Journal of Diabetes Science and Technology最新文献

{"title":"Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions.","authors":"Elena Idi, Andrea Facchinetti, Giovanni Sparacino, Simone Del Favero","doi":"10.1177/19322968241248402","DOIUrl":"10.1177/19322968241248402","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1341-1352"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849770","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":"Comparative Effectiveness of Hybrid Closed-Loop Automated Insulin Delivery Systems Among Patients with Type 1 Diabetes.","authors":"Sara Folk, Janet Zappe, Kathleen Wyne, Kathleen M Dungan","doi":"10.1177/19322968241234948","DOIUrl":"10.1177/19322968241234948","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Result: </strong>Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, (<i>P</i> = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (<i>P</i> = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (<i>P</i> = .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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1374-1384"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140335767","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":"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 Trial.","authors":"Dominic Ehrmann, Norbert Hermanns, Katharina Finke-Gröne, Timm Roos, Johanna Kober, Vanessa Schäfer, Michael Krichbaum, Thomas Haak, Ralph Ziegler, Lutz Heinemann, Cosima Rieger, Ezgi Bingol, Bernhard Kulzer, Stephan Silbermann","doi":"10.1177/19322968241239870","DOIUrl":"10.1177/19322968241239870","url":null,"abstract":"<p><strong>Background: </strong>In a randomized controlled trial, the efficacy of a digital diabetes diary regarding a reduction of diabetes distress was evaluated.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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]: -2.50 to 8.11) in the intervention group and 1.25 (IQR: -5.00 to 7.50) in the control group. The model-based adjusted between-group difference was significant (-2.20, IQR: -4.02 to -0.38, <i>P</i> = .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.</p><p><strong>Conclusions: </strong>The efficacy of the digital diabetes logbook was demonstrated regarding improvements in mental health in people with type 1, type 2, and gestational diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1362-1373"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140287559","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":"Using Journey Mapping and Service Blueprinting to Design Digital Health Behavior Change Innovations: A Scoping Review.","authors":"Paula Voorheis, Julia Victoria Wong, Natasa Lazarevic, Bisma Imtiaz, Aunima Bhuiya, Carolyn Steele Gray","doi":"10.1177/19322968251334396","DOIUrl":"10.1177/19322968251334396","url":null,"abstract":"<p><strong>Introduction: </strong>Solutions to support disease self-management and health-related behavior changes require a deep understanding of patient experiences, needs, and challenges across the care journey. Journey mapping (JM) and service blueprinting (SB) are valuable tools for visualizing user experiences and system processes over time. This scoping review explores how JM/SBs have been applied to design digitally enabled interventions targeting health-related behaviors among patients and the public.</p><p><strong>Methods: </strong>The JBI reviewer manual was used to guide the review. Studies were sourced from Embase, Psych Info, PubMed, Medline, Web of Science, and Scopus. Inclusion criteria required studies to describe how JM/SBs informed the design of a digitally enabled health innovation that aimed to impact health or health care-related behaviors of patients or the public. A two-level screening process and iterative data extraction were applied.</p><p><strong>Results: </strong>A total of 28 studies met the inclusion criteria, with a majority published between 2019 and 2024. The JM/SBs rarely used behavioral science theory and were structured, organized, and presented in diverse ways. Most studies designed their digital health behavior change innovations by using JM/SB to identify relevant innovation touchpoints across the patient journey. Patients frequently participated in the digital health behavior change innovation design process, with JM/SBs often serving as sensemaking tools. Innovations tended to address multifaceted health service problems through multimodal, digitally enabled solutions.</p><p><strong>Conclusions: </strong>JM/SBs are emerging as versatile tools to help digital health innovations to conduct user research, engage diverse partners, identify complex problems, and ideate creative solutions. However, limited integration of behavioral science theory indicates an area for future exploration.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1196-1214"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026486","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":"Probabilistic Meal Detection and Estimation in Type 1 Diabetes With Extreme Shape Variability.","authors":"Mrunal Sontakke, Faye Cameron, B Wayne Bequette","doi":"10.1177/19322968251363234","DOIUrl":"10.1177/19322968251363234","url":null,"abstract":"<p><strong>Background: </strong>The study conceptualizes the application of particle filters for meal detection and shape estimation, aimed at assisting patients with type 1 diabetes (T1D) in meal announcements. It concentrates on leveraging continuous glucose monitoring (CGM) and insulin pump data to detect meals, which will eventually enhance prior research that relied on accelerometer data for event detection.</p><p><strong>Method: </strong>The research employs glucose appearance rate (RA) curves derived from 21 triple-tracer studies. These curves are normalized and adjusted based on population-level meal size distributions obtained from the National Health and Nutrition Examination Survey (NHANES). Furthermore, glucose response curves resulting from insulin, as reported in existing literature focusing on fast-acting insulin analogs, are integrated into the analysis. This normalization of the population level probability distribution facilitates individualized scaling, taking into account insulin sensitivity and carbohydrate-to-insulin ratios.</p><p><strong>Results: </strong>Preliminary findings from the Tidepool data set suggest that the algorithm is effective in meal detection.</p><p><strong>Conclusions: </strong>This innovative approach holds the potential to help individuals with T1D better manage their blood glucose levels by providing information regarding glucose response to meals and estimated meal sizes for closed-loop control. Future research can focus on enhancing key components of the algorithm and incorporating additional data types to improve its performance further.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251363234"},"PeriodicalIF":3.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956172","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":"Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.","authors":"Rawan AlSaad, Ali Elhenidy, Aliya Tabassum, Nour Odeh, Eman AboArqoub, Aya Odeh, Maya AlTamimi, Alaa Abd-Alrazaq, Rajat Thomas, Mohammed Bashir, Javaid Sheikh","doi":"10.1177/19322968251355967","DOIUrl":"https://doi.org/10.1177/19322968251355967","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has emerged as a transformative tool for advancing gestational diabetes mellitus (GDM) care, offering dynamic, data-driven methods for early detection, management, and personalized intervention.</p><p><strong>Objective: </strong>This systematic review aims to comprehensively explore and synthesize the use of AI models in GDM care, including screening, diagnosis, management, and prediction of maternal and neonatal outcomes. Specifically, we examine (1) study designs and population characteristics; (2) the use of AI across different aspects of GDM care; (3) types of input data used for AI modeling; and (4) AI model types, validation strategies, and performance metrics.</p><p><strong>Methods: </strong>A systematic search was conducted across six electronic databases, identifying 126 eligible studies published up to February 2025. Data extraction and quality appraisal were independently conducted by six reviewers, using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for risk of bias assessment.</p><p><strong>Results: </strong>Among 126 studies, 75% employed retrospective designs, with sample sizes ranging from 17 to over 100 000 participants. Most AI applications (85%) focused on early GDM prediction, while fewer addressed management, outcomes, or monitoring. Classical machine learning dominated (84%), with logistic regression and random forest frequently used. Internal validation was common (68%), but external validation was rare (6%). Our risk of bias appraisal indicated an overall moderate-to-good methodological quality, with notable deficiencies in analysis reporting.</p><p><strong>Conclusions: </strong>AI demonstrates strong potential to improve GDM prediction, screening, and management. Nonetheless, broader validation, enhanced model interpretability, and prospective studies in diverse populations are needed to translate these technologies into clinical practice.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251355967"},"PeriodicalIF":3.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956150","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":"Waste Reduction With Diabetes Devices: Barriers and Ways Ahead.","authors":"Stefanie Hossmann, Derek Brandt, Lutz Heinemann","doi":"10.1177/19322968251367063","DOIUrl":"https://doi.org/10.1177/19322968251367063","url":null,"abstract":"<p><p>Reducing the environmental footprint of medical devices used by people with diabetes remains a challenge within the constraints of current regulatory frameworks. These frameworks, which rightly prioritize patient safety, sterility, and traceability, introduce substantial scientific, operational, and legal barriers to sustainable innovation. Efforts to integrate recycled or biodegradable materials, minimize packaging, or redesign products are often impeded by the complexity of revalidation processes, extensive documentation requirements, and a general lack of regulatory precedent. This commentary examines how such structural constraints hinder progress toward sustainable device design, despite mounting evidence of the environmental toll of single-use technologies. It advocates for a strategic shift toward \"Design for Sustainability.\" Coordinated systemic incentives and regulatory evolution are essential to align sustainability objectives with prevailing safety and quality imperatives.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251367063"},"PeriodicalIF":3.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956120","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":"Smartphone Application-Based Rehabilitation Program for Older Adults With Type 2 Diabetes Mellitus: Insights From the Design, Development, and Validation.","authors":"Tina Agnes, G Arun Maiya, Muralidhar M Kulakarni, Sawan Ramesh Hegde, Kirthinath Ballal, Mamatha Balachandra","doi":"10.1177/19322968251365664","DOIUrl":"https://doi.org/10.1177/19322968251365664","url":null,"abstract":"<p><strong>Background: </strong>Older adults facing chronic health challenges may experience significant advantages from smartphone technologies. This study aimed to design, develop, and validate a smartphone application to deliver rehabilitation programs to older adults with type 2 diabetes mellitus.</p><p><strong>Methods: </strong>This study consisted of three phases: (1) Assessment of need, (2) Design and development of the smartphone application, and (3) Evaluation of the smartphone application by content validation. The Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) validated the smartphone application. This validation was performed through direct interaction between the validators and the system, relevant observations were identified, and suggestions were noted during and after the test.</p><p><strong>Results: </strong>The smartphone application consisted of six modules, with content delivered through photos, videos, text, and flyers. Eight experts and twenty-five older adults over 60 years and diagnosed with type 2 diabetes mellitus completed the validation. The smartphone application received a good median understandability score of over 90 from experts and over 80 by end users, and a median actionability score of 100 by experts and over 100 by end users on the PEMAT-A/V.</p><p><strong>Conclusion: </strong>This newly developed smartphone application has been validated and is now ready for evaluation to assess its effectiveness in improving the delivery of rehabilitation programs in older adults with type 2 diabetes mellitus.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251365664"},"PeriodicalIF":3.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956167","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":"Divergent Interleukin-6 Responses to Hypoglycemia and Hyperglycemia in Adults With Type 1 Diabetes: Insights From Continuous Glucose Monitoring.","authors":"Theocharis Koufakis, Areti Kourti, Paraskevi Karalazou, Katerina Thisiadou, Kali Makedou, Ioanna Zografou","doi":"10.1177/19322968251370298","DOIUrl":"https://doi.org/10.1177/19322968251370298","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251370298"},"PeriodicalIF":3.7,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956131","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":"In Response to the Letter to the Editor From Petry et al Regarding \"100 Million Pens a Year in Germany-and Then in the Trash?\"","authors":"Brian Brandell","doi":"10.1177/19322968251366337","DOIUrl":"https://doi.org/10.1177/19322968251366337","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251366337"},"PeriodicalIF":3.7,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956112","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}