Journal of Diabetes Science and Technology最新文献

{"title":"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.","authors":"Katharine Barnard-Kelly, Florian Thienel, Julia K Mader, Nick Oliver, Edward Franek, Iris Vesper, Nicole Dagenbach, Gerhard Vogt, Tobias Etter, Thomas Künsting","doi":"10.1177/19322968241234055","DOIUrl":"10.1177/19322968241234055","url":null,"abstract":"<p><strong>Background: </strong>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).</p><p><strong>Materials and methods: </strong>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, HbA_1c values were measured.</p><p><strong>Results: </strong>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 \"change\" 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]) (<i>P</i> = .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 (<i>P</i> = .382). HbA_1c increased by 0.2% ± 0.7% in the MDI group and decreased in both pump groups (Solo group -0.2% ± 0.8% and Omnipod group -0.1% ± 0.8%). Differences in HbA_1c between the Solo group and the MDI group were significant (<i>P</i> = .009), but not between the Solo group and the Omnipod group (<i>P</i> = .896).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Clinicaltrials: </strong>gov is NCT03478969.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1310-1316"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140059550","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":"Diabetes and Wellness Smartphone Applications for Self-Management Among Adults With Diabetes in the United States.","authors":"Sherecce Fields, Kianna Arthur, Samantha R Philip, Rachel Smallman, Vishaka Kalra, Kirsten Yehl, Felix Lee, David Kerr","doi":"10.1177/19322968251322189","DOIUrl":"10.1177/19322968251322189","url":null,"abstract":"<p><strong>Background: </strong>Diabetes self-management plays a vital role in improving clinical outcomes and the quality of life of individuals living with diabetes. Despite considerable research on its impact on clinical outcomes, diabetes self-management continues to be challenging for many individuals living with the condition. As part of the growth in digital health technologies for diabetes care, smartphone applications present potential opportunities to bridge the existing gaps in self-management and improve patient outcomes.</p><p><strong>Method: </strong>Participants (<i>N</i> = 3241 people with diabetes) were recruited to answer questions about diabetes self-management, including their use of digital tools, their preferences for smartphone applications for diabetes, and the preferred functions of these applications they found useful. Frequency distributions and chi-square analyses were performed to examine the demographic differences among users of diabetes and general wellness applications.</p><p><strong>Results: </strong>Among participants, 30.2% reported using health applications specifically made for diabetes management, while 33.9% reported using health applications that were not diabetes-specific. Considerable differences in demographic characteristics were found between users and nonusers of both diabetes-specific and general health applications groups. The most preferred applications provided the opportunity to engage with continuous glucose monitoring data (i.e., continuous measurement; 47.4%) followed by glucose monitoring (i.e., single reading measurement; 20.9%), food intake trackers (23.6%), and fitness goal trackers (22.8%).</p><p><strong>Conclusion: </strong>These findings suggest that the use of digital health technologies is popular for people living with diabetes, but more needs to be done to ensure wider adoption and sustained use.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1230-1238"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752944","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":"Comparator Value Pairing Impacts Reported Continuous Glucose Monitoring System Accuracy.","authors":"Stefan Pleus, Guido Freckmann, Robbert J Slingerland, Peter Diem, Elisabet Eriksson Boija, Marion Fokkert, Rolf Hinzmann, Johan Jendle, David C Klonoff, Jingyi Lu, Konstantinos Makris, Viswanathan Mohan, James H Nichols, John Pemberton, Elizabeth Selvin, Andreas Thomas, Nam K Tran, Lilian Witthauer, Manuel Eichenlaub","doi":"10.1177/19322968251344303","DOIUrl":"10.1177/19322968251344303","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1423-1426"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127743","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":"Triage Point-of-Care Ketone Measurements: Association With Diabetic Ketoacidosis and Severity.","authors":"Daniel F Suarez, Rachel Ancona, Ryan M Schneider, Nathan Haas, Richard T Griffey","doi":"10.1177/19322968251345500","DOIUrl":"10.1177/19322968251345500","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1434-1435"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144816809","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":"Human Factors Engineering and Diabetes Technology: A Close Relationship.","authors":"Valerie Eichinger, Mirjam de Klepper, Stephan Silbermann, Hunter Roux, Lutz Heinemann","doi":"10.1177/19322968241267826","DOIUrl":"10.1177/19322968241267826","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1169-1171"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141874968","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":"A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data.","authors":"Flavia Giammarino, Ransalu Senanayake, Priya Prahalad, David M Maahs, David Scheinker","doi":"10.1177/19322968241236208","DOIUrl":"10.1177/19322968241236208","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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 patient's 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).</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1353-1361"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140039522","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":"Postprandial Glucose Control With Different Hybrid Closed-Loop Systems According to Type of Meal in Adults With Type 1 Diabetes.","authors":"Giuseppe Scidà, Alessandra Corrado, Jumana Abuqwider, Roberta Lupoli, Carmen Rainone, Giuseppe Della Pepa, Maria Masulli, Giovanni Annuzzi, Lutgarda Bozzetto","doi":"10.1177/19322968241256475","DOIUrl":"10.1177/19322968241256475","url":null,"abstract":"<p><strong>Background: </strong>Hybrid Closed-Loop Systems (HCLs) may not perform optimally on postprandial glucose control. We evaluated how first-generation and advanced HCLs manage meals varying in carbohydrates, fat, and protein.</p><p><strong>Method: </strong>According to a cross-sectional design, seven-day food records and HCLs reports from 120 adults with type 1 diabetes (MiniMed670G: n = 40, MiniMed780G: n = 49, Control-IQ [C-IQ]: n = 31) were analyzed. Breakfasts (n = 570), lunches (n = 658), and dinners (n = 619) were divided according to the median of their carbohydrate (g)/fat (g) <i>plus</i> protein (g) ratio (C/FP). After breakfast (4-hour), lunch (6-hour), and dinner (6-hour), continuous glucose monitoring (CGM) metrics and early and late glucose incremental area under the curves (iAUCs) and delivered insulin doses were evaluated. The association of C/FP and HCLs with postprandial glucose and insulin patterns was analyzed by univariate analysis of variance (ANOVA) with a two-factor design.</p><p><strong>Results: </strong>Postprandial glucose time-in-range 70 to 180 mg/dL was optimal after breakfast (78.3 ± 26.9%), lunch (72.7 ± 26.1%), and dinner (70.8 ± 27.3%), with no significant differences between HCLs. Independent of C/FP, late glucose-iAUC after lunch was significantly lower in C-IQ users than 670G and 780G (<i>P</i> < .05), with no significant differences at breakfast and dinner. Postprandial insulin pattern (Ins_3-6h <i>minus</i> Ins_0-3h) differed by type of HCLs at lunch (<i>P</i> = .026) and dinner (<i>P</i> < .001), being the early insulin dose (Ins_0-3h) higher than the late dose (Ins_3-6h) in 670G and 780G users with an opposite pattern in C-IQ users.</p><p><strong>Conclusions: </strong>Independent of different proportions of dietary carbohydrates, fat, and protein, postprandial glucose response was similar in users of different HCLs, although obtained through different automatic insulin delivery patterns.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1331-1340"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261654","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":"Leveraging Co-Design, Design Thinking, and Service Blueprinting to Create Digital Health Behavior Change Innovations: Insights From a Co-Design Workshop With Type 2 Diabetes Remission Health Coaches.","authors":"Raluca Vela, Paula Voorheis, Jeremy Petch, Hertzel Gerstein, Diana Sherifali","doi":"10.1177/19322968251335303","DOIUrl":"10.1177/19322968251335303","url":null,"abstract":"<p><strong>Introduction: </strong>Digital health innovations are increasingly being designed to support chronic disease management. Digital health innovations may be particularly valuable for supporting health coaching interventions for type 2 diabetes (T2D) remission. To design more effective digitally enabled health coaching for T2D remission, design methods that utilize co-design, design thinking, and service blueprinting may be advantageous.</p><p><strong>Methods: </strong>A one-day collaborative design thinking workshop in Toronto, Canada involved health coaches from pan-Canadian T2D remission research sites. Health coaches reflected on their experiences and identified digital innovation opportunities. Workshop activities included empathizing with each other, defining clear opportunities, and ideating solutions. Data were collected through audio recordings, field notes, and activity outputs, and then analyzed using qualitative content analysis. Researchers synthesized the data into a service blueprint, which outlined specific needs for delivering future digitally enabled T2D remission programming.</p><p><strong>Results: </strong>Health coaches emphasized the importance of personalized goal setting, deep relationship building, and responsive behavioral recommendations in effective T2D remission coaching. Coaches envisioned digital tools as fundamental for improving information accessibility, streamlining workflows, and delivering tailored support throughout the T2D remission journey. The developed service blueprint pinpointed key opportunities where digital technology could enhance the coaching process over time, offering actionable solutions to address patient, coach, provider, and system needs.</p><p><strong>Conclusion: </strong>This study demonstrates the transformative potential of using co-design, design thinking, and service blueprinting to create more meaningful digital health self-management interventions. Future research should validate the developed service blueprint in real-world settings and explore the impact of digitally enabled health coaching on long-term T2D remission outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1215-1229"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12040880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993708","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":"An Automatable Algorithm for In-Hospital Hypoglycemic Medication Dose Reduction Technology Development in Adults Living With Diabetes Mellitus.","authors":"Heather Lochnan, Julie Shaw, Lynn Gosselin-Mcrae, Jacob Sartor, Annie Garon-Mailer, Cathy J Sun","doi":"10.1177/19322968251350723","DOIUrl":"10.1177/19322968251350723","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1429-1431"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528210","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":"Technical Innovations for Deploying Implantation of an Implanted Continuous Glucose Monitor.","authors":"Baudolino Mussa, Mariagrazia Tammaro, Barbara Defrancisco","doi":"10.1177/19322968251356504","DOIUrl":"10.1177/19322968251356504","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1432-1433"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584070","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}