Journal of Diabetes Science and Technology最新文献

{"title":"Real-Life Wear Time and Reasons for Reduced Wear Time of Glucose Sensors of Continuous Glucose Monitoring Systems: Findings from the DiaLink Panel.","authors":"Dominic Ehrmann, Birgit Olesen, Timm Roos, Bernhard Kulzer, Norbert Hermanns, Lutz Heinemann","doi":"10.1177/19322968241310889","DOIUrl":"10.1177/19322968241310889","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"584-586"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2024 Diabetes Technology Meeting Agenda.","authors":"","doi":"10.1177/19322968251314274","DOIUrl":"https://doi.org/10.1177/19322968251314274","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":"19 2","pages":"481-491"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-World Evidence Analysis of a Hybrid Closed-Loop System.","authors":"Heba Alwan, Malgorzata E Wilinska, Yue Ruan, Julien Da Silva, Roman Hovorka","doi":"10.1177/19322968231185348","DOIUrl":"10.1177/19322968231185348","url":null,"abstract":"<p><strong>Background: </strong>We analyzed real-world evidence to assess the performance of the mylife CamAPS FX hybrid closed-loop system.</p><p><strong>Methods: </strong>Users from 15 countries across different age groups who used the system between May 9, 2022, and December 3, 2022, and who had ≥30 days of continuous glucose monitor data, and ≥30% of closed-loop usage were included in the current analysis (N = 1805).</p><p><strong>Results: </strong>Time in range (3.9-10 mmol/L) was 72.6 ± 11.5% (mean ± SD) for all users and increased by age from 66.9 ± 11.7% for users ≤6 years old to 81.8 ± 8.7% for users ≥65 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 ± 1.1 mmol/L and 6.9%, respectively. Time using closed-loop was high at 94.7% [90.0, 96.9].</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"385-389"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9764472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"European Survey on Adult People With Type 1 Diabetes and Their Caregivers: Insights into Perceptions of Technology.","authors":"Alfred Penfornis, Su Down, Antoine Seignez, Alizé Vives, Mireille Bonnemaire, Bernhard Kulzer","doi":"10.1177/19322968231208690","DOIUrl":"10.1177/19322968231208690","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>PWD and caregivers (≥18 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.</p><p><strong>Results: </strong>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 <i>P</i> < .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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"407-414"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71482119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use.","authors":"Halis K Akturk, Casey Sakamoto, Tim Vigers, Viral N Shah, Laura Pyle","doi":"10.1177/19322968231200901","DOIUrl":"10.1177/19322968231200901","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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 (<i>R</i>²).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"345-351"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41146970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.","authors":"John J Wroblewski, Ermilo Sanchez-Buenfil, Miguel Inciarte, Jay Berdia, Lewis Blake, Simon Wroblewski, Alexandria Patti, Gretchen Suter, George E Sanborn","doi":"10.1177/19322968231194644","DOIUrl":"10.1177/19322968231194644","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"370-376"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10108921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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.","authors":"Shekhar Sehgal, Mona Elbalshy, Jonathan Williman, Barbara Galland, Hamish Crocket, Rosemary Hall, Ryan Paul, Robert Leikis, Martin de Bock, Benjamin J Wheeler","doi":"10.1177/19322968231196562","DOIUrl":"10.1177/19322968231196562","url":null,"abstract":"<p><strong>Aim: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 -1.7% <i>P</i> = .593), glycosylated hemoglobin (57.0 ± 17.8 vs 61.4 ± 12.2 mmol/L; <i>P</i> = .593), and time in hypoglycemia <3.9 mmol/L (3.9 ± 3.8% vs 3.8 ± 4.0%; <i>P</i> = .947). Hypoglycemia Fear Survey total score (1.17 ± 0.52 vs 0.97 ± 0.54; <i>P</i> = .02) and fear of hypoglycemia score (1.18 ± 0.64 vs 0.97 ± 0.45; <i>P</i> = .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 ± 5.8 vs 26.0 ± 5.8; <i>P</i> = .04), whereas diabetes-related quality of life was slightly lower (DAWN2 Impact of Diabetes score: 3.11 ± 0.4 vs 3.32 ± 0.51; <i>P</i> = .045); sleep quality did not differ between the two groups.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"415-425"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10153536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Glycemic Control Between In-Person and Virtual Diabetes Consults in Hospitalized Patients With Diabetes.","authors":"Maria Gracia Luzuriaga, Monica Lieberman, Ruixuan Ma, Sabina Casula, Violet Lagari-Libhaber, Shari Messinger, Hua Li, Bresta Miranda, David A Baidal, Ernesto Bernal Mizrachi, Gianluca Iacobellis, Rajesh Garg, Francesco Vendrame","doi":"10.1177/19322968231199470","DOIUrl":"10.1177/19322968231199470","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>Patient-day-weighted 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 ± SD blood glucose (mg/dL) compared with others (206.7 ± 49.6 In-person, 214.6 ± 56.2 Virtual-All, 206.5 ± 57.2 Virtual-COVID-19-Neg, 229.7 ± 51.6 Virtual-COVID-19-Pos; <i>P</i> = .015). A significantly less percentage of patients in this group also achieved a mean ± SD glucose target of 140 to 180 mg/dL (23.8 ± 22.5 In-person, 21.5 ± 20.5 Virtual-All, 25.3 ± 20.8 Virtual-COVID-19-Neg, and 14.4±18.1 Virtual-COVID-19-Pos, <i>P</i> = .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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"400-406"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41124025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Insulin Delivery System and People With Type 2 Diabetes: A Topic With Many Facets.","authors":"Lutz Heinemann","doi":"10.1177/19322968231204625","DOIUrl":"10.1177/19322968231204625","url":null,"abstract":"<p><p>Optimizing glucose control is of interest also for patients with type 2 diabetes (T2D). While systems for automated insulin delivery are widely used for patients with type 1 diabetes, as documented by many publications, this is not the case with T2D. Because of the number of such patients, this will change drastically in the next years. Manufacturers can transfer many learnings from type 1 to type 2; however, specific clinical aspects have to be considered. This commentary will discuss these aspects and some of the current activities. Future automated insulin delivery (AID) systems will take data from multisensor systems into account to individualize the AID algorithm, supported by artificial intelligence. There is a high need to document the benefits of AID systems in this patient group.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"475-480"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41131754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Smartphone-Based Activity Tracking and Nocturnal Hypoglycemia in People With Type 1 Diabetes.","authors":"Daphne Gardner, Hong Chang Tan, Gek Hsiang Lim, May Zin Oo, Xiaohui Xin, Andrew Kingsworth, Pratik Choudhary, Suresh Rama Chandran","doi":"10.1177/19322968231186401","DOIUrl":"10.1177/19322968231186401","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Adults with T1D for ≥ 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 ≥15 minutes), while adjusting for TDC and TDD of insulin, and treating participants as a random effect.</p><p><strong>Results: </strong>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, <i>P</i> = .04]. There was no significant effect on NH <54 mg/dl.</p><p><strong>Conclusion: </strong>Daily PA measured by a smartphone-based step tracker was associated with the risk of NH in people with type 1 diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"377-384"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9770287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}