JMIR Diabetes最新文献

{"title":"Bridging the Digital Divide in Diabetes Care With an AI-Powered Health Coaching Intervention: Qualitative Formative Study.","authors":"Tejossy John, Sangeetha Mohanraj, Tanjila Nawshin, Tapan Mehta, Mohanraj Thirumalai","doi":"10.2196/81441","DOIUrl":"10.2196/81441","url":null,"abstract":"<p><strong>Background: </strong>Despite advancements in digital health coaching (DHC) for type 2 diabetes self-management, a persistent digital divide limits access among underserved populations, necessitating low-tech, telephone-based alternatives.</p><p><strong>Objective: </strong>This formative qualitative study explored the lived experiences of type 2 diabetes mellitus (T2DM) self-management challenges among adults in Alabama and elicited preferences for telephone-based DHC to inform intervention optimization.</p><p><strong>Methods: </strong>This study was part of a larger National Institutes of Health clinical trial (5R01DK129378) that examined the feasibility and needs of a telephone-based DHC intervention for self-management of T2DM. The study employed a qualitative, phenomenological approach to assess patients' needs surrounding digital coaching. Twelve patients diagnosed with T2DM were recruited between August and December 2022. Data collection involved in-depth, semistructured interviews conducted via telephone calls or secure Zoom sessions. All interviews were audio-recorded, professionally transcribed, and verified for accuracy. Two independent coders analyzed the data using NVivo software version 14 Plus.</p><p><strong>Results: </strong>Two themes emerged: (1) multifaceted barriers to diabetes self-management spanning behavioral (dietary adherence and nutritional gaps), physical/environmental (mobility limitations and weather constraints), and structural domains (medication shortages, insurance coverage limiting continuous glucose monitoring); (2) expectations for telephone-based DHC, including targeted education (meal planning, exercise adaptations, and medication effects), health coaches as accountability partners, daily reminders and check-ins via texts/calls, and incentives as extrinsic motivators to adherence. Insights from the study directly shaped the intervention components of a pilot feasibility trial (NCT05344859).</p><p><strong>Conclusions: </strong>Key challenges in day-to-day T2DM management included diet, physical activity, medication unavailability, and lack of insurance coverage for continuous glucose monitors. Expectations for a potential telephone-based DHC intervention included targeted education on diet, exercise, and medication effects; perceptions of health coaches as accountability partners; reminders via texts/calls as beneficial; and monetary incentives as extrinsic motivators for adherence to the intervention.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e81441"},"PeriodicalIF":2.9,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13450810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690159","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 Lived Experience of Adolescents With Type 1 Diabetes Mellitus in Bahir Dar, Ethiopia: Phenomenological Study.","authors":"Yassin Hussin, Desalegn Awle, Hailemariam Mekonnen Workie, Desalegne Amare","doi":"10.2196/71179","DOIUrl":"10.2196/71179","url":null,"abstract":"<p><strong>Background: </strong>Type 1 diabetes mellitus (T1DM) is one of the most common chronic diseases among adolescents and is posing a threat to health and potentially endangering life. It can have a significant impact on the physical, social, and emotional development of adolescents. Understanding the lived experiences of adolescents with T1DM is crucial for improving their health outcomes and future aspirations. However, there is currently limited research, and existing studies on psychosocial and self-care perspectives do not highlight their lived experiences in Ethiopia.</p><p><strong>Objective: </strong>This study aimed to explore the lived experiences of adolescents living with T1DM in Bahir Dar, Ethiopia.</p><p><strong>Methods: </strong>A descriptive phenomenological design was used. The study was conducted at Felege Hiwot Comprehensive Specialized Hospital in Bahir Dar from March to May 2023. Ten adolescents aged 10-19 years receiving follow-up care at the hospital were selected using purposive sampling. Data were collected through face-to-face, in-depth interviews using a semistructured guide translated into Amharic. All interviews were conducted by the principal investigator, audio-recorded, transcribed verbatim, and translated conceptually. Thematic analysis was conducted using ATLAS.ti (8.4.24; ATLAS.ti Scientific Software Development GmbH) software, guided by the Colaizzi phenomenological method.</p><p><strong>Results: </strong>The study included 10 participants (7 female and 3 male), aged 13-18 years, with the duration of living with T1DM ranging from 6 months to 11 years. It identified 4 key themes in adolescents managing insulin dependence. First, psychoemotional factors were significant, with participants experiencing stress, anxiety, low self-esteem, and negative emotions such as fear and hopelessness, although some also reported positive feelings from strong social support. Second, supportive factors were crucial, as family, friends, teachers, health care providers, and community health insurance offered essential emotional and practical help. Third, significant challenges included insulin unavailability, financial issues, fasting-related nonadherence, fear of hypoglycemia, and sleep disturbances, all impacting their well-being. Finally, adolescents used various coping strategies, such as avoidance, acceptance, prayer, and social engagement, to manage emotional distress and maintain daily functioning.</p><p><strong>Conclusions: </strong>Adolescents with T1DM in Bahir Dar face multifaceted challenges that affect their emotional well-being and diabetes management. Despite these difficulties, they demonstrate resilience through various coping strategies and benefit from strong support systems. These findings underscore the need for adolescent-centered diabetes care, improved insulin access, and psychosocial support in both clinical and community settings.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e71179"},"PeriodicalIF":2.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13448013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686689","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 Evolving Landscape of Continuous Glucose Monitoring Metrics in Type 1 Diabetes: Narrative Literature Review.","authors":"Eric Williams, Casey Rand, Alessandra Ayers, Cindy Ho, Ashley Dunova, David Klonoff, Juan Espinoza","doi":"10.2196/92455","DOIUrl":"10.2196/92455","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Continuous glucose monitoring (CGM) has transformed diabetes management and research by providing high-frequency data that address many of the limitations of hemoglobin A1c, enabling more precise clinical treatment targets and responsive trial endpoints. The richness and complexity of high-resolution time-series CGM data have spurred the development of numerous metrics for both clinical care and research applications. Beyond established metrics, there is a growing set of clinical, composite, and research-oriented measures that may support clinical decision support, intervention planning, risk stratification, and discovery-oriented research. This proliferation has created significant challenges in metric selection, interpretation, calculation, and standardization, particularly when metrics are applied across different devices, populations, software packages, and study designs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective is to map the current landscape of CGM metrics and address ongoing challenges in metric selection, clinical and research interpretation, and standardization. We further sought to distinguish between metrics primarily suited for routine clinical interpretation and those designed to explore more granular or multidimensional features of glycemia in research settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We identified the literature focusing on the calculation, application, and interpretation of the following categories of CGM metrics: (1) standardized, (2) clinical, (3) emerging, and (4) composite. CGM metrics included in this study were identified from the 27 metrics included in the Diabetes Research Hub platform, additional published standardized and composite metrics, metrics used in established CGM analysis software, and emerging metrics identified during review. We narratively reviewed each metric's definitions, calculation methods, interpretation, clinical and research utility, and strengths and limitations. In total, 102 articles were reviewed, supporting the synthesis of 36 distinct CGM-derived metrics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The review identifies a fundamental divide in the CGM metric landscape. Standardized and clinical metrics, including time in range, mean glucose, coefficient of variation, and similar, prioritize simplicity and actionability. These metrics facilitate rapid decision-making in clinical settings but potentially mask granular glycemic fluctuations, event patterns, and discordance between average glucose values and variability. Emerging and composite metrics offer deeper insights into glycemic patterns, risk, and variability. However, many rely on specialized software or complex formulas, lack standardized thresholds or clear relationships to clinical outcomes, and do not have consensus methods of calculation and interpretation, limiting their adoption and hindering cross-study comparison.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While consensus exists for core clinical metri","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e92455"},"PeriodicalIF":2.9,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13394860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581131","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":"Assessing Quality, Readability, and Transparency of Online and AI-Generated Information on Type 2 Diabetes Mellitus: Cross-Sectional Exploratory Study.","authors":"Sripradha Srinivasan, Ravanth Baskaran, Srinjay Mukhopadhyay, Mayank Patel","doi":"10.2196/93822","DOIUrl":"10.2196/93822","url":null,"abstract":"<p><strong>Background: </strong>Type 2 diabetes mellitus (T2DM) affects approximately 590 million people worldwide, and its management relies heavily on patient education. With the emergence of online health information and artificial intelligence (AI) large language models, patients are increasingly sourcing medical information independently.</p><p><strong>Objective: </strong>This study compared the quality, readability, and transparency of websites and AI-generated leaflets (AIGLs) related to T2DM.</p><p><strong>Methods: </strong>Four predefined search terms (\"type 2 diabetes,\" \"type 2 diabetes mellitus,\" \"T2DM,\" and \"adult diabetes\") were entered into 3 major search engines (Google, Yahoo, and Bing), and the top 20 search results were retrieved. AIGLs with patient information about T2DM were produced using a standardized prompt in 4 AI large language models (ChatGPT, Gemini, DeepSeek, and Grok). Information quality was assessed using the DISCERN score, calculated by 3 independent raters and ChatGPT. The Journal of the American Medical Association (JAMA) benchmarks were used to measure reliability and transparency. The Flesch-Kincaid Grade Level was used to determine readability.</p><p><strong>Results: </strong>Seventy-five websites and 4 AIGLs were evaluated. Mean author-rated DISCERN scores were 42.6 (SD 11.3) for websites and 43.9 (SD 1.74) for AIGLs, corresponding to fair quality (DISCERN 41-51). In contrast, ChatGPT-rated mean DISCERN scores were higher, with 58.5 (SD 11.5) for websites and 61.0 (SD 2.94) for AIGLs, corresponding to good quality (DISCERN 52-63). Mean JAMA benchmark scores were 2.74 (SD 0.965) for websites, indicating moderate reliability (2-3 out of 4 points), whereas all AIGLs scored 0 out of 4 points. Mean Flesch-Kincaid Grade Level scores for websites were 8.67 (SD 2.23) and 8.30 (SD 1.92) for AIGLs, corresponding to an eighth- to ninth-grade comprehension level. Spearman rank correlation demonstrated minimal variability among the 3 independent raters but showed a significant difference between ChatGPT and the 3 independent raters.</p><p><strong>Conclusions: </strong>Given the high prevalence of T2DM, both websites and AIGLs demonstrated suboptimal quality, readability, and transparency. Increasing patient reliance on digital health information calls for improved readability standards and stronger safeguards for AI-generated content. Both websites and AIGLs require an eighth- to ninth-grade comprehension level, far above the average reading age of 9 years in the United Kingdom (fourth- to fifth-grade level). This reduces the accessibility of online health information. The landscape of medical consultations is evolving, with patients increasingly presenting with preconceived notions based on online health information; hence, health care professionals should adapt to this shift.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e93822"},"PeriodicalIF":2.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457712","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":"Wearable Technologies for Diabetic Foot Ulcer Monitoring and Risk Prediction: Systematic Review.","authors":"Houriyeh Ehtemam, Simin Salehinejad, Dan Robbins, Alireza Sanaei, Hassan Shirvani, Shabnam Sadeghi-Esfahlani","doi":"10.2196/84463","DOIUrl":"10.2196/84463","url":null,"abstract":"<p><strong>Background: </strong>Wearable technologies, including smart insoles and sensor-equipped footwear, enable continuous monitoring of key foot parameters such as plantar pressure and temperature in individuals at risk of diabetic foot ulcers (DFUs).</p><p><strong>Objective: </strong>This systematic review aimed to evaluate the technological characteristics and clinical applications of wearable devices for monitoring DFU-related parameters.</p><p><strong>Methods: </strong>This review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Journal articles, theses, and dissertations evaluating wearable technologies for DFU prevention or monitoring were eligible if they involved human participants and were published in English or Persian. Studies focused on nonwearable or non-foot-based systems were excluded. A comprehensive search was conducted in PubMed, Embase, Web of Science, and Scopus from July 2024 to May 2025. Two reviewers (HE and SS) independently screened studies and extracted data. The methodological quality of included studies was assessed using the Mixed Methods Appraisal Tool (MMAT) 2018. Results were synthesized using descriptive synthesis.</p><p><strong>Results: </strong>A total of 1088 records were identified, of which 23 studies met the inclusion criteria. The included studies varied in design, sample size, and follow-up duration. Wearable devices included smart insoles, socks, and external sensors, primarily monitoring plantar pressure and temperature. Devices differed in sensor type, placement, number, communication protocols, and data acquisition rates. Participants typically had diabetes, and many had a history of neuropathy or prior DFUs.</p><p><strong>Conclusions: </strong>Wearable technologies show promise for monitoring DFU risk factors and supporting early detection. However, the evidence base is limited by heterogeneity in study designs, small sample sizes, and short follow-up periods. Further high-quality studies are required to evaluate their potential clinical benefits, long-term outcomes, and role in preventing DFUs and improving patient care.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e84463"},"PeriodicalIF":2.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13361614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148438719","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":"AI-Based Models for Diabetic Foot Ulcer Assessment: Scoping Review.","authors":"Muhamad Zulfiqar, Saldy Yusuf, Muhammad Jufri Taming, Herlina Burhan","doi":"10.2196/77925","DOIUrl":"10.2196/77925","url":null,"abstract":"<p><strong>Background: </strong>Diabetic foot ulcers (DFU) are serious complications of diabetes that contribute substantially to morbidity, mortality, and health care burden. Accurate and timely wound assessment is essential for effective DFU management; however, conventional assessment methods are limited by subjectivity, time constraints, and interobserver variability.</p><p><strong>Objective: </strong>This scoping review aimed to map and synthesize evidence regarding the development and application of artificial intelligence (AI)-based models for DFU assessment.</p><p><strong>Methods: </strong>A scoping review was conducted following the Arksey and O'Malley framework and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Literature searches were performed in PubMed, ProQuest, and Scopus for studies published between 2014 and 2026. Study selection and data charting were conducted independently by two reviewers using predefined inclusion criteria based on the PCC (population, concept, context) framework. Extracted data were synthesized narratively and categorized according to major AI application domains.</p><p><strong>Results: </strong>A total of 654 records were identified, of which 46 studies met the inclusion criteria. The included studies predominantly focused on image segmentation, diagnostic classification, and risk prediction or monitoring of DFUs. Convolutional neural networks were the most commonly applied models, with performance evaluated using metrics such as accuracy, Dice similarity coefficient, and area under the curve. Most studies relied on retrospective, single-center datasets, with limited external validation and minimal real-world clinical implementation.</p><p><strong>Conclusions: </strong>AI-based models demonstrate strong potential to enhance DFU assessment and monitoring by improving accuracy and efficiency. However, significant gaps remain in terms of dataset diversity, external validation, and integration into clinical workflows. Future research should prioritize prospective validation, standardized datasets, and real-world implementation to support safe and effective clinical adoption.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e77925"},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413808","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":"App-Based Digital Therapeutics Integrating Continuous Glucose Monitoring for Glycemic Control in Type 2 Diabetes: Prospective Observational Cohort Study.","authors":"Banshi Saboo, Mudit Sabharwal, Mihir Gharia, Devina Aswal, Parth Modi, Twinkle Maheshwari, Jaymin Parikh, Simpel Saanyal, Jaikishan Agrawal","doi":"10.2196/86651","DOIUrl":"10.2196/86651","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital therapeutics integrating continuous glucose monitoring (CGM) with personalized lifestyle coaching can enhance glycemic control in individuals with type 2 diabetes mellitus (T2DM). However, real-world evidence evaluating such multicomponent interventions-combining CGM with nutrition coaching, physiotherapy, and cognitive behavioral therapy within a unified digital platform-remains limited in South Asian populations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the effectiveness of an app-based lifestyle-integrated intervention program (Glycemic Lifestyle Intervention in Diabetes Empowerment-CGM; GLIDE-C) on glycemic, anthropometric, lipid profile, quality of life (QoL), and behavioral outcomes, as well as outcomes related to HEOR (health economics and outcomes research), in adults with mildly uncontrolled T2DM.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A prospective observational cohort study was conducted among adults aged ≥18 years with confirmed T2DM and baseline hemoglobin A1c (HbA1c) between 7.5% and 9.5%. The intervention was delivered via the Goodflip mobile app, including a 15-day CGM period and a structured analysis of glucose variations, among other clinical assessments, with follow-up on days 15, 30, and 60. The digital intervention combined CGM-guided personalized diet, exercise, and cognitive behavioral therapy plans delivered through the mobile app, supported by multidisciplinary coaching. Primary outcomes were changes in HbA1c, fasting plasma glucose, postprandial glucose, and CGM metrics; secondary outcomes included lipid profile, anthropometric indices, QoL, and behavioral determinants of glycemic improvement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Eighteen participants (mean age 49.45, SD 10.91 years; n=11, 61.1% male) completed the program. Mean HbA1c decreased by 0.59% (P=.05), with postprandial glucose decreasing by 25.42 mg/dL (P=.06) and time in range increasing by 9.98%. Modest reductions, consistent with the short intervention duration, occurred in low-density lipoprotein cholesterol (-8.38 mg/dL), body fat (-1.13 kg), and waist circumference (-1.56 cm). QoL improved significantly for sleep quality (P=.04). Improvements were also observed for motivation (P=.06), stress management (P=.08), and mood (P=.12), although these changes were not statistically significant. Behavioral analysis demonstrated strong associations with glycemic outcomes; all participants who reduced their consumption of energy-dense, nutrient-poor foods experienced improvements in HbA1c, whereas those who did not reduce energy-dense, nutrient-poor intake showed less favorable outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The novel GLIDE-C program demonstrated clinically meaningful improvements in glycemic control, behavioral outcomes, and QoL in adults with T2DM, without pharmacological modification. These findings strengthen the role of integrated, CGM-guided digital therapeutic platforms as an effective adjunct ","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e86651"},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413084","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":"Digital Twin Applications in Diabetes Management: Scoping Review.","authors":"Fatemeh Sarani Rad, Maryam Jafarpour, Ehsan Bitaraf, Katayoon Khaleghdadi, Juan Li","doi":"10.2196/83059","DOIUrl":"10.2196/83059","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital twin (DT) systems have emerged as a promising approach in health care, enabling real-time, patient-specific virtual modeling and personalized interventions. In diabetes care, DTs offer the potential to revolutionize glucose management, decision support, and therapy personalization through integration of real-time and longitudinal patient data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This scoping review mapped the current landscape of DT applications in diabetes and synthesized evidence across 13 research questions organized into 7 thematic domains: system design, target conditions, data sources, personalization strategies, intelligence and adaptability, validation methods, and implementation considerations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and JBI methodological guidance for scoping reviews. A literature search was performed in PubMed, IEEE Xplore, Scopus, and Web of Science for studies published up to April 2025; all databases were last searched on June 23, 2025. Eligible studies were original empirical articles in English that described patient-specific DT systems or closely related individualized virtual models applied to diabetes diagnosis, monitoring, management, treatment, or complication-related care. Reviews, editorials, commentaries, theoretical papers without original data, and studies not focused on diabetes were excluded. Furthermore, FSR, MJ, and KK independently screened records and assessed full texts, with disagreements resolved through discussion and, when needed, by EB. Data were charted using a structured framework based on 13 predefined research questions, and were synthesized descriptively and thematically.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 208 records identified, 123 underwent title and abstract screening, 39 full texts were assessed for eligibility, and 28 studies were included. Most studies focused on type 1 or type 2 diabetes and used data-driven, hybrid, or simulation-based DT approaches. Common clinical applications included therapeutic control, glucose prediction, decision support, and disease management. Lifestyle data, wearables, continuous glucose monitoring, and electronic health records were the dominant inputs, while personalization relied on adaptive feedback, insulin optimization, and behavior-driven tools. Intelligent features, such as adaptive learning, explainable artificial intelligence, and real-time synchronization, enhanced adaptability, although human oversight was rare. Validation was mainly retrospective or simulation-based, with few clinical trials; reported outcomes included improved hemoglobin A1c, time-in-range, and reduced hypoglycemia. Ethical discussions focused on data privacy, while implementation barriers centered on validation gaps, data quality, and workflow integration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclus","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e83059"},"PeriodicalIF":2.9,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13277824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279907","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":"Training Community Health Workers for Diabetes Management in Low- and Middle-Income Countries: Systematic Review.","authors":"Anirudh Gaurang Gudlavalleti, Sureshkumar Kamalakannan, Sophie Vanbelle S, Venkata Satyanarayana Murthy Gudlavalleti, Nicolaas C Schaper, Giridhara R Babu, Onno Cp van Schayck","doi":"10.2196/84508","DOIUrl":"10.2196/84508","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Type 2 diabetes mellitus, a public health challenge, disproportionately impacts low- and middle-income countries (LMICs), accounting for 73% of global cases. Due to resource constraints, these nations have adopted task-shifting strategies using community health workers (CHWs). However, evidence on the effectiveness of training CHWs in diabetes management is limited and, at most, indirect due to the limited studies, variable training methods, and complex interventions that make it difficult to isolate training effects.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;A systematic review was conducted to answer the question: Does training CHWs in type 2 diabetes improve the efficacy of diabetes screening and management at the community level in LMICs?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 2 reviewers, supervised by 2 supervisors, conducted the review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. They searched databases, including PubMed, MEDLINE Ovid, Scopus, EBMR, and CINAHL, for studies published between January 2000 and April 2024, including randomized and nonrandomized controlled trials and observational studies assessing CHW training in diabetes management in LMICs. The primary outcome was the mean change in glycated hemoglobin (HbA1c) percentage levels. Data were narratively synthesized for training characteristics and study outcomes, and quality was assessed using the Risk of Bias 2 and ROBINS-I tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 3387 studies were screened; 69 were eligible for full-text review, and 4 studies (3 randomized controlled trials [RCTs] and 1 observational stepped-wedge study, ~1000 patients) were included for narrative analysis. One of the 3 RCTs reported a statistically significant mean HbA1c reduction of -0.24% (P=.001), but HbA1c was not the primary outcome, and most patients were normoglycemic, prediabetic, or had diabetes. Other studies reported nonsignificant HbA1c reductions. The risk of bias among RCTs was moderate (some concerns, 1 trial at high risk), and the observational study had a serious risk of bias. No meta-analysis was performed due to the limited number of RCTs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Training CHWs in type 2 diabetes management has shown limited and, at most, indirect effects in improving glycemic control in LMIC settings. These findings are constrained by the small number of eligible studies, heterogeneity in training methodologies, and the multicomponent nature of the included interventions, with 1 trial demonstrating a statistically significant yet small reduction in HbA1c (-0.24%). Our review included only 4 eligible studies with a small representation of CHWs and multicomponent interventions. Considering the limited number of eligible studies, the heterogeneity in training methodologies and study designs, and the multicomponent nature of the included interventions, the existing evidence remains in","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e84508"},"PeriodicalIF":2.9,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13252886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220783","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":"Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.","authors":"Ernest M Taku, Vibhuti Gupta, Ashutosh Singhal","doi":"10.2196/79195","DOIUrl":"10.2196/79195","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of glucose metabolism. Traditional static insulin regimens fail to adapt to individual variability in diet, physical activity, stress, and metabolic fluctuations, leading to suboptimal glycemic control. Reinforcement learning (RL) offers a promising alternative by enabling personalized, real-time insulin adjustments that improve the balance between hyperglycemia and hypoglycemia.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to develop a deep Q-network (DQN)-based RL system that dynamically personalizes insulin dosing recommendations using continuous glucose monitoring data, meal intake, and physical activity levels. By leveraging real-time data, the model adapts to patients' evolving physiological states, enhancing glucose control and patient safety.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used the OhioT1DM dataset (2018 and 2020), which includes 8 weeks of continuous glucose measurements, insulin dosing records, and physical activity data for twelve people with type 1 diabetes. The RL agent was designed with a state representation consisting of recent blood glucose levels, insulin doses, and lifestyle factors over a 2-hour window. The 2-hour window was selected based on the known pharmacodynamic profile of rapid-acting insulin (peak action within 90-120 min), as well as the typical lag in glycemic response following meals or exercise. This window size captures both recent and delayed physiological effects while balancing data density and model stability. The action space included discrete insulin dose recommendations (eg, 0.5 U, 1 U, and 1.5 U). A reward function incentivized glucose levels within the target range (70-180 mg/dL) while penalizing extreme deviations. The DQN model was trained to maximize reward by learning optimal dosing strategies through iterative trial and error.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Performance evaluation was conducted using both qualitative and quantitative metrics. Time-series analysis compared actual and predicted glucose levels, demonstrating effective glucose regulation. The RL model achieved a mean glucose level of 80.06 mg/dL, with a reward score of 10 during evaluation, indicating that most glucose predictions were maintained within the desired clinical range. This suggests the model has learned to regulate blood glucose effectively through adaptive insulin dosing. The root mean square error (12.39 mg/dL) was slightly higher than the mean absolute error (9.85 mg/dL), indicating stable predictions. Additionally, the percentage time in target range was 64.06%, suggesting that the model-maintained glucose within the clinically safe range for a majority of the time.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The DQN-based RL model demonstrated its effectiveness in personalized insulin dosing while minimizing the risk of hypo- and hyperglycemia. This sugg","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e79195"},"PeriodicalIF":2.9,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148158980","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}