JMIR Diabetes最新文献

{"title":"Social Determinants of Health Among Chinese Americans at Risk for Diabetes in a Mobile Diabetes Prevention Trial: Cross-Sectional Baseline Analysis.","authors":"Nan Jiang, Jing Liu, Haili Song, Yanan Zhao, Huilin Li, Stella S Yi, Jeannette M Beasley, Lu Hu","doi":"10.2196/95295","DOIUrl":"10.2196/95295","url":null,"abstract":"<p><strong>Background: </strong>Prediabetes is common in the United States, and adverse social determinants of health (SDOH) are known to undermine diabetes prevention efforts. Chinese Americans experience a disproportionately high prevalence of prediabetes, yet the SDOH profiles of this population remain understudied.</p><p><strong>Objective: </strong>This study assessed SDOH among Chinese Americans at risk for diabetes and examined the associations between sociodemographic characteristics and SDOH barriers.</p><p><strong>Methods: </strong>We conducted a cross-sectional analysis of baseline survey data from the Integrating Cultural Aspects into Diabetes Education (INCLUDE) study, a randomized controlled trial of a culturally and linguistically tailored mobile diabetes prevention intervention for Chinese Americans. Participants at risk for diabetes were enrolled between April 2023 and June 2024 in New York City (N=150). Measures included in the analyses were a 14-item SDOH scale (range 0-14, with higher scores indicating more barriers) and sociodemographic characteristics. Due to the small frequencies of high SDOH scores, we collapsed the outcome into 5 categories (0, 1, 2, 3, and 4-14) to improve model stability. We first used univariable logistic regression models to examine associations between each sociodemographic factor (age, sex, years of US residence, English proficiency, education, marital status, employment status, and annual household income) and the collapsed SDOH category, followed by a multivariable ordinal regression model including all sociodemographic variables.</p><p><strong>Results: </strong>A total of 150 participants had a mean age of 49.9 (SD 12.6) years. Most were female (n=124, 82.7%), born outside the United States (n=149, 99.3%), and reported speaking English less than very well (n=132, 88.0%). Among respondents to the SDOH items (n=149), the mean SDOH score was 2.4 (SD 2.3), and 81.9% (n=122) reported at least 1 SDOH barrier. The three most frequently reported barriers were (1) the need to improve English proficiency, reading skills, or educational attainment (n=77, 51.7%); (2) experiences of racial discrimination (n=49, 32.9%); and (3) adverse housing conditions (n=38, 25.5%). After collapsing the original SDOH score, 27 (18.2%) participants had a score of 0, 39 (26.2%) had a score of 1, 23 (15.4%) had a score of 2, 27 (18.2%) had a score of 3, and 33 (22.1%) had scores of 4 to 14. In the multivariable analysis, female sex (vs male) was associated with higher SDOH score categories (odds ratio 3.83, 95% CI 1.65-9.16; P=.002).</p><p><strong>Conclusions: </strong>SDOH-related barriers were prevalent among Chinese Americans at risk for diabetes. Diabetes prevention efforts should incorporate routine SDOH screening and structured resource navigation or referral pathways, with particular attention to subgroups at higher risk, such as female individuals.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e95295"},"PeriodicalIF":2.9,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13232781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148159067","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":"Quality and Guideline Adherence of Mobile Nutrition Management Apps for Diabetes: Evaluation Study.","authors":"Sidra Jahanzeb, Nadia Davoody","doi":"10.2196/80890","DOIUrl":"10.2196/80890","url":null,"abstract":"<p><strong>Background: </strong>Diabetes mellitus is a chronic metabolic disorder marked by elevated blood glucose levels and has emerged as a global epidemic that requires management strategies for effective glycemic control through diet. In recent years, mobile apps have emerged as valuable tools for supporting self-management in chronic diseases such as diabetes, particularly for the nutritional aspects of the disease. However, the quality, accuracy, and adherence of these apps to established dietary guidelines remain underexplored and inconsistent.</p><p><strong>Objective: </strong>The study aims to evaluate the quality and adherence to guidelines of digital nutrition management apps for diabetes, with a focus on dietary guidelines from the American Diabetes Association (ADA), World Health Organization (WHO), European Association for the Study of Diabetes (EASD), and Diabetes Canada (DC).</p><p><strong>Methods: </strong>A quality evaluation was performed, involving the identification of mobile apps from the Google Play Store and Apple App Store. A total of 24 apps were selected based on predefined inclusion and exclusion criteria. Apps were analyzed for their content and features using a compliance checklist derived from official dietary guidelines for diabetes, including carbohydrate tracking, meal planning, glycemic control, fiber intake, physical activity, weight management, and education about diabetes. Additionally, the Mobile App Rating Scale was used to evaluate app quality in terms of engagement, functionality, aesthetics, and information. Intraclass correlation coefficients were used to calculate interrater reliability. Pearson correlation coefficients were also calculated.</p><p><strong>Results: </strong>Only 2 apps showed full compliance with the dietary guidelines, while most apps showed partial adherence. The Mobile App Rating Scale evaluation revealed significant variability in app quality, with mean total scores ranging from 9.46 to 17.69. This finding indicated major gaps in user engagement, functionality, educational content, and personalization. The intraclass correlation coefficient was 0.86, which indicates good interrater reliability, and the Pearson correlation coefficient was 0.88, suggesting good consistency between the authors.</p><p><strong>Conclusions: </strong>Even with the growing availability of nutrition management apps for diabetes, many lack full compliance with dietary guidelines and show room for improvement in their content quality. Collaboration between health care professionals, developers, and patients is essential for the future development of these tools to effectively support diabetes self-management. Strengthening guideline adherence and content quality can increase the effectiveness of these digital tools in promoting self-management and improving health outcomes for individuals with diabetes.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e80890"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13225222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145614","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":"Cardiovascular-Kidney-Metabolic Syndrome: Development of an <i>ICD-10-CM</i> Coding Framework.","authors":"Minzhe Zhao, Junhao Zhang, Huiyun Wang, Chongjun Xu, Jiahui Li, Chenghua Li, Xuemei He, Xueying Zheng","doi":"10.2196/91827","DOIUrl":"10.2196/91827","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular-kidney-metabolic (CKM) syndrome is a multisystem construct describing the intertwined progression of cardiometabolic risk factors, chronic kidney disease (CKD), and cardiovascular disease. The American Heart Association (AHA) proposed CKM stages (0-4) for risk stratification and prevention. However, CKM lacks a single ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) code, which hinders standardized stage identification in electronic health records and claims data.</p><p><strong>Objective: </strong>This study aimed to develop an AHA-aligned ICD-10-CM coding framework as an implementation template that operationalizes CKM stages 0-4 for reproducible cohort identification and stage-based analyses in real-world data.</p><p><strong>Methods: </strong>We mapped AHA CKM stages (0-4) to ICD-10-CM diagnosis code sets using Fiscal Year 2026 conventions, code-set engineering best practices, and clinician review. To improve reproducibility, we defined a hierarchical staging algorithm, co-occurrence rules, and recommended lookback and encounter-confirmation thresholds. Stage 3 includes guidance for electronic health record-enhanced ascertainment and claims-only proxies.</p><p><strong>Results: </strong>We provide stage-specific ICD-10-CM code sets for CKM stages 0-4. Stage 1 captures excess or dysfunctional adiposity or prediabetes. Stage 2 captures established metabolic disease and earlier-stage CKD. Stage 3 captures subclinical cardiovascular injury or very high-risk CKD. Stage 4 captures overt clinical cardiovascular disease events, with or without kidney failure.</p><p><strong>Conclusions: </strong>This implementation framework enables transparent, reproducible CKM staging in real-world datasets and supports stage-based epidemiologic and health-system applications. Empirical validation and local implementation testing are needed before clinical deployment.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e91827"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13224923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139676","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 Digital Health Solutions to Increasing Diabetes-Related Knowledge and Behavioral Outcomes: Results From a Randomized Controlled Trial.","authors":"Marcia Ory, Christi H Esquivel, Gang Han, Samuel D Castiglione Towne, SangNam Ahn, Matthew Lee Smith","doi":"10.2196/87364","DOIUrl":"10.2196/87364","url":null,"abstract":"<p><strong>Background: </strong>The prevalence of diabetes in the United States necessitates investigations into how to better enable adults with type 2 diabetes mellitus (T2DM) to manage their health using easy-to-access and personally adaptable technologies. The ubiquity of digital content further justifies the need to consider the impact of different digital intervention modalities in diabetes self-care activities.</p><p><strong>Objective: </strong>This study aimed to compare the impact of 2 digital diabetes self-care education programs delivered separately and in combination to adults with T2DM across various settings in Texas.</p><p><strong>Methods: </strong>We conducted a randomized controlled trial in Texas with 188 adults with T2DM to assess whether 2 different interventions alone (Virtual Making Moves with Diabetes or Technology-Based Education and Support) or in combination (Virtual Making Moves with Diabetes followed by Technology-Based Education and Support) improved multiple outcomes associated with diabetes self-management. We used several estimation techniques, including generalized estimating equations, to account for multiple factors simultaneously.</p><p><strong>Results: </strong>All 3 digital intervention modalities led to statistically significant improvements in diabetes-related confidence, distress, and self-care behaviors, with significance from baseline through 6 months and supported by moderate to strong effect sizes (Cohen d) ranging from 0.446 to 0.827 at 3-month follow-up versus baseline and from 0.538 to 0.888 at 6-month follow-up versus baseline. No statistically significant superiority was observed among the intervention modalities. Higher self-care behaviors were significantly associated with higher baseline confidence and lower distress. Those in the most disadvantaged positions (less education, less financial stability, and no health insurance) showed significantly larger improvement in self-care behaviors.</p><p><strong>Conclusions: </strong>Given the benefits associated with this study's interventions, we suggest future work to further develop digital content that can be tailored to individuals with T2DM to help them manage their chronic conditions in a cost-effective manner.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e87364"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13225500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145504","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":"Obstacles and Enablers Related to Gestational Diabetes Self-Management: Systematic Review Using the Socio-Eecological Model.","authors":"Bronya H K Luk, Polly H X Ma, Rosenna Wai Ming Chung, Wendy W Zhang, William Wing-Kuen Lam","doi":"10.2196/86767","DOIUrl":"10.2196/86767","url":null,"abstract":"<p><strong>Background: </strong>Gestational diabetes mellitus (GDM) requires effective self-management to mitigate associated health risks. A comprehensive understanding of the multilevel factors influencing adherence is essential to designing effective, holistic support strategies.</p><p><strong>Objective: </strong>This systematic review aimed to synthesize the obstacles and enablers related to GDM self-management across all 5 levels of the socioecological model (SEM).</p><p><strong>Methods: </strong>A systematic search was conducted across 6 databases (MEDLINE, PsycINFO, CINAHL, PubMed, Cochrane Library, and Web of Science) for literature published from January 2010 to October 2025. Thirty studies (24 qualitative, 4 quantitative, and 2 mixed methods) were included. Data on obstacles and enablers were extracted and synthesized using the SEM as an analytical framework.</p><p><strong>Results: </strong>The analysis identified 11 key factors across the intrapersonal (eg, knowledge and emotional response), interpersonal (eg, functional support network), organizational (eg, health care access and workplace demands), community (eg, food environment and digital information landscape), and policy levels (eg, funding and economic support). The synthesis reveals how these factors interact across levels, creating systemic challenges such as fragmentation of support and information inequity.</p><p><strong>Conclusions: </strong>Successful GDM self-management support requires integrated, multilevel strategies that address factors ranging from the individual level to the policy level. This review provides an evidence-based SEM framework to inform the development of comprehensive support interventions, including those leveraging digital health platforms, to improve maternal and neonatal outcomes.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e86767"},"PeriodicalIF":2.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13225225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145514","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":"Exploring User Experiences With the Lift App for Emotional Well-Being Among Youth With Type 1 Diabetes: Qualitative Study.","authors":"Katie M Babbott, Kristin Harrison Ginsberg, Anna L Boggiss, Joanna McClintock, Chloe Hudson, Paul Hofman, Ryan Paul, Alana Cavadino, Hiran Thabrew, Craig Jefferies, Martin de Bock, Jennifer L Sherr, Anna Serlachius","doi":"10.2196/79896","DOIUrl":"10.2196/79896","url":null,"abstract":"<p><strong>Background: </strong>Individuals living with type 1 diabetes (T1D) are at an increased risk of experiencing psychological distress; however, there remains a scarcity of scalable and widely accessible support services, particularly for adolescents and young adults. To address this gap, digital mental health interventions are becoming an increasingly important area of innovation in diabetes care.</p><p><strong>Objective: </strong>This study aimed to explore qualitative feedback regarding the \"Lift: Thriving with Diabetes\" (Lift) well-being app, designed to support emotional well-being among adolescents and young adults with T1D, which was recently tested in a 12-week feasibility trial conducted in New Zealand and the United States.</p><p><strong>Methods: </strong>Of the 59 adolescents and young adults and 22 support people who participated in the main Lift feasibility trial, 13 agreed to participate in this secondary qualitative study. Participants attended a virtual focus group or 1-on-1 interview to discuss their experiences using the app and to explore their perspectives on the app's engagement, functionality, and perceived impact on well-being and diabetes-related coping. Transcribed audio recordings were analyzed using directed content analysis, guided by the Mobile Application Rating Scale end-user framework (with topics of engagement, functionality, aesthetics, and information quality) and interpreted from a realist theoretical position.</p><p><strong>Results: </strong>In total, 9 adolescents and young adults (mean age 21.5, SD 2.06 years; n=5, 56% men) and 4 support people (2 fathers, 1 friend, and 1 partner; mean age 31.3, SD 18.92 years; n=2, 50% men) completed interviews. Overall, participants viewed Lift as engaging, easy to use, and emotionally impactful. The most positive feedback focused on the app's interactive features, particularly a well-being tree that \"grew\" with increased engagement, and its \"calming\" visual aesthetics. Users also reported meaningful emotional or behavioral impact, particularly in promoting connection, self-awareness, and practical coping strategies in living with T1D. However, user feedback also highlighted areas for improvement, including the need for improved content pacing, personalization, connection with other digital health tools, and greater gamification to sustain long-term engagement. Participants consistently expressed a desire for content tailored to their age, role (eg, support person vs young person), and personal preferences (eg, voice, pace, tone, and interactivity).</p><p><strong>Conclusions: </strong>Findings underscore the potential of user-driven, emotionally intelligent digital tools to enhance well-being and connection for young people with T1D, as well as their support people. These insights can inform the refinement of Lift and the development of broader digital health interventions aimed at promoting well-being and fostering meaningful, sustained impact.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e79896"},"PeriodicalIF":2.9,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148058237","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":"Age- and Sex-Specific Distribution of the Triglyceride-Glucose Index in a Large Chinese Population: Cross-Sectional Study.","authors":"Hanze Du, Shuaihua Song, Xiao Zhai, Junchi Liu, Yue Jiang, Yaoda Hu, Huijing He, Daowei Li, Huijuan Zhu, Shi Chen, Guangliang Shan, Hui Pan","doi":"10.2196/95855","DOIUrl":"10.2196/95855","url":null,"abstract":"<p><strong>Background: </strong>The triglyceride-glucose (TyG) index has demonstrated promising predictive capability in clinical studies, but its distribution characteristics across different age and sex groups in the Chinese population have not been fully characterized.</p><p><strong>Objective: </strong>This study aimed to describe the population-based distribution of the TyG index.</p><p><strong>Methods: </strong>A total of 4621 participants aged 20-80 years from the China National Health Survey were included in this study. The TyG index was calculated from fasting blood glucose and triglycerides. The age- and sex-specific distribution values of the TyG index were obtained using the percentiles method.</p><p><strong>Results: </strong>Males had higher BMI (23.32, SD 2.63 vs 22.72, SD 2.60 kg/m2), triglyceride (133.78, SD 78.07 vs 111.58, SD 61.18 mg/dL), fasting glucose (97.05, SD 11.95 vs 94.34, SD 10.46 mg/dL), and TyG index values (8.63, SD 0.54 vs 8.45, SD 0.50) than females. The TyG index of males reached its peak value at approximately 40 years of age. The lower limit percentile values for females exceeded that of males around age 50. After the age of 60, the upper limit of the distribution values in females was higher than in males.</p><p><strong>Conclusions: </strong>This study characterized the age- and sex-specific distribution of the TyG index among Chinese adults aged 20-80 years. The results of this study contribute to a more precise assessment of glucolipid metabolism.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e95855"},"PeriodicalIF":2.9,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13215049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148037433","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":"Predicting Treatment Failure With Sodium-Glucose Cotransporter-2 Inhibitors in People With Type 2 Diabetes: Novel Artificial Intelligence and Machine Learning Approach.","authors":"Doyoung Kwak, Xi Tan, Yuanjie Liang, Caroline Swift, Chalak Muhammad, Xu Shi","doi":"10.2196/85372","DOIUrl":"10.2196/85372","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rate of treatment failure with sodium-glucose cotransporter-2 inhibitors (SGLT2i) is high among individuals with type 2 diabetes (T2D). Accurately predicting SGLT2i treatment failure is important for improving the clinical management of T2D.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study aimed to use machine learning (ML) models to identify factors predicting treatment failure with SGLT2i in T2D and to evaluate model performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective observational cohort study included adults with T2D treated with SGLT2i (2016-2024). The primary outcome was overall treatment failure with SGLT2i during follow-up (≥180 days after SGLT2i initiation). The secondary outcome was subtypes of treatment failure with SGLT2i (treatment discontinuation, failure with action, and inertial failure) or nonfailure, which was defined as not meeting the definition for one of the failure subtypes. Variables potentially associated with treatment failure were assessed during the year before SGLT2i treatment initiation (analysis 1) and the year before SGLT2i treatment failure (analysis 2). Using these variables, ML models-logistic regression (LR), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and Transformer-were used to identify significant predictors of the outcomes. Model performance metrics (accuracy, area under the curve, precision, recall, and F1-score) were calculated. Using Shapley Additive Explanations methodology, key features were identified based on their impact on model predictions. LR and Transformer models using key features were further evaluated for their potential to support the development of a risk score for predicting treatment failure with SGLT2i.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among all individuals in the study (N=62,222), 71% (n=44,156) had treatment failure with SGLT2i. Across subtypes, failure with action (n=23,839, 38.3%) was more common than treatment discontinuation (n=16,449, 26.4%) and inertial failure (n=3868, 6.2%). Model performance was moderate in both analyses. In analysis 1, the accuracy ranged from 0.72 to 0.73 for predicting overall treatment failure and from 0.56 to 0.57 for predicting the subtype of treatment failure. In analysis 2, the accuracy ranged from 0.74 to 0.75 for predicting overall treatment failure and from 0.61 to 0.63 for predicting the subtype of treatment failure. XGBoost, MLP, and Transformer models showed small improvements compared with LR. Using the top 9 key features identified from the Shapley Additive Explanations analysis, the Transformer model performed similarly in accuracy and area under the curve to its counterpart using the full feature set.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Performance across the LR, MLP, XGBoost, and Transformer models was moderate. The advanced ML models performed slightly better than LR. Overall, the results suggest that further model advancements and increased data availability are needed to be","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e85372"},"PeriodicalIF":2.9,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13189570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147984038","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":"Promoting Diabetes Self-Management Among Vietnamese Americans: Mixed Methods Pilot Study.","authors":"Anna Nguyen, Valerie Eschiti, Thanh C Bui, Katherine O'Neal, Tam Nguyen, Angelina P Nguyen, Hanxia Li, Michael Machiorlatti, Kathleen Dwyer","doi":"10.2196/80177","DOIUrl":"10.2196/80177","url":null,"abstract":"<p><strong>Background: </strong>Participating in a Diabetes Self-Management Education and Support (DSMES) program improves self-care behaviors, quality of life, and health outcomes. However, language barriers and cultural differences can hinder participation, leaving many Vietnamese Americans with limited access to DSMES services.</p><p><strong>Objective: </strong>This study aims to evaluate the feasibility, acceptability, and preliminary efficacy of a 3-month Blended Automated Links Augmented by Nurse Call and Engagement (BALANCE) intervention designed to deliver culturally tailored DSMES in the Vietnamese language, with participants monitored for 12 months afterward to assess sustained effects on key outcomes.</p><p><strong>Methods: </strong>An explanatory sequential mixed methods design was used, guided by the Practical, Robust Implementation and Sustainability Model (PRISM) framework. Feasibility and acceptability were measured by the participation rate of eligible clinics and patients, patient message response rate, and retention rate. Focus groups were conducted to assess adoption and sustainability. A pilot single-arm, prospective interventional trial was conducted with a sample of 88 Vietnamese American adults with type 2 diabetes from 10 primary care clinics. Surveys were administered at baseline and every 3 months over 12 months. Repeated measures ANOVA assessed changes in clinical outcomes at 3, 6, 9, and 12 months. Qualitative data from in-depth interviews and focus groups were thematically analyzed to validate and expand on quantitative findings. Integrated analysis using joint display enabled meta-inferences across data sources.</p><p><strong>Results: </strong>Among 88 participants (mean age 68, SD 9.8; range 35-86 years), the intervention did not significantly affect glycated hemoglobin A1c (P=.63) but led to a statistically and clinically significant reduction in low-density lipoprotein (P=.001) and improvement in exercise performance (P=.04). Qualitative data from 45 patient interviews reached data saturation, with 80% (n=36) describing the intervention as \"convenient\" and \"helpful.\" Clinic staff (n=18) participated in 3 focus groups and endorsed the intervention as acceptable and feasible. Mixed methods analysis confirmed high feasibility (83% clinic participation and 100% clinic retention) and acceptability (90.9% patient retention). Key barriers to sustainability included limited staffing and supply infrastructure.</p><p><strong>Conclusions: </strong>Intervention feasibility and acceptability were demonstrated but require further refinement to achieve long-term, consistent glycemic control. Findings indicated that clinic staff workload and clinic workflow were key determinants of the study's feasibility and acceptability. Future research should test BALANCE in a fully powered randomized controlled trial to evaluate intervention effectiveness.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e80177"},"PeriodicalIF":2.9,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846053","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":"Continuous Glucose Monitoring-Derived Metrics and Cardiovascular Risk Among People With Diabetes: Systematic Scoping Review.","authors":"Helene Bei Thomsen, Benjamin Lebiecka-Johansen, Ole Nørgaard, Tue Helms Andersen, Signe Toft Andersen, Guy Fagherazzi, Adam Hulman, Anders Aasted Isaksen","doi":"10.2196/89374","DOIUrl":"10.2196/89374","url":null,"abstract":"<p><strong>Background: </strong>Conventional clinical markers guide cardiovascular risk stratification; however, continuous glucose monitoring (CGM) data remain absent from prediction models. A synthesis of the current literature is needed to clarify the prognostic relevance of CGM data for cardiovascular outcomes in people with diabetes.</p><p><strong>Objective: </strong>This scoping review aimed to identify published studies examining (1) the associations between glycemic control and cardiovascular outcomes and (2) the predictive value of CGM-derived metrics in cardiovascular risk assessment.</p><p><strong>Methods: </strong>MEDLINE and Embase were searched from inception to March 11, 2025, for peer-reviewed, original research that included CGM-derived metrics and cardiovascular disease (CVD) outcomes. Two reviewers screened the records independently.</p><p><strong>Results: </strong>A total of 53 studies were identified. These studies focused on type 1 diabetes, type 2 diabetes, both diabetes types, or prediabetes. Clinical outcomes were examined in 16 studies, while subclinical outcomes were assessed in 40 studies. Of the 53 studies, 47 were cross-sectional studies and 6 were longitudinal studies. All studies were association studies, and 3 included secondary analyses of predictive performance. However, none applied machine learning-based methods. A wide range of CGM-derived metrics and CVD outcomes, both clinical and subclinical, were studied in the literature.</p><p><strong>Conclusions: </strong>Overall, the findings were inconsistent across studies, and this was likely due to methodological weaknesses such as underpowered analyses. Time-in-range was both the most studied metric and associated with cardiovascular risk in the largest single study. Only the mean amplitude of glycemic excursions was consistently associated with CVD in most studies investigating this metric, when using statistical significance as a pragmatic indicator of consistency across heterogeneous studies. The prognostic value of CGM-derived metrics for CVD outcomes is currently underexplored. Longitudinal prediction studies on clinical CVD outcomes, leveraging the potential of routinely collected CGM data, are needed.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e89374"},"PeriodicalIF":2.9,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13148326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846064","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}