were registered. Compliance with General Data Protection Regulation (GDPR) security requirements varied, with some software relying on adequacy decisions (8/12), whereas others did not (4/12).Conclusions:The study highlights the dominance of non-European Economic Area (EEA) companies in medical device technology development. It also identifies the lack of registration for medical equipment and software in the EUDAMED database, which is currently not mandatory. These findings underscore the need for further attention to ensure regulatory compliance and improve data-sharing practices in the context of diabetes management.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1991:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Individuals with diabetes rely on medical equipment (eg, continuous glucose monitoring (CGM), hybrid closed-loop systems) and mobile applications to manage their condition, providing valuable data to health care providers. Data sharing from this equipment is regulated via Terms of Service (ToS) and Privacy Policy documents. The introduction of the Medical Devices Regulation (MDR) and In Vitro Diagnostic Medical Devices Regulation (IVDR) in the European Union has established updated rules for medical devices, including software.Objective:This study examines how data sharing is regulated by the ToS and Privacy Policy documents of approved diabetes medical equipment and associated software. It focuses on the equipment approved by the Norwegian Regional Health Authorities.Methods:A document analysis was conducted on the ToS and Privacy Policy documents of diabetes medical equipment and software applications approved in Norway.Results:The analysis identified 11 medical equipment and 12 software applications used for diabetes data transfer and analysis in Norway. Only 3 medical equipment (OmniPod Dash, Accu-Chek Insight, and Accu-Chek Solo) were registered in the European Database on Medical Devices (EUDAMED) database, whereas none of their respective software applications were registered. Compliance with General Data Protection Regulation (GDPR) security requirements varied, with some software relying on adequacy decisions (8/12), whereas others did not (4/12).Conclusions:The study highlights the dominance of non-European Economic Area (EEA) companies in medical device technology development. It also identifies the lack of registration for medical equipment and software in the EUDAMED database, which is currently not mandatory. These findings underscore the need for further attention to ensure regulatory compliance and improve data-sharing practices in the context of diabetes management.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:70:"Privacy Concerns Related to Data Sharing for European Diabetes Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231210548";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-14T07:08:50Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:14:"Pietro Randine";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Matthias Pocs";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"John Graham Cooper";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Dimitrios Tsolovos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Miroslav Muzny";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"Rouven Besters";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Eirik ร rsand";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:70:"Privacy Concerns Related to Data Sharing for European Diabetes Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231210548";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231210548?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:74;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231213378?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231213378?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1427:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1427:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231213378";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-13T06:06:23Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:15:"Cynthia Baseman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Maya Fayfman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Marcos C. 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Arriaga";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:138:"Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231213378";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231213378?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:75;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231209339?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:51:"Ketone-Based Alert System for Insulin Pump Failures";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231209339?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1703:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:An increasing number of individuals with type 1 diabetes (T1D) manage glycemia with insulin pumps containing short-acting insulin. If insulin delivery is interrupted for even a few hours due to pump or infusion site malfunction, the resulting insulin deficiency can rapidly initiate ketogenesis and diabetic ketoacidosis (DKA).Methods:To detect an event of accidental cessation of insulin delivery, we propose the design of ketone-based alert system (K-AS). This system relies on an extended Kalman filter based on plasma 3-beta-hydroxybutyrate (BOHB) measurements to estimate the disturbance acting on the insulin infusion/injection input. The alert system is based on a novel physiological model capable of simulating the ketone body turnover in response to a change in plasma insulin levels. Simulated plasma BOHB levels were compared with plasma BOHB levels available in the literature. We evaluated the performance of the K-AS on 10 in silico subjects using the S2014 UVA/Padova simulator for two different scenarios.Results:The K-AS achieves an average detection time of 84 and 55.5 minutes in fasting and postprandial conditions, respectively, which compares favorably and improves against a detection time of 193 and 120 minutes, respectively, based on the current guidelines.Conclusions:The K-AS leverages the rapid rate of increase of plasma BOHB to achieve short detection time in order to prevent BOHB levels from rising to dangerous levels, without any false-positive alarms. Moreover, the proposed novel insulin-BOHB model will allow us to understand the efficacy of treatment without compromising patient safety.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1703:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:An increasing number of individuals with type 1 diabetes (T1D) manage glycemia with insulin pumps containing short-acting insulin. If insulin delivery is interrupted for even a few hours due to pump or infusion site malfunction, the resulting insulin deficiency can rapidly initiate ketogenesis and diabetic ketoacidosis (DKA).Methods:To detect an event of accidental cessation of insulin delivery, we propose the design of ketone-based alert system (K-AS). This system relies on an extended Kalman filter based on plasma 3-beta-hydroxybutyrate (BOHB) measurements to estimate the disturbance acting on the insulin infusion/injection input. The alert system is based on a novel physiological model capable of simulating the ketone body turnover in response to a change in plasma insulin levels. Simulated plasma BOHB levels were compared with plasma BOHB levels available in the literature. We evaluated the performance of the K-AS on 10 in silico subjects using the S2014 UVA/Padova simulator for two different scenarios.Results:The K-AS achieves an average detection time of 84 and 55.5 minutes in fasting and postprandial conditions, respectively, which compares favorably and improves against a detection time of 193 and 120 minutes, respectively, based on the current guidelines.Conclusions:The K-AS leverages the rapid rate of increase of plasma BOHB to achieve short detection time in order to prevent BOHB levels from rising to dangerous levels, without any false-positive alarms. Moreover, the proposed novel insulin-BOHB model will allow us to understand the efficacy of treatment without compromising patient safety.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:51:"Ketone-Based Alert System for Insulin Pump Failures";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231209339";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-10T06:15:17Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:18:"Eleonora M. Aiello";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Lori M. Laffel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:20:"Mary-Elizabeth Patti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Francis J. Doyle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:51:"Ketone-Based Alert System for Insulin Pump Failures";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231209339";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231209339?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:76;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231204376?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Results From a Virtual Clinic for the Follow-up of Patients Using the Advanced Hybrid Closed-Loop System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231204376?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1852:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Evidence regarding the implementation of medium-term strategies in advanced hybrid closed-loop (AHCL) system users is limited. Therefore, this study aimed to describe the efficacy and safety of the AHCL system in patients with type 1 diabetes (T1D) on a six-month follow-up in a virtual diabetes clinic (VDC).Method:A prospective cohort of adult patients with T1D treated using the AHCL system (Mini Med 780G; Medtronic, Northridge, California) in a VDC follow-up. Standardized training and follow-up were conducted virtually. Clinical data and metabolic control outcomes were reported at baseline, and at three and six months.Results:Sixty-four patients (mean age = 42 ยฑ 14.6 years, 65% men, 54% with graduate education) were included. Percentage time in range (%TIR) increased significantly regardless of prior therapy with intermittently scanned continuous glucose monitoring + multiple daily injections and sensor-augmented pump therapy with predictive low-glucose management after starting AHCL and persisted during the follow-up period with no hypoglycemic events. The %TIR 70 to 180 mg/dL according to socioeconomic strata was 73.4% ยฑ 5.3%, 78.1% ยฑ 8.1%, and 84.2% ยฑ 7.5% for the lower, middle, and upper strata, respectively. The sensor was used more frequently in the population with a higher education level. Adherence to sensor use and SmartGuard retention were higher in patients who underwent the VDC follow-up.Conclusions:Medium-term follow-up of users of AHCL systems in a VDC contributes to safely achieving %TIR goals. Virtual diabetes clinic follow-up favored adherence to sensor use and continuous SmartGuard use. Socioeconomic strata were associated with a better glycemic profile and education level was associated with better adherence to sensor use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1852:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Evidence regarding the implementation of medium-term strategies in advanced hybrid closed-loop (AHCL) system users is limited. Therefore, this study aimed to describe the efficacy and safety of the AHCL system in patients with type 1 diabetes (T1D) on a six-month follow-up in a virtual diabetes clinic (VDC).Method:A prospective cohort of adult patients with T1D treated using the AHCL system (Mini Med 780G; Medtronic, Northridge, California) in a VDC follow-up. Standardized training and follow-up were conducted virtually. Clinical data and metabolic control outcomes were reported at baseline, and at three and six months.Results:Sixty-four patients (mean age = 42 ยฑ 14.6 years, 65% men, 54% with graduate education) were included. Percentage time in range (%TIR) increased significantly regardless of prior therapy with intermittently scanned continuous glucose monitoring + multiple daily injections and sensor-augmented pump therapy with predictive low-glucose management after starting AHCL and persisted during the follow-up period with no hypoglycemic events. The %TIR 70 to 180 mg/dL according to socioeconomic strata was 73.4% ยฑ 5.3%, 78.1% ยฑ 8.1%, and 84.2% ยฑ 7.5% for the lower, middle, and upper strata, respectively. The sensor was used more frequently in the population with a higher education level. Adherence to sensor use and SmartGuard retention were higher in patients who underwent the VDC follow-up.Conclusions:Medium-term follow-up of users of AHCL systems in a VDC contributes to safely achieving %TIR goals. Virtual diabetes clinic follow-up favored adherence to sensor use and continuous SmartGuard use. Socioeconomic strata were associated with a better glycemic profile and education level was associated with better adherence to sensor use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Results From a Virtual Clinic for the Follow-up of Patients Using the Advanced Hybrid Closed-Loop System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231204376";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-09T09:57:59Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:24:"Ana Marรญa Gรณmez Medina";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:29:"Diana Cristina Henao Carrillo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:23:"Julio David Silva Leรณn";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:31:"Javier Alberto Gรณmez Gonzรกlez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:30:"Oscar Mauricio Muรฑoz Velandia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:18:"Lucia Conde Brahim";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:30:"Guillermo Andrรฉs Mecรณn Prada";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:25:"Martin Rondรณn Sepรบlveda";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:104:"Results From a Virtual Clinic for the Follow-up of Patients Using the Advanced Hybrid Closed-Loop System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231204376";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231204376?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:77;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231208690?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"European Survey on Adult People With Type 1 Diabetes and Their Caregivers: Insights Into Perceptions of Technology";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231208690?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1741:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Type 1 diabetes (T1D) is a complex condition requiring constant monitoring and self-management. The landscape of diabetes management is evolving with the development of new technologies. This survey aimed to gain insight into the perceptions and experiences of people with T1D (PWD) and their caregivers on the use of technology in diabetes care, and identify future needs for T1D management.Methods:PWD and caregivers (โฅ18 years) living in five European countries (France, Germany, Italy, Spain, and the United Kingdom) completed an online survey. Data were collected during July and August 2021.Results:Responders included 458 PWD and 54 caregivers. More than 60% of PWD perceived devices/digital tools for diabetes management as useful and 63% reported that access to monitoring device data made their life easier. Nearly half of participants hoped for new devices and/or digital tools. While approximately one-third of all PWD had used teleconsultation, perceptions and usage varied significantly between countries and by age (both P < .0001), with the lowest use in Germany (20%) and the highest in Spain (48%). The proportions of PWD contributing to diabetes care costs varied by device and were highest for smart insulin pen users at 83% compared with 44% for insulin pen users and 37% for insulin pump users. One-quarter (24%) of PWD and 15% of caregivers felt they lacked knowledge about devices/digital tools for T1D.Conclusions:Most PWD and caregivers had positive perceptions and experiences of new technologies/digital solutions for diabetes management, although improved support and structured education for devices/digital tools are still required.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1744:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Type 1 diabetes (T1D) is a complex condition requiring constant monitoring and self-management. The landscape of diabetes management is evolving with the development of new technologies. This survey aimed to gain insight into the perceptions and experiences of people with T1D (PWD) and their caregivers on the use of technology in diabetes care, and identify future needs for T1D management.Methods:PWD and caregivers (โฅ18 years) living in five European countries (France, Germany, Italy, Spain, and the United Kingdom) completed an online survey. Data were collected during July and August 2021.Results:Responders included 458 PWD and 54 caregivers. More than 60% of PWD perceived devices/digital tools for diabetes management as useful and 63% reported that access to monitoring device data made their life easier. Nearly half of participants hoped for new devices and/or digital tools. While approximately one-third of all PWD had used teleconsultation, perceptions and usage varied significantly between countries and by age (both P < .0001), with the lowest use in Germany (20%) and the highest in Spain (48%). The proportions of PWD contributing to diabetes care costs varied by device and were highest for smart insulin pen users at 83% compared with 44% for insulin pen users and 37% for insulin pump users. One-quarter (24%) of PWD and 15% of caregivers felt they lacked knowledge about devices/digital tools for T1D.Conclusions:Most PWD and caregivers had positive perceptions and experiences of new technologies/digital solutions for diabetes management, although improved support and structured education for devices/digital tools are still required.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"European Survey on Adult People With Type 1 Diabetes and Their Caregivers: Insights Into Perceptions of Technology";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231208690";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-08T11:26:16Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:16:"Alfred Penfornis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:7:"Su Down";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Antoine Seignez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Alizรฉ Vives";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:19:"Mireille Bonnemaire";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Bernhard Kulzer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:114:"European Survey on Adult People With Type 1 Diabetes and Their Caregivers: Insights Into Perceptions of Technology";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231208690";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231208690?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:78;a:6:{s:4:"data";s:158:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231204584?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"The Role of Ultra-Rapid-Acting Insulin Analogs in Diabetes: An Expert Consensus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231204584?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1101:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Ultra-rapid-acting insulin analogs (URAA) are a further development and refinement of rapid-acting insulin analogs. Because of their adapted formulation, URAA provide an even faster pharmacokinetics and thus an accelerated onset of insulin action than conventional rapid-acting insulin analogs, allowing for a more physiologic delivery of exogenously applied insulin. Clinical trials have confirmed the superiority of URAA in controlling postprandial glucose excursions, with a safety profile that is comparable to the rapid-acting insulins. Consequently, many individuals with diabetes mellitus may benefit from URAA in terms of prandial glycemic control. Unfortunately, there are only few available recommendations from authoritative sources for use of URAA in clinical practice. Therefore, this expert consensus report aims to define populations of people with diabetes mellitus for whom URAA may be beneficial and to provide health care professionals with concrete, practical recommendations on how best to use URAA in this context.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1101:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Ultra-rapid-acting insulin analogs (URAA) are a further development and refinement of rapid-acting insulin analogs. Because of their adapted formulation, URAA provide an even faster pharmacokinetics and thus an accelerated onset of insulin action than conventional rapid-acting insulin analogs, allowing for a more physiologic delivery of exogenously applied insulin. Clinical trials have confirmed the superiority of URAA in controlling postprandial glucose excursions, with a safety profile that is comparable to the rapid-acting insulins. Consequently, many individuals with diabetes mellitus may benefit from URAA in terms of prandial glycemic control. Unfortunately, there are only few available recommendations from authoritative sources for use of URAA in clinical practice. Therefore, this expert consensus report aims to define populations of people with diabetes mellitus for whom URAA may be beneficial and to provide health care professionals with concrete, practical recommendations on how best to use URAA in this context.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:79:"The Role of Ultra-Rapid-Acting Insulin Analogs in Diabetes: An Expert Consensus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231204584";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-11-08T11:24:04Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:18:"Francesco Giorgino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Tadej Battelino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:21:"Richard M. Bergenstal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Thomas Forst";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"Jennifer B. Green";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Chantal Mathieu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:17:"Helena W. Rodbard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Oliver Schnell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:14:"Emma G. Wilmot";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:79:"The Role of Ultra-Rapid-Acting Insulin Analogs in Diabetes: An Expert Consensus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231204584";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231204584?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:79;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231209999?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:74:"Insulin Pump Alarms During Adverse Events: A Qualitative Descriptive Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231209999?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1775:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:Explore alarm signals cited in insulin pump-associated adverse events (AEs), describe the clinical consequences and other root cause informing remarks that cooccurred with the alarm signals, and identify opportunities for improvements to patient education, instructional materials, and alarm systems to prevent future AEs.Research Design and Methods:We explored the type, frequency, and associated clinical consequences of alarm signals cited in a pre-coded data set of 2294 insulin pump-associated AEs involving the MiniMed 670G, MiniMed 630G, and t:slim X2. We also explored the clinical consequences and other root cause informing remarks that cooccurred with the top 10 most frequently cited alarm signals.Results:Overall, 403 AEs narratives cited at least one alarm signal. Of the 40 unique alarm signals cited, 42.5% were โalarms,โ 25.0% were โalerts,โ and 32.5% were not referenced in the instructional materials packaged with the corresponding pump. The top 10 most frequently cited alarm signals included two obstruction of flow alarms, which accounted for 49.9% of all AEs citing at least one alarm, and two unreferenced alarms. The most frequent cooccurring root cause informing remark varied across the top 10 alarm signals and revealed valuable insight into why these alarms may have occurred.Conclusions:Our findings demonstrate the value of analyzing alarm signals cited in insulin pump-associated AEs and reveal multiple opportunities for providers to educate patients on how to respond to alarm signals and manage their pumps to avoid AEs, and for insulin pump manufacturers to update instructional materials and improve alarm systems to support appropriate patient response.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1775:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:Explore alarm signals cited in insulin pump-associated adverse events (AEs), describe the clinical consequences and other root cause informing remarks that cooccurred with the alarm signals, and identify opportunities for improvements to patient education, instructional materials, and alarm systems to prevent future AEs.Research Design and Methods:We explored the type, frequency, and associated clinical consequences of alarm signals cited in a pre-coded data set of 2294 insulin pump-associated AEs involving the MiniMed 670G, MiniMed 630G, and t:slim X2. We also explored the clinical consequences and other root cause informing remarks that cooccurred with the top 10 most frequently cited alarm signals.Results:Overall, 403 AEs narratives cited at least one alarm signal. Of the 40 unique alarm signals cited, 42.5% were โalarms,โ 25.0% were โalerts,โ and 32.5% were not referenced in the instructional materials packaged with the corresponding pump. The top 10 most frequently cited alarm signals included two obstruction of flow alarms, which accounted for 49.9% of all AEs citing at least one alarm, and two unreferenced alarms. The most frequent cooccurring root cause informing remark varied across the top 10 alarm signals and revealed valuable insight into why these alarms may have occurred.Conclusions:Our findings demonstrate the value of analyzing alarm signals cited in insulin pump-associated AEs and reveal multiple opportunities for providers to educate patients on how to respond to alarm signals and manage their pumps to avoid AEs, and for insulin pump manufacturers to update instructional materials and improve alarm systems to support appropriate patient response.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:74:"Insulin Pump Alarms During Adverse Events: A Qualitative Descriptive Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231209999";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-31T01:09:43Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:15:"Jamie L. Estock";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Ronald A. Codario";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Margaret F. Zupa";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Shimrit Keddem";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"Keri L. Rodriguez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:74:"Insulin Pump Alarms During Adverse Events: A Qualitative Descriptive Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231209999";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231209999?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:80;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231207861?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:144:"Reliability of Handheld Blood Glucose Monitors in Neonates: Trustworthy Arterial Readings but Capillary Results Warrant Caution for Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231207861?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1653:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Accurate glucose monitoring is vitally important in neonatal intensive care units (NICUs) and clinicians use blood glucose monitors (BGM), such as the Inform II, for bedside glucose monitoring. Studies on BGM use in neonates have demonstrated good reliability; however, most studies only included healthy-term neonates. Therefore, the applicability of results to the preterm and/or ill neonate is limited.Objectives:In preterm and ill neonates, quantify differences in glucose concentrations between (1) capillary glucose (measured by BGM) and arterial glucose (measured by YSI 2300 Stat Plus) and (2) between aliquots from the same arterial blood sample, one measured by BGM versus one by YSI.Design/Methods:Forty neonates were included in the study. Using Inform II, we measured glucose concentrations on blood samples simultaneously collected from capillary circulation via heel puncture and from arterial circulation via an umbilical catheter. Plasma was then separated from the remainder of the arterial whole blood sample and a YSI 2300 Stat Plus measured plasma glucose concentration.Results:The dominant majority of arterial BGM results met the Clinical and Laboratory Standard Institute (CLSI) and Food and Drug Administration (FDA) tolerance criteria. Greater discrepancy was observed with capillary BGM values with an average of 27.5% of results falling outside tolerance criteria.Conclusions:Blood glucose monitor testing provided reliable results from arterial blood. However, users should interpret hypoglycemic results obtained from capillary blood with caution.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1653:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Accurate glucose monitoring is vitally important in neonatal intensive care units (NICUs) and clinicians use blood glucose monitors (BGM), such as the Inform II, for bedside glucose monitoring. Studies on BGM use in neonates have demonstrated good reliability; however, most studies only included healthy-term neonates. Therefore, the applicability of results to the preterm and/or ill neonate is limited.Objectives:In preterm and ill neonates, quantify differences in glucose concentrations between (1) capillary glucose (measured by BGM) and arterial glucose (measured by YSI 2300 Stat Plus) and (2) between aliquots from the same arterial blood sample, one measured by BGM versus one by YSI.Design/Methods:Forty neonates were included in the study. Using Inform II, we measured glucose concentrations on blood samples simultaneously collected from capillary circulation via heel puncture and from arterial circulation via an umbilical catheter. Plasma was then separated from the remainder of the arterial whole blood sample and a YSI 2300 Stat Plus measured plasma glucose concentration.Results:The dominant majority of arterial BGM results met the Clinical and Laboratory Standard Institute (CLSI) and Food and Drug Administration (FDA) tolerance criteria. Greater discrepancy was observed with capillary BGM values with an average of 27.5% of results falling outside tolerance criteria.Conclusions:Blood glucose monitor testing provided reliable results from arterial blood. However, users should interpret hypoglycemic results obtained from capillary blood with caution.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:144:"Reliability of Handheld Blood Glucose Monitors in Neonates: Trustworthy Arterial Readings but Capillary Results Warrant Caution for Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231207861";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-21T06:02:14Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:12:"David Brooks";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"James C. Slaughter";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"James H. Nichols";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Justin M. 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Although the trial included only 20 participants during a relatively short 4-week intervention period, glycemic outcomes attained were similar to commercial AID systems and there were no safety concerns. Validation of open-source AID systems in studies such as this should help address clinician hesitancy regarding these systems, and affirms the role of patient-centered innovation and self-management in diabetes care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:722:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>In an article in Journal of Diabetes Science and Technology, Nanayakkara and colleagues assessed the glycemic efficacy and safety of AndroidAPS, an open-source automated delivery (AID) system, in a crossover randomized controlled trial. Although the trial included only 20 participants during a relatively short 4-week intervention period, glycemic outcomes attained were similar to commercial AID systems and there were no safety concerns. Validation of open-source AID systems in studies such as this should help address clinician hesitancy regarding these systems, and affirms the role of patient-centered innovation and self-management in diabetes care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Analysis of โHybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetesโ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231208216";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-18T11:24:37Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:2:{i:0;a:5:{s:4:"data";s:16:"Tom M. Wilkinson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Martin de Bock";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:117:"Analysis of โHybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetesโ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231208216";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231208216?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:82;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231206155?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"First International Survey on Diabetes Providersโ Assessment of Skin Reactions in Youth With Type 1 Diabetes Using Technological Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231206155?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1621:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Advances in diabetes technological devices led to optimization of diabetes care; however, long-lasting skin exposure to devices may be accompanied by an increasing occurrence of cutaneous reactions.Methods:We used an open-link web-based survey to evaluate diabetes-care providersโ viewpoint on prevalence, management practices, and knowledge related to skin reactions with the use of diabetes technological devices. A post hoc analysis was applied to investigate differences in the level of awareness on this topic in relation to the experience in diabetes technology.Results:One hundred twenty-five responses from 39 different countries were collected. Most respondents (69%) routinely examine patientsโ skin at each visit. All the preventive measures are not clear and, mainly, homogenously put into clinical practice. Contact dermatitis was the most frequently reported cutaneous complication due to diabetes devices, and its most common provocative causes are not yet fully known by diabetes-care providers. Almost half of the respondents (42%) had discussed the presence of harmful allergens contained in adhesives with device manufacturers. There is general agreement on the need to strengthen knowledge on dermatological complications.Conclusions:Although diabetes-care providers are quite aware of the chance to develop skin reactions in people with diabetes using technological devices, there are still some unmet needs. Large follow-up studies and further dissemination tools are awaited to address the gaps revealed by our survey.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1621:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Advances in diabetes technological devices led to optimization of diabetes care; however, long-lasting skin exposure to devices may be accompanied by an increasing occurrence of cutaneous reactions.Methods:We used an open-link web-based survey to evaluate diabetes-care providersโ viewpoint on prevalence, management practices, and knowledge related to skin reactions with the use of diabetes technological devices. A post hoc analysis was applied to investigate differences in the level of awareness on this topic in relation to the experience in diabetes technology.Results:One hundred twenty-five responses from 39 different countries were collected. Most respondents (69%) routinely examine patientsโ skin at each visit. All the preventive measures are not clear and, mainly, homogenously put into clinical practice. Contact dermatitis was the most frequently reported cutaneous complication due to diabetes devices, and its most common provocative causes are not yet fully known by diabetes-care providers. Almost half of the respondents (42%) had discussed the presence of harmful allergens contained in adhesives with device manufacturers. There is general agreement on the need to strengthen knowledge on dermatological complications.Conclusions:Although diabetes-care providers are quite aware of the chance to develop skin reactions in people with diabetes using technological devices, there are still some unmet needs. Large follow-up studies and further dissemination tools are awaited to address the gaps revealed by our survey.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:138:"First International Survey on Diabetes Providersโ Assessment of Skin Reactions in Youth With Type 1 Diabetes Using Technological Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231206155";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-17T09:59:54Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:17:"Stefano Passanisi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:19:"Anna Korsgaard Berg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Agata Chobot";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:25:"Tiago Jeronimo Dos Santos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:19:"Claudia Anita Piona";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Laurel Messer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:18:"Fortunato Lombardo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:138:"First International Survey on Diabetes Providersโ Assessment of Skin Reactions in Youth With Type 1 Diabetes Using Technological Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231206155";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231206155?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:83;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231186428?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Glycemic and Psychosocial Correlates of Continuous Glucose Monitor Use Among Adolescents With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231186428?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1052:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background: Continuous glucose monitor (CGM) use has been linked with better glycemic outcomes (HbA1c), yet many adolescents with type 1 diabetes (T1D) struggle to maintain optimal CGM use. Methods: This study examined CGM use and its association with HbA1c and psychosocial factors among adolescents with T1D experiencing at least moderate diabetes distress (N = 198). We examined mean differences in HbA1c, diabetes distress, diabetes-related family conflict, and quality of life among CGM user groups (Current Users, Past Users, and Never Users). Results: Current Users demonstrated significantly lower HbA1c than Never Users and significantly lower diabetes distress than Past Users. CGM use was not associated with family conflict or quality of life. Conclusions: CGM use was associated with lower HbA1c and diabetes distress but not with other psychosocial outcomes. Longitudinal data may explain why many adolescents do not experience improvements in quality of life with CGM use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1052:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background: Continuous glucose monitor (CGM) use has been linked with better glycemic outcomes (HbA1c), yet many adolescents with type 1 diabetes (T1D) struggle to maintain optimal CGM use. Methods: This study examined CGM use and its association with HbA1c and psychosocial factors among adolescents with T1D experiencing at least moderate diabetes distress (N = 198). We examined mean differences in HbA1c, diabetes distress, diabetes-related family conflict, and quality of life among CGM user groups (Current Users, Past Users, and Never Users). Results: Current Users demonstrated significantly lower HbA1c than Never Users and significantly lower diabetes distress than Past Users. CGM use was not associated with family conflict or quality of life. Conclusions: CGM use was associated with lower HbA1c and diabetes distress but not with other psychosocial outcomes. Longitudinal data may explain why many adolescents do not experience improvements in quality of life with CGM use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Glycemic and Psychosocial Correlates of Continuous Glucose Monitor Use Among Adolescents With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231186428";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-17T08:10:32Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:12:"Emma Straton";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Hailey Inverso";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Hailey Moore";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Kashope Anifowoshe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:18:"Kendall Washington";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Randi Streisand";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Karishma Datye";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Sarah S. 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knowledge level of Bard and ChatGPT in the areas of endocrinology, diabetes, and diabetes technology through a multiple-choice question (MCQ) examination format.Methods:Initially, a 100-MCQ bank was established based on MCQs in endocrinology, diabetes, and diabetes technology. The MCQs were created from physiology, medical textbooks, and academic examination pools in the areas of endocrinology, diabetes, and diabetes technology and academic examination pools. The study team members analyzed the MCQ contents to ensure that they were related to the endocrinology, diabetes, and diabetes technology. The number of MCQs from endocrinology was 50, and that from diabetes and science technology was also 50. The knowledge level of Googleโs Bard and ChatGPT was assessed with an MCQ-based examination.Results:In the endocrinology examination section, ChatGPT obtained 29 marks (correct responses) of 50 (58%), and Bard obtained a similar score of 29 of 50 (58%). However, in the diabetes technology examination section, ChatGPT obtained 23 marks of 50 (46%), and Bard obtained 20 marks of 50 (40%). Overall, in the entire three-part examination, ChatGPT obtained 52 marks of 100 (52%), and Bard obtained 49 marks of 100 (49%). ChatGPT obtained slightly more marks than Bard. However, both ChatGPT and Bard did not achieve satisfactory scores in endocrinology or diabetes/technology of at least 60%.Conclusions:The overall MCQ-based performance of ChatGPT was slightly better than that of Googleโs Bard. However, both ChatGPT and Bard did not achieve appropriate scores in endocrinology and diabetes/diabetes technology. The study indicates that Bard and ChatGPT have the potential to facilitate medical students and faculty in academic medical education settings, but both artificial intelligence tools need more updated information in the fields of endocrinology, diabetes, and diabetes technology.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2021:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The present study aimed to investigate the knowledge level of Bard and ChatGPT in the areas of endocrinology, diabetes, and diabetes technology through a multiple-choice question (MCQ) examination format.Methods:Initially, a 100-MCQ bank was established based on MCQs in endocrinology, diabetes, and diabetes technology. The MCQs were created from physiology, medical textbooks, and academic examination pools in the areas of endocrinology, diabetes, and diabetes technology and academic examination pools. The study team members analyzed the MCQ contents to ensure that they were related to the endocrinology, diabetes, and diabetes technology. The number of MCQs from endocrinology was 50, and that from diabetes and science technology was also 50. The knowledge level of Googleโs Bard and ChatGPT was assessed with an MCQ-based examination.Results:In the endocrinology examination section, ChatGPT obtained 29 marks (correct responses) of 50 (58%), and Bard obtained a similar score of 29 of 50 (58%). However, in the diabetes technology examination section, ChatGPT obtained 23 marks of 50 (46%), and Bard obtained 20 marks of 50 (40%). Overall, in the entire three-part examination, ChatGPT obtained 52 marks of 100 (52%), and Bard obtained 49 marks of 100 (49%). ChatGPT obtained slightly more marks than Bard. However, both ChatGPT and Bard did not achieve satisfactory scores in endocrinology or diabetes/technology of at least 60%.Conclusions:The overall MCQ-based performance of ChatGPT was slightly better than that of Googleโs Bard. However, both ChatGPT and Bard did not achieve appropriate scores in endocrinology and diabetes/diabetes technology. The study indicates that Bard and ChatGPT have the potential to facilitate medical students and faculty in academic medical education settings, but both artificial intelligence tools need more updated information in the fields of endocrinology, diabetes, and diabetes technology.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:153:"The Scientific Knowledge of Bard and ChatGPT in Endocrinology, Diabetes, and Diabetes Technology: Multiple-Choice Questions Examination-Based Performance";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231203987";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-06T06:26:54Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:16:"Sultan Ayoub Meo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Thamir Al-Khlaiwi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:25:"Abdulelah Adnan AbuKhalaf";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Anusha Sultan Meo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:16:"David C. 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However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1676:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231203237";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-06T06:24:24Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:18:"Michael Schoemaker";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Anna Martensson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Julia K. Mader";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Kirsten Nรธrgaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Guido Freckmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:20:"Pierre-Yves Benhamou";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:10:"Peter Diem";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Lutz Heinemann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:130:"Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231203237";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231203237?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:88;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231201400?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:150:"Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231201400?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1700:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aims:For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges.Objective:To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework.Methods:A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features.Results:Participants had a mean age of approximately 59โyears, a mean duration of diabetes of 12โyears, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55.Conclusions:A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1700:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aims:For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges.Objective:To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework.Methods:A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features.Results:Participants had a mean age of approximately 59โyears, a mean duration of diabetes of 12โyears, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55.Conclusions:A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:150:"Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231201400";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-03T06:04:26Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:29:"Camilla Heisel Nyholm Thomsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Thomas Kronborg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Stine Hangaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Peter Vestergaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Ole Hejlesen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:26:"Morten Hasselstrรธm Jensen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:150:"Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231201400";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231201400?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:89;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231201862?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"System Accuracy and Interference Evaluation of a New Glucose Dehydrogenase-Based Blood Glucose Meter for Patient Self-Testing";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231201862?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:942:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>New European medical device regulations require the performance of postmarketing surveillance evaluations for blood glucose meters (BGMs). We conducted an ISO15197:2015-conform system performance evaluation with the approved glucose dehydrogenase (GDH)-based Wellion NEWTON BGM. One hundred subjects were enrolled into the study (44 female, 56 male, 43 healthy subjects, 23 type 1 diabetes, 34 type 2 diabetes, age: 53.7 ยฑ 15.8 years). In addition, manipulated heparinized whole blood was used for a laboratory interference test with ten selected substances (interference definition: substance-induced bias > 10%). The mean absolute relative difference (MARD) was 4.7%, and 100% of the values were in zones A (99.7%) and B (0.3%), respectively, of the consensus error grid. Interference was observed with xylose only, which is a known interfering substance for GDH-based BGMs.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:945:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>New European medical device regulations require the performance of postmarketing surveillance evaluations for blood glucose meters (BGMs). We conducted an ISO15197:2015-conform system performance evaluation with the approved glucose dehydrogenase (GDH)-based Wellion NEWTON BGM. One hundred subjects were enrolled into the study (44 female, 56 male, 43 healthy subjects, 23 type 1 diabetes, 34 type 2 diabetes, age: 53.7 ยฑ 15.8 years). In addition, manipulated heparinized whole blood was used for a laboratory interference test with ten selected substances (interference definition: substance-induced bias > 10%). The mean absolute relative difference (MARD) was 4.7%, and 100% of the values were in zones A (99.7%) and B (0.3%), respectively, of the consensus error grid. Interference was observed with xylose only, which is a known interfering substance for GDH-based BGMs.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:125:"System Accuracy and Interference Evaluation of a New Glucose Dehydrogenase-Based Blood Glucose Meter for Patient Self-Testing";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231201862";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-10-03T05:56:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:17:"Andreas Pfรผtzner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Daiva Kalasauske";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:10:"Mina Hanna";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:21:"Daniela Sachsenheimer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Gerhard Raab";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:20:"Silvia Weissenbacher";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Nicole Thomรฉ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:125:"System Accuracy and Interference Evaluation of a New Glucose Dehydrogenase-Based Blood Glucose Meter for Patient Self-Testing";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231201862";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231201862?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:90;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231198871?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231198871?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1447:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Open-source automated insulin delivery (OS-AID) systems combine commercially available insulin pumps and continuous glucose monitors with open-source algorithms to automate insulin dosing for people with insulin-requiring diabetes. Two data sets (OPEN and the OpenAPS Data Commons) contain anonymized OS-AID user data.Methods:We assessed glycemic variability (GV) outcomes in the OPEN data set and characterized it alongside a comparison to the n = 122 version of the OpenAPS Data Commons. Glucose data are analyzed using an unsupervised machine learning algorithm for clustering, and GV metrics are quantified using statistical tests for distribution comparison. Demographic data are also analyzed quantitatively.Results:The n = 75 OPEN data set contains 36โ827 days worth of data. Mean TIR is 82.08% (TOR < 70: 3.66%; TOR > 180: 14.3%). LBGI (P < .05) differs by gender whereas HBGI distributions are similar (P > .05). GV metrics (except TOR < 70, LBGI) show a statistically significant difference (P < .05) between data sets.Conclusions:Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1471:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Open-source automated insulin delivery (OS-AID) systems combine commercially available insulin pumps and continuous glucose monitors with open-source algorithms to automate insulin dosing for people with insulin-requiring diabetes. Two data sets (OPEN and the OpenAPS Data Commons) contain anonymized OS-AID user data.Methods:We assessed glycemic variability (GV) outcomes in the OPEN data set and characterized it alongside a comparison to the n = 122 version of the OpenAPS Data Commons. Glucose data are analyzed using an unsupervised machine learning algorithm for clustering, and GV metrics are quantified using statistical tests for distribution comparison. Demographic data are also analyzed quantitatively.Results:The n = 75 OPEN data set contains 36โ827 days worth of data. Mean TIR is 82.08% (TOR < 70: 3.66%; TOR > 180: 14.3%). LBGI (P < .05) differs by gender whereas HBGI distributions are similar (P > .05). GV metrics (except TOR < 70, LBGI) show a statistically significant difference (P < .05) between data sets.Conclusions:Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:122:"Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231198871";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-26T09:03:59Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:11:"Drew Cooper";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Bernd Reinhold";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Arsalan Shahid";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Dana M. 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However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days.Methods:Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2).Results:For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use.Conclusion:We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1564:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days.Methods:Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2).Results:For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use.Conclusion:We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:156:"Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231200901";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-25T12:15:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:15:"Halis K. Akturk";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Casey Sakamoto";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:10:"Tim Vigers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Viral N. Shah";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:10:"Laura Pyle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:156:"Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231200901";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231200901?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:92;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231202803?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:188:"Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231202803?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1731:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment.Methods:We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79โ000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations.Results:Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively).Conclusions:We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1740:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment.Methods:We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79โ000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations.Results:Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively).Conclusions:We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:188:"Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231202803";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-25T06:57:09Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:19:"Franck Diaz-Garelli";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Aakash Shah";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Arthur Mikhno";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Pratik Agrawal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:19:"Amanda Kinnischtzke";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:18:"Robert A. Vigersky";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:188:"Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231202803";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231202803?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:93;a:6:{s:4:"data";s:186:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231199470?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Comparison of Glycemic Control Between In-Person and Virtual Diabetes Consults in Hospitalized Patients With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231199470?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2054:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:There is limited evidence that the diabetes in-person consult in hospitalized patients can be replaced by a virtual consult. During COVID-19 pandemic, the diabetes in-person consult service at the University of Miami and Miami Veterans Affairs Healthcare System transitioned to a virtual model. The aim of this study was to assess the impact of telemedicine on glycemic control after this transition.Methods:We retrospectively analyzed glucose metrics from in-person consults (In-person) during January 16 to March 14, 2020 and virtual consults during March 15 to May 14, 2020. Data from virtual consults were analyzed by separating patients infected with COVID-19, who were seen only virtually (Virtual-COVID-19-Pos), and patients who were not infected (Virtual-COVID-19-Neg), or by combining the two groups (Virtual-All).Results:Patient-dayโweighted blood glucose was not significantly different between In-person, Virtual-All, and Virtual-COVID-19-Neg, but Virtual-COVID-19-Pos had significantly higher mean ยฑ SD blood glucose (mg/dL) compared with others (206.7 ยฑ 49.6 In-person, 214.6 ยฑ 56.2 Virtual-All, 206.5 ยฑ 57.2 Virtual-COVID-19-Neg, 229.7 ยฑ 51.6 Virtual-COVID-19-Pos; P = .015). A significantly less percentage of patients in this group also achieved a mean ยฑ SD glucose target of 140 to 180 mg/dL (23.8 ยฑ 22.5 In-person, 21.5 ยฑ 20.5 Virtual-All, 25.3 ยฑ 20.8 Virtual-COVID-19-Neg, and 14.4ยฑ18.1 Virtual-COVID-19-Pos, P = .024), but there was no significant difference between In-person, Virtual-All, and Virtual-COVID-19-Neg. The occurrence of hypoglycemia was not significantly different among groups.Conclusions:In-person and virtual consults delivered by a diabetes team at an academic institution were not associated with significant differences in glycemic control. These real-world data suggest that telemedicine could be used for in-patient diabetes management, although additional studies are needed to better assess clinical outcomes and safety.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2054:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:There is limited evidence that the diabetes in-person consult in hospitalized patients can be replaced by a virtual consult. During COVID-19 pandemic, the diabetes in-person consult service at the University of Miami and Miami Veterans Affairs Healthcare System transitioned to a virtual model. The aim of this study was to assess the impact of telemedicine on glycemic control after this transition.Methods:We retrospectively analyzed glucose metrics from in-person consults (In-person) during January 16 to March 14, 2020 and virtual consults during March 15 to May 14, 2020. Data from virtual consults were analyzed by separating patients infected with COVID-19, who were seen only virtually (Virtual-COVID-19-Pos), and patients who were not infected (Virtual-COVID-19-Neg), or by combining the two groups (Virtual-All).Results:Patient-dayโweighted blood glucose was not significantly different between In-person, Virtual-All, and Virtual-COVID-19-Neg, but Virtual-COVID-19-Pos had significantly higher mean ยฑ SD blood glucose (mg/dL) compared with others (206.7 ยฑ 49.6 In-person, 214.6 ยฑ 56.2 Virtual-All, 206.5 ยฑ 57.2 Virtual-COVID-19-Neg, 229.7 ยฑ 51.6 Virtual-COVID-19-Pos; P = .015). A significantly less percentage of patients in this group also achieved a mean ยฑ SD glucose target of 140 to 180 mg/dL (23.8 ยฑ 22.5 In-person, 21.5 ยฑ 20.5 Virtual-All, 25.3 ยฑ 20.8 Virtual-COVID-19-Neg, and 14.4ยฑ18.1 Virtual-COVID-19-Pos, P = .024), but there was no significant difference between In-person, Virtual-All, and Virtual-COVID-19-Neg. The occurrence of hypoglycemia was not significantly different among groups.Conclusions:In-person and virtual consults delivered by a diabetes team at an academic institution were not associated with significant differences in glycemic control. These real-world data suggest that telemedicine could be used for in-patient diabetes management, although additional studies are needed to better assess clinical outcomes and safety.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:117:"Comparison of Glycemic Control Between In-Person and Virtual Diabetes Consults in Hospitalized Patients With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231199470";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-20T08:15:58Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:13:{i:0;a:5:{s:4:"data";s:22:"Maria Gracia Luzuriaga";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Monica Lieberman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:10:"Ruixuan MA";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Sabina Casula";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:22:"Violet Lagari-Libhaber";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Shari Messinger";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:6:"Hua Li";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Bresta Miranda";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:15:"David A. Baidal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:23:"Ernesto Bernal Mizrachi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:19:"Gianluca Iacobellis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:11:"Rajesh Garg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:18:"Francesco Vendrame";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:117:"Comparison of Glycemic Control Between In-Person and Virtual Diabetes Consults in Hospitalized Patients With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231199470";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231199470?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:94;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231199113?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:137:"Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231199113?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1591:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The magnitude and importance of higher HbA1c levels not due to mean blood glucose (MBG) in non-Hispanic black (B) versus non-Hispanic white (W) individuals is controversial. We sought to clarify the relationship of HbA1c with glucose data from continuous glucose monitoring (CGM) in a young biracial population.Methods:Glycemic data of 33 B and 85 W, healthy youth with type 1 diabetes (age 14.7 ยฑ 4.8 years, M/F = 51/67, duration of diabetes 5.4 ยฑ 4.7 years) from a factory-calibrated CGM was compared with HbA1c. Hemoglobin glycation index (HGI) = assayed HbA1c โ glucose management index (GMI).Results:B patients had higher unadjusted levels of HbA1c, MBG, MBGSD, GMI, and HGI than W patients. Percent glucose time in range (TIR) and percent sensor use (PSU) were lower for B patients. Average HbA1c in B patients 8.3% was higher than 7.7% for W (P < .0001) after statistical adjustment for MBG, age, gender, insulin delivery method, and accounting for a race by PSU interaction effect. Higher HbA1c persisted in B patients when TIR was substituted for MBG. Predicted MBG was higher in B patients at any level of PSU. The 95th percentile for HGI was 0.47 in W patients, and 52% of B patients had HGI โฅ 0.5. Time below range was similar for both.Conclusions:Young B patients have clinically relevant higher average HbA1c at any given level of MBG or TIR than W patients, which may pose an additional risk for diabetes complications development. HGI โฅ 0.5 may be an easy way to identify high-risk patients.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1594:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The magnitude and importance of higher HbA1c levels not due to mean blood glucose (MBG) in non-Hispanic black (B) versus non-Hispanic white (W) individuals is controversial. We sought to clarify the relationship of HbA1c with glucose data from continuous glucose monitoring (CGM) in a young biracial population.Methods:Glycemic data of 33 B and 85 W, healthy youth with type 1 diabetes (age 14.7 ยฑ 4.8 years, M/F = 51/67, duration of diabetes 5.4 ยฑ 4.7 years) from a factory-calibrated CGM was compared with HbA1c. Hemoglobin glycation index (HGI) = assayed HbA1c โ glucose management index (GMI).Results:B patients had higher unadjusted levels of HbA1c, MBG, MBGSD, GMI, and HGI than W patients. Percent glucose time in range (TIR) and percent sensor use (PSU) were lower for B patients. Average HbA1c in B patients 8.3% was higher than 7.7% for W (P < .0001) after statistical adjustment for MBG, age, gender, insulin delivery method, and accounting for a race by PSU interaction effect. Higher HbA1c persisted in B patients when TIR was substituted for MBG. Predicted MBG was higher in B patients at any level of PSU. The 95th percentile for HGI was 0.47 in W patients, and 52% of B patients had HGI โฅ 0.5. Time below range was similar for both.Conclusions:Young B patients have clinically relevant higher average HbA1c at any given level of MBG or TIR than W patients, which may pose an additional risk for diabetes complications development. HGI โฅ 0.5 may be an easy way to identify high-risk patients.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:137:"Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231199113";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-13T06:27:31Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:22:"Nicholas J. Christakis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Marcella Gioe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Ricardo Gomez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Dania Felipe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Arlette Soros";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Robert McCarter";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Stuart Chalew";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:137:"Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231199113";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231199113?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:95;a:6:{s:4:"data";s:214:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231197423?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:199:"The Effect of Insulin Degludec Versus Insulin Glargine U100 on Glucose Metrics Recorded During Continuous Glucose Monitoring in People With Type 1 Diabetes and Recurrent Nocturnal Severe Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231197423?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1810:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:Comparing continuous glucose monitoring (CGM)-recorded metrics during treatment with insulin degludec (IDeg) versus insulin glargine U100 (IGlar-100) in people with type 1 diabetes (T1D) and recurrent nocturnal severe hypoglycemia.Materials and methods:This is a multicenter, two-year, randomized, crossover trial, including 149 adults with T1D and minimum one episode of nocturnal severe hypoglycemia within the last two years. Participants were randomized 1:1 to treatment with IDeg or IGlar-100 and given the option of six days of blinded CGM twice during each treatment. CGM traces were reviewed for the percentage of time-within-target glucose range (TIR), time-below-range (TBR), time-above-range (TAR), and coefficient of variation (CV).Results:Seventy-four participants were included in the analysis. Differences between treatments were greatest during the night (23:00-06:59). Treatment with IGlar-100 resulted in 54.0% vs 49.0% with IDeg TIR (70-180 mg/dL) (estimated treatment difference [ETD]: โ4.6%, 95% confidence interval [CI]: โ9.1, โ0.0, P = .049). TBR was lower with IDeg at level 1 (54-69 mg/dL) (ETD: โ1.7% [95% CI: โ2.9, โ0.5], P < .05) and level 2 (<54 mg/dL) (ETD: โ1.3% [95% CI: โ2.1, โ0.5], P = .001). TAR was higher with IDeg compared with IGlar-100 at level 1 (181-250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.3], P < .05) and level 2 (> 250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.2], P < .05). The mean CV was lower with IDeg than that with IGlar-100 (ETD: โ3.4% [95% CI: โ5.6, โ1.2], P < .05).Conclusion:For people with T1D suffering from recurrent nocturnal severe hypoglycemia, treatment with IDeg, compared with IGlar-100, results in a lower TBR and CV during the night at the expense of more TAR.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1828:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:Comparing continuous glucose monitoring (CGM)-recorded metrics during treatment with insulin degludec (IDeg) versus insulin glargine U100 (IGlar-100) in people with type 1 diabetes (T1D) and recurrent nocturnal severe hypoglycemia.Materials and methods:This is a multicenter, two-year, randomized, crossover trial, including 149 adults with T1D and minimum one episode of nocturnal severe hypoglycemia within the last two years. Participants were randomized 1:1 to treatment with IDeg or IGlar-100 and given the option of six days of blinded CGM twice during each treatment. CGM traces were reviewed for the percentage of time-within-target glucose range (TIR), time-below-range (TBR), time-above-range (TAR), and coefficient of variation (CV).Results:Seventy-four participants were included in the analysis. Differences between treatments were greatest during the night (23:00-06:59). Treatment with IGlar-100 resulted in 54.0% vs 49.0% with IDeg TIR (70-180 mg/dL) (estimated treatment difference [ETD]: โ4.6%, 95% confidence interval [CI]: โ9.1, โ0.0, P = .049). TBR was lower with IDeg at level 1 (54-69 mg/dL) (ETD: โ1.7% [95% CI: โ2.9, โ0.5], P < .05) and level 2 (<54 mg/dL) (ETD: โ1.3% [95% CI: โ2.1, โ0.5], P = .001). TAR was higher with IDeg compared with IGlar-100 at level 1 (181-250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.3], P < .05) and level 2 (> 250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.2], P < .05). The mean CV was lower with IDeg than that with IGlar-100 (ETD: โ3.4% [95% CI: โ5.6, โ1.2], P < .05).Conclusion:For people with T1D suffering from recurrent nocturnal severe hypoglycemia, treatment with IDeg, compared with IGlar-100, results in a lower TBR and CV during the night at the expense of more TAR.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:199:"The Effect of Insulin Degludec Versus Insulin Glargine U100 on Glucose Metrics Recorded During Continuous Glucose Monitoring in People With Type 1 Diabetes and Recurrent Nocturnal Severe Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231197423";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-06T11:26:26Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:17:{i:0;a:5:{s:4:"data";s:28:"Julie Maria Bรธggild Brรธsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Rikke Mette Agesen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:21:"Amra Ciric Alibegovic";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:22:"Henrik Ullits Andersen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:20:"Henning Beck-Nielsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:16:"Peter Gustenhoff";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:20:"Troels Krarup Hansen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:20:"Christoffer Hedetoft";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:18:"Tonny Joran Jensen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:15:"Claus Bogh Juhl";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:23:"Charlotte Rรธn Stolberg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:23:"Susanne Sรธgaard Lerche";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:17:"Kirsten Nรธrgaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:13;a:5:{s:4:"data";s:19:"Hans-Henrik Parving";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:14;a:5:{s:4:"data";s:11:"Lise Tarnow";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:15;a:5:{s:4:"data";s:20:"Birger Thorsteinsson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:16;a:5:{s:4:"data";s:24:"Ulrik Pedersen-Bjergaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:199:"The Effect of Insulin Degludec Versus Insulin Glargine U100 on Glucose Metrics Recorded During Continuous Glucose Monitoring in People With Type 1 Diabetes and Recurrent Nocturnal Severe Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231197423";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231197423?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:96;a:6:{s:4:"data";s:165:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231196562?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:185:"The Effect of Do-It-Yourself Real-Time Continuous Glucose Monitoring on Glycemic Variables and Participant-Reported Outcomes in Adults With Type 1 Diabetes: A Randomized Crossover Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231196562?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1976:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:Real-time continuous glucose monitoring (rtCGM) has several advantages over intermittently scanned continuous glucose monitoring (isCGM) but generally comes at a higher cost. Do-it-yourself rtCGM (DIY-rtCGM) potentially has benefits similar to those of rtCGM. This study compared outcomes in adults with type 1 diabetes using DIY-rtCGM versus isCGM.Methods:In this crossover trial, adults with type 1 diabetes were randomized to use isCGM or DIY-rtCGM for eight weeks before crossover to use the other device for eight weeks, after a four-week washout period where participants reverted back to isCGM. The primary endpoint was time in range (TIR; 3.9-10 mmol/L). Secondary endpoints included other glycemic control measures, psychosocial outcomes, and sleep quality.Results:Sixty participants were recruited, and 52 (87%) completed follow-up. Glucose outcomes were similar in the DIY-rtCGM and isCGM groups, including TIR (53.1% vs 51.3%; mean difference โ1.7% P = .593), glycosylated hemoglobin (57.0 ยฑ 17.8 vs 61.4 ยฑ 12.2 mmol/L; P = .593), and time in hypoglycemia <3.9 mmol/L (3.9 ยฑ 3.8% vs 3.8 ยฑ 4.0%; P = .947). Hypoglycemia Fear Survey total score (1.17 ยฑ 0.52 vs 0.97 ยฑ 0.54; P = .02) and fear of hypoglycemia score (1.18 ยฑ 0.64 vs 0.97 ยฑ 0.45; P = .02) were significantly higher during DIY-rtCGM versus isCGM. Diabetes Treatment Satisfaction Questionnaire status (DTSQS) score was also higher with DIY-rtCGM versus isCGM (28.7 ยฑ 5.8 vs 26.0 ยฑ 5.8; P = .04), whereas diabetes-related quality of life was slightly lower (DAWN2 Impact of Diabetes score: 3.11 ยฑ 0.4 vs 3.32 ยฑ 0.51; P = .045); sleep quality did not differ between the two groups.Conclusion:Although the use of DIY-rtCGM did not improve glycemic outcomes compared with isCGM, it positively impacted several patient-reported psychosocial variables. DIY-rtCGM potentially provides an alternative, cost-effective rtCGM option.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1979:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:Real-time continuous glucose monitoring (rtCGM) has several advantages over intermittently scanned continuous glucose monitoring (isCGM) but generally comes at a higher cost. Do-it-yourself rtCGM (DIY-rtCGM) potentially has benefits similar to those of rtCGM. This study compared outcomes in adults with type 1 diabetes using DIY-rtCGM versus isCGM.Methods:In this crossover trial, adults with type 1 diabetes were randomized to use isCGM or DIY-rtCGM for eight weeks before crossover to use the other device for eight weeks, after a four-week washout period where participants reverted back to isCGM. The primary endpoint was time in range (TIR; 3.9-10 mmol/L). Secondary endpoints included other glycemic control measures, psychosocial outcomes, and sleep quality.Results:Sixty participants were recruited, and 52 (87%) completed follow-up. Glucose outcomes were similar in the DIY-rtCGM and isCGM groups, including TIR (53.1% vs 51.3%; mean difference โ1.7% P = .593), glycosylated hemoglobin (57.0 ยฑ 17.8 vs 61.4 ยฑ 12.2 mmol/L; P = .593), and time in hypoglycemia <3.9 mmol/L (3.9 ยฑ 3.8% vs 3.8 ยฑ 4.0%; P = .947). Hypoglycemia Fear Survey total score (1.17 ยฑ 0.52 vs 0.97 ยฑ 0.54; P = .02) and fear of hypoglycemia score (1.18 ยฑ 0.64 vs 0.97 ยฑ 0.45; P = .02) were significantly higher during DIY-rtCGM versus isCGM. Diabetes Treatment Satisfaction Questionnaire status (DTSQS) score was also higher with DIY-rtCGM versus isCGM (28.7 ยฑ 5.8 vs 26.0 ยฑ 5.8; P = .04), whereas diabetes-related quality of life was slightly lower (DAWN2 Impact of Diabetes score: 3.11 ยฑ 0.4 vs 3.32 ยฑ 0.51; P = .045); sleep quality did not differ between the two groups.Conclusion:Although the use of DIY-rtCGM did not improve glycemic outcomes compared with isCGM, it positively impacted several patient-reported psychosocial variables. DIY-rtCGM potentially provides an alternative, cost-effective rtCGM option.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:185:"The Effect of Do-It-Yourself Real-Time Continuous Glucose Monitoring on Glycemic Variables and Participant-Reported Outcomes in Adults With Type 1 Diabetes: A Randomized Crossover Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231196562";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-06T11:17:46Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:10:{i:0;a:5:{s:4:"data";s:14:"Shekhar Sehgal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Mona Elbalshy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Jonathan Williman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Barbara Galland";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Hamish Crocket";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Rosemary Hall";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:9:"Ryan Paul";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:13:"Robert Leikis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:14:"Martin de Bock";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:19:"Benjamin J. 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To address this gap, we conducted a quasi-experimental prospective study to assess psychosocial, glycemic, and behavioral changes over six months in T2D adults on multiple daily injections (MDI) who were interested in starting Omnipod DASH, comparing those who did versus did not start on it.Methods:In total, 458 adults with T2D completed baseline questionnaires assessing psychosocial dimensions (eg, diabetes distress), clinical metrics (eg, HbA1c [glycosylated hemoglobin]), and behavioral measures (eg, missed mealtime boluses). Six months later, 220 (48.0%) completed the same questionnaire again. To examine differences in outcomes over time between those who began CSII (n = 176) versus those who remained on MDI (n = 44), a latent change score approach was used.Results:The CSII users reported greater gains than MDI users on all major psychosocial metrics, including overall well-being (P < .001) diabetes distress (P < .001), perceived T2D impact on quality of life (P = .003), and hypoglycemic worries and concerns (P < .001). The CSII users similarly reported a larger decline in HbA1c than MDI users (P < .05) and greater declines in two critical self-care behaviors: number of missed mealtime boluses (P < .001) and number of days of perceived overeating (P = .001).Conclusions:The introduction of CSII (Omnipod DASH) in T2D adults can contribute to significant psychosocial, glycemic, and behavioral benefits, indicating that broader use of CSII in the T2D population may be of value.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1812:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous subcutaneous insulin infusion (CSII) use in adults with type 1 diabetes offers psychosocial and clinical benefits, but little is known about its impact on such outcomes in the type 2 diabetes (T2D) population. To address this gap, we conducted a quasi-experimental prospective study to assess psychosocial, glycemic, and behavioral changes over six months in T2D adults on multiple daily injections (MDI) who were interested in starting Omnipod DASH, comparing those who did versus did not start on it.Methods:In total, 458 adults with T2D completed baseline questionnaires assessing psychosocial dimensions (eg, diabetes distress), clinical metrics (eg, HbA1c [glycosylated hemoglobin]), and behavioral measures (eg, missed mealtime boluses). Six months later, 220 (48.0%) completed the same questionnaire again. To examine differences in outcomes over time between those who began CSII (n = 176) versus those who remained on MDI (n = 44), a latent change score approach was used.Results:The CSII users reported greater gains than MDI users on all major psychosocial metrics, including overall well-being (P < .001) diabetes distress (P < .001), perceived T2D impact on quality of life (P = .003), and hypoglycemic worries and concerns (P < .001). The CSII users similarly reported a larger decline in HbA1c than MDI users (P < .05) and greater declines in two critical self-care behaviors: number of missed mealtime boluses (P < .001) and number of days of perceived overeating (P = .001).Conclusions:The introduction of CSII (Omnipod DASH) in T2D adults can contribute to significant psychosocial, glycemic, and behavioral benefits, indicating that broader use of CSII in the T2D population may be of value.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:144:"Psychosocial and Glycemic Benefits for Insulin-Using Adults With Type 2 Diabetes After Six Months of Pump Therapy: A Quasi-Experimental Approach";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231198533";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-09-05T06:07:27Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:2:{i:0;a:5:{s:4:"data";s:19:"William H. 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A method of integrating CGM data with the EHR that relies on the Dexcom API was developed by Northwestern Medicine and Dexcom to address these challenges. Here, we describe the data management steps and user interface of the integrated system. Providers can access patientsโ historical and latest daily CGM data in the form of modal day plots and stacked columns showing time in various glucose concentration ranges. The integration facilitates the acquisition, storage, analysis, and display of CGM data within an EHR system and may be appropriate for deployment in other health care facilities.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:847:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Managing data from continuous glucose monitoring (CGM) systems presents challenges to health care provider teams that rely on the electronic health record (EHR) during patient visits. A method of integrating CGM data with the EHR that relies on the Dexcom API was developed by Northwestern Medicine and Dexcom to address these challenges. Here, we describe the data management steps and user interface of the integrated system. Providers can access patientsโ historical and latest daily CGM data in the form of modal day plots and stacked columns showing time in various glucose concentration ranges. The integration facilitates the acquisition, storage, analysis, and display of CGM data within an EHR system and may be appropriate for deployment in other health care facilities.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Integration of Continuous Glucose Monitoring Data into an Electronic Health Record System: Single-Center Implementation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231196168";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-08-30T07:06:03Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:13:"Grazia Aleppo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Ryan Chmiel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Andrew Zurn";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Ryan Bandoske";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Patrick Creamer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:17:"Nicholas Neubauer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:7:"Jo Wong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"Sarah B. 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Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard.Results:A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%).Conclusions:Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1632:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting.Methods:In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard.Results:A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%).Conclusions:Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:154:"Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231194644";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-08-29T07:24:32Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:18:"John J. Wroblewski";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Ermilo Sanchez-Buenfil";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Miguel Inciarte";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"Jay Berdia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Lewis Blake";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:16:"Simon Wroblewski";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Alexandria Patti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Gretchen Suter";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:17:"George E. Sanborn";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:154:"Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231194644";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231194644?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:100;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231192979?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Mealtime Insulin BOLUS Score More Strongly Predicts HbA1c Than the Self-Care Inventory in Youth With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231192979?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1688:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To meet their glycated hemoglobin (HbA1c) goals, youth with type 1 diabetes (T1D) need to engage with their daily T1D treatment. The mealtime insulin Bolus score (BOLUS) is an objective measure of youthโs T1D engagement which we have previously shown to be superior to other objective engagement measures in predicting youthโs HbA1c. Here, to further assess the BOLUS scoreโs validity, we compared the strengths of the associations between youthโs HbA1c with their mean insulin BOLUS score and a valid, self-report measure of T1D engagement, the Self-Care Inventory (SCI).Methods:One-hundred and five youth with T1D self-reported their T1D engagement using the SCI. We also collected two weeks of insulin pump data and a concurrent HbA1c level. We scored youthโs SCI and calculated their mean insulin BOLUS score using standardized methods. For the analyses, we performed simple correlations, partial correlations, and multiple regression models.Results:Youth had a mean age of 15.03 ยฑ 1.97 years, mean time since diagnosis of 8.11 ยฑ 3.26 years, and a mean HbA1c of 8.78 ยฑ 1.49%. The sample included n = 58 boys (55%) and n = 96 families (91%) self-identified as white. Simple correlations between youthโs age, HbA1c, SCI total score, and BOLUS score were all significant. Partial correlation and regression models revealed that youthโs insulin BOLUS score was more strongly associated with HbA1c than the SCI.Conclusions:Youthsโ BOLUS score has better concurrent validity with HbA1c than the SCI. We should consider reporting the BOLUS score as an outcome metric in insulin pump data reports.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1688:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To meet their glycated hemoglobin (HbA1c) goals, youth with type 1 diabetes (T1D) need to engage with their daily T1D treatment. The mealtime insulin Bolus score (BOLUS) is an objective measure of youthโs T1D engagement which we have previously shown to be superior to other objective engagement measures in predicting youthโs HbA1c. Here, to further assess the BOLUS scoreโs validity, we compared the strengths of the associations between youthโs HbA1c with their mean insulin BOLUS score and a valid, self-report measure of T1D engagement, the Self-Care Inventory (SCI).Methods:One-hundred and five youth with T1D self-reported their T1D engagement using the SCI. We also collected two weeks of insulin pump data and a concurrent HbA1c level. We scored youthโs SCI and calculated their mean insulin BOLUS score using standardized methods. For the analyses, we performed simple correlations, partial correlations, and multiple regression models.Results:Youth had a mean age of 15.03 ยฑ 1.97 years, mean time since diagnosis of 8.11 ยฑ 3.26 years, and a mean HbA1c of 8.78 ยฑ 1.49%. The sample included n = 58 boys (55%) and n = 96 families (91%) self-identified as white. Simple correlations between youthโs age, HbA1c, SCI total score, and BOLUS score were all significant. Partial correlation and regression models revealed that youthโs insulin BOLUS score was more strongly associated with HbA1c than the SCI.Conclusions:Youthsโ BOLUS score has better concurrent validity with HbA1c than the SCI. We should consider reporting the BOLUS score as an outcome metric in insulin pump data reports.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:116:"Mealtime Insulin BOLUS Score More Strongly Predicts HbA1c Than the Self-Care Inventory in Youth With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231192979";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-08-12T04:44:35Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:15:"Jordan Christie";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Mark A. Clements";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"David D. Williams";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Joseph Cernich";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:16:"Susana R. Patton";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:116:"Mealtime Insulin BOLUS Score More Strongly Predicts HbA1c Than the Self-Care Inventory in Youth With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231192979";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231192979?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:101;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231191544?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:197:"Age and Red Blood Cell Parameters Mainly Explain the Differences Between HbA1c and Glycemic Management Indicator Among Patients With Type 1 Diabetes Using Intermittent Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231191544?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1767:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Glycated hemoglobin (HbA1c) is the gold standard to assess glycemic control in patients with diabetes. Glucose management indicator (GMI), a metric generated by continuous glucose monitoring (CGM), has been proposed as an alternative to HbA1c, but the two values may differ, complicating clinical decision-making. This study aimed to identify the factors that may explain the discrepancy between them.Methods:Subjects were patients with type 1 diabetes, with one or more HbA1c measurements after starting the use of the Freestyle Libre 2 intermittent CGM, who shared their data with the center on the Libreview platform. The 14-day glucometric reports were retrieved, with the end date coinciding with the date of each HbA1c measurement, and those with sensor use โฅ70% were selected. Clinical data prior to the start of CGM use, glucometric data from each report, and other simultaneous laboratory measurements with HbA1c were collected.Results:A total of 646 HbA1c values and their corresponding glucometric reports were obtained from 339 patients. The absolute difference between HbA1c and GMI was <0.3% in only 38.7% of cases. Univariate analysis showed that the HbA1c-GMI value was associated with age, diabetes duration, estimated glomerular filtration rate, mean corpuscular volume (MCV), red cell distribution width (RDW), and time with glucose between 180 and 250 mg/dL. In a multilevel model, only age and RDW, positively, and MCV, negatively, were correlated to HbA1c-GMI.Conclusion:The difference between HbA1c and GMI is clinically relevant in a high percentage of cases. Age and easily accessible hematological parameters (MCV and RDW) can help to interpret these differences.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1770:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Glycated hemoglobin (HbA1c) is the gold standard to assess glycemic control in patients with diabetes. Glucose management indicator (GMI), a metric generated by continuous glucose monitoring (CGM), has been proposed as an alternative to HbA1c, but the two values may differ, complicating clinical decision-making. This study aimed to identify the factors that may explain the discrepancy between them.Methods:Subjects were patients with type 1 diabetes, with one or more HbA1c measurements after starting the use of the Freestyle Libre 2 intermittent CGM, who shared their data with the center on the Libreview platform. The 14-day glucometric reports were retrieved, with the end date coinciding with the date of each HbA1c measurement, and those with sensor use โฅ70% were selected. Clinical data prior to the start of CGM use, glucometric data from each report, and other simultaneous laboratory measurements with HbA1c were collected.Results:A total of 646 HbA1c values and their corresponding glucometric reports were obtained from 339 patients. The absolute difference between HbA1c and GMI was <0.3% in only 38.7% of cases. Univariate analysis showed that the HbA1c-GMI value was associated with age, diabetes duration, estimated glomerular filtration rate, mean corpuscular volume (MCV), red cell distribution width (RDW), and time with glucose between 180 and 250 mg/dL. In a multilevel model, only age and RDW, positively, and MCV, negatively, were correlated to HbA1c-GMI.Conclusion:The difference between HbA1c and GMI is clinically relevant in a high percentage of cases. Age and easily accessible hematological parameters (MCV and RDW) can help to interpret these differences.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:197:"Age and Red Blood Cell Parameters Mainly Explain the Differences Between HbA1c and Glycemic Management Indicator Among Patients With Type 1 Diabetes Using Intermittent Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231191544";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-08-12T04:41:27Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:14:"Pablo Azcoitia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:28:"Raquel Rodrรญguez-Castellano";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Pedro Saavedra";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:19:"Marรญa P. Alberiche";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Dunia Marrero";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"Ana M. Wรคgner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Antonio Ojeda";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:13:"Mauro Boronat";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:197:"Age and Red Blood Cell Parameters Mainly Explain the Differences Between HbA1c and Glycemic Management Indicator Among Patients With Type 1 Diabetes Using Intermittent Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231191544";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231191544?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:102;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231190408?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Novel Robust Needle Tip Design Enables Needle Reuse and Reduced Skin Trauma in Combination With Autoinjector Needle Shields";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231190408?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1644:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Pen needles and autoinjectors are necessary for millions of patients needing injectable drug treatment but pose economic and environmental burdens. A durable device with a multiuse needle could reduce cost and improve user experience. This study explores a novel robust needle tip (EXP) designed for multiple uses and durability against hooking.Method:Needle robustness was investigated through a structural analysis. Furthermore, EXP and control needles (NF30, NF28) were evaluated in an in-vivo porcine model as pen needles or embedded in autoinjectors to study the resulting increase in skin blood perfusion (SBP). The SBP was assessed by laser speckle contrast analysis (LASCA) of 192 randomized and blinded needle insertions.Results:Forming a 33 ยตm hook against a hard surface requires 0.92 N for the NF30 control needle and 5.38 N for EXP. The EXP did not induce more tissue trauma than the NF30. There was a positive relation between needle diameter and SBP (P < .05). Furthermore, the presence of an autoinjector shield and applied force of 10 N was found to significantly reduce SBP for worn EXP needles (P < .05) compared to insertions without autoinjector shield.Conclusions:The investigated robust needle EXP is on par with the single-use needle NF30 in terms of tissue trauma, which is further reduced by combining the needle with a needle shield. These results should encourage the innovation and development of durable, reusable injection systems with pharmacoeconomic and environmental value and a simplified and enhanced user experience for patients.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1650:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Pen needles and autoinjectors are necessary for millions of patients needing injectable drug treatment but pose economic and environmental burdens. A durable device with a multiuse needle could reduce cost and improve user experience. This study explores a novel robust needle tip (EXP) designed for multiple uses and durability against hooking.Method:Needle robustness was investigated through a structural analysis. Furthermore, EXP and control needles (NF30, NF28) were evaluated in an in-vivo porcine model as pen needles or embedded in autoinjectors to study the resulting increase in skin blood perfusion (SBP). The SBP was assessed by laser speckle contrast analysis (LASCA) of 192 randomized and blinded needle insertions.Results:Forming a 33 ยตm hook against a hard surface requires 0.92 N for the NF30 control needle and 5.38 N for EXP. The EXP did not induce more tissue trauma than the NF30. There was a positive relation between needle diameter and SBP (P < .05). Furthermore, the presence of an autoinjector shield and applied force of 10 N was found to significantly reduce SBP for worn EXP needles (P < .05) compared to insertions without autoinjector shield.Conclusions:The investigated robust needle EXP is on par with the single-use needle NF30 in terms of tissue trauma, which is further reduced by combining the needle with a needle shield. These results should encourage the innovation and development of durable, reusable injection systems with pharmacoeconomic and environmental value and a simplified and enhanced user experience for patients.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Novel Robust Needle Tip Design Enables Needle Reuse and Reduced Skin Trauma in Combination With Autoinjector Needle Shields";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231190408";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-08-10T05:27:04Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:25:"Anne-Sofie Madsen Staples";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Julie Schwartz";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:26:"Kezia Ann Friis Prรฆstmark";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Marie Sand Traberg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:123:"Novel Robust Needle Tip Design Enables Needle Reuse and Reduced Skin Trauma in Combination With Autoinjector Needle Shields";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231190408";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231190408?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:103;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231190413?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:158:"Assessing the Effects of Pulsed Electromagnetic Therapy on Painful Diabetic Distal Symmetric Peripheral Neuropathy: A Double-Blind Randomized Controlled Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231190413?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1765:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Significant complications of diabetes include pain and the loss of sensation in peripheral limbs. Pain management of diabetic symmetric peripheral neuropathy (DSPN) remains challenging. This study reports on utilizing pulsed electromagnetic field therapy (PEMF) to reduce pain and improve skin perfusion pressure (SPP) in subjects with DSPN.Methods:A randomized, sham-controlled, double-blind, clinical trial was conducted on subjects afflicted with foot pain associated with DSPN. Following informed consent, 182 subjects with diabetes and confirmed DSPN were entered into the trial for a period of 18 weeks. Subjects were randomized into active PEMF treatment or nonactive sham and instructed to treat to their feet for 30 minutes, twice daily and report daily pain scores. Some patients in the active arm experienced a transient low field strength notification (LFSN) due to improper pad placement during treatment. Skin perfusion pressure measurements were also collected at two and seven weeks to assess peripheral arterial disease effects via measurement of local microcirculatory flow and blood pressure.Results:Patients in the active arm who did not receive an LFSN experienced a clinically significant 30% reduction in pain from baseline compared to sham (P < .05). Though not statistically significant, SPP in the active group trended toward improvement compared to sham.Conclusions:Pulsed electromagnetic field therapy appears effective as a nonpharmacological means for reduction of pain associated with diabetic peripheral neuropathy and holds promise for improvement of vascular physiology in microcirculatory dysfunction associated with diabetic peripheral arterial disease.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1768:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Significant complications of diabetes include pain and the loss of sensation in peripheral limbs. Pain management of diabetic symmetric peripheral neuropathy (DSPN) remains challenging. This study reports on utilizing pulsed electromagnetic field therapy (PEMF) to reduce pain and improve skin perfusion pressure (SPP) in subjects with DSPN.Methods:A randomized, sham-controlled, double-blind, clinical trial was conducted on subjects afflicted with foot pain associated with DSPN. Following informed consent, 182 subjects with diabetes and confirmed DSPN were entered into the trial for a period of 18 weeks. Subjects were randomized into active PEMF treatment or nonactive sham and instructed to treat to their feet for 30 minutes, twice daily and report daily pain scores. Some patients in the active arm experienced a transient low field strength notification (LFSN) due to improper pad placement during treatment. Skin perfusion pressure measurements were also collected at two and seven weeks to assess peripheral arterial disease effects via measurement of local microcirculatory flow and blood pressure.Results:Patients in the active arm who did not receive an LFSN experienced a clinically significant 30% reduction in pain from baseline compared to sham (P < .05). Though not statistically significant, SPP in the active group trended toward improvement compared to sham.Conclusions:Pulsed electromagnetic field therapy appears effective as a nonpharmacological means for reduction of pain associated with diabetic peripheral neuropathy and holds promise for improvement of vascular physiology in microcirculatory dysfunction associated with diabetic peripheral arterial disease.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:158:"Assessing the Effects of Pulsed Electromagnetic Therapy on Painful Diabetic Distal Symmetric Peripheral Neuropathy: A Double-Blind Randomized Controlled Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231190413";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-08-05T04:50:21Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:16:"Erica E. Tassone";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Jeffrey C. Page";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Marvin J. Slepian";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:158:"Assessing the Effects of Pulsed Electromagnetic Therapy on Painful Diabetic Distal Symmetric Peripheral Neuropathy: A Double-Blind Randomized Controlled Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231190413";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231190413?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:104;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231187915?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Real-World Evidence of the Cambridge Hybrid Closed-Loop App With a Novel Real-Time Continuous Glucose Monitoring System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231187915?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:795:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>We evaluated the performance of the interoperable Cambridge hybrid closed-loop app with FreeStyle Libreโ3 glucose sensor, and YpsoPump insulin pump in a real-world setting. Data from 100 users (63 adults [mean ยฑ SD age 41.9 ยฑ 14.0 years], 15โchildren [8.6 ยฑ 5.2 years)] and 22 users of unreported age) for a period of 28 days were analyzed. Time in range (3.91- 10.0mmol/L) was 72.6 ยฑ 11.1% overall. Time below range (<3.9mmol/L) was 3.1% (1.4-5.1) (median [interquartile range]). Auto-mode was active for 95.8% (91.8-97.9) of time. This real-world analysis suggests that the performance of Cambridge hybrid closed-loop app with this glucose sensor is comparable to other commercially available hybrid closed-loop systems.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:798:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>We evaluated the performance of the interoperable Cambridge hybrid closed-loop app with FreeStyle Libreโ3 glucose sensor, and YpsoPump insulin pump in a real-world setting. Data from 100 users (63 adults [mean ยฑ SD age 41.9 ยฑ 14.0 years], 15โchildren [8.6 ยฑ 5.2 years)] and 22 users of unreported age) for a period of 28 days were analyzed. Time in range (3.91- 10.0mmol/L) was 72.6 ยฑ 11.1% overall. Time below range (<3.9mmol/L) was 3.1% (1.4-5.1) (median [interquartile range]). Auto-mode was active for 95.8% (91.8-97.9) of time. This real-world analysis suggests that the performance of Cambridge hybrid closed-loop app with this glucose sensor is comparable to other commercially available hybrid closed-loop systems.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:119:"Real-World Evidence of the Cambridge Hybrid Closed-Loop App With a Novel Real-Time Continuous Glucose Monitoring System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231187915";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-28T11:38:10Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:16:"Christine Newman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Sara Hartnell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Malgorzata Wilinska";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"Heba Alwan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Roman Hovorka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:119:"Real-World Evidence of the Cambridge Hybrid Closed-Loop App With a Novel Real-Time Continuous Glucose Monitoring System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231187915";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231187915?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:105;a:6:{s:4:"data";s:186:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183974?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Treatment Satisfaction and Well-Being With CGM in People With T1D: An Analysis Based on the GOLD Randomized Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183974?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2110:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The GOLD trial demonstrated that continuous glucose monitoring (CGM) in people with type 1 diabetes (T1D) managed with multiple daily insulin injections (MDI) improved not only glucose control but also overall well-being and treatment satisfaction. This analysis investigated which factors contributed to improved well-being and treatment satisfaction with CGM.Methods:The GOLD trial was a randomized crossover trial comparing CGM versus self-monitored blood glucose (SMBG) over 16 months. Endpoints included well-being measured by the World Health OrganizationโFive Well-Being Index (WHO-5) and treatment satisfaction by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) as well as glucose metrics. Multivariable R2-decomposition was used to understand which variables contributed most to treatment satisfaction.Results:A total of 139 participants were included. Multivariable analyses revealed that increased convenience and flexibility contributed to 60% (95% confidence interval [CI] = 50%-69%) of the improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire change version [DTSQc]) observed with CGM, whereas perceived effects on hypoglycemia and hyperglycemia only contributed to 6% (95% CI = 2%-11%) of improvements. Significant improvements in well-being (WHO-5) by CGM were observed for the following: feeling cheerful (P = .025), calm and relaxed (P = .024), being active (P = .046), and waking up fresh and rested (P = .044). HbA1c reductions and increased time in range (TIR) were associated with increased treatment satisfaction, whereas glycemic variability was not. HbA1c reduction showed also an association with increased well-being and increased TIR with less diabetes-related distress.Conclusions:While CGM improves glucose control in people with T1D on MDI, increased convenience and flexibility through CGM is of even greater importance for treatment satisfaction and patient well-being. These CGM-mediated effects should be taken into account when considering CGM initiation.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2110:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The GOLD trial demonstrated that continuous glucose monitoring (CGM) in people with type 1 diabetes (T1D) managed with multiple daily insulin injections (MDI) improved not only glucose control but also overall well-being and treatment satisfaction. This analysis investigated which factors contributed to improved well-being and treatment satisfaction with CGM.Methods:The GOLD trial was a randomized crossover trial comparing CGM versus self-monitored blood glucose (SMBG) over 16 months. Endpoints included well-being measured by the World Health OrganizationโFive Well-Being Index (WHO-5) and treatment satisfaction by the Diabetes Treatment Satisfaction Questionnaire (DTSQ) as well as glucose metrics. Multivariable R2-decomposition was used to understand which variables contributed most to treatment satisfaction.Results:A total of 139 participants were included. Multivariable analyses revealed that increased convenience and flexibility contributed to 60% (95% confidence interval [CI] = 50%-69%) of the improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire change version [DTSQc]) observed with CGM, whereas perceived effects on hypoglycemia and hyperglycemia only contributed to 6% (95% CI = 2%-11%) of improvements. Significant improvements in well-being (WHO-5) by CGM were observed for the following: feeling cheerful (P = .025), calm and relaxed (P = .024), being active (P = .046), and waking up fresh and rested (P = .044). HbA1c reductions and increased time in range (TIR) were associated with increased treatment satisfaction, whereas glycemic variability was not. HbA1c reduction showed also an association with increased well-being and increased TIR with less diabetes-related distress.Conclusions:While CGM improves glucose control in people with T1D on MDI, increased convenience and flexibility through CGM is of even greater importance for treatment satisfaction and patient well-being. 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Needlestick injuries not only are associated with an increased risk of infections caused by bloodborne pathogens but are also a primary source of emotional distress and job burnout for HCWs and patients. Insulin injectionโrelated NSIs are common among HCWs working in hospitals in the Asia-Pacific (APAC) region and impose a significant burden. Insulin pen needles have a high risk of transmitting infections (at both the patient-end and cartridge end of the sharp) after use. Recapping a needle after administering an insulin injection poses a major risk to HCWs. Currently, several safety-engineered needle devices (SENDs) are available with active or passive safety mechanisms. Passive insulin safety pen needles with dual-ended protection and automatic recapping capabilities have resulted in a significant drop in accidental punctures to HCWs while administering insulin to patients with diabetes. In this article, we have reviewed the burden and common causes of NSIs with insulin injections among HCWs in the APAC region. We have discussed current approaches to address the issues associated with NSIs and the benefits of introducing SENDs in health care settings, including long-term care facilities, nursing homes, and home care settings where patients may require assisted insulin injections. This review also summarizes key strategies/recommendations to prevent NSIs in HCWs and patients with diabetes in the APAC region.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1612:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Globally, health care workers (HCWs) are at a high risk of occupational exposure to needlestick injuries (NSIs). Needlestick injuries not only are associated with an increased risk of infections caused by bloodborne pathogens but are also a primary source of emotional distress and job burnout for HCWs and patients. Insulin injectionโrelated NSIs are common among HCWs working in hospitals in the Asia-Pacific (APAC) region and impose a significant burden. Insulin pen needles have a high risk of transmitting infections (at both the patient-end and cartridge end of the sharp) after use. Recapping a needle after administering an insulin injection poses a major risk to HCWs. Currently, several safety-engineered needle devices (SENDs) are available with active or passive safety mechanisms. Passive insulin safety pen needles with dual-ended protection and automatic recapping capabilities have resulted in a significant drop in accidental punctures to HCWs while administering insulin to patients with diabetes. In this article, we have reviewed the burden and common causes of NSIs with insulin injections among HCWs in the APAC region. We have discussed current approaches to address the issues associated with NSIs and the benefits of introducing SENDs in health care settings, including long-term care facilities, nursing homes, and home care settings where patients may require assisted insulin injections. This review also summarizes key strategies/recommendations to prevent NSIs in HCWs and patients with diabetes in the APAC region.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:146:"Needlestick Injuries With Insulin Injections: Risk Factors, Concerns, and Implications of the Use of Safety Pen Needles in the Asia-Pacific Region";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231186402";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-21T07:33:03Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:15:"Mafauzy Mohamed";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Nikhil Tandon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Youngsoon Kim";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"Irene Kopp";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Nagaaki Tanaka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:16:"Hiroshige Mikamo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Kevin Friedman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:17:"Shailendra Bajpai";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:146:"Needlestick Injuries With Insulin Injections: Risk Factors, Concerns, and Implications of the Use of Safety Pen Needles in the Asia-Pacific Region";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231186402";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231186402?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:107;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231182406?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:151:"Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231182406?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2133:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).Methods:Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohenโs d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.Results:Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).Conclusion:AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2151:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).Methods:Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohenโs d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.Results:Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).Conclusion:AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:151:"Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231182406";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-14T09:02:25Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:17:"Susan Kohl Malone";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Austin M. Matus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Anneliese J. Flatt";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Amy J. Peleckis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Laura Grunin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:7:"Gary Yu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:12:"Sooyong Jang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:12:"James Weimer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:9:"Insup Lee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:18:"Michael R. Rickels";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:10:"Namni Goel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:151:"Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231182406";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231182406?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:108;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231186401?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231186401?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1544:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH.Methods:Adults with T1D for โฅ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for โฅ15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect.Results:Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl.Conclusion:Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1556:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Nocturnal hypoglycemia (NH) remains a major burden for people with type 1 diabetes (T1D). Daytime physical activity (PA) increases the risk of NH. This pilot study tested whether cumulative daytime PA measured using a smartphone-based step tracker was associated with NH.Methods:Adults with T1D for โฅ 5 years (y) on multiple daily insulin or continuous insulin infusion, not using continuous glucose monitoring and HbA1c 6 to 10% wore blinded Freestyle Libre Pro sensors and recorded total daily carbohydrate (TDC) and total daily dose (TDD) of insulin. During this time, daily step count (DSC) was tracked using the smartphone-based Fitbit MobileTrack application. Mixed effects logistic regression was used to estimate the effect of DSC on NH (sensor glucose <70, <54 mg/dl for โฅ15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect.Results:Twenty-six adults, with 65.4% females, median age 27 years (interquartile range: 26-32) mean body mass index 23.9 kg/m2, median HbA1c 7.6% (7.1-8.1) and mean Gold Score 2.1 (standard deviation 1.0) formed the study population. The median DSC for the whole group was 2867 (1820-4807). There was a significant effect of DSC on NH episodes <70 mg/dl. (odds ratio 1.11 [95% CI: 1.01-1.23, P = .04]. There was no significant effect on NH <54 mg/dl.Conclusion:Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:107:"Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231186401";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-13T06:30:59Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:14:"Daphne Gardner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Hong Chang Tan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Gek Hsiang Lim";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"May Zin Oo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Xiaohui Xin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:17:"Andrew Kingsworth";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Pratik Choudhary";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:20:"Suresh Rama Chandran";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:107:"Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231186401";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231186401?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:109;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185796?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185796?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1754:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH.Methods:We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each.Results:At population-level, SVM outperforms RF algorithm with a receiver operating characteristicโarea under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%).Conclusions:Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1754:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH.Methods:We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each.Results:At population-level, SVM outperforms RF algorithm with a receiver operating characteristicโarea under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%).Conclusions:Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:113:"Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231185796";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-12T05:24:00Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:17:"Ioannis Afentakis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Rebecca Unsworth";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Pau Herrero";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Nick Oliver";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Monika Reddy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:17:"Pantelis Georgiou";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:113:"Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231185796";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185796?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:110;a:6:{s:4:"data";s:158:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185115?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:171:"Real-World Performance of First- Versus Second-Generation Automated Insulin Delivery Systems on a Pediatric Population With Type 1 Diabetes: A One-Year Observational Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185115?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1850:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The aim of this single-center observational study was to assess the real-world performance of first- and second-generation automated insulin delivery (AID) systems in a cohort of children and adolescents with type 1 diabetes over a one-year follow-up.Methods:Demographic, anamnestic, and clinical data of the study cohort were collected at the start of automatic mode. Data on continuous glucose monitoring metrics, system settings, insulin requirements, and anthropometric parameters at three different time points (start period, six months, 12 months) were retrospectively gathered and statistically analyzed.Results:Fifty-four individuals (55.6% of females) aged 7 to 18 years switching to AID therapy were included in the analysis. Two weeks after starting automatic mode, subjects using advanced hybrid closed-loop (AHCL) showed a better response than hybrid closed-loop (HCL) users in terms of time in range (P = .016), time above range 180 to 250 mg/dl (P = .022), sensor mean glucose (P = .047), and glycemia risk index (P = .012). After 12 months, AHCL group maintained better mean sensor glucose (P = .021) and glucose management indicator (P = .027). Noteworthy, both HCL and AHCL users achieved the recommended clinical targets over the entire study period. The second-generation AID system registered longer time spent with automatic mode activated and fewer shifts to manual mode at every time point (P < .001).Conclusions:Both systems showed sustained and successful glycemic outcomes in the first year of use. However, AHCL users achieved tighter glycemic targets, without an increase of hypoglycemia risk. Improved usability of the device may also have contributed to optimal glycemic outcomes by ensuring better continuity of the automatic mode activation.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1853:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The aim of this single-center observational study was to assess the real-world performance of first- and second-generation automated insulin delivery (AID) systems in a cohort of children and adolescents with type 1 diabetes over a one-year follow-up.Methods:Demographic, anamnestic, and clinical data of the study cohort were collected at the start of automatic mode. Data on continuous glucose monitoring metrics, system settings, insulin requirements, and anthropometric parameters at three different time points (start period, six months, 12 months) were retrospectively gathered and statistically analyzed.Results:Fifty-four individuals (55.6% of females) aged 7 to 18 years switching to AID therapy were included in the analysis. Two weeks after starting automatic mode, subjects using advanced hybrid closed-loop (AHCL) showed a better response than hybrid closed-loop (HCL) users in terms of time in range (P = .016), time above range 180 to 250 mg/dl (P = .022), sensor mean glucose (P = .047), and glycemia risk index (P = .012). After 12 months, AHCL group maintained better mean sensor glucose (P = .021) and glucose management indicator (P = .027). Noteworthy, both HCL and AHCL users achieved the recommended clinical targets over the entire study period. The second-generation AID system registered longer time spent with automatic mode activated and fewer shifts to manual mode at every time point (P < .001).Conclusions:Both systems showed sustained and successful glycemic outcomes in the first year of use. However, AHCL users achieved tighter glycemic targets, without an increase of hypoglycemia risk. Improved usability of the device may also have contributed to optimal glycemic outcomes by ensuring better continuity of the automatic mode activation.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:171:"Real-World Performance of First- Versus Second-Generation Automated Insulin Delivery Systems on a Pediatric Population With Type 1 Diabetes: A One-Year Observational Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231185115";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-11T12:17:19Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:13:"Bruno Bombaci";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Stefano Passanisi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Mariella Valenzise";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Fabio Macrรฌ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Marco Calderone";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Senad Hasaj";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:11:"Sofia Zullo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:18:"Giuseppina Salzano";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:18:"Fortunato Lombardo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:171:"Real-World Performance of First- Versus Second-Generation Automated Insulin Delivery Systems on a Pediatric Population With Type 1 Diabetes: A One-Year Observational Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231185115";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185115?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:111;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185348?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:59:"Real-World Evidence Analysis of a Hybrid Closed-Loop System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185348?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1102:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:We analyzed real-world evidence to assess the performance of the mylife CamAPS FX hybrid closed-loop system.Methods:Users from 15 countries across different age groups who used the system between May 9, 2022, and December 3, 2022, and who had โฅ30 days of continuous glucose monitor data, and โฅ30% of closed-loop usage were included in the current analysis (N = 1805).Results:Time in range (3.9-10 mmol/L) was 72.6 ยฑ 11.5% (mean ยฑ SD) for all users and increased by age from 66.9 ยฑ 11.7% for users โค6 years old to 81.8 ยฑ 8.7% for users โฅ65 years. Time spent in hypoglycemia (<3.9 mmol/L) was 2.3% [1.3, 3.6] (median [interquartile range]). Mean glucose and glucose management indicator were 8.4 ยฑ 1.1 mmol/L and 6.9%, respectively. Time using closed-loop was high at 94.7% [90.0, 96.9].Conclusions:Glycemic outcomes from the present real-world evidence are comparable to results obtained from previous randomized controlled studies and confirm the efficacy of this hybrid closed-loop system in real-world settings.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1105:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:We analyzed real-world evidence to assess the performance of the mylife CamAPS FX hybrid closed-loop system.Methods:Users from 15 countries across different age groups who used the system between May 9, 2022, and December 3, 2022, and who had โฅ30 days of continuous glucose monitor data, and โฅ30% of closed-loop usage were included in the current analysis (N = 1805).Results:Time in range (3.9-10 mmol/L) was 72.6 ยฑ 11.5% (mean ยฑ SD) for all users and increased by age from 66.9 ยฑ 11.7% for users โค6 years old to 81.8 ยฑ 8.7% for users โฅ65 years. Time spent in hypoglycemia (<3.9 mmol/L) was 2.3% [1.3, 3.6] (median [interquartile range]). Mean glucose and glucose management indicator were 8.4 ยฑ 1.1 mmol/L and 6.9%, respectively. Time using closed-loop was high at 94.7% [90.0, 96.9].Conclusions:Glycemic outcomes from the present real-world evidence are comparable to results obtained from previous randomized controlled studies and confirm the efficacy of this hybrid closed-loop system in real-world settings.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:59:"Real-World Evidence Analysis of a Hybrid Closed-Loop System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231185348";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-08T10:55:46Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:10:"Heba Alwan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Malgorzata E. Wilinska";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:8:"Yue Ruan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Julien Da Silva";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Roman Hovorka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:59:"Real-World Evidence Analysis of a Hybrid Closed-Loop System";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231185348";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231185348?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:112;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179728?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Selective Collection of Exhaled Breath Condensate for Noninvasive Screening of Breath Glucose";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179728?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1050:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Although exhaled breath condensate (EBC) is a promising noninvasive sample for detecting respiratory analytes such as glucose, current EBC collection methods yield inconsistent results.Methods:We developed a custom EBC collection device with a temperature-based algorithm to selectively condense alveolar air for reproducible EBC glucose detection. We characterized the condensate volumes and the corresponding glucose concentrations. We performed a pilot study demonstrating its use during oral glucose tolerance tests.Results:The novel device selectively captured alveolar air resulting in slightly higher and less variable glucose concentrations than the overall EBC. Participants with type 2 diabetes demonstrated significantly higher blood plasma-EBC glucose ratios than normoglycemic participants.Conclusions:Temperature-based selective EBC collection allows EBC glucose measurement and is a promising sampling method to distinguish patients with and without diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1050:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Although exhaled breath condensate (EBC) is a promising noninvasive sample for detecting respiratory analytes such as glucose, current EBC collection methods yield inconsistent results.Methods:We developed a custom EBC collection device with a temperature-based algorithm to selectively condense alveolar air for reproducible EBC glucose detection. We characterized the condensate volumes and the corresponding glucose concentrations. We performed a pilot study demonstrating its use during oral glucose tolerance tests.Results:The novel device selectively captured alveolar air resulting in slightly higher and less variable glucose concentrations than the overall EBC. Participants with type 2 diabetes demonstrated significantly higher blood plasma-EBC glucose ratios than normoglycemic participants.Conclusions:Temperature-based selective EBC collection allows EBC glucose measurement and is a promising sampling method to distinguish patients with and without diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:93:"Selective Collection of Exhaled Breath Condensate for Noninvasive Screening of Breath Glucose";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231179728";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-04T12:08:42Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:12:"Aditya Desai";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Divya Tankasala";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Gabriel P. Ng";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Pankti Thakkar";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:18:"Orlando S. Hoilett";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:16:"Kieren J. Mather";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:20:"Jacqueline C. 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The objective of this review was to summarize and critically analyze the current body of economic evaluation (EE) studies for mHealth interventions for type 2 diabetes.Methods:Using a comprehensive search strategy, five databases were searched for full and partial EE studies for mHealth interventions for type 2 diabetes from January 2007 to March 2022. โmHealthโ was defined as any intervention that used a mobile device with cellular technology to collect and/or provide data or information for the management of type 2 diabetes. The CHEERS 2022 checklist was used to appraise the reporting of the full EEs.Results:Twelve studies were included in the review; nine full and three partial evaluations. Text messages smartphone applications were the most common mHealth features. The majority of interventions also included a Bluetooth-connected medical device, eg, glucose or blood pressure monitors. All studies reported their intervention to be cost-effective or cost-saving, however, most studiesโ reporting were of moderate quality with a median CHEERS score of 59%.Conclusion:The current literature indicates that mHealth interventions for type 2 diabetes can be cost-saving or cost-effective, however, the quality of the reporting can be substantially improved. Heterogeneity makes it difficult to compare study outcomes, and the failure to report on key items leaves insufficient information for decision-makers to consider.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1744:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:There is plenty of evidence supporting the clinical benefits of mHealth interventions for type 2 diabetes, but despite often being promoted as cost-effective or cost-saving, there is still limited research to support such claims. The objective of this review was to summarize and critically analyze the current body of economic evaluation (EE) studies for mHealth interventions for type 2 diabetes.Methods:Using a comprehensive search strategy, five databases were searched for full and partial EE studies for mHealth interventions for type 2 diabetes from January 2007 to March 2022. โmHealthโ was defined as any intervention that used a mobile device with cellular technology to collect and/or provide data or information for the management of type 2 diabetes. The CHEERS 2022 checklist was used to appraise the reporting of the full EEs.Results:Twelve studies were included in the review; nine full and three partial evaluations. Text messages smartphone applications were the most common mHealth features. The majority of interventions also included a Bluetooth-connected medical device, eg, glucose or blood pressure monitors. All studies reported their intervention to be cost-effective or cost-saving, however, most studiesโ reporting were of moderate quality with a median CHEERS score of 59%.Conclusion:The current literature indicates that mHealth interventions for type 2 diabetes can be cost-saving or cost-effective, however, the quality of the reporting can be substantially improved. Heterogeneity makes it difficult to compare study outcomes, and the failure to report on key items leaves insufficient information for decision-makers to consider.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:101:"Economic Evaluations of mHealth Interventions for the Management of Type 2 Diabetes: A Scoping Review";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231183956";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-03T09:40:42Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:12:"Ida Tornvall";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Danelle Kenny";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:25:"Befikadu Legesse Wubishet";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Anthony Russell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Anish Menon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Tracy Comans";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:101:"Economic Evaluations of mHealth Interventions for the Management of Type 2 Diabetes: A Scoping Review";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231183956";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183956?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:114;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183985?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:174:"A Retrospective Cohort Study of Racial/Ethnic and Socioeconomic Disparities in Initiation and Meaningful Use of Continuous Glucose Monitoring among Youth With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183985?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1824:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous glucose monitor (CGM) use improves type 1 diabetes (T1D) outcomes, yet children from diverse backgrounds and on public insurance have worse outcomes and lower CGM utilization. Using novel CGM data acquisition and analysis of two T1D cohorts, we test the hypothesis that T1D youth from different backgrounds experience disparities in meaningful CGM use following both T1D diagnosis and CGM uptake.Methods:Cohorts drawn from a pediatric T1D program were followed for one year beginning at diagnosis (n = 815, 2016-2020) or CGM uptake (n = 1392, 2015-2020). Using chart and CGM data, CGM start and meaningful use outcomes between racial/ethnic and insurance groups were compared using median days, one-year proportions, and survival analysis.Results:Publicly compared with privately insured were slower to start CGM (233, 151 days, P < .01), had fewer use-days in the year following uptake (232, 324, P < .001), and had faster first discontinuation rates (hazard ratio [HR] = 1.61, P < .001). Disparities were more pronounced among Hispanic and black compared with white subjects for CGM start time (312, 289, 149, P = .0013) and discontinuation rates (Hispanic HR = 2.17, P < .001; black HR = 1.45, P = .038), and remained even among privately insured (Hispanic/black HR = 1.44, P = .0286).Conclusions:Given the impact of insurance and race/ethnicity on CGM initiation and use, it is imperative that we target interventions to support universal access and sustained CGM use to mitigate the potential impact of provider biases and systemic disadvantage and racism. By enabling more equitable and meaningful T1D technology use, such interventions will begin to alleviate outcome disparities between youth with T1D from different backgrounds.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1836:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Continuous glucose monitor (CGM) use improves type 1 diabetes (T1D) outcomes, yet children from diverse backgrounds and on public insurance have worse outcomes and lower CGM utilization. Using novel CGM data acquisition and analysis of two T1D cohorts, we test the hypothesis that T1D youth from different backgrounds experience disparities in meaningful CGM use following both T1D diagnosis and CGM uptake.Methods:Cohorts drawn from a pediatric T1D program were followed for one year beginning at diagnosis (n = 815, 2016-2020) or CGM uptake (n = 1392, 2015-2020). Using chart and CGM data, CGM start and meaningful use outcomes between racial/ethnic and insurance groups were compared using median days, one-year proportions, and survival analysis.Results:Publicly compared with privately insured were slower to start CGM (233, 151 days, P < .01), had fewer use-days in the year following uptake (232, 324, P < .001), and had faster first discontinuation rates (hazard ratio [HR] = 1.61, P < .001). Disparities were more pronounced among Hispanic and black compared with white subjects for CGM start time (312, 289, 149, P = .0013) and discontinuation rates (Hispanic HR = 2.17, P < .001; black HR = 1.45, P = .038), and remained even among privately insured (Hispanic/black HR = 1.44, P = .0286).Conclusions:Given the impact of insurance and race/ethnicity on CGM initiation and use, it is imperative that we target interventions to support universal access and sustained CGM use to mitigate the potential impact of provider biases and systemic disadvantage and racism. By enabling more equitable and meaningful T1D technology use, such interventions will begin to alleviate outcome disparities between youth with T1D from different backgrounds.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:174:"A Retrospective Cohort Study of Racial/Ethnic and Socioeconomic Disparities in Initiation and Meaningful Use of Continuous Glucose Monitoring among Youth With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231183985";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-07-03T07:19:11Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:24:"Elise Schlissel Tremblay";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:16:"Allison Bernique";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Katherine Garvey";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:19:"Christina M. Astley";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:174:"A Retrospective Cohort Study of Racial/Ethnic and Socioeconomic Disparities in Initiation and Meaningful Use of Continuous Glucose Monitoring among Youth With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231183985";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183985?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:115;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231184497?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:145:"Development and Validation of an Electronic Health RecordโBased Risk Assessment Tool for Hypoglycemia in Patients With Type 2 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231184497?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1971:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The recent availability of high-quality data from clinical trials, together with machine learning (ML) techniques, presents exciting opportunities for developing prediction models for clinical outcomes.Methods:As a proof-of-concept, we translated a hypoglycemia risk model derived from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study into the HypoHazardScore, a risk assessment tool applicable to electronic health record (EHR) data. To assess its performance, we conducted a 16-week clinical study at the University of Minnesota where participants (N = 40) with type 2 diabetes mellitus (T2DM) had hypoglycemia assessed prospectively by continuous glucose monitoring (CGM).Results:The HypoHazardScore combines 16 risk factors commonly found within the EHR. The HypoHazardScore successfully predicted (area under the curve [AUC] = 0.723) whether participants experienced least one CGM-assessed hypoglycemic event (glucose <54 mg/dL for โฅ15 minutes from two CGMs) while significantly correlating with frequency of CGM-assessed hypoglycemic events (r = 0.38) and percent time experiencing CGM-assessed hypoglycemia (r = 0.39). Compared to participants with a low HypoHazardScore (N = 19, score <4, median score of 4), participants with a high HypoHazardScore (N = 21, score โฅ4) had more frequent CGM-assessed hypoglycemic events (high: 1.6 ยฑ 2.2 events/week; low: 0.3 ยฑ 0.5 events/week) and experienced more CGM-assessed hypoglycemia (high: 1.4% ยฑ 2.0%; low: 0.2% ยฑ 0.4% time) during the 16-week follow-up.Conclusions:We demonstrated that a hypoglycemia risk model can be successfully adapted from the ACCORD data to the EHR, with validation by a prospective study using CGM-assessed hypoglycemia. The HypoHazardScore represents a significant advancement toward implementing an EHR-based decision support system that can help reduce hypoglycemia in patients with T2DM.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1977:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The recent availability of high-quality data from clinical trials, together with machine learning (ML) techniques, presents exciting opportunities for developing prediction models for clinical outcomes.Methods:As a proof-of-concept, we translated a hypoglycemia risk model derived from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study into the HypoHazardScore, a risk assessment tool applicable to electronic health record (EHR) data. To assess its performance, we conducted a 16-week clinical study at the University of Minnesota where participants (N = 40) with type 2 diabetes mellitus (T2DM) had hypoglycemia assessed prospectively by continuous glucose monitoring (CGM).Results:The HypoHazardScore combines 16 risk factors commonly found within the EHR. The HypoHazardScore successfully predicted (area under the curve [AUC] = 0.723) whether participants experienced least one CGM-assessed hypoglycemic event (glucose <54 mg/dL for โฅ15 minutes from two CGMs) while significantly correlating with frequency of CGM-assessed hypoglycemic events (r = 0.38) and percent time experiencing CGM-assessed hypoglycemia (r = 0.39). Compared to participants with a low HypoHazardScore (N = 19, score <4, median score of 4), participants with a high HypoHazardScore (N = 21, score โฅ4) had more frequent CGM-assessed hypoglycemic events (high: 1.6 ยฑ 2.2 events/week; low: 0.3 ยฑ 0.5 events/week) and experienced more CGM-assessed hypoglycemia (high: 1.4% ยฑ 2.0%; low: 0.2% ยฑ 0.4% time) during the 16-week follow-up.Conclusions:We demonstrated that a hypoglycemia risk model can be successfully adapted from the ACCORD data to the EHR, with validation by a prospective study using CGM-assessed hypoglycemia. The HypoHazardScore represents a significant advancement toward implementing an EHR-based decision support system that can help reduce hypoglycemia in patients with T2DM.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:145:"Development and Validation of an Electronic Health RecordโBased Risk Assessment Tool for Hypoglycemia in Patients With Type 2 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231184497";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-29T06:14:26Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:7:"Sisi Ma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Alison Alvear";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Pamela J. Schreiner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:21:"Elizabeth R. Seaquist";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Thomas Kirsh";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Lisa S. 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Secondary outcomes were HbA1c, depression, diabetes distress, anxiety, functional health status, and healthcare professional burnout. Machine learning models were utilized to analyze the data collected from the Spotlight-AQ platform to validate the reliability of question-concern association; as well as to identify key features that distinguish people with type 1 and type 2 diabetes, as well as important features that distinguish different levels of HbA1c.Results:n = 98 adults with T1D or T2D; any HbA1c and receiving any diabetes treatment participated (n = 49 intervention). Consultation duration for intervention participants was reduced in intervention consultations by 0.5 to 4.1 minutes (3%-14%) versus no change in the control group (โ0.9 to +1.28 minutes). HbA1c improved in the intervention group by 6 mmol/mol (range 0-30) versus control group 3 mmol/mol (range 0-8). Moderate improvements in psychosocial outcomes were seen in the intervention group for functional health status; reduced anxiety, depression, and diabetes distress and improved well-being. None were statistically significant. HCPs reported improved communication and greater focus on patient priorities in consultations. Artificial Intelligence examination highlighted therapy and psychological burden were most important in predicting HbA1c levels. The Natural Language Processing semantic analysis confirmed the mapping relationship between questions and their corresponding concerns. Machine learning model revealed type 1 and type 2 patients have different concerns regarding psychological burden and knowledge. Moreover, the machine learning model emphasized that individuals with varying levels of HbA1c exhibit diverse levels of psychological burden and therapy-related concerns.Conclusion:Spotlight-AQ was associated with shorter, more useful consultations; with improved HbA1c and moderate benefits on psychosocial outcomes. Results reflect the importance of a biopsychosocial approach to routine care visits. Spotlight-AQ is viable across health care settings for improved outcomes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2734:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Annual national diabetes audit data consistently shows most people with diabetes do not consistently achieve blood glucose targets for optimal health, despite the large range of treatment options available.Aim:To explore the efficacy of a novel clinical intervention to address physical and mental health needs within routine diabetes consultations across health care settings.Methods:A multicenter, parallel group, individually randomized trial comparing consultation duration in adults diagnosed with T1D or T2D for โฅ6 months using the Spotlight-AQ platform versus usual care. Secondary outcomes were HbA1c, depression, diabetes distress, anxiety, functional health status, and healthcare professional burnout. Machine learning models were utilized to analyze the data collected from the Spotlight-AQ platform to validate the reliability of question-concern association; as well as to identify key features that distinguish people with type 1 and type 2 diabetes, as well as important features that distinguish different levels of HbA1c.Results:n = 98 adults with T1D or T2D; any HbA1c and receiving any diabetes treatment participated (n = 49 intervention). Consultation duration for intervention participants was reduced in intervention consultations by 0.5 to 4.1 minutes (3%-14%) versus no change in the control group (โ0.9 to +1.28 minutes). HbA1c improved in the intervention group by 6 mmol/mol (range 0-30) versus control group 3 mmol/mol (range 0-8). Moderate improvements in psychosocial outcomes were seen in the intervention group for functional health status; reduced anxiety, depression, and diabetes distress and improved well-being. None were statistically significant. HCPs reported improved communication and greater focus on patient priorities in consultations. Artificial Intelligence examination highlighted therapy and psychological burden were most important in predicting HbA1c levels. The Natural Language Processing semantic analysis confirmed the mapping relationship between questions and their corresponding concerns. Machine learning model revealed type 1 and type 2 patients have different concerns regarding psychological burden and knowledge. Moreover, the machine learning model emphasized that individuals with varying levels of HbA1c exhibit diverse levels of psychological burden and therapy-related concerns.Conclusion:Spotlight-AQ was associated with shorter, more useful consultations; with improved HbA1c and moderate benefits on psychosocial outcomes. Results reflect the importance of a biopsychosocial approach to routine care visits. Spotlight-AQ is viable across health care settings for improved outcomes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Delivering Biopsychosocial Health Care Within Routine Care: Spotlight-AQ Pivotal Multicenter Randomized Controlled Trial Results";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231183436";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-23T07:19:18Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:10:{i:0;a:5:{s:4:"data";s:18:"Ryan Charles Kelly";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Hermione Price";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Peter Phiri";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Michael Cummings";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:8:"Amar Ali";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Mayank Patel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Ethan Barnard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:9:"Yufan Liu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:12:"Oscar Mendez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:23:"Katharine Barnard-Kelly";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:128:"Delivering Biopsychosocial Health Care Within Routine Care: Spotlight-AQ Pivotal Multicenter Randomized Controlled Trial Results";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231183436";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231183436?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:117;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231181138?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:80:"Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231181138?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1739:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance.Methods:To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model).Results:Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets.Conclusions:We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1739:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance.Methods:To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model).Results:Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets.Conclusions:We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:80:"Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231181138";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-23T07:11:00Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:14:"Louis A. Gomez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:21:"Adedolapo Aishat Toye";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"R. Stanley Hum";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Samantha Kleinberg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:80:"Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231181138";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231181138?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:118;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179740?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:159:"Identification of Core Outcome Domains and Design of a Survey Questionnaire to Evaluate Impacts of Digital Health Solutions That Matter to People With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179740?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1877:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Digital health solutions (DHS) are increasingly used to support people with diabetes (PwD) to help manage their diabetes and to gather and manage health and treatment data. There is a need for scientifically reliable and valid methods to measure the value and impact of DHS on outcomes that matter to PwD. Here, we describe the development of a survey questionnaire designed to assess the perceptions of PwD toward DHS and their prioritized outcomes for DHS evaluation.Method:We applied a structured process for engagement of a total of nine PwD and representatives of diabetes advocacy organizations. Questionnaire development consisted of a scoping literature review, individual interviews, workshops, asynchronous virtual collaboration, and cognitive debriefing interviews.Results:We identified three overarching categories of DHS, which were meaningful to PwD and crucial for the identification of relevant outcomes: (1) online/digital tools for information, education, support, motivation; (2) personal health monitoring to support self-management; (3) digital and telehealth solutions for engaging with health professionals. Overall outcome domains identified to be important were diabetes-related quality of life, distress, treatment burden, and confidence in self-management. Additional positive and negative outcomes specific to DHS were identified and corresponding questions were incorporated into the survey questionnaire.Conclusion:We identified the need for self-reporting of quality of life, diabetes distress, treatment burden, and confidence in self-management, as well as specific positive and negative impacts of DHS. We designed a survey questionnaire to further assess the perceptions and perspectives of people with type 1 and 2 diabetes on outcomes relevant for DHS evaluations.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1877:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Digital health solutions (DHS) are increasingly used to support people with diabetes (PwD) to help manage their diabetes and to gather and manage health and treatment data. There is a need for scientifically reliable and valid methods to measure the value and impact of DHS on outcomes that matter to PwD. Here, we describe the development of a survey questionnaire designed to assess the perceptions of PwD toward DHS and their prioritized outcomes for DHS evaluation.Method:We applied a structured process for engagement of a total of nine PwD and representatives of diabetes advocacy organizations. Questionnaire development consisted of a scoping literature review, individual interviews, workshops, asynchronous virtual collaboration, and cognitive debriefing interviews.Results:We identified three overarching categories of DHS, which were meaningful to PwD and crucial for the identification of relevant outcomes: (1) online/digital tools for information, education, support, motivation; (2) personal health monitoring to support self-management; (3) digital and telehealth solutions for engaging with health professionals. Overall outcome domains identified to be important were diabetes-related quality of life, distress, treatment burden, and confidence in self-management. Additional positive and negative outcomes specific to DHS were identified and corresponding questions were incorporated into the survey questionnaire.Conclusion:We identified the need for self-reporting of quality of life, diabetes distress, treatment burden, and confidence in self-management, as well as specific positive and negative impacts of DHS. We designed a survey questionnaire to further assess the perceptions and perspectives of people with type 1 and 2 diabetes on outcomes relevant for DHS evaluations.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:159:"Identification of Core Outcome Domains and Design of a Survey Questionnaire to Evaluate Impacts of Digital Health Solutions That Matter to People With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231179740";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-20T11:50:35Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:18:"Soren Eik Skovlund";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Scibilia Renza";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Julie Laurent";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Paco Cerletti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:159:"Identification of Core Outcome Domains and Design of a Survey Questionnaire to Evaluate Impacts of Digital Health Solutions That Matter to People With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231179740";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179740?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:119;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179164?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:207:"Two-Way Crossover Phase 1 Bioequivalence and Safety Studies in Healthy Adults for a Ready-to-Use, Room-Temperature, Liquid-Stable Glucagon Administered by Autoinjector, Prefilled Syringe, or Vial and Syringe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179164?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1760:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:To demonstrate bioequivalence and safety for a ready-to-use room-temperature liquid-stable glucagon administered subcutaneously (SC) through a glucagon autoinjector (GAI) or a glucagon vial and syringe kit (GVS), versus a glucagon prefilled syringe (G-PFS).Methods:Healthy adults (N = 32) were randomly assigned to receive 1-mg glucagon as GAI or G-PFS, and then as the alternative three to seven days later. Other healthy adults (N = 40) were randomly assigned to receive 1-mg glucagon as GVS or G-PFS, and then as the alternative two days later. Samples for plasma glucagon were obtained through 240 minutes after glucagon injection. Bioequivalence was declared when the geometric mean estimate ratio of the area under-the-concentration-versus-time curve from 0 to 240 minutes (AUC0-240) and maximum concentration (Cmax) for plasma glucagon between treatment groups was contained within the bounds of 80% and 125%. Adverse events (AEs) were recorded.Results:The 90% confidence intervals (CIs) for AUC0-240 and Cmax geometric mean ratios for G-PFS to GAI and GVS to G-PFS were contained within the bounds 80% to 125% (G-PFS:GAI AUC0-240 95.05%, 119.67% and Cmax 88.01%, 120.24%; GVS:G-PFS AUC0-240 87.39%, 100.66% and Cmax 89.08%, 106.08%). At least one AE occurred in 15.6% (5/32) participants with GAI, 25% (18/72) with G-PFS, and 32.5% (13/40) with GVS. Sixty-nine of 73 (94.5%) AEs were mild, and none were serious. Nausea was the most common (33/73 [45%]).Conclusions:Bioequivalence and safety were established after 1 mg of this ready-to-use room-temperature liquid-stable glucagon, administered SC to healthy adults, by autoinjector, prefilled syringe, or vial and syringe kit.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1760:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:To demonstrate bioequivalence and safety for a ready-to-use room-temperature liquid-stable glucagon administered subcutaneously (SC) through a glucagon autoinjector (GAI) or a glucagon vial and syringe kit (GVS), versus a glucagon prefilled syringe (G-PFS).Methods:Healthy adults (N = 32) were randomly assigned to receive 1-mg glucagon as GAI or G-PFS, and then as the alternative three to seven days later. Other healthy adults (N = 40) were randomly assigned to receive 1-mg glucagon as GVS or G-PFS, and then as the alternative two days later. Samples for plasma glucagon were obtained through 240 minutes after glucagon injection. Bioequivalence was declared when the geometric mean estimate ratio of the area under-the-concentration-versus-time curve from 0 to 240 minutes (AUC0-240) and maximum concentration (Cmax) for plasma glucagon between treatment groups was contained within the bounds of 80% and 125%. Adverse events (AEs) were recorded.Results:The 90% confidence intervals (CIs) for AUC0-240 and Cmax geometric mean ratios for G-PFS to GAI and GVS to G-PFS were contained within the bounds 80% to 125% (G-PFS:GAI AUC0-240 95.05%, 119.67% and Cmax 88.01%, 120.24%; GVS:G-PFS AUC0-240 87.39%, 100.66% and Cmax 89.08%, 106.08%). At least one AE occurred in 15.6% (5/32) participants with GAI, 25% (18/72) with G-PFS, and 32.5% (13/40) with GVS. Sixty-nine of 73 (94.5%) AEs were mild, and none were serious. Nausea was the most common (33/73 [45%]).Conclusions:Bioequivalence and safety were established after 1 mg of this ready-to-use room-temperature liquid-stable glucagon, administered SC to healthy adults, by autoinjector, prefilled syringe, or vial and syringe kit.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:207:"Two-Way Crossover Phase 1 Bioequivalence and Safety Studies in Healthy Adults for a Ready-to-Use, Room-Temperature, Liquid-Stable Glucagon Administered by Autoinjector, Prefilled Syringe, or Vial and Syringe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231179164";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-09T05:10:13Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:17:"M. Khaled Junaidi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Matthew R. Krecic";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Nicole C. Close";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:20:"Valentina Conoscenti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:207:"Two-Way Crossover Phase 1 Bioequivalence and Safety Studies in Healthy Adults for a Ready-to-Use, Room-Temperature, Liquid-Stable Glucagon Administered by Autoinjector, Prefilled Syringe, or Vial and Syringe";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231179164";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231179164?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:120;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178020?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:132:"Feasibility and Preliminary Behavioral and Clinical Efficacy of a Diabetes Education Chatbot Pilot Among Adults With Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178020?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1827:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Diabetes self-management education and support (DSMES) improves diabetes outcomes yet remains consistently underutilized. Chatbot technology offers the potential to increase access to and engagement in DSMES. Evidence supporting the case for chatbot uptake and efficacy in people with diabetes (PWD) is needed.Method:A diabetes education and support chatbot was deployed in a regional health care system. Adults with type 2 diabetes with an A1C of 8.0% to 8.9% and/or having recently completed a 12-week diabetes care management program were enrolled in a pilot program. Weekly chats included three elements: knowledge assessment, limited self-reporting of blood glucose data and medication taking behaviors, and education content (short videos and printable materials). A clinician facing dashboard identified need for escalation via flags based on participant responses. Data were collected to assess satisfaction, engagement, and preliminary glycemic outcomes.Results:Over 16 months, 150 PWD (majority above 50 years of age, female, and African American) were enrolled. The unenrollment rate was 5%. Most escalation flags (N = 128) were for hypoglycemia (41%), hyperglycemia (32%), and medication issues (11%). Overall satisfaction was high for chat content, length, and frequency, and 87% reported increased self-care confidence. Enrollees completing more than one chat had a mean drop in A1C of โ1.04%, whereas those completing one chat or less had a mean increase in A1C of +0.09% (P = .008).Conclusion:This diabetes education chatbot pilot demonstrated PWD acceptability, satisfaction, and engagement plus preliminary evidence of self-care confidence and A1C improvement. Further efforts are needed to validate these promising early findings.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1827:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Diabetes self-management education and support (DSMES) improves diabetes outcomes yet remains consistently underutilized. Chatbot technology offers the potential to increase access to and engagement in DSMES. Evidence supporting the case for chatbot uptake and efficacy in people with diabetes (PWD) is needed.Method:A diabetes education and support chatbot was deployed in a regional health care system. Adults with type 2 diabetes with an A1C of 8.0% to 8.9% and/or having recently completed a 12-week diabetes care management program were enrolled in a pilot program. Weekly chats included three elements: knowledge assessment, limited self-reporting of blood glucose data and medication taking behaviors, and education content (short videos and printable materials). A clinician facing dashboard identified need for escalation via flags based on participant responses. Data were collected to assess satisfaction, engagement, and preliminary glycemic outcomes.Results:Over 16 months, 150 PWD (majority above 50 years of age, female, and African American) were enrolled. The unenrollment rate was 5%. Most escalation flags (N = 128) were for hypoglycemia (41%), hyperglycemia (32%), and medication issues (11%). Overall satisfaction was high for chat content, length, and frequency, and 87% reported increased self-care confidence. Enrollees completing more than one chat had a mean drop in A1C of โ1.04%, whereas those completing one chat or less had a mean increase in A1C of +0.09% (P = .008).Conclusion:This diabetes education chatbot pilot demonstrated PWD acceptability, satisfaction, and engagement plus preliminary evidence of self-care confidence and A1C improvement. Further efforts are needed to validate these promising early findings.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:132:"Feasibility and Preliminary Behavioral and Clinical Efficacy of a Diabetes Education Chatbot Pilot Among Adults With Type 2 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178020";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-06T07:53:41Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:16:"Carine M. 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That is why the purpose of this work is to study the average level of fructosamine in apparently healthy individuals and individuals with diabetes mellitus (DM), as well as the possibility to use it when evaluating the effectiveness of inpatient treatment of patients with hyperglycemia on the seven to ten days of hospitalization.Methods:This research work was carried out in Alma-Ata, Republic of Kazakhstan, based on the endocrinology department in the period from 2020 to 2022. The work consists of a retrospective analysis of previously examined patients and a prospective stage. The statistical evaluation was carried out with the calculation of reliability coefficient, confidence interval, and criteria for testing for normality. The level of fructosamine in healthy individuals in the corresponding region was analyzed in this article for the first time, and the correlation between this indicator and the level of glycated hemoglobin was found.Results:The effectiveness of treatment of the Type 2 DM (according to the treatment protocol) has also been studied in stationary conditions for the seven to ten days, which makes it possible to judge the effectiveness of the prescribed therapy.Conclusions:These results will allow identifying the irrationality of the prescribed therapy at an early stage, which is especially important for the correct management of patients with this pathology, and minimizing the possible complications.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1693:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The use of fructosamine to assess the glycemic control represents a new step in diagnostics, and it has been accompanied by the active scientific discussion in recent years. That is why the purpose of this work is to study the average level of fructosamine in apparently healthy individuals and individuals with diabetes mellitus (DM), as well as the possibility to use it when evaluating the effectiveness of inpatient treatment of patients with hyperglycemia on the seven to ten days of hospitalization.Methods:This research work was carried out in Alma-Ata, Republic of Kazakhstan, based on the endocrinology department in the period from 2020 to 2022. The work consists of a retrospective analysis of previously examined patients and a prospective stage. The statistical evaluation was carried out with the calculation of reliability coefficient, confidence interval, and criteria for testing for normality. The level of fructosamine in healthy individuals in the corresponding region was analyzed in this article for the first time, and the correlation between this indicator and the level of glycated hemoglobin was found.Results:The effectiveness of treatment of the Type 2 DM (according to the treatment protocol) has also been studied in stationary conditions for the seven to ten days, which makes it possible to judge the effectiveness of the prescribed therapy.Conclusions:These results will allow identifying the irrationality of the prescribed therapy at an early stage, which is especially important for the correct management of patients with this pathology, and minimizing the possible complications.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:102:"The Importance of Fructosamine for Monitoring the Compensation and Effectiveness of Diabetes Treatment";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231174921";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-06T07:51:01Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:16:"Natalya Akhetova";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Zhangentkhan Abylaiuly";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Svetlana Bolshakova";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:102:"The Importance of Fructosamine for Monitoring the Compensation and Effectiveness of Diabetes Treatment";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231174921";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231174921?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:122;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178016?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"An Empirical Review of Key Glucose Monitoring Devices: Product Iterations and Patent Protection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178016?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2002:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Each year, people with diabetes and their insurers or governments spend billions of dollars on blood glucose monitors and their associated components. These monitors have evolved substantially since their introduction in the 1970s, and manufacturers frequently protect original medical devices and their modifications by applying for and obtaining patent protection.Research Design and Methods:We tracked the product iterations of five widely used blood glucose monitorsโmanufactured by LifeScan, Dexcom, Abbott, Roche, and Trividiaโfrom information published by the U.S. Food and Drug Administration (FDA), and extracted relevant U.S. patents.Results:We found 384 products made by the five manufacturers of interest, including 130 devices cleared through the 510(k) pathway, 251 approved via the premarket approval (PMA) pathway or via PMA supplements, and three for which de novo requests were granted. We identified 8095 patents potentially relevant to these devices, 2469 (31%) of which were likely to have expired by July 2021.Conclusions:Manufacturers of blood glucose monitoring systems frequently modified their devices and obtained patent protection related to these device modifications. The therapeutic value of these new modifications should be critically evaluated and balanced against their additional cost. Older glucose monitoring devices that were marketed in decades past are now in the public domain and no longer protected by patents. Newer devices will join them as their patents expire. Increased demand from people with diabetes and the health care system for older, off-patent devices would provide an incentive for the medical device industry to make these devices more widely available, enabling good care at lower cost when such devices are substantially equivalent in effectiveness and safety. In turn, availability and awareness of older, off-patent devices could help stimulate such demand.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2002:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Each year, people with diabetes and their insurers or governments spend billions of dollars on blood glucose monitors and their associated components. These monitors have evolved substantially since their introduction in the 1970s, and manufacturers frequently protect original medical devices and their modifications by applying for and obtaining patent protection.Research Design and Methods:We tracked the product iterations of five widely used blood glucose monitorsโmanufactured by LifeScan, Dexcom, Abbott, Roche, and Trividiaโfrom information published by the U.S. Food and Drug Administration (FDA), and extracted relevant U.S. patents.Results:We found 384 products made by the five manufacturers of interest, including 130 devices cleared through the 510(k) pathway, 251 approved via the premarket approval (PMA) pathway or via PMA supplements, and three for which de novo requests were granted. We identified 8095 patents potentially relevant to these devices, 2469 (31%) of which were likely to have expired by July 2021.Conclusions:Manufacturers of blood glucose monitoring systems frequently modified their devices and obtained patent protection related to these device modifications. The therapeutic value of these new modifications should be critically evaluated and balanced against their additional cost. Older glucose monitoring devices that were marketed in decades past are now in the public domain and no longer protected by patents. Newer devices will join them as their patents expire. Increased demand from people with diabetes and the health care system for older, off-patent devices would provide an incentive for the medical device industry to make these devices more widely available, enabling good care at lower cost when such devices are substantially equivalent in effectiveness and safety. In turn, availability and awareness of older, off-patent devices could help stimulate such demand.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:95:"An Empirical Review of Key Glucose Monitoring Devices: Product Iterations and Patent Protection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178016";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-05T10:37:51Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:18:"Jonathan J. Darrow";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:19:"Victor Van de Wiele";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"David Beran";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:19:"Aaron S. Kesselheim";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:95:"An Empirical Review of Key Glucose Monitoring Devices: Product Iterations and Patent Protection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178016";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178016?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:123;a:6:{s:4:"data";s:102:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178525?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:67:"Adverse Event Causes From 2022 for Four Continuous Glucose Monitors";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178525?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1305:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Adverse events for continuous glucose monitors (CGMs) represent a significant issue for people with diabetes with 281 963 CGM adverse events occurring in 2022. The process to obtain adverse events and the US Food and Drug Administration (FDA) database that contains them are reviewed.Methods:Tables were created in SQL Server for four CGM products (Dexcom G6, all versions of Abbott Libre, Medtronic Guardian 3, and Senseonics Eversense) containing either malfunction or injury adverse events sorted by the manufacturerโs chosen product code. As the product code is not always clear (or appropriate), the causes of the events were determined from the text description of the adverse event. The resulting causes were listed in decreasing order in tables for each product and event type.Results:A common effect of several event causes prevented the user from obtaining a result. Inaccuracy was also a frequent complaint. Other causes were specific to that device.Conclusions:Creating tables based on manufacturer problem codes for their CGMs, followed by analysis of the adverse event text, facilitates the analysis of event causes. Analyzing adverse event data is the first step in trying to reduce the number of adverse events.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1305:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Adverse events for continuous glucose monitors (CGMs) represent a significant issue for people with diabetes with 281 963 CGM adverse events occurring in 2022. The process to obtain adverse events and the US Food and Drug Administration (FDA) database that contains them are reviewed.Methods:Tables were created in SQL Server for four CGM products (Dexcom G6, all versions of Abbott Libre, Medtronic Guardian 3, and Senseonics Eversense) containing either malfunction or injury adverse events sorted by the manufacturerโs chosen product code. As the product code is not always clear (or appropriate), the causes of the events were determined from the text description of the adverse event. The resulting causes were listed in decreasing order in tables for each product and event type.Results:A common effect of several event causes prevented the user from obtaining a result. Inaccuracy was also a frequent complaint. Other causes were specific to that device.Conclusions:Creating tables based on manufacturer problem codes for their CGMs, followed by analysis of the adverse event text, facilitates the analysis of event causes. Analyzing adverse event data is the first step in trying to reduce the number of adverse events.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:67:"Adverse Event Causes From 2022 for Four Continuous Glucose Monitors";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178525";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-02T06:21:17Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"Jan S. Krouwer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:67:"Adverse Event Causes From 2022 for Four Continuous Glucose Monitors";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178525";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178525?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:124;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178022?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Current Perspective on the Potential Benefits of Smart Insulin Pens on Glycemic Control in Patients With Diabetes: Spanish Delphi Consensus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178022?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1662:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Diabetes mellitus (DM) is a chronic disease with high morbidity and mortality, and glycemic control is key to avoiding complications. Technological innovations have led to the development of new tools to help patients with DM manage their condition.Objective:This consensus assesses the current perspective of physicians on the potential benefits of using smart insulin pens in the glycemic control of patients with type 1 diabetes (DM1) in Spain.Methods:The Delphi technique was used by 110 physicians who were experts in managing patients with DM1. The questionnaire consisted of 94 questions.Results:The consensus obtained was 95.74%. The experts recommended using the ambulatory glucose profile report and the different time-in-range (TIR) metrics to assess poor glycemic control. Between 31% and 65% of patients had TIR values less than 70% and were diagnosed based on glycosylated hemoglobin values. They believed that less than 10% of patients needed to remember to administer the basal insulin dose and between 10% and 30% needed to remember the prandial insulin dose.Conclusions:The perception of physicians in their usual practice leads them to recommend the use of ambulatory glucose profile and time in range for glycemic control. Forgetting to administer insulin is a very common problem and the actual occurrence rate does not correspond with cliniciansโ perceptions. Technological improvements and the use of smart insulin pens can increase treatment adherence, strengthen the doctorโpatient relationship, and help improve patientsโ education and quality of life.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1662:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Diabetes mellitus (DM) is a chronic disease with high morbidity and mortality, and glycemic control is key to avoiding complications. Technological innovations have led to the development of new tools to help patients with DM manage their condition.Objective:This consensus assesses the current perspective of physicians on the potential benefits of using smart insulin pens in the glycemic control of patients with type 1 diabetes (DM1) in Spain.Methods:The Delphi technique was used by 110 physicians who were experts in managing patients with DM1. The questionnaire consisted of 94 questions.Results:The consensus obtained was 95.74%. The experts recommended using the ambulatory glucose profile report and the different time-in-range (TIR) metrics to assess poor glycemic control. Between 31% and 65% of patients had TIR values less than 70% and were diagnosed based on glycosylated hemoglobin values. They believed that less than 10% of patients needed to remember to administer the basal insulin dose and between 10% and 30% needed to remember the prandial insulin dose.Conclusions:The perception of physicians in their usual practice leads them to recommend the use of ambulatory glucose profile and time in range for glycemic control. Forgetting to administer insulin is a very common problem and the actual occurrence rate does not correspond with cliniciansโ perceptions. Technological improvements and the use of smart insulin pens can increase treatment adherence, strengthen the doctorโpatient relationship, and help improve patientsโ education and quality of life.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Current Perspective on the Potential Benefits of Smart Insulin Pens on Glycemic Control in Patients With Diabetes: Spanish Delphi Consensus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178022";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-02T06:18:37Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:23:"Jesรบs Moreno-Fernandez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Gonzalo Dรญaz-Soto";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Juan Girbes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:23:"Francisco Javier Arroyo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:139:"Current Perspective on the Potential Benefits of Smart Insulin Pens on Glycemic Control in Patients With Diabetes: Spanish Delphi Consensus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178022";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178022?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:125;a:6:{s:4:"data";s:179:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178017?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:212:"An Observational Crossover Study of People Using Real-Time Continuous Glucose Monitors Versus Self-Monitoring of Blood Glucose: Real-World Evidence Using EMR Data From More Than 12,000 People With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231178017?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1570:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:We used real-world electronic health record (EHR) data to examine HbA1c levels among children and adults with type 1 diabetes (T1D) who are classified as continuous glucose monitor (CGM) users after T1D diagnosis and switch to self-monitoring of blood glucose (SMBG) during follow-up, versus people who opt for SMBG after T1D diagnosis and switch to CGM during follow-up visits.Methods:We conducted an observational, case-crossover study using electronic medical record (EMR) data from the T1D Exchange Quality Improvement Collaborative. The primary outcome in this study was HbA1c. Baseline HbA1c levels were taken at the index date, corresponding to initial device classification, and compared with HbA1c value recorded at the clinic visit following device switch.Results:Of all patients classified in the SMBG group, 7,706 switched to CGM use within the 5-year study time frame, and 5,123 of all initial CGM users switched to SMBG within the study time frame and were included in this analysis. At baseline, median (interquartile range [IQR]) HbA1c for SMBG use was 8.1 (2.4), whereas postcrossover to CGM use, there was a decline in median (IQR) levels to 7.7 (1.9) (P < .001). For baseline CGM users, median (IQR) HbA1c levels were 7.9 (2.0), and postcrossover to SMBG, median (IQR) HbA1c levels increased to 8.0 (2.9) (P < .001).Conclusion:We found that people who switched to CGM use had significantly improved HbA1c levels compared to those who switched to glucose monitoring with SMBG.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1576:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:We used real-world electronic health record (EHR) data to examine HbA1c levels among children and adults with type 1 diabetes (T1D) who are classified as continuous glucose monitor (CGM) users after T1D diagnosis and switch to self-monitoring of blood glucose (SMBG) during follow-up, versus people who opt for SMBG after T1D diagnosis and switch to CGM during follow-up visits.Methods:We conducted an observational, case-crossover study using electronic medical record (EMR) data from the T1D Exchange Quality Improvement Collaborative. The primary outcome in this study was HbA1c. Baseline HbA1c levels were taken at the index date, corresponding to initial device classification, and compared with HbA1c value recorded at the clinic visit following device switch.Results:Of all patients classified in the SMBG group, 7,706 switched to CGM use within the 5-year study time frame, and 5,123 of all initial CGM users switched to SMBG within the study time frame and were included in this analysis. At baseline, median (interquartile range [IQR]) HbA1c for SMBG use was 8.1 (2.4), whereas postcrossover to CGM use, there was a decline in median (IQR) levels to 7.7 (1.9) (P < .001). For baseline CGM users, median (IQR) HbA1c levels were 7.9 (2.0), and postcrossover to SMBG, median (IQR) HbA1c levels increased to 8.0 (2.9) (P < .001).Conclusion:We found that people who switched to CGM use had significantly improved HbA1c levels compared to those who switched to glucose monitoring with SMBG.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:212:"An Observational Crossover Study of People Using Real-Time Continuous Glucose Monitors Versus Self-Monitoring of Blood Glucose: Real-World Evidence Using EMR Data From More Than 12,000 People With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231178017";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-06-02T06:15:15Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:12:{i:0;a:5:{s:4:"data";s:11:"Nudrat Noor";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Gregory Norman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Rona Sonabend";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:9:"Lily Chao";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Manmohan Kamboj";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Lauren Golden";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"M. 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The cornerstone for optimizing diabetes management and treatment outcomes is glucose monitoring, the techniques of which have evolved from self-monitoring of blood glucose (SMBG) to glycated hemoglobin (HbA1c), and to continuous glucose monitoring (CGM). Contextual differences with Western populations and limited regionally generated clinical evidence warrant regional standards of diabetes care, including glucose monitoring in APAC. Hence, the APAC Diabetes Care Advisory Board convened to gather insights into clinician-reported CGM utilization for optimized glucose monitoring and diabetes management in the region. We discuss the findings from a pre-meeting survey and an expert panel meeting regarding glucose monitoring patterns and influencing factors, patient profiles for CGM initiation and continuation, CGM benefits, and CGM optimization challenges and potential solutions in APAC. While CGM is becoming the new standard of care and a useful adjunct to HbA1c and SMBG globally, glucose monitoring type, timing, and frequency should be individualized according to local and patient-specific contexts. The results of this APAC survey guide methods for the formulation of future APAC-specific consensus guidelines for the application of CGM in people living with diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1469:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Diabetes is prevalent, and it imposes a substantial public health burden globally and in the Asia-Pacific (APAC) region. The cornerstone for optimizing diabetes management and treatment outcomes is glucose monitoring, the techniques of which have evolved from self-monitoring of blood glucose (SMBG) to glycated hemoglobin (HbA1c), and to continuous glucose monitoring (CGM). Contextual differences with Western populations and limited regionally generated clinical evidence warrant regional standards of diabetes care, including glucose monitoring in APAC. Hence, the APAC Diabetes Care Advisory Board convened to gather insights into clinician-reported CGM utilization for optimized glucose monitoring and diabetes management in the region. We discuss the findings from a pre-meeting survey and an expert panel meeting regarding glucose monitoring patterns and influencing factors, patient profiles for CGM initiation and continuation, CGM benefits, and CGM optimization challenges and potential solutions in APAC. While CGM is becoming the new standard of care and a useful adjunct to HbA1c and SMBG globally, glucose monitoring type, timing, and frequency should be individualized according to local and patient-specific contexts. The results of this APAC survey guide methods for the formulation of future APAC-specific consensus guidelines for the application of CGM in people living with diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:104:"Asia-Pacific Perspectives on the Role of Continuous Glucose Monitoring in Optimizing Diabetes Management";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231176533";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-05-26T12:10:43Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:13:{i:0;a:5:{s:4:"data";s:13:"Stephen Twigg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:7:"Soo Lim";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Seung-Hyun Yoo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Liming Chen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:10:"Yuqian Bao";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:10:"Alice Kong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:10:"Ester Yeoh";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:15:"Siew Pheng Chan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:18:"Jeremyjones Robles";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:17:"Viswanathan Mohan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:11:"Neale Cohen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:15:"Margaret McGill";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:9:"Linong Ji";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:104:"Asia-Pacific Perspectives on the Role of Continuous Glucose Monitoring in Optimizing Diabetes Management";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231176533";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231176533?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:127;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231175920?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Assessing Barriers and Adherence to Insulin Injection Technique in People With Diabetes: Development and Validation of New Assessment Tools";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231175920?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1828:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The correct injection technique is crucial for people with insulin therapy. However, barriers to insulin injections exist, which can lead to problems with injections. In addition, injection behavior may deviate from recommendations leading to lower adherence to the correct injection technique. We developed two scales to assess barriers and adherence to the correct technique.Methods:Two item pools were created to assess barriers to insulin injections (barriers scale) and adherence to the correct technique (adherence scale). In an evaluation study, participants completed the two newly created scales, as well as other questionnaires used for criterion validity. Exploratory factor analysis, correlational analysis, and receiver operating characteristics analysis were computed to analyze the validity of the scales.Results:A total of 313 people with type 1 and type 2 diabetes using an insulin pen for insulin injections participated. For the barriers scale, 12 items were selected achieving a reliability of 0.74. The factor analysis revealed three factors namely emotional, cognitive, and behavioral barriers. For the adherence scale, nine items were selected achieving a reliability of 0.78. Both scales showed significant associations with diabetes self-management, diabetes distress, diabetes acceptance, and diabetes empowerment. Receiver operating characteristics analysis showed significant area under the curves for both scales in classifying people with current skin irritations.Conclusions:Reliability and validity of the two scales assessing barriers and adherence to insulin injection technique were demonstrated. The two scales can be used in clinical practice to identify persons in need of education in insulin injection technique.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1828:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The correct injection technique is crucial for people with insulin therapy. However, barriers to insulin injections exist, which can lead to problems with injections. In addition, injection behavior may deviate from recommendations leading to lower adherence to the correct injection technique. We developed two scales to assess barriers and adherence to the correct technique.Methods:Two item pools were created to assess barriers to insulin injections (barriers scale) and adherence to the correct technique (adherence scale). In an evaluation study, participants completed the two newly created scales, as well as other questionnaires used for criterion validity. Exploratory factor analysis, correlational analysis, and receiver operating characteristics analysis were computed to analyze the validity of the scales.Results:A total of 313 people with type 1 and type 2 diabetes using an insulin pen for insulin injections participated. For the barriers scale, 12 items were selected achieving a reliability of 0.74. The factor analysis revealed three factors namely emotional, cognitive, and behavioral barriers. For the adherence scale, nine items were selected achieving a reliability of 0.78. Both scales showed significant associations with diabetes self-management, diabetes distress, diabetes acceptance, and diabetes empowerment. Receiver operating characteristics analysis showed significant area under the curves for both scales in classifying people with current skin irritations.Conclusions:Reliability and validity of the two scales assessing barriers and adherence to insulin injection technique were demonstrated. The two scales can be used in clinical practice to identify persons in need of education in insulin injection technique.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:139:"Assessing Barriers and Adherence to Insulin Injection Technique in People With Diabetes: Development and Validation of New Assessment Tools";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231175920";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-05-20T09:51:03Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:15:"Dominic Ehrmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Bernhard Kulzer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Inka Wienbarg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Jochen Sieber";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Siegfried Weber";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Thomas Haak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Norbert Hermanns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:139:"Assessing Barriers and Adherence to Insulin Injection Technique in People With Diabetes: Development and Validation of New Assessment Tools";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231175920";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231175920?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:128;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231174040?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231174040?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:900:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:900:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:130:"Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231174040";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-05-10T11:53:05Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:10:"Jean C. Lu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Petrova Lee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Francesco Ierino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:19:"Richard J. MacIsaac";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Elif Ekinci";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"David OโNeal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:130:"Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231174040";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231174040?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:129;a:6:{s:4:"data";s:109:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231168379?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:57:"Rebound Hypoglycemia and Hyperglycemia in Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231168379?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1365:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aims:The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics.Methods:Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes.Results:A total of 10โ977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ยฑ 1.6 mmol/l before Rhypo; 12.8 ยฑ 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearmanโs rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13).Conclusions:The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1377:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aims:The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics.Methods:Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes.Results:A total of 10โ977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ยฑ 1.6 mmol/l before Rhypo; 12.8 ยฑ 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearmanโs rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13).Conclusions:The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:57:"Rebound Hypoglycemia and Hyperglycemia in Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231168379";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-05-04T05:54:59Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:2:{i:0;a:5:{s:4:"data";s:15:"Klavs W. 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Bibby";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:57:"Rebound Hypoglycemia and Hyperglycemia in Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231168379";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231168379?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:130;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231170242?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Noninvasive Continuous Glucose Monitoring With a Novel Wearable Dial Resonating Sensor: A Clinical Proof-of-Concept Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231170242?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1799:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:A noninvasive, wearable continuous glucose monitor would be a major advancement in diabetes therapy. This trial investigated a novel noninvasive glucose monitor which analyzes spectral variations in radio frequency/microwave signals reflected from the wrist.Methods:A single-arm, open-label, experimental study compared glucose values from a prototype investigational device with laboratory glucose measurements from venous blood samples (Super GL Glucose Analyzer, Dr. Mรผller Gerรคtebau GmbH) at varying levels of glycemia. The study included 29 male participants with type 1 diabetes (age range = 19-56 years). The study comprised three stages with the following aims: (1) demonstrate initial proof-of-principle, (2) test an improved device design, and (3) test performance on two consecutive days without device recalibration. The co-primary endpoints in all trial stages were median and mean absolute relative difference (ARD) calculated across all data points.Results:In stage 1, the median and mean ARDs were 30% and 46%, respectively. Stage 2 produced marked performance improvements with a median and mean ARD of 22% and 28%, respectively. Stage 3 showed that, without recalibration, the device performed as well as the initial prototype (stage 1) with a median and mean ARD of 35% and 44%, respectively.Conclusion:This proof-of-concept study shows that a novel noninvasive continuous glucose monitor was capable of detecting glucose levels. Furthermore, the ARD results are comparable to first models of commercially available minimally invasive products without the need to insert a needle. The prototype has been further developed and is being tested in subsequent studies.Trial registration number:NCT05023798.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1799:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:A noninvasive, wearable continuous glucose monitor would be a major advancement in diabetes therapy. This trial investigated a novel noninvasive glucose monitor which analyzes spectral variations in radio frequency/microwave signals reflected from the wrist.Methods:A single-arm, open-label, experimental study compared glucose values from a prototype investigational device with laboratory glucose measurements from venous blood samples (Super GL Glucose Analyzer, Dr. Mรผller Gerรคtebau GmbH) at varying levels of glycemia. The study included 29 male participants with type 1 diabetes (age range = 19-56 years). The study comprised three stages with the following aims: (1) demonstrate initial proof-of-principle, (2) test an improved device design, and (3) test performance on two consecutive days without device recalibration. The co-primary endpoints in all trial stages were median and mean absolute relative difference (ARD) calculated across all data points.Results:In stage 1, the median and mean ARDs were 30% and 46%, respectively. Stage 2 produced marked performance improvements with a median and mean ARD of 22% and 28%, respectively. Stage 3 showed that, without recalibration, the device performed as well as the initial prototype (stage 1) with a median and mean ARD of 35% and 44%, respectively.Conclusion:This proof-of-concept study shows that a novel noninvasive continuous glucose monitor was capable of detecting glucose levels. Furthermore, the ARD results are comparable to first models of commercially available minimally invasive products without the need to insert a needle. The prototype has been further developed and is being tested in subsequent studies.Trial registration number:NCT05023798.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Noninvasive Continuous Glucose Monitoring With a Novel Wearable Dial Resonating Sensor: A Clinical Proof-of-Concept Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231170242";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-04-27T12:03:12Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:14:"Consuelo Handy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Mohamed Sabih Chaudhry";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:28:"Muhammad Rafaqat Ali Qureshi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Bradley Love";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"John Shillingford";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:20:"Leona Plum-Mรถrschel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Eric Zijlstra";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:121:"Noninvasive Continuous Glucose Monitoring With a Novel Wearable Dial Resonating Sensor: A Clinical Proof-of-Concept Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231170242";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231170242?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:131;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231171399?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:173:"Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231171399?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:976:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:976:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:173:"Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231171399";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-04-27T11:33:11Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:16:"Ashley Y. DuBord";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Emily W. Paolillo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Adam M. Staffaroni";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:173:"Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231171399";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231171399?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:132;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231169722?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Impact of X-Ray Exposure From Computed Tomography on Wearable Insulin Delivery Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231169722?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1684:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To investigate the impact of radiation exposure from a computed tomography (CT) scanner on the functional integrity of a wearable insulin delivery system.Methods:A total of 160 Omnipods and four personal diabetes managers (PDMs) were evenly divided into four groups: (1) control group (no radiation exposure), (2) typical radiation exposure group, (3) 4ร typical radiation exposure group, and (4) scatter radiation group. Pods were attached to an anthropomorphic torso phantom on the abdomen (direct irradiation) or shoulder (scatter radiation) region. A third-generation dual-source CT scanner was used to scan the pods using either a typical exposure (used for routine CT abdominal study of a median size patient) or 4ร typical exposure. A manufacturer-recommended 20-step functionality test was performed for all 160 Omnipods.Results:The radiation dose (measured in volume CT Dose index) was 16 mGy for a typical exposure, and 64 mGy for 4ร typical exposure. The scatter radiation is less than 0.1 mGy. All Pods passed the functionality test except one pod in the scatter radiation group, which sounded an alarm due to occlusion. The blockage to the fluid was due to a kink in the soft cannula, a mechanical issue not caused by the radiation exposure.Conclusions:This study suggests X-ray exposure levels used in radiological imaging procedures do not negatively impact the functional integrity of Omnipods. This finding may support the potential for the manufacturer to remove the warning that patients should remove the Pod for X-ray imaging procedures, which will have a huge impact on patient care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1684:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To investigate the impact of radiation exposure from a computed tomography (CT) scanner on the functional integrity of a wearable insulin delivery system.Methods:A total of 160 Omnipods and four personal diabetes managers (PDMs) were evenly divided into four groups: (1) control group (no radiation exposure), (2) typical radiation exposure group, (3) 4ร typical radiation exposure group, and (4) scatter radiation group. Pods were attached to an anthropomorphic torso phantom on the abdomen (direct irradiation) or shoulder (scatter radiation) region. A third-generation dual-source CT scanner was used to scan the pods using either a typical exposure (used for routine CT abdominal study of a median size patient) or 4ร typical exposure. A manufacturer-recommended 20-step functionality test was performed for all 160 Omnipods.Results:The radiation dose (measured in volume CT Dose index) was 16 mGy for a typical exposure, and 64 mGy for 4ร typical exposure. The scatter radiation is less than 0.1 mGy. All Pods passed the functionality test except one pod in the scatter radiation group, which sounded an alarm due to occlusion. The blockage to the fluid was due to a kink in the soft cannula, a mechanical issue not caused by the radiation exposure.Conclusions:This study suggests X-ray exposure levels used in radiological imaging procedures do not negatively impact the functional integrity of Omnipods. This finding may support the potential for the manufacturer to remove the warning that patients should remove the Pod for X-ray imaging procedures, which will have a huge impact on patient care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:86:"Impact of X-Ray Exposure From Computed Tomography on Wearable Insulin Delivery Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231169722";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-04-26T12:15:31Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:10:"Frank Dong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Paul Johnson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:10:"Grant Fong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Alex Nguyen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Felipe Lauand";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Todd Vienneau";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:86:"Impact of X-Ray Exposure From Computed Tomography on Wearable Insulin Delivery Devices";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231169722";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231169722?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:133;a:6:{s:4:"data";s:179:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231167853?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:134:"Digital Health Solutions for Community-Based Control of Diabetes During COVID-19 Pandemic: A Scoping Review of Implementation Outcomes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231167853?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1870:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The COVID-19 pandemic has added to the pre-existing challenges of diabetes management in many countries. It has accelerated the wider use of digital health solutions which have tremendous potential to improve health outcomes for people with diabetes. However, little is known about the attributes and the implementation of these solutions.Objective:To identify and describe digital health solutions for community-based diabetes management and to highlight their key implementation outcomes.Methods:We searched Ovid Medline, CINAHL, Embase, PsycINFO, and Web of Science for relevant articles. A purposive search was also used to identify grey literature. Articles that described digital health solutions that aimed to improve community-based diabetes management were included in this review. We applied a thematic synthesis of evidence to describe the characteristics of digital health solutions, and to summarize their key implementation outcomes.Results:We included 15 articles that reported digital health solutions that primarily focused on community-based diabetes management. Nine of the 15 innovations involved were mobile applications and/or web-based platforms, and five were based on social media platforms. The majority of the digital health solutions were used for diabetes education and support. High engagement, utilization, and satisfaction rates with digital health solutions were observed. The use of digital health solutions was also associated with improvement in self-management, taking medication, and reduction in glycated hemoglobin (HbA1c) levels.Conclusion:COVID-19 triggered digital health solutions have tremendous potential to improve health outcomes for people with diabetes. Further studies are needed to evaluate the sustainability and scale-up of these solutions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1870:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The COVID-19 pandemic has added to the pre-existing challenges of diabetes management in many countries. It has accelerated the wider use of digital health solutions which have tremendous potential to improve health outcomes for people with diabetes. However, little is known about the attributes and the implementation of these solutions.Objective:To identify and describe digital health solutions for community-based diabetes management and to highlight their key implementation outcomes.Methods:We searched Ovid Medline, CINAHL, Embase, PsycINFO, and Web of Science for relevant articles. A purposive search was also used to identify grey literature. Articles that described digital health solutions that aimed to improve community-based diabetes management were included in this review. We applied a thematic synthesis of evidence to describe the characteristics of digital health solutions, and to summarize their key implementation outcomes.Results:We included 15 articles that reported digital health solutions that primarily focused on community-based diabetes management. Nine of the 15 innovations involved were mobile applications and/or web-based platforms, and five were based on social media platforms. The majority of the digital health solutions were used for diabetes education and support. High engagement, utilization, and satisfaction rates with digital health solutions were observed. The use of digital health solutions was also associated with improvement in self-management, taking medication, and reduction in glycated hemoglobin (HbA1c) levels.Conclusion:COVID-19 triggered digital health solutions have tremendous potential to improve health outcomes for people with diabetes. Further studies are needed to evaluate the sustainability and scale-up of these solutions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:134:"Digital Health Solutions for Community-Based Control of Diabetes During COVID-19 Pandemic: A Scoping Review of Implementation 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poor glycemic control. Electronic dashboards summating patient data have been shown to improve patient outcomes in other conditions. In addition, educating patients on T1DM has shown to improve glycated hemoglobin (A1C) levels. We hypothesized that using data from the electronic dashboard to monitor defined diabetes management activities to implement population-based interventions would improve patient outcomes.Methods:Inclusion criteria included patients aged 0 to 18 years at Phoenix Childrenโs Hospital with T1DM. Patient data were collected via the electronic dashboard, and both diabetes management activities (A1C, patient admissions, and visits to the emergency department) and patient outcomes (patient education, appointment compliance, follow-up after hospital admission) were analyzed.Results:This study revealed that following implementation of the electronic dashboard, the percentage of patients receiving appropriate education increased from 48% to 80% (Z-score = 23.55, P < .0001), the percentage of patients attending the appropriate number of appointments increased from 50% to 68.2%, and the percentage of patients receiving follow-up care within 40 days after a hospital admission increased from 43% to 70%. The median A1C level decreased from 9.1% to 8.2% (Z-score = โ6.74, P < .0001), and patient admissions and visits to the emergency department decreased by 20%.Conclusions:This study shows, with the implementation of an electronic dashboard, we were able to improve outcomes for our pediatric patients with T1DM. This tool can be used at other institutions to improve care and outcomes for pediatric patients with T1DM and other chronic conditions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1869:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background and Objectives:Incidence of type 1 diabetes mellitus (T1DM) is increasing, and these patients often have poor glycemic control. Electronic dashboards summating patient data have been shown to improve patient outcomes in other conditions. In addition, educating patients on T1DM has shown to improve glycated hemoglobin (A1C) levels. We hypothesized that using data from the electronic dashboard to monitor defined diabetes management activities to implement population-based interventions would improve patient outcomes.Methods:Inclusion criteria included patients aged 0 to 18 years at Phoenix Childrenโs Hospital with T1DM. Patient data were collected via the electronic dashboard, and both diabetes management activities (A1C, patient admissions, and visits to the emergency department) and patient outcomes (patient education, appointment compliance, follow-up after hospital admission) were analyzed.Results:This study revealed that following implementation of the electronic dashboard, the percentage of patients receiving appropriate education increased from 48% to 80% (Z-score = 23.55, P < .0001), the percentage of patients attending the appropriate number of appointments increased from 50% to 68.2%, and the percentage of patients receiving follow-up care within 40 days after a hospital admission increased from 43% to 70%. The median A1C level decreased from 9.1% to 8.2% (Z-score = โ6.74, P < .0001), and patient admissions and visits to the emergency department decreased by 20%.Conclusions:This study shows, with the implementation of an electronic dashboard, we were able to improve outcomes for our pediatric patients with T1DM. This tool can be used at other institutions to improve care and outcomes for pediatric patients with T1DM and other chronic conditions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:92:"Electronic Dashboard to Improve Outcomes in Pediatric Patients With Type 1 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159401";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-04-07T07:05:21Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:13:"Lily Sandblom";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Chirag Kapadia";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Vinay Vaidya";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Melissa Chambers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Rob Gonsalves";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:19:"Lea Ann Holzmeister";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Fran Hoekstra";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:15:"Stewart Goldman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:92:"Electronic Dashboard to Improve Outcomes in Pediatric Patients With Type 1 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159401";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159401?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:136;a:6:{s:4:"data";s:179:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231164151?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231164151?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1923:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The Glycemia Risk Index (GRI) was introduced as a single value derived from the ambulatory glucose profile that identifies patients who need attention. This study describes participants in each of the five GRI zones and examines the percentage of variation in GRI scores that is explained by sociodemographic and clinical variables among diverse adults with type 1 diabetes.Methods:A total of 159 participants provided blinded continuous glucose monitoring (CGM) data over 14 days (mean age [SD] = 41.4 [14.5] years; female = 54.1%, Hispanic = 41.5%). Glycemia Risk Index zones were compared on CGM, sociodemographic, and clinical variables. Shapley value analysis examined the percentage of variation in GRI scores explained by different variables. Receiver operating characteristic curves examined GRI cutoffs for those more likely to have experienced ketoacidosis or severe hypoglycemia.Results:Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones (P values < .001). Multiple sociodemographic indices also differed across zones, including education level, race/ethnicity, age, and insurance status. Sociodemographic and clinical variables collectively explained 62.2% of variance in GRI scores. A GRI score โฅ84.5 reflected greater likelihood of ketoacidosis (area under the curve [AUC] = 0.848), and scores โฅ58.2 reflected greater likelihood of severe hypoglycemia (AUC = 0.729) over the previous six months.Conclusions:Results support the use of the GRI, with GRI zones identifying those in need of clinical attention. Findings highlight the need to address health inequities. Treatment differences associated with the GRI also suggest behavioral and clinical interventions including starting individuals on CGM or automated insulin delivery systems.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1926:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The Glycemia Risk Index (GRI) was introduced as a single value derived from the ambulatory glucose profile that identifies patients who need attention. This study describes participants in each of the five GRI zones and examines the percentage of variation in GRI scores that is explained by sociodemographic and clinical variables among diverse adults with type 1 diabetes.Methods:A total of 159 participants provided blinded continuous glucose monitoring (CGM) data over 14 days (mean age [SD] = 41.4 [14.5] years; female = 54.1%, Hispanic = 41.5%). Glycemia Risk Index zones were compared on CGM, sociodemographic, and clinical variables. Shapley value analysis examined the percentage of variation in GRI scores explained by different variables. Receiver operating characteristic curves examined GRI cutoffs for those more likely to have experienced ketoacidosis or severe hypoglycemia.Results:Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones (P values < .001). Multiple sociodemographic indices also differed across zones, including education level, race/ethnicity, age, and insurance status. Sociodemographic and clinical variables collectively explained 62.2% of variance in GRI scores. A GRI score โฅ84.5 reflected greater likelihood of ketoacidosis (area under the curve [AUC] = 0.848), and scores โฅ58.2 reflected greater likelihood of severe hypoglycemia (AUC = 0.729) over the previous six months.Conclusions:Results support the use of the GRI, with GRI zones identifying those in need of clinical attention. Findings highlight the need to address health inequities. Treatment differences associated with the GRI also suggest behavioral and clinical interventions including starting individuals on CGM or automated insulin delivery systems.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:85:"Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231164151";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-31T06:23:06Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:12:{i:0;a:5:{s:4:"data";s:21:"Claire J. Hoogendoorn";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Raymond Hernandez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Stefan Schneider";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Mark Harmel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Loree T. Pham";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:19:"Gladys Crespo-Ramos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:15:"Shivani Agarwal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:13:"Jill Crandall";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:14:"Anne L. Peters";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:18:"Donna Spruijt-Metz";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:19:"Jeffrey S. Gonzalez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:19:"Elizabeth A. Pyatak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:85:"Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231164151";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231164151?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:137;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161320?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:175:"Advanced Hybrid Closed Loop in Adult Population With Type 1 Diabetes: A Substudy From the ADAPT Randomized Controlled Trial in Users of Real-Time Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161320?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1841:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This analysis reports the findings from a predefined exploratory cohort (cohort B) from the ADAPT (ADvanced Hybrid Closed Loop Study in Adult Population with Type 1 Diabetes) study. Adults with type 1 diabetes (T1D) with suboptimal glucose control were randomly allocated to an advanced hybrid closed-loop (AHCL) system or multiple daily injections of insulin (MDI) plus real-time continuous glucose monitoring (RT-CGM).Methods:In this prospective, multicenter, exploratory, open-label, randomized controlled trial, 13 participants using MDI + RT-CGM and with HbA1c โฅ8.0% were randomized to switch to AHCL (n = 8) or continue with MDI + RT-CGM (n = 5) for six months. Prespecified endpoints included the between-group difference in mean change from baseline in HbA1c, CGM-derived measures of glycemic control, and safety.Results:The mean HbA1c level decreased by 1.70 percentage points in the AHCL group versus a 0.60 percentage point decrease in the MDI + RT-CGM group, with a model-based treatment effect of โ1.08 percentage points (95% confidence interval [CI] = โ2.17 to 0.00 percentage points; P = .0508) in favor of AHCL. The percentage of time spent with sensor glucose levels between 70 and 180โmg/dL in the study phase was 73.6% in the AHCL group and 46.4% in the MDI + RT-CGM group; model-based between-group difference of 28.8 percentage points (95% CI = 12.3 to 45.3โpercentage points; P = .0035). No diabetic ketoacidosis or severe hypoglycemia occurred in either group.Conclusions:In people with T1D with HbA1c โฅ8.0%, the use of AHCL resulted in improved glycemic control relative to MDI + RT-CGM. The scale of improvement suggests that AHCL should be considered as an option for people not achieving good glycemic control on MDI + RT-CGM.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1841:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This analysis reports the findings from a predefined exploratory cohort (cohort B) from the ADAPT (ADvanced Hybrid Closed Loop Study in Adult Population with Type 1 Diabetes) study. Adults with type 1 diabetes (T1D) with suboptimal glucose control were randomly allocated to an advanced hybrid closed-loop (AHCL) system or multiple daily injections of insulin (MDI) plus real-time continuous glucose monitoring (RT-CGM).Methods:In this prospective, multicenter, exploratory, open-label, randomized controlled trial, 13 participants using MDI + RT-CGM and with HbA1c โฅ8.0% were randomized to switch to AHCL (n = 8) or continue with MDI + RT-CGM (n = 5) for six months. Prespecified endpoints included the between-group difference in mean change from baseline in HbA1c, CGM-derived measures of glycemic control, and safety.Results:The mean HbA1c level decreased by 1.70 percentage points in the AHCL group versus a 0.60 percentage point decrease in the MDI + RT-CGM group, with a model-based treatment effect of โ1.08 percentage points (95% confidence interval [CI] = โ2.17 to 0.00 percentage points; P = .0508) in favor of AHCL. The percentage of time spent with sensor glucose levels between 70 and 180โmg/dL in the study phase was 73.6% in the AHCL group and 46.4% in the MDI + RT-CGM group; model-based between-group difference of 28.8 percentage points (95% CI = 12.3 to 45.3โpercentage points; P = .0035). No diabetic ketoacidosis or severe hypoglycemia occurred in either group.Conclusions:In people with T1D with HbA1c โฅ8.0%, the use of AHCL resulted in improved glycemic control relative to MDI + RT-CGM. The scale of improvement suggests that AHCL should be considered as an option for people not achieving good glycemic control on MDI + RT-CGM.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:175:"Advanced Hybrid Closed Loop in Adult Population With Type 1 Diabetes: A Substudy From the ADAPT Randomized Controlled Trial in Users of Real-Time Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231161320";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-23T05:55:13Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:18:"Tim van den Heuvel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Ralf Kolassa";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Winfried Keuthage";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Jens Kroeger";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Roseline Rรฉ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Simona de Portu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Linda Vorrink";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:9:"John Shin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:17:"Javier Castaรฑeda";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:15:"Robert Vigersky";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:10:"Ohad Cohen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:175:"Advanced Hybrid Closed Loop in Adult Population With Type 1 Diabetes: A Substudy From the ADAPT Randomized Controlled Trial in Users of Real-Time Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231161320";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161320?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:138;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231162601?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Implementation and Evaluation of an Automated Text MessageโBased Diabetes Prevention Program for Adults With Pre-diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231162601?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1843:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Despite the efficacy of diabetes prevention programs, only an estimated 5% of people with pre-diabetes actually participate. Mobile health (mHealth) holds promise to engage patients with pre-diabetes into lifestyle modification programs by decreasing the referral burden, centralizing remote enrollment, removing the physical requirement of a brick-and-mortar location, lowering operating costs through automation, and reducing time and transportation barriers.Methods:Non-randomized implementation study enrolling patients with pre-diabetes from a large health care organization. Patients were exposed to a text messageโbased program combining live human coaching guidance and support with automated scheduled, interactive, data-driven, and on-demand messages. The primary analysis examined predicted weight outcomes at 6 and 12 months. Secondary outcomes included predicted changes in HbA1c and minutes of exercise at 6 and 12 months.Results:Of the 163 participants included in the primary analysis, participants had a mean predicted weight loss of 5.5% at six months (P < .001) and of 4.3% at 12 months (P < .001). We observed a decrease in predicted HbA1c from 6.1 at baseline to 5.8 at 6 and 12 months (P < .001). Activity minutes were statistically similar from a baseline of 155.5 minutes to 146.0 minutes (P = .567) and 142.1 minutes (P = .522) at 6 and 12 months, respectively, for the overall cohort.Conclusions:In this real-world implementation of the myAgileLife Diabetes Prevention Program among patients with pre-diabetes, we observed significant decreases in weight and HbA1c at 6 and 12 months. mHealth may represent an effective and easily scalable potential solution to deliver impactful diabetes prevention curricula to large numbers of patients.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1852:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Despite the efficacy of diabetes prevention programs, only an estimated 5% of people with pre-diabetes actually participate. Mobile health (mHealth) holds promise to engage patients with pre-diabetes into lifestyle modification programs by decreasing the referral burden, centralizing remote enrollment, removing the physical requirement of a brick-and-mortar location, lowering operating costs through automation, and reducing time and transportation barriers.Methods:Non-randomized implementation study enrolling patients with pre-diabetes from a large health care organization. Patients were exposed to a text messageโbased program combining live human coaching guidance and support with automated scheduled, interactive, data-driven, and on-demand messages. The primary analysis examined predicted weight outcomes at 6 and 12 months. Secondary outcomes included predicted changes in HbA1c and minutes of exercise at 6 and 12 months.Results:Of the 163 participants included in the primary analysis, participants had a mean predicted weight loss of 5.5% at six months (P < .001) and of 4.3% at 12 months (P < .001). We observed a decrease in predicted HbA1c from 6.1 at baseline to 5.8 at 6 and 12 months (P < .001). Activity minutes were statistically similar from a baseline of 155.5 minutes to 146.0 minutes (P = .567) and 142.1 minutes (P = .522) at 6 and 12 months, respectively, for the overall cohort.Conclusions:In this real-world implementation of the myAgileLife Diabetes Prevention Program among patients with pre-diabetes, we observed significant decreases in weight and HbA1c at 6 and 12 months. mHealth may represent an effective and easily scalable potential solution to deliver impactful diabetes prevention curricula to large numbers of patients.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:123:"Implementation and Evaluation of an Automated Text MessageโBased Diabetes Prevention Program for Adults With Pre-diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231162601";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-22T12:29:59Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:12:"Sanjay Arora";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Chun Nok Lam";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Elizabeth Burner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Michael Menchine";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:123:"Implementation and Evaluation of an Automated Text MessageโBased Diabetes Prevention Program for Adults With Pre-diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231162601";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231162601?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:139;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161361?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:52:"Vacuum-Assisted Needle-Free Capillary Blood Sampling";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161361?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1680:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Poor glycemic management persists among people practicing insulin therapy in relation to type 1 and 2 diabetes despite a clear relationship with negative health outcomes. Skin penetration by jet injection has recently been shown as a viable method for inducing blood release from fingertips. This study examines the use of vacuum to enhance the volume of blood released and quantifies any dilution of the collected blood.Methods:A single-blind crossover study involving 15 participants, each receiving four different interventions, was conducted wherein each participant served as their own control. Each participant experienced fingertip lancing and fingertip jet injection, both with and without applied vacuum. Participants were divided into three equal groups to explore different vacuum pressures.Results:This study found that glucose concentration in blood collected under vacuum following jet injection and lancing were equivalent. We found that applying a 40 kPa vacuum following jet injection produced a 35-fold increase in the collected volume. We determined the limited extent to which the injectate dilutes blood collected following jet injection. The mean dilution of blood collected by jet injection was 5.5%. We show that jet injection is as acceptable to patients as lancing, while being equally suited for conducting glucose measurements.Conclusions:Vacuum significantly enhances the volume of capillary blood released from the fingertip without any difference in pain. The blood collected by jet injection with vacuum is equivalent to that from lancing for glucose measurement purposes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1680:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Poor glycemic management persists among people practicing insulin therapy in relation to type 1 and 2 diabetes despite a clear relationship with negative health outcomes. Skin penetration by jet injection has recently been shown as a viable method for inducing blood release from fingertips. This study examines the use of vacuum to enhance the volume of blood released and quantifies any dilution of the collected blood.Methods:A single-blind crossover study involving 15 participants, each receiving four different interventions, was conducted wherein each participant served as their own control. Each participant experienced fingertip lancing and fingertip jet injection, both with and without applied vacuum. Participants were divided into three equal groups to explore different vacuum pressures.Results:This study found that glucose concentration in blood collected under vacuum following jet injection and lancing were equivalent. We found that applying a 40 kPa vacuum following jet injection produced a 35-fold increase in the collected volume. We determined the limited extent to which the injectate dilutes blood collected following jet injection. The mean dilution of blood collected by jet injection was 5.5%. We show that jet injection is as acceptable to patients as lancing, while being equally suited for conducting glucose measurements.Conclusions:Vacuum significantly enhances the volume of capillary blood released from the fingertip without any difference in pain. The blood collected by jet injection with vacuum is equivalent to that from lancing for glucose measurement purposes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:52:"Vacuum-Assisted Needle-Free Capillary Blood Sampling";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231161361";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-18T06:08:27Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:15:"Michael Hoffman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"James McKeage";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Bryan Ruddy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Poul Nielsen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Andrew Taberner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:52:"Vacuum-Assisted Needle-Free Capillary Blood Sampling";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231161361";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161361?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:140;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161317?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:142:"All Children Deserve to Be Safe, Mothers Too: Evidence and Rationale Supporting CGM Use in Gestational Diabetes Within the Medicaid Population";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161317?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1157:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Gestational diabetes mellitus (GDM) is a common metabolic disease of pregnancy that threatens the health of several million women and their offspring. The highest prevalence of GDM is seen in women of low socioeconomic status. Women with GDM are at increased risk of adverse maternal outcomes, including increased rates of Cesarean section delivery, preeclampsia, perineal tears, and postpartum hemorrhage. However, of even greater concern is the increased risk to the fetus and long-term health of the child due to elevated glycemia during pregnancy. Although the use of continuous glucose monitoring (CGM) has been shown to reduce the incidence of maternal and fetal complications in pregnant women with type 1 diabetes and type 2 diabetes, most state Medicaid programs do not cover CGM for women with GDM. This article reviews current statistics relevant to the incidence and costs of GDM among Medicaid beneficiaries, summarizes key findings from pregnancy studies using CGM, and presents a rationale for expanding and standardizing CGM coverage for GDM within state Medicaid populations.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1157:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Gestational diabetes mellitus (GDM) is a common metabolic disease of pregnancy that threatens the health of several million women and their offspring. The highest prevalence of GDM is seen in women of low socioeconomic status. Women with GDM are at increased risk of adverse maternal outcomes, including increased rates of Cesarean section delivery, preeclampsia, perineal tears, and postpartum hemorrhage. However, of even greater concern is the increased risk to the fetus and long-term health of the child due to elevated glycemia during pregnancy. Although the use of continuous glucose monitoring (CGM) has been shown to reduce the incidence of maternal and fetal complications in pregnant women with type 1 diabetes and type 2 diabetes, most state Medicaid programs do not cover CGM for women with GDM. This article reviews current statistics relevant to the incidence and costs of GDM among Medicaid beneficiaries, summarizes key findings from pregnancy studies using CGM, and presents a rationale for expanding and standardizing CGM coverage for GDM within state Medicaid populations.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:142:"All Children Deserve to Be Safe, Mothers Too: Evidence and Rationale Supporting CGM Use in Gestational Diabetes Within the Medicaid Population";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231161317";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-15T09:48:04Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:13:"Carol J. Levy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Rodolfo J. Galindo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:21:"Christopher G. Parkin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Jacob Gillis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:19:"Nicholas B. Argento";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:142:"All Children Deserve to Be Safe, Mothers Too: Evidence and Rationale Supporting CGM Use in Gestational Diabetes Within the Medicaid Population";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231161317";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231161317?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:141;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159411?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Updated Psychosocial Surveys With Continuous Glucose Monitoring Items for Youth With Type 1 Diabetes and Their Caregivers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159411?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1875:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:We added items relevant to continuous glucose monitoring (CGM) to the Diabetes Family Conflict Scale (DFC), Diabetes Family Responsibility Questionnaire (DFR), and Blood Glucose Monitoring Communication Questionnaire (GMC) and evaluated the psychometric properties of the updated surveys.Research Design and Methods:Youth with type 1 diabetes who recently started CGM and their parents completed the updated surveys and additional psychosocial surveys. Medical data were collected from self-reports and review of the medical record.Results:Youth (N = 114, 49% adolescent girls) were aged 13.3 ยฑ 2.7 years and had mean glycated hemoglobin (HbA1c) 7.9 ยฑ 0.9%; 87% of them used pump therapy. The updated surveys demonstrated high internal consistency (DFC youth: ฮฑ = .91, parent: ฮฑ = .81; DFR youth: ฮฑ = .88, parent: ฮฑ = .93; and GMC youth: ฮฑ = .88, parent: ฮฑ = .86). Higher youth and parent DFC scores (more diabetes-specific family conflict) and GMC scores (more negative affect related to glucose monitoring) were associated with more youth and parent depressive symptoms (r = 0.28-0.60, P โค .003), more diabetes burden (r = 0.31-0.71, P โค .0009), more state anxiety (r = 0.24 to r = 0.46, P โค .01), and lower youth quality of life (r = โ0.29 to โ0.50, P โค .002). Higher youth and parent DFR scores (more parent involvement in diabetes management) were associated with younger youth age (youth: r = โ0.76, P < .0001; parent: r = โ0.81, P < .0001) and more frequent blood glucose monitoring (youth: r = 0.27, P = .003; parent: r = 0.35, P = .0002).Conclusions:The updated DFC, DFR, and GMC surveys maintain good psychometric properties. The addition of CGM items expands the relevance of these surveys for youth with type 1 diabetes who are using CGM and other diabetes technologies.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1881:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:We added items relevant to continuous glucose monitoring (CGM) to the Diabetes Family Conflict Scale (DFC), Diabetes Family Responsibility Questionnaire (DFR), and Blood Glucose Monitoring Communication Questionnaire (GMC) and evaluated the psychometric properties of the updated surveys.Research Design and Methods:Youth with type 1 diabetes who recently started CGM and their parents completed the updated surveys and additional psychosocial surveys. Medical data were collected from self-reports and review of the medical record.Results:Youth (N = 114, 49% adolescent girls) were aged 13.3 ยฑ 2.7 years and had mean glycated hemoglobin (HbA1c) 7.9 ยฑ 0.9%; 87% of them used pump therapy. The updated surveys demonstrated high internal consistency (DFC youth: ฮฑ = .91, parent: ฮฑ = .81; DFR youth: ฮฑ = .88, parent: ฮฑ = .93; and GMC youth: ฮฑ = .88, parent: ฮฑ = .86). Higher youth and parent DFC scores (more diabetes-specific family conflict) and GMC scores (more negative affect related to glucose monitoring) were associated with more youth and parent depressive symptoms (r = 0.28-0.60, P โค .003), more diabetes burden (r = 0.31-0.71, P โค .0009), more state anxiety (r = 0.24 to r = 0.46, P โค .01), and lower youth quality of life (r = โ0.29 to โ0.50, P โค .002). Higher youth and parent DFR scores (more parent involvement in diabetes management) were associated with younger youth age (youth: r = โ0.76, P < .0001; parent: r = โ0.81, P < .0001) and more frequent blood glucose monitoring (youth: r = 0.27, P = .003; parent: r = 0.35, P = .0002).Conclusions:The updated DFC, DFR, and GMC surveys maintain good psychometric properties. The addition of CGM items expands the relevance of these surveys for youth with type 1 diabetes who are using CGM and other diabetes technologies.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:121:"Updated Psychosocial Surveys With Continuous Glucose Monitoring Items for Youth With Type 1 Diabetes and Their Caregivers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159411";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-13T08:41:41Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:12:"Amit Shapira";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Charlotte W. Chen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Lisa K. Volkening";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Lori M. Laffel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:121:"Updated Psychosocial Surveys With Continuous Glucose Monitoring Items for Youth With Type 1 Diabetes and Their Caregivers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159411";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159411?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:142;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159360?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:228:"Regulatory Verification by Health Canada of Content in Recombinant Human Insulin, Human Insulin Analog, and Porcine Insulin Drug Products in the Canadian Market Using Validated Pharmacopoeial Methods Over Nonvalidated Approaches";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159360?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1920:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:For diabetes mellitus treatment plans, the consistency and quality of insulin drug products are crucial for patient well-being. Because biologic drugs, such as insulin, are complex heterogeneous products, the methods for drug product evaluation should be carefully validated for use. As such, these criteria are rigorously evaluated and monitored by national authorities. Consequently, reports that describe significantly lower insulin content than their label claims are a concern. This issue was raised by a past publication analyzing insulin drug products available in Canada, and, as a result, consumers and major patient organizations have requested clarification.Methods:To address these concerns, this study independently analyzed insulin drug products purchased from local Canadian pharmaciesโincluding human insulin, insulin analogs, and porcine insulinโby compendial and noncompendial reversed-phase high-performance liquid chromatography (RP-HPLC) methods.Results:We demonstrated the importance of using methods fit for purpose when assessing insulin quality. In a preliminary screen, the expected insulin peak was seen in all products except two insulin analogsโinsulin detemir and insulin degludec. Further investigation showed that this was not caused by low insulin content but insufficient solvent conditions, which demonstrated the necessity for methods to be adequately validated for product-specific use. When drug products were appropriately assessed for content using the validated type-specific compendial RP-HPLC methods for insulin quantitation, values agreed with the label claim content.Conclusions:Because insulin drug products are used daily by over a million Canadians, it is important that researchers and journals present data using methods fit for purpose and that readers evaluate such reports critically.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1920:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:For diabetes mellitus treatment plans, the consistency and quality of insulin drug products are crucial for patient well-being. Because biologic drugs, such as insulin, are complex heterogeneous products, the methods for drug product evaluation should be carefully validated for use. As such, these criteria are rigorously evaluated and monitored by national authorities. Consequently, reports that describe significantly lower insulin content than their label claims are a concern. This issue was raised by a past publication analyzing insulin drug products available in Canada, and, as a result, consumers and major patient organizations have requested clarification.Methods:To address these concerns, this study independently analyzed insulin drug products purchased from local Canadian pharmaciesโincluding human insulin, insulin analogs, and porcine insulinโby compendial and noncompendial reversed-phase high-performance liquid chromatography (RP-HPLC) methods.Results:We demonstrated the importance of using methods fit for purpose when assessing insulin quality. In a preliminary screen, the expected insulin peak was seen in all products except two insulin analogsโinsulin detemir and insulin degludec. Further investigation showed that this was not caused by low insulin content but insufficient solvent conditions, which demonstrated the necessity for methods to be adequately validated for product-specific use. When drug products were appropriately assessed for content using the validated type-specific compendial RP-HPLC methods for insulin quantitation, values agreed with the label claim content.Conclusions:Because insulin drug products are used daily by over a million Canadians, it is important that researchers and journals present data using methods fit for purpose and that readers evaluate such reports critically.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:228:"Regulatory Verification by Health Canada of Content in Recombinant Human Insulin, Human Insulin Analog, and Porcine Insulin Drug Products in the Canadian Market Using Validated Pharmacopoeial Methods Over Nonvalidated Approaches";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159360";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-13T08:37:29Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:16:"Barry Lorbetskie";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Stewart Bigelow";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Lisa Walrond";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:14:"Agnes V. Klein";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Shih-Miin Loo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Nancy Green";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:18:"Michael Rosu-Myles";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:8:"Xu Zhang";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:9:"Huixin Lu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:13:"Michel Girard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:12:"Simon Sauvรฉ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:228:"Regulatory Verification by Health Canada of Content in Recombinant Human Insulin, Human Insulin Analog, and Porcine Insulin Drug Products in the Canadian Market Using Validated Pharmacopoeial Methods Over Nonvalidated Approaches";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159360";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159360?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:143;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231158663?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"Clinical Accuracy of a Glucose OxidaseโBased Blood Glucose Test-Strip Across Extremes of Oxygen Partial Pressure";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231158663?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1759:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Glucose oxidase (GOx)-based blood glucose monitors (BGMs) are influenced by the partial pressure of oxygen (Po2) within the applied sample. Limited in-clinic data exists regarding the quantitative effect of Po2 in unmanipulated capillary fingertip blood samples across physiologically representative glucose and Po2 ranges.Method:Clinical accuracy data were collected as part of a BGM manufacturerโs ongoing post-market surveillance program for a commercially available GOx-based BGM test-strip. The data set comprised 29โ901 paired BGM-comparator readings and corresponding Po2 values from 5 428 blood samples from a panel of 975 subjects.Results:A linear regression-calculated bias range of 5.22% (+0.72% [low Po2: 45 mm Hg] to โ4.5% [high Po2: 105 mm Hg]); biases calculated as absolute at <100 mg/dL glucose was found. Below the nominal Po2 of 75 mm Hg, a linear regression bias of +3.14% was calculated at low Po2, while negligible impact on bias (regression slope: +0.002%) was observed at higher than nominal levels (>75 mm Hg). When evaluating BGM performance under corner conditions of low (<70 mg/dL) and high (>180 mg/dL) glucose, combined with low and high Po2, linear regression biases ranged from +1.52% to โ5.32% within this small group of subjects and with no readings recorded at <70 mg/dL glucose at low and high Po2.Conclusions:Data from this large-scale clinical study, performed on unmanipulated fingertip capillary bloods from a diverse diabetes population, indicate Po2 sensitivity of the BGM to be markedly lower than published studies, which are mainly laboratory-based, requiring artificial manipulation of oxygen levels in aliquots of venous blood.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1774:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Glucose oxidase (GOx)-based blood glucose monitors (BGMs) are influenced by the partial pressure of oxygen (Po2) within the applied sample. Limited in-clinic data exists regarding the quantitative effect of Po2 in unmanipulated capillary fingertip blood samples across physiologically representative glucose and Po2 ranges.Method:Clinical accuracy data were collected as part of a BGM manufacturerโs ongoing post-market surveillance program for a commercially available GOx-based BGM test-strip. The data set comprised 29โ901 paired BGM-comparator readings and corresponding Po2 values from 5 428 blood samples from a panel of 975 subjects.Results:A linear regression-calculated bias range of 5.22% (+0.72% [low Po2: 45 mm Hg] to โ4.5% [high Po2: 105 mm Hg]); biases calculated as absolute at <100 mg/dL glucose was found. Below the nominal Po2 of 75 mm Hg, a linear regression bias of +3.14% was calculated at low Po2, while negligible impact on bias (regression slope: +0.002%) was observed at higher than nominal levels (>75 mm Hg). When evaluating BGM performance under corner conditions of low (<70 mg/dL) and high (>180 mg/dL) glucose, combined with low and high Po2, linear regression biases ranged from +1.52% to โ5.32% within this small group of subjects and with no readings recorded at <70 mg/dL glucose at low and high Po2.Conclusions:Data from this large-scale clinical study, performed on unmanipulated fingertip capillary bloods from a diverse diabetes population, indicate Po2 sensitivity of the BGM to be markedly lower than published studies, which are mainly laboratory-based, requiring artificial manipulation of oxygen levels in aliquots of venous blood.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:114:"Clinical Accuracy of a Glucose OxidaseโBased Blood Glucose Test-Strip Across Extremes of Oxygen Partial Pressure";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231158663";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-03-07T05:36:02Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:14:"Steven Setford";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Stuart Phillips";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Hilary Cameron";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"Mike Grady";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:114:"Clinical Accuracy of a Glucose OxidaseโBased Blood Glucose Test-Strip Across Extremes of Oxygen Partial Pressure";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231158663";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231158663?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:144;a:6:{s:4:"data";s:165:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231156601?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:251:"Use of Continuous Glucose Monitoring in Pump Therapy Sensor Augmented Pump or Automated Insulin Delivery in Different Age Groups (0.5 to <26 Years) With Type 1 Diabetes From 2018 to 2021: Analysis of the German/Austrian/Swiss/Luxemburg DPV Registry";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231156601?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1639:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:Insulin pump, continuous glucose monitoring (CGM), and sensor augmented pump (SAP) technology have evolved continuously leading to the development of automated insulin delivery (AID) systems. Evaluation of the use of diabetes technologies in people with T1D from January 2018 to December 2021.Methods:A patient registry (Diabetes Prospective Follow-up Database [DPV]) was analyzed for use of SAP (insulin pump + CGM โฅ90โdays, no automated dose adjustment) and AID (HCL or LGS/PLGS). In total 46,043 people with T1D aged 0.5 to <26โyears treated in 416โdiabetes centers (Germany, Austria, Luxemburg, and Switzerland) were included and stratified into 4 groups A-D according to age. Additionally, TiR and HbA1c were analyzed.Results:From 2018 to 2021, there was a significant increase from 28.7% to 32.9% (sensor augmented pump [SAP]) and 3.5% to 16.6% (AID) across all age groups, with the most frequent use in group A (<7โyears, 38.8%-40.2% and 10.3%-28.5%). A similar increase in SAP and AID use was observed in groups B (7 to <11โyears) and C (11 to <16โyears): B: +15.8โPP, C: +15.9โPP. HbA1c improved significantly in groups C and D (16 to <26 years) (both P < .01). Time in range (TiR) increased in all groups (A: +3โPP; B: +5โPP; C: +5โPP; D: +5โPP; P < 0.01 for each group). Insulin pumps (61.0% versus 53.4% male) and SAP (33.5% versus 28.9% male) are used more frequently in females.Conclusion:In recent years, we found an increasing use of new diabetes technologies and an improvement in metabolic control (TiR) across all age groups.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1660:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:Insulin pump, continuous glucose monitoring (CGM), and sensor augmented pump (SAP) technology have evolved continuously leading to the development of automated insulin delivery (AID) systems. Evaluation of the use of diabetes technologies in people with T1D from January 2018 to December 2021.Methods:A patient registry (Diabetes Prospective Follow-up Database [DPV]) was analyzed for use of SAP (insulin pump + CGM โฅ90โdays, no automated dose adjustment) and AID (HCL or LGS/PLGS). In total 46,043 people with T1D aged 0.5 to <26โyears treated in 416โdiabetes centers (Germany, Austria, Luxemburg, and Switzerland) were included and stratified into 4 groups A-D according to age. Additionally, TiR and HbA1c were analyzed.Results:From 2018 to 2021, there was a significant increase from 28.7% to 32.9% (sensor augmented pump [SAP]) and 3.5% to 16.6% (AID) across all age groups, with the most frequent use in group A (<7โyears, 38.8%-40.2% and 10.3%-28.5%). A similar increase in SAP and AID use was observed in groups B (7 to <11โyears) and C (11 to <16โyears): B: +15.8โPP, C: +15.9โPP. HbA1c improved significantly in groups C and D (16 to <26 years) (both P < .01). Time in range (TiR) increased in all groups (A: +3โPP; B: +5โPP; C: +5โPP; D: +5โPP; P < 0.01 for each group). Insulin pumps (61.0% versus 53.4% male) and SAP (33.5% versus 28.9% male) are used more frequently in females.Conclusion:In recent years, we found an increasing use of new diabetes technologies and an improvement in metabolic control (TiR) across all age groups.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:248:"Use of Continuous Glucose Monitoring in Pump Therapy Sensor Augmented Pump or Automated Insulin Delivery in Different Age Groups (0.5 to <26 Years) With Type 1 Diabetes From 2018 to 2021: Analysis of the German/Austrian/Swiss/Luxemburg DPV Registry";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231156601";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-25T12:34:31Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:10:{i:0;a:5:{s:4:"data";s:19:"Louisa van den Boom";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Marie Auzanneau";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Joachim Woelfle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Marina Sindichakis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"Antje Herbst";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"Dagmar Meraner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:12:"Kathrin Hake";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:17:"Christof Klinkert";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:14:"Bettina Gohlke";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:16:"Reinhard W. Holl";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:248:"Use of Continuous Glucose Monitoring in Pump Therapy Sensor Augmented Pump or Automated Insulin Delivery in Different Age Groups (0.5 to <26 Years) With Type 1 Diabetes From 2018 to 2021: Analysis of the German/Austrian/Swiss/Luxemburg DPV Registry";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231156601";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231156601?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:145;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231157431?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"Diagnostic Accuracy of Perception Threshold Tracking in the Detection of Small Fiber Damage in Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231157431?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1513:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:An objective assessment of small nerve fibers is key to the early detection of diabetic peripheral neuropathy (DPN). This study investigates the diagnostic accuracy of a novel perception threshold tracking technique in detecting small nerve fiber damage.Methods:Participants with type 1 diabetes (T1DM) without DPN (n = 20), with DPN (n = 20), with painful DPN (n = 20) and 20 healthy controls (HCs) underwent perception threshold tracking on the foot and corneal confocal microscopy. Diagnostic accuracy of perception threshold tracking compared to corneal confocal microscopy was analyzed using logistic regression.Results:The rheobase, corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), and corneal nerve fiber length (CNFL) (all P < .001) differed between groups. The diagnostic accuracy of perception threshold tracking (rheobase) was excellent for identifying small nerve fiber damage, especially for CNFL with a sensitivity of 94%, specificity 94%, positive predictive value 97%, and negative predictive value 89%. There was a significant correlation between rheobase with CNFD, CNBD, CNFL, and Michigan Neuropathy Screening Instrument (all P < .001).Conclusion:Perception threshold tracking had a very high diagnostic agreement with corneal confocal microscopy for detecting small nerve fiber loss and may have clinical utility for assessing small nerve fiber damage and hence early DPN.Clinical Trials:NCT04078516";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1519:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Aim:An objective assessment of small nerve fibers is key to the early detection of diabetic peripheral neuropathy (DPN). This study investigates the diagnostic accuracy of a novel perception threshold tracking technique in detecting small nerve fiber damage.Methods:Participants with type 1 diabetes (T1DM) without DPN (n = 20), with DPN (n = 20), with painful DPN (n = 20) and 20 healthy controls (HCs) underwent perception threshold tracking on the foot and corneal confocal microscopy. Diagnostic accuracy of perception threshold tracking compared to corneal confocal microscopy was analyzed using logistic regression.Results:The rheobase, corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), and corneal nerve fiber length (CNFL) (all P < .001) differed between groups. The diagnostic accuracy of perception threshold tracking (rheobase) was excellent for identifying small nerve fiber damage, especially for CNFL with a sensitivity of 94%, specificity 94%, positive predictive value 97%, and negative predictive value 89%. There was a significant correlation between rheobase with CNFD, CNBD, CNFL, and Michigan Neuropathy Screening Instrument (all P < .001).Conclusion:Perception threshold tracking had a very high diagnostic agreement with corneal confocal microscopy for detecting small nerve fiber loss and may have clinical utility for assessing small nerve fiber damage and hence early DPN.Clinical Trials:NCT04078516";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:110:"Diagnostic Accuracy of Perception Threshold Tracking in the Detection of Small Fiber Damage in Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231157431";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-24T11:11:26Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:14:"Johan Rรธikjer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:31:"Suganthiya Santhiapillai Croosu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:21:"Benn Falch Sejergaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Tine Maria Hansen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:23:"Jens Brรธndum Frรธkjรฆr";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:23:"Chris Bath Sรธndergaard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:22:"Ioannis N. Petropoulos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Rayaz A. 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Furthermore, these devices are inherently prone to malfunction, adhesive failure, and issues with insertion that can lead to a reduction in wear time. Prescription and dispensing practices provide an exact number of sensors per month without redundancy to account for the realities of daily CGM use.Methods:A RedCap survey was completed by adult patients with type 1 or type 2 diabetes (T1D or T2D) who utilize CGM followed in the Diabetes Center at Washington University in St Louis.Results:Of 384 surveys sent, 99 were completed. Participants had a mean age of 54 years, T1D 69%, female 70%, White 96%, non-Hispanic 96%, and a mean duration of diabetes mellitus (DM) 28 years. Of the cohort, 100% used CGM (80.2% Dexcom, 13.5% Freestyle Libre, 6.3% Medtronic), 61% insulin pump, and 41% Hybrid closed-loop (HCL) systems. CGM-related disruption events included device malfunction (in 85.4% of participants), insertion problems (63.5%), and falling off (61.4%). Medical careโrelated disruption occurred most frequently in the setting of imaging (41.7%), followed by surgery/procedures (11.7%) and hospitalization (4.4%). Adverse glycemic events attributed to CGM disruption, including hyperglycemia and hypoglycemia, occurred โฅ4 times in 36.5% and 12.4% of the cohort, respectively.Conclusions:Disruption in CGM use is common. Lack of redundancy of CGM supplies contributes to care disruption and adverse glycemic events.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1718:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Removal of diabetes devices, including insulin pumps and continuous glucose monitoring (CGM), is a common practice due to hospital policies, interference with imaging studies, medications, and surgical interventions. Furthermore, these devices are inherently prone to malfunction, adhesive failure, and issues with insertion that can lead to a reduction in wear time. Prescription and dispensing practices provide an exact number of sensors per month without redundancy to account for the realities of daily CGM use.Methods:A RedCap survey was completed by adult patients with type 1 or type 2 diabetes (T1D or T2D) who utilize CGM followed in the Diabetes Center at Washington University in St Louis.Results:Of 384 surveys sent, 99 were completed. Participants had a mean age of 54 years, T1D 69%, female 70%, White 96%, non-Hispanic 96%, and a mean duration of diabetes mellitus (DM) 28 years. Of the cohort, 100% used CGM (80.2% Dexcom, 13.5% Freestyle Libre, 6.3% Medtronic), 61% insulin pump, and 41% Hybrid closed-loop (HCL) systems. CGM-related disruption events included device malfunction (in 85.4% of participants), insertion problems (63.5%), and falling off (61.4%). Medical careโrelated disruption occurred most frequently in the setting of imaging (41.7%), followed by surgery/procedures (11.7%) and hospitalization (4.4%). Adverse glycemic events attributed to CGM disruption, including hyperglycemia and hypoglycemia, occurred โฅ4 times in 36.5% and 12.4% of the cohort, respectively.Conclusions:Disruption in CGM use is common. Lack of redundancy of CGM supplies contributes to care disruption and adverse glycemic events.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:55:"Interruption of CGM: Frequency and Adverse Consequences";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231156572";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-24T07:17:14Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:19:"Alexander M. Markov";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Petra Krutilova";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Andrea E. Cedeno";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"Janet B. McGill";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Alexis M. McKee";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:55:"Interruption of CGM: Frequency and Adverse Consequences";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231156572";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231156572?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:147;a:6:{s:4:"data";s:158:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159657?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Performance Assessment of Three Continuous Glucose Monitoring Systems in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159657?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1653:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:FIND, the global alliance for diagnostics, identified the nonmarket-approved continuous glucose monitoring (CGM) system, FiberSense system (FBS), as a potential device for use in low- and middle-income countries. Together with two market-approved, factory-calibrated CGM systems, namely, the FreeStyle Libre 2 (FL2) and the GlucoRx AiDEX (ADX), the FBS was subjected to a clinical performance evaluation.Methods:Thirty adult participants with type 1 diabetes were enrolled. The study was mainly conducted at home, with three in-clinic sessions conducted over the study period of 28 days. Comparator measurements were collected from capillary samples, using a high-quality blood glucose monitoring system.Results:Data from 31, 70, and 78 sensors of FBS, FL2, and ADX, respectively, were included in the performance analysis. The mean absolute relative differences between CGM and comparator data for FBS, FL2, and ADX were 14.7%, 9.2%, and 21.9%, and relative biases were โ2.1%, โ2.5%, and โ18.5%, respectively. Analysis of individual sensor accuracy revealed low, moderate, and high sensor-to-sensor variability for FBS, FL2, and ADX, respectively. Sensor survival probabilities until the end of sensor life were 47.2% for FBS (28 days), 71.3% for FL2 (14 days), and 48.4% for ADX (14 days).Conclusions:The results of FBS were encouraging enough to conduct further performance and usability evaluations in a low- and middle-income country. The results of FL2 mainly agreed with existing studies, whereas ADX showed substantial deviations from previously reported results.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1653:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:FIND, the global alliance for diagnostics, identified the nonmarket-approved continuous glucose monitoring (CGM) system, FiberSense system (FBS), as a potential device for use in low- and middle-income countries. Together with two market-approved, factory-calibrated CGM systems, namely, the FreeStyle Libre 2 (FL2) and the GlucoRx AiDEX (ADX), the FBS was subjected to a clinical performance evaluation.Methods:Thirty adult participants with type 1 diabetes were enrolled. The study was mainly conducted at home, with three in-clinic sessions conducted over the study period of 28 days. Comparator measurements were collected from capillary samples, using a high-quality blood glucose monitoring system.Results:Data from 31, 70, and 78 sensors of FBS, FL2, and ADX, respectively, were included in the performance analysis. The mean absolute relative differences between CGM and comparator data for FBS, FL2, and ADX were 14.7%, 9.2%, and 21.9%, and relative biases were โ2.1%, โ2.5%, and โ18.5%, respectively. Analysis of individual sensor accuracy revealed low, moderate, and high sensor-to-sensor variability for FBS, FL2, and ADX, respectively. Sensor survival probabilities until the end of sensor life were 47.2% for FBS (28 days), 71.3% for FL2 (14 days), and 48.4% for ADX (14 days).Conclusions:The results of FBS were encouraging enough to conduct further performance and usability evaluations in a low- and middle-income country. The results of FL2 mainly agreed with existing studies, whereas ADX showed substantial deviations from previously reported results.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Performance Assessment of Three Continuous Glucose Monitoring Systems in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159657";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-22T08:17:40Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:12:"Julia Kรถlle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Manuel Eichenlaub";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Jochen Mende";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Manuela Link";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Beatrice Vetter";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Elvis Safary";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:12:"Stefan Pleus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:13:"Cornelia Haug";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:15:"Guido Freckmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:100:"Performance Assessment of Three Continuous Glucose Monitoring Systems in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231159657";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231159657?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:148;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153896?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153896?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1675:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control.Methods:We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records.Results:Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity.Conclusions:The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1675:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control.Methods:We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records.Results:Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity.Conclusions:The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:115:"Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153896";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-17T10:37:54Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:12:"Sunghyun Cho";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Eleonora M. Aiello";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:13:"Basak Ozaslan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"Michael C. Riddell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Peter Calhoun";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:12:"Robin L. Gal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Francis J. Doyle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:115:"Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153896";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153896?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:149;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231154561?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Glycemia Risk Index Assessment in a Pediatric and Adult Patient Cohort With Type 1 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231154561?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1981:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To evaluate the glycemia risk index (GRI) as a new glucometry in pediatric and adult populations with type 1 diabetes (T1D) in clinical practice.Methods:A cross-sectional study of 202 patients with T1D receiving intensive treatment with insulin (25.2% continuous subcutaneous insulin infusion [CSII]) and intermittent scanning (flash) glucose monitoring (isCGM). Clinical and glucometric isCGM data were collected, as well as the component of hypoglycemia (CHypo) and component of hyperglycemia (CHyper) of the GRI.Results:A total of 202 patients (53% males and 67.8% adults) with a mean age of 28.6 ยฑ 15.7 years and 12.5 ยฑ 10.9 years of T1D evolution were evaluated.Adult patients (>19 years) presented higher glycated hemoglobin (HbA1c) (7.4 ยฑ 1.1 vs 6.7 ยฑ 0.6%; P < .01) and lower time in range (TIR) (55.4 ยฑ 17.5 vs 66.5 ยฑ 13.1%; P < .01) values than the pediatric population, with lower coefficient of variation (CV) (38.6 ยฑ 7.2 vs 42.4 ยฑ 8.9%; P < .05). The GRI was significantly lower in pediatric patients (48.0 ยฑ 22.2 vs 56.8 ยฑ 23.4; P < .05) associated with higher CHypo (7.1 ยฑ 5.1 vs 5.0 ยฑ 4.5; P < .01) and lower CHyper (16.8 ยฑ 9.8 vs 26.5 ยฑ 15.1; P < .01) than in adults.When analyzing treatment with CSII compared with multiple doses of insulin (MDI), a nonsignificant trend to a lower GRI was observed in CSII (51.0 ยฑ 15.3 vs 55.0 ยฑ 25.4; P= .162), with higher levels of CHypo (6.5 ยฑ 4.1 vs 5.4 ยฑ 5.0; P < .01) and lower CHyper (19.6 ยฑ 10.6 vs 24.6 ยฑ 15.2; P < .05) compared with MDI.Conclusions:In pediatric patients and in those with CSII treatment, despite a better control by classical and GRI parameters, higher overall CHypo was observed than in adults and MDI, respectively. The present study supports the usefulness of the GRI as a new glucometric parameter to evaluate the global risk of hypoglycemia-hyperglycemia in both pediatric and adult patients with T1D.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2008:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:To evaluate the glycemia risk index (GRI) as a new glucometry in pediatric and adult populations with type 1 diabetes (T1D) in clinical practice.Methods:A cross-sectional study of 202 patients with T1D receiving intensive treatment with insulin (25.2% continuous subcutaneous insulin infusion [CSII]) and intermittent scanning (flash) glucose monitoring (isCGM). Clinical and glucometric isCGM data were collected, as well as the component of hypoglycemia (CHypo) and component of hyperglycemia (CHyper) of the GRI.Results:A total of 202 patients (53% males and 67.8% adults) with a mean age of 28.6 ยฑ 15.7 years and 12.5 ยฑ 10.9 years of T1D evolution were evaluated.Adult patients (>19 years) presented higher glycated hemoglobin (HbA1c) (7.4 ยฑ 1.1 vs 6.7 ยฑ 0.6%; P < .01) and lower time in range (TIR) (55.4 ยฑ 17.5 vs 66.5 ยฑ 13.1%; P < .01) values than the pediatric population, with lower coefficient of variation (CV) (38.6 ยฑ 7.2 vs 42.4 ยฑ 8.9%; P < .05). The GRI was significantly lower in pediatric patients (48.0 ยฑ 22.2 vs 56.8 ยฑ 23.4; P < .05) associated with higher CHypo (7.1 ยฑ 5.1 vs 5.0 ยฑ 4.5; P < .01) and lower CHyper (16.8 ยฑ 9.8 vs 26.5 ยฑ 15.1; P < .01) than in adults.When analyzing treatment with CSII compared with multiple doses of insulin (MDI), a nonsignificant trend to a lower GRI was observed in CSII (51.0 ยฑ 15.3 vs 55.0 ยฑ 25.4; P= .162), with higher levels of CHypo (6.5 ยฑ 4.1 vs 5.4 ยฑ 5.0; P < .01) and lower CHyper (19.6 ยฑ 10.6 vs 24.6 ยฑ 15.2; P < .05) compared with MDI.Conclusions:In pediatric patients and in those with CSII treatment, despite a better control by classical and GRI parameters, higher overall CHypo was observed than in adults and MDI, respectively. The present study supports the usefulness of the GRI as a new glucometric parameter to evaluate the global risk of hypoglycemia-hyperglycemia in both pediatric and adult patients with T1D.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:100:"Glycemia Risk Index Assessment in a Pediatric and Adult Patient Cohort With Type 1 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231154561";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-16T12:48:16Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:18:"Gonzalo Dรญaz-Soto";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:20:"Paloma Pรฉrez-Lรณpez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:24:"Pablo Fรฉrnandez-Velasco";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:32:"Marรญa de la O Nieto de la Marca";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Esther Delgado";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Sofia del Amo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Daniel de Luis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:22:"Pilar Bahillo-Curieses";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:100:"Glycemia Risk Index Assessment in a Pediatric and Adult Patient Cohort With Type 1 Diabetes Mellitus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231154561";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231154561?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:150;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153883?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:142:"Increasing Frequency of Hemoglobin A1C Measurements in Hospitalized Patients With Diabetes: A Quality Improvement Project Using Lean Six Sigma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153883?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1888:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The American Diabetes Association (ADA) recommends measuring A1C in all inpatients with diabetes if not performed in the prior three months. Our objective was to determine the impact of utilizing Lean Six Sigma to increase the frequency of A1C measurements in hospitalized patients.Methods:We evaluated inpatients with diabetes mellitus consecutively admitted in a community hospital between January 2016 and June 2021, excluding those who had an A1C in the electronic health record (EHR) in the previous three months. Lean Six Sigma was utilized to define the extent of the problem and devise solutions. The intervention bundle delivered between November 2017 and February 2018 included (1) provider education on the utility of A1C, (2) more rapid turnaround of A1C results, and (3) an EHR glucose-management tab and insulin order set that included A1C. Hospital encounter and patient-level data were extracted from the EHR via bulk query. Frequency of A1C measurement was compared before (January 2016-November 2017) and after the intervention (March 2018-June 2021) using ฯ2 analysis.Results:Demographics did not differ preintervention versus postintervention (mean age [range]: 70.9 [18-104] years, sex: 52.2% male, race: 57.0% white). A1C measurements significantly increased following implementation of the intervention bundle (61.2% vs 74.5%, P < .001). This level was sustained for more than two years following the initial intervention. Patients seen by the diabetes consult service (40.4% vs 51.7%, P < 0.001) and length of stay (mean: 135 hours vs 149 hours, P < 0.001) both increased postintervention.Conclusions:We demonstrate a novel approach in improving A1C in hospitalized patients. Lean Six Sigma may represent a valuable methodology for community hospitals to improve inpatient diabetes care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1897:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The American Diabetes Association (ADA) recommends measuring A1C in all inpatients with diabetes if not performed in the prior three months. Our objective was to determine the impact of utilizing Lean Six Sigma to increase the frequency of A1C measurements in hospitalized patients.Methods:We evaluated inpatients with diabetes mellitus consecutively admitted in a community hospital between January 2016 and June 2021, excluding those who had an A1C in the electronic health record (EHR) in the previous three months. Lean Six Sigma was utilized to define the extent of the problem and devise solutions. The intervention bundle delivered between November 2017 and February 2018 included (1) provider education on the utility of A1C, (2) more rapid turnaround of A1C results, and (3) an EHR glucose-management tab and insulin order set that included A1C. Hospital encounter and patient-level data were extracted from the EHR via bulk query. Frequency of A1C measurement was compared before (January 2016-November 2017) and after the intervention (March 2018-June 2021) using ฯ2 analysis.Results:Demographics did not differ preintervention versus postintervention (mean age [range]: 70.9 [18-104] years, sex: 52.2% male, race: 57.0% white). A1C measurements significantly increased following implementation of the intervention bundle (61.2% vs 74.5%, P < .001). This level was sustained for more than two years following the initial intervention. Patients seen by the diabetes consult service (40.4% vs 51.7%, P < 0.001) and length of stay (mean: 135 hours vs 149 hours, P < 0.001) both increased postintervention.Conclusions:We demonstrate a novel approach in improving A1C in hospitalized patients. Lean Six Sigma may represent a valuable methodology for community hospitals to improve inpatient diabetes care.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:142:"Increasing Frequency of Hemoglobin A1C Measurements in Hospitalized Patients With Diabetes: A Quality Improvement Project Using Lean Six Sigma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153883";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-15T06:26:42Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:14:"Sara Atiq Khan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:20:"Andrew P. Demidowich";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:16:"Megan M. Tschudy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Joyce Wedler";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:11:"Wilson Lamy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:17:"Iniuboho Akpandak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:17:"Lee Ann Alexander";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:10:"Isha Misra";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:14:"Aniket Sidhaye";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:11:"Leo Rotello";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:17:"Mihail Zilbermint";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:142:"Increasing Frequency of Hemoglobin A1C Measurements in Hospitalized Patients With Diabetes: A Quality Improvement Project Using Lean Six Sigma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153883";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153883?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:151;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153882?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153882?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2027:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:There is increasing use of open-source artificial pancreas systems (APS) in the management of Type 1 diabetes. Our aim was to assess the safety and efficacy of the automated insulin delivery system AndroidAPS (AAPS), compared with stand-alone pump therapy in people with type 1 diabetes. The primary outcome was the difference in the percentage of time in range (TIR, 70-180 mg/dL). Secondary aims included mean sensor glucose value and percent continuous glucose monitor (CGM) time below range (TBR, <70 mg/dL).Research Design and Methods:This open-label single-center randomized crossover study (ANZCTR, Australian New Zealand clinical trial registry, ANZCTR-ACTRN12620001191987) comprised 20 participants with type 1 diabetes on established pump therapy, assigned to either stand-alone insulin pump therapy or the open-source AAPS hybrid closed-loop system for four weeks, with crossover to the alternate arm for the following four weeks. The CGM outcome parameters were measured by seven-day CGM at baseline and the final week of each four-week study arm.Results:Twenty participants were recruited (60% women), aged 45.8 ยฑ 15.9 years, with mean diabetes duration of 23.9 ยฑ 13.2 years, baseline glycated hemoglobin (HbA1c) 7.5% ยฑ 0.5% (58 ยฑ 6 mmol/mol) and mean TIR 62.3% ยฑ 12.9%. The change in TIR from baseline for AAPS compared with stand-alone pump therapy was 18.6% (11.4-25.9), (P < .001), TIR 76.6% ยฑ 11.7%, 58.0% ยฑ 15.6%, for AAPS and stand-alone pump, respectively. Time glucose <54 mg/dL was not increased (mean = โ2.0%, P = .191). No serious adverse events or episodes of severe hypoglycemia were recorded.Conclusions:This clinical trial of the open-source AAPS hybrid closed-loop system performed in an at-home setting demonstrated comparable safety to stand-alone pump therapy. The glycemic outcomes of AAPS were superior with improved TIR, and there was no significant difference in TBR compared with stand-alone pump therapy.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2036:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:There is increasing use of open-source artificial pancreas systems (APS) in the management of Type 1 diabetes. Our aim was to assess the safety and efficacy of the automated insulin delivery system AndroidAPS (AAPS), compared with stand-alone pump therapy in people with type 1 diabetes. The primary outcome was the difference in the percentage of time in range (TIR, 70-180 mg/dL). Secondary aims included mean sensor glucose value and percent continuous glucose monitor (CGM) time below range (TBR, <70 mg/dL).Research Design and Methods:This open-label single-center randomized crossover study (ANZCTR, Australian New Zealand clinical trial registry, ANZCTR-ACTRN12620001191987) comprised 20 participants with type 1 diabetes on established pump therapy, assigned to either stand-alone insulin pump therapy or the open-source AAPS hybrid closed-loop system for four weeks, with crossover to the alternate arm for the following four weeks. The CGM outcome parameters were measured by seven-day CGM at baseline and the final week of each four-week study arm.Results:Twenty participants were recruited (60% women), aged 45.8 ยฑ 15.9 years, with mean diabetes duration of 23.9 ยฑ 13.2 years, baseline glycated hemoglobin (HbA1c) 7.5% ยฑ 0.5% (58 ยฑ 6 mmol/mol) and mean TIR 62.3% ยฑ 12.9%. The change in TIR from baseline for AAPS compared with stand-alone pump therapy was 18.6% (11.4-25.9), (P < .001), TIR 76.6% ยฑ 11.7%, 58.0% ยฑ 15.6%, for AAPS and stand-alone pump, respectively. Time glucose <54 mg/dL was not increased (mean = โ2.0%, P = .191). No serious adverse events or episodes of severe hypoglycemia were recorded.Conclusions:This clinical trial of the open-source AAPS hybrid closed-loop system performed in an at-home setting demonstrated comparable safety to stand-alone pump therapy. The glycemic outcomes of AAPS were superior with improved TIR, and there was no significant difference in TBR compared with stand-alone pump therapy.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:99:"Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153882";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-15T06:20:22Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:19:"Natalie Nanayakkara";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:12:"Amin Sharifi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"David Burren";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Yasser Elghattis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:19:"Dulari K Jayarathna";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Neale Cohen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:99:"Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153882";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153882?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:152;a:6:{s:4:"data";s:158:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153419?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Highly Miniaturized, Low-Power CMOS ASIC Chip for Long-Term Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153419?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1966:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The objective of this work is to develop a highly miniaturized, low-power, biosensing platform for continuous glucose monitoring (CGM). This platform is based on an application-specific integrated circuit (ASIC) chip that interfaces with an amperometric glucose-sensing element. To reduce both size and power requirements, this custom ASIC chip was implemented using 65-nm complementary metal oxide semiconductor (CMOS) technology node. Interfacing this chip to a frequency-counting microprocessor with storage capabilities, a miniaturized transcutaneous CGM system can be constructed for small laboratory animals, with long battery life.Method:A 0.45 mm ร 1.12 mm custom ASIC chip was first designed and implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS technology node. This ASIC chip was then interfaced with a multi-layer amperometric glucose-sensing element and a frequency-counting microprocessor with storage capabilities. Variation in glucose levels generates a linear increase in frequency response of this ASIC chip. In vivo experiments were conducted in healthy Sprague Dawley rats.Results:This highly miniaturized, 65-nm custom ASIC chip has an overall power consumption of circa 36 ยตW. In vitro testing shows that this ASIC chip produces a linear (R2 = 99.5) frequency response to varying glucose levels (from 2 to 25 mM), with a sensitivity of 1278 Hz/mM. In vivo testing in unrestrained healthy rats demonstrated long-term CGM (six days/per charge) with rapid glucose response to glycemic variations induced by isoflurane anesthesia and tail vein injection.Conclusions:The miniature footprint of the biosensor platform, together with its low-power consumption, renders this CMOS ASIC chip a versatile platform for a variety of highly miniaturized devices, intended to improve the quality of life of patients with type 1 and type 2 diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1966:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The objective of this work is to develop a highly miniaturized, low-power, biosensing platform for continuous glucose monitoring (CGM). This platform is based on an application-specific integrated circuit (ASIC) chip that interfaces with an amperometric glucose-sensing element. To reduce both size and power requirements, this custom ASIC chip was implemented using 65-nm complementary metal oxide semiconductor (CMOS) technology node. Interfacing this chip to a frequency-counting microprocessor with storage capabilities, a miniaturized transcutaneous CGM system can be constructed for small laboratory animals, with long battery life.Method:A 0.45 mm ร 1.12 mm custom ASIC chip was first designed and implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS technology node. This ASIC chip was then interfaced with a multi-layer amperometric glucose-sensing element and a frequency-counting microprocessor with storage capabilities. Variation in glucose levels generates a linear increase in frequency response of this ASIC chip. In vivo experiments were conducted in healthy Sprague Dawley rats.Results:This highly miniaturized, 65-nm custom ASIC chip has an overall power consumption of circa 36 ยตW. In vitro testing shows that this ASIC chip produces a linear (R2 = 99.5) frequency response to varying glucose levels (from 2 to 25 mM), with a sensitivity of 1278 Hz/mM. In vivo testing in unrestrained healthy rats demonstrated long-term CGM (six days/per charge) with rapid glucose response to glycemic variations induced by isoflurane anesthesia and tail vein injection.Conclusions:The miniature footprint of the biosensor platform, together with its low-power consumption, renders this CMOS ASIC chip a versatile platform for a variety of highly miniaturized devices, intended to improve the quality of life of patients with type 1 and type 2 diabetes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:89:"Highly Miniaturized, Low-Power CMOS ASIC Chip for Long-Term Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153419";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-02-11T09:19:00Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:9:{i:0;a:5:{s:4:"data";s:22:"Raja Hari Gudlavalleti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:10:"Xiangyi Xi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Allen Legassey";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Pik-Yiu Chan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:6:"Jin Li";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Diane Burgess";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:16:"Charles Giardina";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:26:"Fotios Papadimitrakopoulos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:11:"Faquir Jain";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:89:"Highly Miniaturized, Low-Power CMOS ASIC Chip for Long-Term Continuous Glucose Monitoring";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968231153419";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968231153419?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:153;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221148764?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:161:"Improved Glycemic Control Using a Bluetoothยฎ-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144 000 People With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221148764?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1786:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The OneTouch Verio Flexยฎ (OTVF) blood glucose (BG) meter features a ColorSureยฎ Range Indicator. Diabetes management is enhanced by connecting the meter to the OneTouch Revealยฎ (OTR) mobile app. We sought to provide real-world evidence (RWE) that combining both devices improves glycemic control.Methods:Anonymized glucose and app analytics were extracted from a server from over 144โ000 people with diabetes (PWDs). Data from their first 14 days using OTVF and OTR were compared with 14 days prior to 90- and 180-day timepoints using paired within-subject differences.Results:In people with type 1 diabetes (PwT1D) or people with type 2 diabetes (PwT2D), readings in-range (RIR) improved by +6.1 (54.5% to 60.6%) and +11.9 percentage points (68.2% to 80.1%), respectively, over 180 days, and hyperglycemia was reduced by โ6.6 (40.5% to 33.9%) and โ12.0 (30.3% to 18.3%). In total, 35% of PwT1D and 40% of PwT2D improved RIR by >10 percentage points. People with type 1 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +5.1 and 7.0, respectively. People with type 2 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +11.6 and 12.0, respectively. In PwT1D or PwT2D, mean BG reduced by โ11.4 and โ19.5 mg/dL, respectively, from baseline to 180 days, with no clinically meaningful changes in percentage of hypoglycemic readings. All glycemic changes were statistically significant (P < .0005 level).Conclusion:Real-world data from over 144โ000 PWDs demonstrated improved percentage readings in-range and reduced hyperglycemia in PWDs using the OneTouch Verio Flex blood glucose meter and OneTouch Reveal app.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1792:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The OneTouch Verio Flexยฎ (OTVF) blood glucose (BG) meter features a ColorSureยฎ Range Indicator. Diabetes management is enhanced by connecting the meter to the OneTouch Revealยฎ (OTR) mobile app. We sought to provide real-world evidence (RWE) that combining both devices improves glycemic control.Methods:Anonymized glucose and app analytics were extracted from a server from over 144โ000 people with diabetes (PWDs). Data from their first 14 days using OTVF and OTR were compared with 14 days prior to 90- and 180-day timepoints using paired within-subject differences.Results:In people with type 1 diabetes (PwT1D) or people with type 2 diabetes (PwT2D), readings in-range (RIR) improved by +6.1 (54.5% to 60.6%) and +11.9 percentage points (68.2% to 80.1%), respectively, over 180 days, and hyperglycemia was reduced by โ6.6 (40.5% to 33.9%) and โ12.0 (30.3% to 18.3%). In total, 35% of PwT1D and 40% of PwT2D improved RIR by >10 percentage points. People with type 1 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +5.1 and 7.0, respectively. People with type 2 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +11.6 and 12.0, respectively. In PwT1D or PwT2D, mean BG reduced by โ11.4 and โ19.5 mg/dL, respectively, from baseline to 180 days, with no clinically meaningful changes in percentage of hypoglycemic readings. All glycemic changes were statistically significant (P < .0005 level).Conclusion:Real-world data from over 144โ000 PWDs demonstrated improved percentage readings in-range and reduced hyperglycemia in PWDs using the OneTouch Verio Flex blood glucose meter and OneTouch Reveal app.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:161:"Improved Glycemic Control Using a Bluetoothยฎ-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144 000 People With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221148764";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-01-30T09:12:53Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:10:"Mike Grady";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Hilary Cameron";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Elizabeth Holt";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:161:"Improved Glycemic Control Using a Bluetoothยฎ-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144 000 People With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221148764";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221148764?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:154;a:6:{s:4:"data";s:151:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221149040?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:188:"Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221149040?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:2079:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.Method:The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.Results:The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.Conclusions:The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2091:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.Method:The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.Results:The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.Conclusions:The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:188:"Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221149040";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-01-11T01:00:27Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:8:{i:0;a:5:{s:4:"data";s:16:"Anna R. Kahkoska";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:14:"Kushal S. Shah";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Michael R. Kosorok";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:16:"Kellee M. Miller";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Michael Rickels";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:17:"Ruth S. Weinstock";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Laura A. Young";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:18:"Richard E. 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Barriers of people with diabetes to implementation of titration include lack of self-efficiency and self-management skills, increased diabetes-related distress, low treatment satisfaction, poor well-being, as well as concerns about hypoglycemia and insulin overdose. My Dose Coach is a digital health tool for optimizing titration of basal insulin that combines a smartphone app for patients with T2DM and a Web portal for health care professionals.Methods/Design:This is a prospective, open-label, multicenter, randomized controlled parallel study conducted in approximately 50 centers in Germany that are specialized in the treatment of diabetes. Patients in the intervention group will use the titration app and will be registered on the Web portal by their treating physician. Control group patients will continue their current basal insulin titration without using the app. The primary outcome is the mean change in HbA1c levels at the 12-week follow-up. The secondary outcome measures include patient-reported outcomes such as diabetes-related distress, self-management, empowerment, self-efficacy, treatment satisfaction, and psychological well-being as well as fasting blood glucose values.Conclusion:This digital health tool has been previously implemented in several independent pilot studies. The findings from this multicenter randomized controlled trial can provide further evidence supporting the effectiveness of this tool in patients with T2DM and serve as a basis for its clinical integration.Trial Registration:German Register for Clinical Studies-DRKS-ID: DRKS00024861";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1795:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Optimal insulin titration is essential in helping people with type 2 diabetes mellitus (T2DM) to achieve adequate glycemic control. Barriers of people with diabetes to implementation of titration include lack of self-efficiency and self-management skills, increased diabetes-related distress, low treatment satisfaction, poor well-being, as well as concerns about hypoglycemia and insulin overdose. My Dose Coach is a digital health tool for optimizing titration of basal insulin that combines a smartphone app for patients with T2DM and a Web portal for health care professionals.Methods/Design:This is a prospective, open-label, multicenter, randomized controlled parallel study conducted in approximately 50 centers in Germany that are specialized in the treatment of diabetes. Patients in the intervention group will use the titration app and will be registered on the Web portal by their treating physician. Control group patients will continue their current basal insulin titration without using the app. The primary outcome is the mean change in HbA1c levels at the 12-week follow-up. The secondary outcome measures include patient-reported outcomes such as diabetes-related distress, self-management, empowerment, self-efficacy, treatment satisfaction, and psychological well-being as well as fasting blood glucose values.Conclusion:This digital health tool has been previously implemented in several independent pilot studies. The findings from this multicenter randomized controlled trial can provide further evidence supporting the effectiveness of this tool in patients with T2DM and serve as a basis for its clinical integration.Trial Registration:German Register for Clinical Studies-DRKS-ID: DRKS00024861";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:152:"Evaluation of a Digital Health Tool for Titration of Basal Insulin in People With Type 2 Diabetes: Rationale and Design of a Randomized Controlled Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221148756";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-01-05T11:00:46Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:16:"Norbert Hermanns";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Dominic Ehrmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:22:"Katharina Finke-Grรถne";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:9:"Timm Roos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Guido Freckmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:15:"Bernhard Kulzer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:152:"Evaluation of a Digital Health Tool for Titration of Basal Insulin in People With Type 2 Diabetes: Rationale and Design of a Randomized Controlled Trial";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221148756";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221148756?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:156;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221147937?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:101:"Continuous Glucose Monitoring of Steroid-Induced Hyperglycemia in Patients With Dermatologic Diseases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221147937?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1510:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background and Objectives:Systemic administration of glucocorticoids is a mainstay therapy for various inflammatory diseases and may lead to hyperglycemia, which carries the risk of worsening preexisting diabetes and triggering steroid-induced diabetes. Therefore, we aimed to identify patients at risk and to quantify severity of steroid-induced hyperglycemia (SIH) by continuous glucose monitoring (CGM) in hospitalized patients needing systemic glucocorticoid treatment.Patients and Methods:This prospective study included 51 steroid-naive, dermatological patients requiring systemic high-dose glucocorticoid treatment at the Department of Dermatology of the University Hospital Essen. After careful diabetes-specific assessment at admission, glucose monitoring was performed using a CGM system and glucose profile was analyzed in patients with and without SIH.Results:SIH occurred in 47.1% of all treated patients, and a relevant part of patients with initial normoglycemia developed SIH (2/10 patients). Doubling of SIH incidence was observed with each severity grade of dysglycemia (4/10 in prediabetes; 9/10 in diabetes). Patients with SIH spend nearly 6 hours daily above targeted glucose range, and severe hyperglycemia was observed for 1.2 hours/day.Conclusions:Our study underlines the need for dedicated glucose monitoring in dermatologic patients on systemic glucocorticoid therapy by demonstrating its impact on glucose metabolism.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1510:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background and Objectives:Systemic administration of glucocorticoids is a mainstay therapy for various inflammatory diseases and may lead to hyperglycemia, which carries the risk of worsening preexisting diabetes and triggering steroid-induced diabetes. Therefore, we aimed to identify patients at risk and to quantify severity of steroid-induced hyperglycemia (SIH) by continuous glucose monitoring (CGM) in hospitalized patients needing systemic glucocorticoid treatment.Patients and Methods:This prospective study included 51 steroid-naive, dermatological patients requiring systemic high-dose glucocorticoid treatment at the Department of Dermatology of the University Hospital Essen. After careful diabetes-specific assessment at admission, glucose monitoring was performed using a CGM system and glucose profile was analyzed in patients with and without SIH.Results:SIH occurred in 47.1% of all treated patients, and a relevant part of patients with initial normoglycemia developed SIH (2/10 patients). Doubling of SIH incidence was observed with each severity grade of dysglycemia (4/10 in prediabetes; 9/10 in diabetes). Patients with SIH spend nearly 6 hours daily above targeted glucose range, and severe hyperglycemia was observed for 1.2 hours/day.Conclusions:Our study underlines the need for dedicated glucose monitoring in dermatologic patients on systemic glucocorticoid therapy by demonstrating its impact on glucose metabolism.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:101:"Continuous Glucose Monitoring of Steroid-Induced Hyperglycemia in Patients With Dermatologic Diseases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221147937";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-01-05T10:52:56Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:16:"Monika Kleinhans";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:20:"Lea Jessica Albrecht";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:11:"Sven Benson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:13:"Dagmar Fuhrer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"Joachim Dissemond";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:11:"Susanne Tan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:101:"Continuous Glucose Monitoring of Steroid-Induced Hyperglycemia in Patients With Dermatologic Diseases";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221147937";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221147937?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:157;a:6:{s:4:"data";s:116:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221147570?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:52:"AGATA: A Toolbox for Automated Glucose Data Analysis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221147570?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1927:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.Methods:Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of usersโ prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATAโs features are compared against those of 12 noncommercial software programs for CGM data analysis.Results:Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.Conclusion:Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1927:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.Methods:Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of usersโ prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATAโs features are compared against those of 12 noncommercial software programs for CGM data analysis.Results:Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.Conclusion:Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:52:"AGATA: A Toolbox for Automated Glucose Data Analysis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221147570";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2023-01-05T10:50:17Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:3:{i:0;a:5:{s:4:"data";s:14:"Giacomo Cappon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Giovanni Sparacino";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:18:"Andrea Facchinetti";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:52:"AGATA: A Toolbox for Automated Glucose Data Analysis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221147570";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221147570?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2023 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:158;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221146808?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Short-term Glycemic Variability and Its Association With Macrovascular and Microvascular Complications in Patients With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221146808?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:932:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>The introduction of continuous glucose monitoring inaugurated a new era in clinical practice by shifting the characterization of glycemic control from HbA1c to novel metrics. The one that gained widespread attention over the past decades was glycemic variability (GV), which typically refers to peaks and nadirs of blood glucose measured over a given time interval. GV can be dichotomized into two main categories: short-term and long-term. Short-term GV reflects within-day and between-day glycemic oscillations, and its contribution to diabetic complications remains an enigma. In this review, we summarize the available data about short-term GV and its possible association with both microvascular and macrovascular complications, evaluating different pathogenic mechanisms and demonstrating nonpharmaceutical, as well as pharmaceutical, therapeutic interventions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:932:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>The introduction of continuous glucose monitoring inaugurated a new era in clinical practice by shifting the characterization of glycemic control from HbA1c to novel metrics. The one that gained widespread attention over the past decades was glycemic variability (GV), which typically refers to peaks and nadirs of blood glucose measured over a given time interval. GV can be dichotomized into two main categories: short-term and long-term. Short-term GV reflects within-day and between-day glycemic oscillations, and its contribution to diabetic complications remains an enigma. In this review, we summarize the available data about short-term GV and its possible association with both microvascular and macrovascular complications, evaluating different pathogenic mechanisms and demonstrating nonpharmaceutical, as well as pharmaceutical, therapeutic interventions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:128:"Short-term Glycemic Variability and Its Association With Macrovascular and Microvascular Complications in Patients With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221146808";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-12-28T10:20:05Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:13:"Ourania Psoma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Marios Makris";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Alexandros Tselepis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:20:"Vasilis Tsimihodimos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:128:"Short-term Glycemic Variability and Its Association With Macrovascular and Microvascular Complications in Patients With Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221146808";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221146808?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:159;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221145964?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:145:"Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221145964?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1880:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.Objective:To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.Methods:The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.Results:In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.Conclusions:Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1880:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.Objective:To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.Methods:The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.Results:In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.Conclusions:Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:145:"Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? 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Severe LON with pain insensitivity can be diagnosed with a mechanical (pinprick) pain stimulus of 512-mN force. A comparable โsuprathresholdโ heat-pain stimulus may have the same potential.Objective:A six-second, 51ยฐC heat-pain stimulus delivered on a 38.5-mmยฒ spot by a commercial medical device (bite awayยฎ, to treat insect bites) was explored in a prospective cross-sectional diagnostic accuracy study to detect DFU-related LON.Methods:Seventy-two participants were studied: 12 with and 30 without diabetic neuropathy according to the conventional criteria, and 30 patients with a history of painless DFU (indicative of end-stage LON, reference standard). The feet were stimulated at the plantar and dorsal sides. A palmar surface was stimulated for control purposes. Participants scored stimulated pain intensity 0 to 10 on a numerical rating scale.Results:At hands, pain intensity was rated six on average by all participants. Persons without neuropathy scored 7 (0-10), median (range), at the plantar side and 8.5 (2-10) at the dorsal side of the foot, while those with DFU scored 0 (0-8) and 0 (0-10), respectively. A pain response of 0 at the foot dorsum detected DFU-related LON with a sensitivity of 65% (specificity, 100%; positive and negative predictive values, 100% and 96%, respectively).Conclusions:Due to its high specificity, the test seems advantageous for diagnostic purposes, complementary to current screening tests.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1705:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Loss of nociception (LON) at the feet of persons with diabetes mellitus develops gradually over years and remains asymptomatic until the first painless diabetic foot ulceration (DFU). Severe LON with pain insensitivity can be diagnosed with a mechanical (pinprick) pain stimulus of 512-mN force. A comparable โsuprathresholdโ heat-pain stimulus may have the same potential.Objective:A six-second, 51ยฐC heat-pain stimulus delivered on a 38.5-mmยฒ spot by a commercial medical device (bite awayยฎ, to treat insect bites) was explored in a prospective cross-sectional diagnostic accuracy study to detect DFU-related LON.Methods:Seventy-two participants were studied: 12 with and 30 without diabetic neuropathy according to the conventional criteria, and 30 patients with a history of painless DFU (indicative of end-stage LON, reference standard). The feet were stimulated at the plantar and dorsal sides. A palmar surface was stimulated for control purposes. Participants scored stimulated pain intensity 0 to 10 on a numerical rating scale.Results:At hands, pain intensity was rated six on average by all participants. Persons without neuropathy scored 7 (0-10), median (range), at the plantar side and 8.5 (2-10) at the dorsal side of the foot, while those with DFU scored 0 (0-8) and 0 (0-10), respectively. A pain response of 0 at the foot dorsum detected DFU-related LON with a sensitivity of 65% (specificity, 100%; positive and negative predictive values, 100% and 96%, respectively).Conclusions:Due to its high specificity, the test seems advantageous for diagnostic purposes, complementary to current screening tests.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:91:"Trial of a Trivial Quantitative Heat-Pain Stimulus for Detecting Severe Loss of Nociception";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221144328";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-12-22T11:48:31Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:2:{i:0;a:5:{s:4:"data";s:21:"Ernst-Adolf Chantelau";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Oliver Schrรถer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:91:"Trial of a Trivial Quantitative Heat-Pain Stimulus for Detecting Severe Loss of Nociception";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221144328";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221144328?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:161;a:6:{s:4:"data";s:137:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221145178?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:143:"Design and Testing of a Smartphone Application for Real-Time Tracking of CSII and CGM Site Rotation Compliance in Patients With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221145178?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1571:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Glycemic control in patients with type 1 diabetes can be difficult to achieve. One critical aspect of insulin delivery is site rotation, which is necessary to reduce dermatologic complications of repeated insulin infusion. No current application is designed to help patients track sites and instruct on overused sites.Objective:The objectives of this study were to (1) design a smartphone app, Insulin Site Guide, to gather real-time information on continuous subcutaneous insulin infusion (CSII) and continuous glucose monitor (CGM) site location and rotation compliance and instruct subjects on the use of an overused site; (2) conduct a usability study to measure site rotation compliance; and (3) report subject satisfaction with the app.Design:The app is installed on the subjectโs smartphone. Subjects use the app to record CSII and CGM placement in real-time. Data are sent to the study team at the end of the study. Subjects complete a questionnaire concerning the app.Results:We report site rotation compliance data for eight subjects and survey responses for 10 subjects. Initial data from eight subjects indicate a high site rotation compliance of 84% for insulin pumps. In general, the majority of users indicate high satisfaction with the app.Conclusions:Insulin Site Guide is a mobile app that uses a novel algorithm to better guide site rotation. Use of the app has the potential to improve site rotation and decrease dermatologic complications of diabetes with long-term use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1571:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Introduction:Glycemic control in patients with type 1 diabetes can be difficult to achieve. One critical aspect of insulin delivery is site rotation, which is necessary to reduce dermatologic complications of repeated insulin infusion. No current application is designed to help patients track sites and instruct on overused sites.Objective:The objectives of this study were to (1) design a smartphone app, Insulin Site Guide, to gather real-time information on continuous subcutaneous insulin infusion (CSII) and continuous glucose monitor (CGM) site location and rotation compliance and instruct subjects on the use of an overused site; (2) conduct a usability study to measure site rotation compliance; and (3) report subject satisfaction with the app.Design:The app is installed on the subjectโs smartphone. Subjects use the app to record CSII and CGM placement in real-time. Data are sent to the study team at the end of the study. Subjects complete a questionnaire concerning the app.Results:We report site rotation compliance data for eight subjects and survey responses for 10 subjects. Initial data from eight subjects indicate a high site rotation compliance of 84% for insulin pumps. In general, the majority of users indicate high satisfaction with the app.Conclusions:Insulin Site Guide is a mobile app that uses a novel algorithm to better guide site rotation. Use of the app has the potential to improve site rotation and decrease dermatologic complications of diabetes with long-term use.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:143:"Design and Testing of a Smartphone Application for Real-Time Tracking of CSII and CGM Site Rotation Compliance in Patients With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221145178";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-12-21T06:05:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:6:{i:0;a:5:{s:4:"data";s:14:"John Blanchard";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:11:"Samir Ahmed";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:12:"Brenda Clark";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:21:"Lourdes Sanchez Cotto";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"Sampath Rangasamy";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:16:"Bithika Thompson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:143:"Design and Testing of a Smartphone Application for Real-Time Tracking of CSII and CGM Site Rotation Compliance in Patients With Type 1 Diabetes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221145178";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221145178?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:162;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221144052?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Increase Access, Reduce Disparities: Recommendations for Modifying Medicaid CGM Coverage Eligibility Criteria";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221144052?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:991:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agencyโs CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:995:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agencyโs CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:109:"Increase Access, Reduce Disparities: Recommendations for Modifying Medicaid CGM Coverage Eligibility Criteria";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221144052";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-12-16T09:54:53Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:18:"Rodolfo J. Galindo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Grazia Aleppo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:21:"Christopher G. Parkin";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:15:"David A. Baidal";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:17:"Anders L. Carlson";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:10:"Eda Cengiz";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:19:"Gregory P. Forlenza";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"Davida F. 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Umpierrez";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:109:"Increase Access, Reduce Disparities: Recommendations for Modifying Medicaid CGM Coverage Eligibility Criteria";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221144052";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221144052?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:163;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221142899?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:66:"A Machine Learning Model for Prediction of Amputation in Diabetics";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221142899?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1983:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Diabetic foot ulcer (DFU) and the resulting lower extremity amputation are associated with a poor survival prognosis. The objective of this study is to generate a model for predicting the probability of major amputation in hospitalized patients with DFU.Methods:The National Inpatient Sample (NIS) database from 2008 to 2014 was used to select patients with DFU, who were then further divided by major amputation status. International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) and Agency for Healthcare Research and Quality (AHRQ) comorbidity codes were used to compare patient characteristics. For the descriptive statistics, the Student t test, the ฯ2 test, and the Spearman correlation were utilized. The five most predictive variables were identified. A decision tree model (CTREE) based on conditional inference framework algorithm and a random forest model were used to develop the algorithm.Results:A total of 326โ853 inpatients with DFU were identified, and 5.9% underwent major amputation. The top five contributory variables (all with P < .001) were gangrene (odds ratio [OR] = 11.8, 95% confidence interval [CI] = 11.5-12.2), peripheral vascular disease (OR = 2.9, 95% CI = 2.8-3.0), weight loss (OR = 2.6, 95% CI = 2.5-2.8), systemic infection (OR = 2.5, 95% CI = 2.4-2.53), and osteomyelitis (OR = 1.7, 95% CI = 1.6-1.73). The model performance of the training data was 77.7% (76.1% sensitivity and 79.3% specificity) and of the testing data was 77.8% (76.2% sensitivity and 79.4% specificity). The model was further validated with boosting and random forest models which demonstrated similar performance and area under the curve (AUC) (0.84, 95% CI = 0.83-0.85).Conclusion:Utilizing machine learning methods, we have developed a clinical algorithm that predicts the risk of major lower extremity amputation for inpatients with diabetes with 77.8% accuracy.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1986:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Diabetic foot ulcer (DFU) and the resulting lower extremity amputation are associated with a poor survival prognosis. The objective of this study is to generate a model for predicting the probability of major amputation in hospitalized patients with DFU.Methods:The National Inpatient Sample (NIS) database from 2008 to 2014 was used to select patients with DFU, who were then further divided by major amputation status. International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) and Agency for Healthcare Research and Quality (AHRQ) comorbidity codes were used to compare patient characteristics. For the descriptive statistics, the Student t test, the ฯ2 test, and the Spearman correlation were utilized. The five most predictive variables were identified. A decision tree model (CTREE) based on conditional inference framework algorithm and a random forest model were used to develop the algorithm.Results:A total of 326โ853 inpatients with DFU were identified, and 5.9% underwent major amputation. The top five contributory variables (all with P < .001) were gangrene (odds ratio [OR] = 11.8, 95% confidence interval [CI] = 11.5-12.2), peripheral vascular disease (OR = 2.9, 95% CI = 2.8-3.0), weight loss (OR = 2.6, 95% CI = 2.5-2.8), systemic infection (OR = 2.5, 95% CI = 2.4-2.53), and osteomyelitis (OR = 1.7, 95% CI = 1.6-1.73). The model performance of the training data was 77.7% (76.1% sensitivity and 79.3% specificity) and of the testing data was 77.8% (76.2% sensitivity and 79.4% specificity). The model was further validated with boosting and random forest models which demonstrated similar performance and area under the curve (AUC) (0.84, 95% CI = 0.83-0.85).Conclusion:Utilizing machine learning methods, we have developed a clinical algorithm that predicts the risk of major lower extremity amputation for inpatients with diabetes with 77.8% accuracy.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:66:"A Machine Learning Model for Prediction of Amputation in Diabetics";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221142899";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-12-08T12:46:07Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:21:"Stavros Stefanopoulos";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:9:"Qiong Qiu";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:8:"Gang Ren";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Ayman Ahmed";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:13:"Mohamed Osman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:21:"F. 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Manual correction boluses may be needed, increasing hypoglycemia risk with overcorrection. The Cambridge HCL system includes a user-initiated algorithm intensification mode (โBoostโ), activation of which increases automated insulin delivery by approximately 35%, while remaining glucose-responsive. In this analysis, we assessed the safety of โBoostโ mode.Methods:We retrospectively analyzed data from closed-loop studies involving young children (1-7 years, n = 24), children and adolescents (10-17 years, n = 19), adults (โฅ24 years, n = 13), and older adults (โฅ60 years, n = 20) with type 1 diabetes. Outcomes were calculated per participant for days with โฅ30 minutes of โBoostโ use versus days with no โBoostโ use. Participants with <10 โBoostโ days were excluded. The main outcome was time spent in hypoglycemia <70 and <54 mg/dL.Results:Eight weeks of data for 76 participants were analyzed. There was no difference in time spent <70 and <54 mg/dL between โBoostโ days and โnon-Boostโ days; mean difference: โ0.10% (95% confidence interval [CI] โ0.28 to 0.07; P = .249) time <70 mg/dL, and 0.03 (โ0.04 to 0.09; P = .416) time < 54 mg/dL. Time in significant hyperglycemia >300 mg/dL was 1.39 percentage points (1.01 to 1.77; P < .001) higher on โBoostโ days, with higher mean glucose and lower time in target range (P < .001).Conclusions:Use of an algorithm intensification mode in HCL therapy is safe across all age groups with type 1 diabetes. The higher time in hyperglycemia observed on โBoostโ days suggests that users are more likely to use algorithm intensification on days with extreme hyperglycemic excursions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1925:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:Many hybrid closed-loop (HCL) systems struggle to manage unusually high glucose levels as experienced with intercurrent illness or pre-menstrually. Manual correction boluses may be needed, increasing hypoglycemia risk with overcorrection. The Cambridge HCL system includes a user-initiated algorithm intensification mode (โBoostโ), activation of which increases automated insulin delivery by approximately 35%, while remaining glucose-responsive. In this analysis, we assessed the safety of โBoostโ mode.Methods:We retrospectively analyzed data from closed-loop studies involving young children (1-7 years, n = 24), children and adolescents (10-17 years, n = 19), adults (โฅ24 years, n = 13), and older adults (โฅ60 years, n = 20) with type 1 diabetes. Outcomes were calculated per participant for days with โฅ30 minutes of โBoostโ use versus days with no โBoostโ use. Participants with <10 โBoostโ days were excluded. The main outcome was time spent in hypoglycemia <70 and <54 mg/dL.Results:Eight weeks of data for 76 participants were analyzed. There was no difference in time spent <70 and <54 mg/dL between โBoostโ days and โnon-Boostโ days; mean difference: โ0.10% (95% confidence interval [CI] โ0.28 to 0.07; P = .249) time <70 mg/dL, and 0.03 (โ0.04 to 0.09; P = .416) time < 54 mg/dL. Time in significant hyperglycemia >300 mg/dL was 1.39 percentage points (1.01 to 1.77; P < .001) higher on โBoostโ days, with higher mean glucose and lower time in target range (P < .001).Conclusions:Use of an algorithm intensification mode in HCL therapy is safe across all age groups with type 1 diabetes. The higher time in hyperglycemia observed on โBoostโ days suggests that users are more likely to use algorithm intensification on days with extreme hyperglycemic excursions.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:105:"Safety of User-Initiated Intensification of Insulin Delivery Using Cambridge Hybrid Closed-Loop Algorithm";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221141924";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-12-08T09:46:31Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:29:{i:0;a:5:{s:4:"data";s:10:"Julia Ware";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:22:"Malgorzata E. 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Besser";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:17:"Fiona M. Campbell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:20:"Katharine Draxlbauer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:14:"Daniela Elleri";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:13:"Mark L. Evans";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:13;a:5:{s:4:"data";s:23:"Elke Frรถhlich-Reiterer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:14;a:5:{s:4:"data";s:14:"Atrayee Ghatak";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:15;a:5:{s:4:"data";s:15:"Sabine E. Hofer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:16;a:5:{s:4:"data";s:18:"Thomas M. Kapellen";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:17;a:5:{s:4:"data";s:20:"Lalantha Leelarathna";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:18;a:5:{s:4:"data";s:14:"Julia K. Mader";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:19;a:5:{s:4:"data";s:15:"Womba M. Mubita";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:20;a:5:{s:4:"data";s:15:"Parth Narendran";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:21;a:5:{s:4:"data";s:13:"Tina Poettler";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:22;a:5:{s:4:"data";s:18:"Birgit Rami-Merhar";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:23;a:5:{s:4:"data";s:17:"Martin Tauschmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:24;a:5:{s:4:"data";s:15:"Tabitha Randell";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:25;a:5:{s:4:"data";s:11:"Hood Thabit";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:26;a:5:{s:4:"data";s:15:"Ajay Thankamony";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:27;a:5:{s:4:"data";s:16:"Nicola Trevelyan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:28;a:5:{s:4:"data";s:13:"Roman Hovorka";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:105:"Safety of User-Initiated Intensification of Insulin Delivery Using Cambridge Hybrid Closed-Loop Algorithm";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221141924";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221141924?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:165;a:6:{s:4:"data";s:123:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221140430?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:64:"Continuous Glucose Monitoring in Pediatric Diabetic Ketoacidosis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221140430?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1748:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Use of real-time continuous glucose monitoring (rtCGM) in ambulatory settings improves overall glycemic control and reduces the incidence of diabetic ketoacidosis (DKA) in adults and children/adolescents with type 1 diabetes (T1D). However, the use of rtCGM in children with DKA has not been well studied.Method:This prospective, single-arm, single-center study assessed the accuracy, reliability, and feasibility of a commercially available rtCGM device compared with point-of-care (POC) capillary and serum glucose values in pediatric patients admitted to the pediatric intensive care unit for DKA. The primary outcome was the accuracy of rtCGM glucose values compared with POC capillary and serum glucose values during standard treatment of DKA as assessed by Clarke Error Grid (CEG) analysis. Secondary outcomes were assessment of the relationship between rtCGM readings and degree of acidosis and mean length of hospital stay (LOS).Results:Data from 35 hospitalized children (mean ยฑ SD age, 11.9 ยฑ 4.1 years) with DKA were included in our analysis. Five hundred twenty-four time-matched glucose values between serum glucose and rtCGM and 91 time-matched glucose values between POC capillary glucose and rtCGM were obtained. The effect of acidosis on accuracy CEG analysis showed 95.4% of the 524 matched CGM/POC pairs and 95.6% of the 91 matched CGM/serum glucose pairs in the clinically acceptable A + B zones. The average LOS was 1.32 ยฑ 0.73 days. Serum bicarbonate level did not appear to affect the accuracy of rtCGM in the setting of DKA.Conclusions:Continuous glucose monitoring use in inpatient pediatric DKA treatment was found to be feasible and reliable.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1748:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:Use of real-time continuous glucose monitoring (rtCGM) in ambulatory settings improves overall glycemic control and reduces the incidence of diabetic ketoacidosis (DKA) in adults and children/adolescents with type 1 diabetes (T1D). However, the use of rtCGM in children with DKA has not been well studied.Method:This prospective, single-arm, single-center study assessed the accuracy, reliability, and feasibility of a commercially available rtCGM device compared with point-of-care (POC) capillary and serum glucose values in pediatric patients admitted to the pediatric intensive care unit for DKA. The primary outcome was the accuracy of rtCGM glucose values compared with POC capillary and serum glucose values during standard treatment of DKA as assessed by Clarke Error Grid (CEG) analysis. Secondary outcomes were assessment of the relationship between rtCGM readings and degree of acidosis and mean length of hospital stay (LOS).Results:Data from 35 hospitalized children (mean ยฑ SD age, 11.9 ยฑ 4.1 years) with DKA were included in our analysis. Five hundred twenty-four time-matched glucose values between serum glucose and rtCGM and 91 time-matched glucose values between POC capillary glucose and rtCGM were obtained. The effect of acidosis on accuracy CEG analysis showed 95.4% of the 524 matched CGM/POC pairs and 95.6% of the 91 matched CGM/serum glucose pairs in the clinically acceptable A + B zones. The average LOS was 1.32 ยฑ 0.73 days. Serum bicarbonate level did not appear to affect the accuracy of rtCGM in the setting of DKA.Conclusions:Continuous glucose monitoring use in inpatient pediatric DKA treatment was found to be feasible and reliable.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:64:"Continuous Glucose Monitoring in Pediatric Diabetic Ketoacidosis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221140430";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-11-23T09:36:08Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:4:{i:0;a:5:{s:4:"data";s:11:"Thomas Pott";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"Jose Jimenez-Vega";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:14:"Jessica Parker";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"Robert Fitzgerald";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:64:"Continuous Glucose Monitoring in Pediatric Diabetic Ketoacidosis";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221140430";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221140430?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:166;a:6:{s:4:"data";s:130:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221139873?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:120:"Real-Time Continuous Glucose Monitoring in Adolescents and Young Adults With Type 2 Diabetes Can Improve Quality of Life";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221139873?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1846:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D.Methods:Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administrationโapproved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data.Results:Participantsโ (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ยฑ2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior.Conclusion:Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1846:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Objective:Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D.Methods:Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administrationโapproved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data.Results:Participantsโ (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ยฑ2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior.Conclusion:Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:120:"Real-Time Continuous Glucose Monitoring in Adolescents and Young Adults With Type 2 Diabetes Can Improve Quality of Life";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221139873";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-11-23T09:31:42Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:5:{i:0;a:5:{s:4:"data";s:14:"Hannah Chesser";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:18:"Shylaja Srinivasan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:15:"Cassidy Puckett";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:19:"Stephen E. Gitelman";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:14:"Jenise C. Wong";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:120:"Real-Time Continuous Glucose Monitoring in Adolescents and Young Adults With Type 2 Diabetes Can Improve Quality of Life";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221139873";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221139873?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:167;a:6:{s:4:"data";s:172:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221134639?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:171:"Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221134639?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1831:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for โintegratedโ CGM systems.Methods:The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA.Results:The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).Conclusions:We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1831:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for โintegratedโ CGM systems.Methods:The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA.Results:The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).Conclusions:We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:171:"Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221134639";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-11-04T05:36:15Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:11:{i:0;a:5:{s:4:"data";s:17:"Manuel Eichenlaub";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:13:"Peter Stephan";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:17:"Delia Waldenmaier";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Stefan Pleus";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:21:"Martina Rothenbรผhler";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"Cornelia Haug";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:13:"Rolf Hinzmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:14:"Andreas Thomas";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:12:"Johan Jendle";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:10:"Peter Diem";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:15:"Guido Freckmann";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:46:"http://prismstandard.org/namespaces/basic/2.0/";a:4:{s:15:"publicationName";a:1:{i:0;a:5:{s:4:"data";s:171:"Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"doi";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221134639";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:3:"url";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221134639?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:9:"copyright";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:168;a:6:{s:4:"data";s:144:" ";s:7:"attribs";a:1:{s:43:"http://www.w3.org/1999/02/22-rdf-syntax-ns#";a:1:{s:5:"about";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221135217?ai=2b4&mi=ehikzz&af=R";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:24:"http://purl.org/rss/1.0/";a:3:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"An Investigation Into Local Infusion Site Pain After Infusion of Ultra Rapid Lispro Excipients Across Sites and Depths";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:84:"https://journals.sagepub.com/doi/abs/10.1177/19322968221135217?ai=2b4&mi=ehikzz&af=R";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:1829:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This phase 1, randomized, one-day, five-period crossover study in adults with type 1 diabetes on continuous subcutaneous insulin infusion investigated local infusion site pain following infusion of the excipients of ultra rapid lispro (URLi; without insulin) across infusion sites and depths.Methods:Forty participants (mean age, 40.5 years; body mass index [BMI], 27.5) were randomized to one of five infusion site sequences consisting of the arm, thigh, buttock (6 mm cannula depth), and abdomen (6 and 9 mm depth). Basal infusion of sodium citrate and treprostinil in diluent with magnesium chloride was initiated (10 ฮผL/h) and at three, six, and nine hours after basal initiation, 15 unit-equivalent boluses (150 ฮผL) were given. Participants rated their pain on a 0 to 100 mm validated visual analog scale (VAS) at 5 minutes pre-bolus and 1 and 15 minutes post-bolus.Results:At one minute post-bolus, increased VAS scores were occasionally reported. Most one minute post-bolus scores were โค10 mm (little to no discomfort) while 7 of 577 were >45 mm (generally considered clinically meaningful pain). Painful infusions were reported more frequently for the arm, and mean VAS scores were higher for the arm compared with the thigh and abdomen. The VAS score distributions were similar between cannula depths. By 15 minutes post-bolus, VAS scores returned to pre-bolus levels.Conclusions:Local infusion site discomfort after infusion of URLi excipients was reported by a small subset of participants; it was transient, tolerable, and dependent on infusion site but not infusion depth. Given differences within individuals, patients may consider using a different infusion site if they experience discomfort.Clinicaltrial.gov identifier:NCT05067270.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:1832:"Journal of Diabetes Science and Technology, Ahead of Print. <br/>Background:This phase 1, randomized, one-day, five-period crossover study in adults with type 1 diabetes on continuous subcutaneous insulin infusion investigated local infusion site pain following infusion of the excipients of ultra rapid lispro (URLi; without insulin) across infusion sites and depths.Methods:Forty participants (mean age, 40.5 years; body mass index [BMI], 27.5) were randomized to one of five infusion site sequences consisting of the arm, thigh, buttock (6 mm cannula depth), and abdomen (6 and 9 mm depth). Basal infusion of sodium citrate and treprostinil in diluent with magnesium chloride was initiated (10 ฮผL/h) and at three, six, and nine hours after basal initiation, 15 unit-equivalent boluses (150 ฮผL) were given. Participants rated their pain on a 0 to 100 mm validated visual analog scale (VAS) at 5 minutes pre-bolus and 1 and 15 minutes post-bolus.Results:At one minute post-bolus, increased VAS scores were occasionally reported. Most one minute post-bolus scores were โค10 mm (little to no discomfort) while 7 of 577 were >45 mm (generally considered clinically meaningful pain). Painful infusions were reported more frequently for the arm, and mean VAS scores were higher for the arm compared with the thigh and abdomen. The VAS score distributions were similar between cannula depths. By 15 minutes post-bolus, VAS scores returned to pre-bolus levels.Conclusions:Local infusion site discomfort after infusion of URLi excipients was reported by a small subset of participants; it was transient, tolerable, and dependent on infusion site but not infusion depth. Given differences within individuals, patients may consider using a different infusion site if they experience discomfort.Clinicaltrial.gov identifier:NCT05067270.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:118:"An Investigation Into Local Infusion Site Pain After Infusion of Ultra Rapid Lispro Excipients Across Sites and Depths";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:10:"identifier";a:1:{i:0;a:5:{s:4:"data";s:25:"10.1177/19322968221135217";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"source";a:1:{i:0;a:5:{s:4:"data";s:0:"";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"date";a:1:{i:0;a:5:{s:4:"data";s:20:"2022-11-03T12:53:08Z";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:6:"rights";a:1:{i:0;a:5:{s:4:"data";s:35:"ยฉ 2022 Diabetes Technology Society";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"creator";a:7:{i:0;a:5:{s:4:"data";s:12:"Debra Ignaut";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:15:"Tsuyoshi Fukuda";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:19:"Ramanjineyulu Bandi";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:12:"Marcel Ermer";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:15:"Marc S. 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