JMIR Diabetes最新文献

{"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":2.6,"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":2.6,"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}

{"title":"User-Centered Development of a Digital Health Service for Diabetic Foot Ulcer Risk Stratification: Usability Study.","authors":"Ulla Hellstrand Tang, Shivani Ravichandran, Stefan Candefjord, Dipu T Sathyapalan, Vivek Lakshmanan","doi":"10.2196/83287","DOIUrl":"https://doi.org/10.2196/83287","url":null,"abstract":"<p><strong>Background: </strong>Globally, 537 million persons live with diabetes, and a lifetime risk of up to 34% of developing diabetic foot ulcers (DFUs) necessitates strengthened preventive initiatives.</p><p><strong>Objective: </strong>The study aimed to develop and evaluate a clinical decision support system (CDSS) to be used by health care professionals in foot assessment and risk stratification as a base for prevention.</p><p><strong>Methods: </strong>Based on principles of human-computer interaction, the CDSS was developed for DFU risk assessment. Users, health care professionals from Region Västra Götaland in Sweden, evaluated the functions regarding effectiveness, efficiency, and satisfaction using a mixed methods usability testing approach. Expectations and experiences of using the CDSS were evaluated with the System Usability Scale (SUS).</p><p><strong>Results: </strong>A total of 9 participants participated. User expectations of the CDSS, measured by SUS, averaged 77.2 (SD 14.6). Posttest SUS scores were 68.9 (SD 14.3), with a mean difference of 8.3 (P=.07), a nonsignificant reduction of usability after testing. The effectiveness of the CDSS in supporting users to complete 9 clinical tasks showed that for 7 (78%) tasks, at least 5 (56%) testers successfully achieved the intended goals. Tasks involving the identification of ingrown toenails and the confirmation of foot status, including risk stratification for the patient, were completed by fewer testers. Efficiency, measured as mean task completion time, ranged from 7 seconds to 9 minutes 20 seconds, and qualitative feedback informed recommendations for further system refinement. Users reported that a structured CDSS has the potential to support more equitable, consistent, and person-centered DFU prevention within a digital health service.</p><p><strong>Conclusions: </strong>A digital health service for DFU risk stratification was developed based on national and international guidelines. Although the users' expectations of the usability were higher compared to how they experienced the CDSS, the SUS test was near a threshold of 70, indicating that the system being tested was above average in usability. Further development and validation, both nationally and internationally, with continued attention to users' needs and contextual factors, are recommended.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e83287"},"PeriodicalIF":2.6,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823376","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":"Integration of Continuous Glucose Monitoring With HbA_1c to Improve the Detection of Prediabetes in Asian Individuals: Model Development Study.","authors":"Michelle H Lee, Shihui Jin, Eveline Febriana, Maybritte Lim, Sonia Baig, Shahmir H Ali, Ian Yi Han Ang, Tze Ping Loh, Ashna Nastar, Kee Seng Chia, Alice Pik-Shan Kong, Faidon Magkos, Alex R Cook, Sue-Anne Toh","doi":"10.2196/81520","DOIUrl":"https://doi.org/10.2196/81520","url":null,"abstract":"<p><strong>Background: </strong>Glycated hemoglobin (HbA1c) is a convenient tool to evaluate glycemic status but its ability to detect individuals at risk for type 2 diabetes is limited.</p><p><strong>Objective: </strong>Exploiting the glycemic variability captured in continuous glucose monitoring (CGM), we used a well-characterized Asian cohort study from Singapore to assess whether utilizing CGM features in a machine learning model can improve the detection of prediabetes as compared to using HbA1c alone.</p><p><strong>Methods: </strong>In this study, 406 nondiabetic Asian participants underwent an oral glucose tolerance test and had their fasting and 2-hour plasma glucose concentrations measured, together with HbA1c, to classify them as with normoglycemia or prediabetes. They also wore a CGM sensor for 14 days. CGM profile features were extracted and prediction models were constructed with random subsampling validation to evaluate predictive efficacy. The use of CGM and HbA1c data alone or in combination was assessed for the ability to correctly distinguish prediabetes from normoglycemia.</p><p><strong>Results: </strong>In this cohort (N=406), 189 (46.6%) individuals had prediabetes. The majority of the cohort were women (n=236, 58.1%) and of Chinese ethnicity (n=267, 65.8%). Those with prediabetes were slightly older, heavier, and had higher glucose levels with more variability than the normoglycemia group. A 2-step approach was used where those with HbA1c ≥5.7% were automatically categorized as having prediabetes; the model then focused on the prediction capability of the CGM features among individuals with HbA1c <5.7%. The prediction models with CGM outperformed the benchmark for comparison defined by HbA1c ≥5.7%, where they yielded an area under the receiver operating characteristic curve of 0.866-0.876, with a lower specificity of 78%-80% but a vastly improved sensitivity of 76%-78%.</p><p><strong>Conclusions: </strong>Adding CGM to HbA1c in a 2-step approach greatly improved the sensitivity of detecting prediabetes in an Asian population. Given the benefits to optimizing lifestyle behaviors and its growing acceptability among the nondiabetic population, CGM is a promising alternative for type 2 diabetes mellitus risk screening.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e81520"},"PeriodicalIF":2.6,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13118137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788972","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 Process of Developing an Intervention to Increase Awareness of Cardiovascular Risk for Persons With Type 2 Diabetes: Co-Creation Study.","authors":"Anna-Lena Stenlund, Karin Hellström Ängerud, Mikael Lilja, Julia Otten, Lena Jutterström","doi":"10.2196/85748","DOIUrl":"https://doi.org/10.2196/85748","url":null,"abstract":"<p><strong>Background: </strong>Many persons with type 2 diabetes (T2D) lack risk awareness or underestimate their cardiovascular risk. Although health care professionals in primary health care strive to implement risk-awareness strategies for cardiovascular risk, persons with T2D report a lack of meaningful dialogue with health care professionals. Co-creation is grounded in participatory action research and involves participants as equal partners across all stages of a project. This study describes the development of an intervention to increase cardiovascular risk awareness in people with T2D.</p><p><strong>Objective: </strong>This study aims to describe the co-creation process of developing an intervention to increase awareness of cardiovascular risk in persons with T2D.</p><p><strong>Methods: </strong>A co-creative design was used to develop an intervention following a participatory action research framework. Four workshops with persons with T2D, diabetes specialist nurses, and physicians in primary health care explored communication about cardiovascular risk, co-identified needs, co-designed solutions, tested prototypes, and redefined and retested the content of the intervention. The data were analyzed using reflexive thematic analysis.</p><p><strong>Results: </strong>The analysis identified 4 themes: co-define: taking the person's voice into account; co-design: problem-solving and generating ideas; prototype and test: drafting intervention proposals; and redefine and retest: reviewing suggested interventions. The workshop discussions highlighted the need for new interventions, including a risk assessment tool, a patient handbook, material to prompt reflection, and a web education for specialist diabetes nurses.</p><p><strong>Conclusions: </strong>This study demonstrates the value of co-creation, which was used to develop an intervention to enhance cardiovascular risk awareness in persons with T2D. Diabetes specialist nurses need to explore patients' perceptions of risk and provide space for emotional responses. The web education is intended to strengthen the person-centered approach of diabetes specialist nurses, the patient handbook encourages reflection and dialogue on personal risk, and the risk assessment tool visualizes individual risk. These components may contribute to increased awareness of cardiovascular risk.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e85748"},"PeriodicalIF":2.6,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789033","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":"Harnessing Digital Innovation for Diabetes Care: Insights From the Action4Diabetes-CorrelAid Data4Good Collaboration.","authors":"Sze May Ng, Michael Aydinbas, Tyla Martin, Darren Jia Chen Toh, Steven James","doi":"10.2196/89357","DOIUrl":"https://doi.org/10.2196/89357","url":null,"abstract":"<p><strong>Unlabelled: </strong>Recent decades have seen a dramatic proliferation of real-world data use and evidence generation from nonresearch settings. Data utilization is particularly revolutionizing the operations and impact of nongovernmental organizations worldwide, especially in low- and middle-income countries. Action4Diabetes, which has incrementally been providing sustainable diabetes care for children, adolescents, and young adults with type 1 diabetes (aged 0-25 y) across Southeast Asia since 2015, is one such organization. Recognizing the importance of data, Action4Diabetes have collaborated with CorrelAid e.V. As part of this, Action4Diabetes has been exchanging patient data with the local program hospitals monthly. A preprocessing pipeline was implemented, extracting patient and medical product data in a standardized and unified manner. Data collected are anonymized and subsequently uploaded to secure public cloud storage, where they are processed and stored in a centralized database. The model used by Action4Diabetes shows that much can be achieved and can perhaps be utilized elsewhere.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e89357"},"PeriodicalIF":2.6,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13089624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718775","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":"Antidiabetic Drug Associations With Heart Failure Outcomes: Real-World Evidence Study Using Electronic Health Records.","authors":"Elzbieta Jodlowska-Siewert, Yunhui Chen, Sinian Zhang, Jia Li, Robert Dellavalle, Rui Zhang, Jue Hou","doi":"10.2196/85083","DOIUrl":"10.2196/85083","url":null,"abstract":"<p><strong>Background: </strong>Patients with type 2 diabetes mellitus (T2D) have a higher risk of cardiovascular disease, including heart failure (HF), leading to health care burden including hospitalization and mortality. Among multiple T2D therapies, there are inadequate head-to-head comparisons of their effects on HF in the real-world patient population.</p><p><strong>Objective: </strong>This study aims to compare the time-to-HF among patients treated with different T2D drugs following metformin.</p><p><strong>Methods: </strong>We conducted a retrospective data analysis on electronic health records of 5000 patients with T2D. The inclusion criteria were previous treatment with metformin and initiation of glucagon-like peptide-1 receptor agonists (GLP1 RAs), dipeptidyl peptidase-4 inhibitors (DPP4i), sulfonylureas, or insulin. We grouped patients by the mechanism of their subsequent therapies and focused on 2 pairs of comparisons classified by insulin resistance: sulfonylureas versus insulin (increased resistance) and GLP1 RA versus DPP4i (decreased resistance). The outcomes were 5-year HF status and the HF-free survival time, which was verified manually by examining clinical notes. We applied doubly robust causal estimation and accounted for confounding by adjusting for coded and natural language processing electronic health record features identified through medical knowledge networks.</p><p><strong>Results: </strong>The study included 939 patients, of whom 204 (21.7%) received insulin, 482 (51.3%) received sulfonylureas, 90 (9.6%) received GLP1 RA, and 163 (17.4%) received DPP4i. Patients in the sulfonylureas group had a significantly higher 5-year HF-free survival compared to the insulin group (survival ratio of insulin/sulfonylureas 0.902, 95% CI 0.840-0.976; P=.01). There was no significant difference between the DPP4i versus GLP1 RA group in 5-year HF-free survival (survival ratio of GLP1 RA/DPP4i was 0.953, 95% CI 0.849-1.067; P=.40). For the occurrence of a HF-related hospitalization within 5 years, there were no significant differences between the sulfonylureas and insulin groups (risk difference 0.057, 95% CI -0.011 to 0.132; P=.11), and between the GLP1 RA and DPP4i groups (risk difference 0.010, 95% CI -0.096 to 0.129).</p><p><strong>Conclusions: </strong>We evaluated real-world evidence on 2 head-to-head comparisons of second-line T2D therapies on 5-year HF outcomes. Patients on sulfonylureas were associated with lower 5-year HF risks than those treated with insulin when measured by risk ratio, but no significant difference was detected when measured by the risk difference. Limitations of this study included potentially inadequate adjustment of confounding in the observational study and a limited sample size with validated HF outcomes.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e85083"},"PeriodicalIF":2.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693575","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 ICD-10-CM Coding Framework.","authors":"Minzhe Zhao, Junhao Zhang, Huiyun Wang, Chongjun Xu, Jiahui Li, Chenghua Li, Xuemei He, Xueying Zheng","doi":"10.2196/91827","DOIUrl":"https://doi.org/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 (CVD). The American Heart Association (AHA) proposed CKM stages (0-4) for risk stratification and prevention. However, CKM lacks a single ICD-10-CM code, which hinders standardized stage identification in EHRs and claims data.</p><p><strong>Objective: </strong>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 American Heart Association CKM stages (0-4) to ICD-10-CM diagnosis code sets using FY2026 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 EHR-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 CVD 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 prior to clinical deployment.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized Digital Health Solutions to Increasing Diabetes-Related Knowledge and Behavioral Outcomes: Results from an RCT .","authors":"Marcia Ory, Christi H Esquivel, Gang Han, Samuel D Castiglione Towne, SangNam Ahn, Matthew Lee Smith","doi":"10.2196/87364","DOIUrl":"https://doi.org/10.2196/87364","url":null,"abstract":"<p><strong>Background: </strong>The prevalence of diabetes in the US necessitates investigations into how to better enable adults with type 2 diabetes 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>The purpose of this study is to compare the impact of two digital diabetes self-care education programs delivered separately, and in combination, to adults with type 2 diabetes across various settings in Texas.</p><p><strong>Methods: </strong>We conducted a randomized control trial (RCT) in Texas with 188 adults with T2DM to assess whether two different interventions alone (vMMWD or TBES) or in combination (vMMWD followed by TBES) improved multiple outcomes associated with diabetes self-management. We employed several estimation techniques including generalized estimating equations (GEE), to account for multiple factors simultaneously.</p><p><strong>Results: </strong>All three digital intervention modalities led to significant improvements (p<.05) in diabetes-related confidence, distress, and self-care behaviors, with significant effects from baseline through 6 months and supported by moderate to strong effect sizes for the total population (ranging from .446 to .827 at 3 months and .538 to .888 at 6 months). 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 selfcare behaviors.</p><p><strong>Conclusions: </strong>Given the benefits associated with the current 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 condition(s) in a cost-effective manner.</p><p><strong>Clinicaltrial: </strong>This trial was registered at ClinicalTrials.gov under ID number NCT06370494.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Needs of Health Professionals for a Type 2 Diabetes Remote Patient Monitoring Dashboard for Personalized Care: Focus Group Study.","authors":"Chiara Lansink, Nienke Beerlage-de Jong, Eclaire Hietbrink, Anouk Middelweerd, Gozewijn Dirk Laverman","doi":"10.2196/84894","DOIUrl":"10.2196/84894","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Effective management of type 2 diabetes mellitus (T2DM) requires monitoring clinical parameters like blood glucose and medication, alongside lifestyle factors such as diet and physical activity. Decision support tools, including dashboards and shared decision-making tools, help with medication adjustments, glucose monitoring, and lifestyle. However, systems rarely integrate home-monitored lifestyle data with personalized guidance and rarely facilitate collaborative goal setting for behavior change. As a result, health care professionals (HCPs) are limited in their ability to support patients' medical and lifestyle management. Blended care, combining in-person consultations with digital monitoring of patient data, can help bridge this gap by providing structured information and data-driven insights to support diabetes management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study aims to identify HCPs' requirements for a remote monitoring dashboard for people with diabetes that integrates clinical and home-monitored lifestyle data, supporting personalized, patient-centered care and collaborative goal setting in blended T2DM management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A qualitative study was conducted using 2 interactive focus group sessions with HCPs involved in the treatment of T2DM. Focus group participants shared experiences, identified practical needs, and collaboratively defined requirements for a dashboard to support personalized diabetes management. Transcripts were coded to identify recurring themes and ideas, which were then consolidated into distinct requirements. Requirements were labeled with an identification code (ID) and categorized in accordance with the FICS framework, distinguishing 4 types of design requirements: functions and events (F), interaction and usability (I), content and structure (C), and style and aesthetics (S). Prioritization of requirements was performed using the must/should/could/will not have method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 9 HCPs participated in 2 focus groups, each lasting approximately 1.5 hours. A total of 50 requirements for a T2DM dashboard were identified. Of these, 31 (62.0%) were functions and events (F), 9 (18.0%) related to interaction and usability (I), 7 (14.0%) concerned content and structure (C), and 3 (6.0%) pertained to style and aesthetics (S). The participants expressed the need for a dashboard that incorporates data-driven lifestyle (eg, physical activity and nutrition) with visual trend analysis and integration of psychosocial aspects. They also emphasized the importance of visualizing how nutrition, physical activity, and medication interact to influence glucose values. In addition, participants expressed the wish for a home screen that provides a quick overview, with the option to click through to more detailed views (eg, per day, week, or month).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings demonstrate a demand among HCPs for an integrated d","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"11 ","pages":"e84894"},"PeriodicalIF":2.6,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147583086","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}