JMIR Diabetes最新文献

{"title":"Agreement Between AI and Nephrologists in Addressing Common Patient Questions About Diabetic Nephropathy: Cross-Sectional Study.","authors":"Niloufar Ebrahimi, Mehrbod Vakhshoori, Seigmund Teichman, Amir Abdipour","doi":"10.2196/65846","DOIUrl":"https://doi.org/10.2196/65846","url":null,"abstract":"<p><strong>Unlabelled: </strong>This research letter presents a cross-sectional analysis comparing the agreement between artificial intelligence models and nephrologists in responding to common patient questions about diabetic nephropathy.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e65846"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025861","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":"mHealth Social Support Versus Standard Support for Diabetes Management in Safety-Net Emergency Department Patients: Randomized Phase-III Trial.","authors":"Elizabeth Burner, Danielle Hazime, Michael Menchine, Wendy Mack, Janisse Mercado, Adriana Aleman, Antonio Hernandez Saenz, Sanjay Arora, Shinyi Wu","doi":"10.2196/56934","DOIUrl":"https://doi.org/10.2196/56934","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobile health (mHealth) is a low-cost method to improve health for patients with diabetes seeking care in safety-net emergency departments, resulting in improved medication adherence and self-management. Additions of social support to mHealth interventions could further enhance diabetes self-management by increasing the gains and the postintervention maintenance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We assessed outcomes of an unblinded, parallel, equal-allocation randomized phase-III trial that tested a social support mHealth intervention to improve emergency department patients' diabetes self-management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Patients with glycated hemoglobin (HbA&lt;sub&gt;1c&lt;/sub&gt;) levels of ≥8.5% mg/dL and a text-capable phone were recruited during their emergency department visit for any reason (diabetes related or not) at a US public hospital along with a friend or family member as a supporter. Patients received 6 months of the Trial to Examine Text Messaging in Emergency Department Patients With Diabetes self-management mHealth program. Supporters were randomized to receive either (1) an mHealth social support program (Family and Friends Network Support)-daily SMS text messages guiding supporters to provide diabetes-related social support-or (2) a non-mHealth social support program as an active control-pamphlet-augmented social support with Family and Friends Network Support content. Point-of-care HbA&lt;sub&gt;1c&lt;/sub&gt; level, self-reported diabetes self-care activities, medication adherence, and safety events were collected. Mixed-effects linear regression models analyzed group differences at the end of the intervention (6 months) and the postintervention phase (12 months) for HbA&lt;sub&gt;1c&lt;/sub&gt; level and behavioral outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 166 patients were randomized. In total, 8.4% (n=14) reported type 1 diabetes, 66.9% (n=111) reported type 2 diabetes, and 24.7% (n=41) did not know their diabetes type; 50% (n=83) reported using insulin for diabetes management. Trial follow-up was completed with 58.4% (n=97) of the patients at 6 months and 63.9% (n=106) of the patients at 12 months. Both groups showed significant HbA&lt;sub&gt;1c&lt;/sub&gt; level improvements (combined group change=1.36%, SD 2.42% mg/dL; 95% CI 0.87-1.83; P&lt;.001), with no group difference (group mean difference=0.14%, SD 4.88% mg/dL; 95% CI -1.11 to 0.83; P=.87) at 6 months. At 12 months, both groups maintained their improved HbA&lt;sub&gt;1c&lt;/sub&gt; levels, with a combined mean change from 6 months of 0.06% (SD 1.89% mg/dL; 95% CI -0.34 to 0.47; P=.76) and no clinically meaningful difference between groups. No differences were observed in safety events. In subgroup analyses, patients recently diagnosed with diabetes in the mHealth social support group improved their glycemic control compared to the standard social support group (between-group difference of 1.96%, SD 9.59% mg/dL; 95% CI -3.81 to -0.125; P=.04).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e56934"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12059508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049558","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 Psychosocial Burdens of Diabetes in Pregnancy and the Feasibility of Technology-Based Support: Qualitative Study.","authors":"Maya V Roytman, Layna Lu, Elizabeth Soyemi, Karolina Leziak, Charlotte M Niznik, Lynn M Yee","doi":"10.2196/53854","DOIUrl":"https://doi.org/10.2196/53854","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Gestational diabetes mellitus and type 2 diabetes mellitus impose psychosocial burdens on pregnant individuals. As there is less evidence about the experience and management of psychosocial burdens of diabetes mellitus during pregnancy, we sought to identify these psychosocial burdens and understand how a novel smartphone app may alleviate them. The app was designed to provide supportive, educational, motivational, and logistical support content, delivered through interactive messages.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study aimed to analyze the qualitative data generated in a feasibility randomized controlled trial of a novel mobile app designed to promote self-management skills, motivate healthy behaviors, and inform low-income pregnant individuals with diabetes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Individuals receiving routine clinical care at a single, large academic medical center in Chicago, Illinois were randomized to use of the SweetMama app (n=30) or usual care (n=10) from diagnosis of diabetes until 6 weeks post partum. All individuals completed exit interviews at delivery about their experience of having diabetes during pregnancy. Interviews were guided by a semistructured interview guide and were conducted by a single interviewer extensively trained in empathic, culturally sensitive qualitative interviewing of pregnant and postpartum people. SweetMama users were also queried about their perspectives on the app. Interview data were audio-recorded and professionally transcribed. Data were analyzed by 2 researchers independently using grounded theory constant comparative techniques.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 40 participants, the majority had gestational diabetes mellitus (n=25, 63%), publicly funded prenatal care (n=33, 83%), and identified as non-Hispanic Black (n=25, 63%) or Hispanic (n=14, 35%). Participants identified multiple psychosocial burdens, including challenges taking action, negative affectivity regarding diagnosis, diet guilt, difficulties managing other responsibilities, and reluctance to use insulin. External factors, such as taking care of children or navigating the COVID-19 pandemic, affected participant self-perception and motivation to adhere to clinical recommendations. SweetMama participants largely agreed that the use of the app helped mitigate these burdens by enhancing self-efficacy, capitalizing on external motivation, validating efforts, maintaining medical nutrition therapy, extending clinical care, and building a sense of community. Participants expressed that SweetMama supported the goals they established with their clinical team and helped them harness motivating factors for self-care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Psychosocial burdens of diabetes during pregnancy present challenges with diabetes self-management. Mobile health support may be an effective tool to provide motivation, behavioral cues, and access to educational and social network resources to a","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e53854"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058243","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":"Correction: Toward Personalized Digital Experiences to Promote Diabetes Self-Management: Mixed Methods Social Computing Approach.","authors":"Tavleen Singh, Kirk Roberts, Kayo Fujimoto, Jing Wang, Constance Johnson, Sahiti Myneni","doi":"10.2196/75497","DOIUrl":"https://doi.org/10.2196/75497","url":null,"abstract":"","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e75497"},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048602","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":"Early Detection of Elevated Ketone Bodies in Type 1 Diabetes Using Insulin and Glucose Dynamics Across Age Groups: Model Development Study.","authors":"Simon Cichosz, Clara Bender","doi":"10.2196/67867","DOIUrl":"https://doi.org/10.2196/67867","url":null,"abstract":"<p><strong>Background: </strong>Diabetic ketoacidosis represents a significant and potentially life-threatening complication of diabetes, predominantly observed in individuals with type 1 diabetes (T1D). Studies have documented suboptimal adherence to diabetes management among children and adolescents, as evidenced by deficient ketone monitoring practices.</p><p><strong>Objective: </strong>The aim of the study was to explore the potential for prediction of elevated ketone bodies from continuous glucose monitoring (CGM) and insulin data in pediatric and adult patients with T1D using a closed-loop system.</p><p><strong>Methods: </strong>Participants used the Dexcom G6 CGM system and the iLet Bionic Pancreas system for insulin administration for up to 13 weeks. We used supervised binary classification machine learning, incorporating feature engineering to identify elevated ketone bodies (>0.6 mmol/L). Features were derived from CGM, insulin delivery data, and self-monitoring of blood glucose to develop an extreme gradient boosting-based prediction model. A total of 259 participants aged 6-79 years with over 49,000 days of full-time monitoring were included in the study.</p><p><strong>Results: </strong>Among the participants, 1768 ketone samples were eligible for modeling, including 383 event samples with elevated ketone bodies (≥0.6 mmol/L). Insulin, self-monitoring of blood glucose, and current glucose measurements provided discriminative information on elevated ketone bodies (receiver operating characteristic area under the curve [ROC-AUC] 0.64-0.69). The CGM-derived features exhibited stronger discrimination (ROC-AUC 0.75-0.76). Integration of all feature types resulted in an ROC-AUC estimate of 0.82 (SD 0.01) and a precision recall-AUC of 0.53 (SD 0.03).</p><p><strong>Conclusions: </strong>CGM and insulin data present a valuable avenue for early prediction of patients at risk of elevated ketone bodies. Furthermore, our findings indicate the potential application of such predictive models in both pediatric and adult populations with T1D.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e67867"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048715","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 Decision Support for Perioperative Care of Patients With Type 2 Diabetes: A Call to Action.","authors":"Jianwen Cai, Peiyi Li, Weimin Li, Xuechao Hao, Sheyu Li, Tao Zhu","doi":"10.2196/70475","DOIUrl":"10.2196/70475","url":null,"abstract":"<p><strong>Unlabelled: </strong>Type 2 diabetes mellitus affects over 500 million people globally, with 10%-20% requiring surgery. Patients with diabetes are at increased risk for perioperative complications, including prolonged hospital stays and higher mortality, primarily due to perioperative hyperglycemia. Managing blood glucose during the perioperative period is challenging, and conventional monitoring is often inadequate to detect rapid fluctuations. Clinical decision support systems (CDSS) are emerging tools to improve perioperative diabetes management by providing real-time glucose data and medication recommendations. This viewpoint examines the role of CDSS in perioperative diabetes care, highlighting their benefits and limitations. CDSS can help manage blood glucose more effectively, preventing both hyperglycemia and hypoglycemia. However, technical and integration challenges, along with clinician acceptance, remain significant barriers.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e70475"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812923","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":"eHealth Literacy and Its Association With Demographic Factors, Disease-Specific Factors, and Well-Being Among Adults With Type 1 Diabetes: Cross-Sectional Survey Study.","authors":"Divya Anna Stephen, Anna Nordin, Unn-Britt Johansson, Jan Nilsson","doi":"10.2196/66117","DOIUrl":"10.2196/66117","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The use of digital health technology in diabetes self-care is increasing, making eHealth literacy an important factor to consider among people with type 1 diabetes. There are very few studies investigating eHealth literacy among adults with type 1 diabetes, highlighting the need to explore this area further.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study was to explore associations between eHealth literacy and demographic factors, disease-specific factors, and well-being among adults with type 1 diabetes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The study used data from a larger cross-sectional survey conducted among adults with type 1 diabetes in Sweden (N=301). Participants were recruited using a convenience sampling method primarily through advertisements on social media. Data were collected between September and November 2022 primarily through a web-based survey, although participants could opt to answer a paper-based survey. Screening questions at the beginning of the survey determined eligibility to participate. In this study, eHealth literacy was assessed using the Swedish version of the eHealth Literacy Scale (Sw-eHEALS). The predictor variables, well-being was assessed using the World Health Organization-5 Well-Being Index and psychosocial self-efficacy using the Swedish version of the Diabetes Empowerment Scale. The survey also included research group-developed questions on demographic and disease-specific variables as well as digital health technology use. Data were analyzed using multiple linear regression presented as nested models. A sample size of 270 participants was required in order to detect an association between the dependent and predictor variables using a regression model based on an F test. The final sample size included in the nested regression model was 285.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The mean Sw-eHEALS score was 33.42 (SD 5.32; range 8-40). The model involving both demographic and disease-specific variables explained 31.5% of the total variation in eHealth literacy and was deemed the best-fitting model. Younger age (P=.01; B=-0.07, SE=0.03;95% CI -0.12 to -0.02), lower self-reported glycated hemoglobin levels (P=.04; B=-0.06, SE=0.03; 95% CI -0.12 to 0.00), and higher psychosocial self-efficacy (P&lt;.001; B=3.72, SE=0.53; 95% CI 2.68-4.75) were found associated with higher Sw-eHEALS scores when adjusted for demographic and disease-specific variables in this model. Well-being was not associated with eHealth literacy in this study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The demographic and disease-specific factors explained the variation in eHealth literacy in this sample. Further studies in this area using newer eHealth literacy tools are important to validate our findings. The study highlights the importance of development and testing of interventions to improve eHealth literacy in this population for better glucose control. These eHealth literacy interventions should be tailored to meet ","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e66117"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11975121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755848","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":"Applications of AI in Predicting Drug Responses for Type 2 Diabetes.","authors":"Shilpa Garg, Robert Kitchen, Ramneek Gupta, Ewan Pearson","doi":"10.2196/66831","DOIUrl":"10.2196/66831","url":null,"abstract":"<p><strong>Unlabelled: </strong>Type 2 diabetes mellitus has seen a continuous rise in prevalence in recent years, and a similar trend has been observed in the increased availability of glucose-lowering drugs. There is a need to understand the variation in treatment response to these drugs to be able to predict people who will respond well or poorly to a drug. Electronic health records, clinical trials, and observational studies provide a huge amount of data to explore predictors of drug response. The use of artificial intelligence (AI), which includes machine learning and deep learning techniques, has the capacity to improve the prediction of treatment response in patients. AI can assist in the analysis of vast datasets to identify patterns and may provide valuable information on selecting an effective drug. Predicting an individual's response to a drug can aid in treatment selection, optimizing therapy, exploring new therapeutic options, and personalized medicine. This viewpoint highlights the growing evidence supporting the potential of AI-based methods to predict drug response with accuracy. Furthermore, the methods highlight a trend toward using ensemble methods as preferred models in drug response prediction studies.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e66831"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732261","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":"School-Partnered Collaborative Care (SPACE) for Pediatric Type 1 Diabetes: Development and Usability Study of a Virtual Intervention With Multisystem Community Partners.","authors":"Christine A March, Elissa Naame, Ingrid Libman, Chelsea N Proulx, Linda Siminerio, Elizabeth Miller, Aaron R Lyon","doi":"10.2196/64096","DOIUrl":"10.2196/64096","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;School-partnered interventions may improve health outcomes for children with type 1 diabetes, though there is limited evidence to support their effectiveness and sustainability. Family, school, or health system factors may interfere with intervention usability and implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To identify and address potential implementation barriers during intervention development, we combined methods in user-centered design and implementation science to adapt an evidence-based psychosocial intervention, the collaborative care model, to a virtual school-partnered collaborative care (SPACE) model for type 1 diabetes between schools and diabetes medical teams.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We recruited patient, family, school, and health system partners (n=20) to cocreate SPACE through iterative, web-based design sessions using a digital whiteboard (phase 1). User-centered design methods included independent and group activities for idea generation, visual voting, and structured critique of the evolving SPACE prototype. In phase 2, the prototype was evaluated with the usability evaluation for evidence-based psychosocial interventions methods. School nurses reviewed the prototype and tasks in cognitive walkthroughs and completed the Intervention Usability Scale (IUS). Two members of the research team independently identified and prioritized (1-3 rating) discrete usability concerns. We evaluated the relationship between prioritization and the percentage of nurses reporting each usability issue with Spearman correlation. Differences in IUS scores by school nurse characteristics were assessed with ANOVA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the design phase, the partners generated over 90 unique ideas for SPACE, prioritizing elements pertaining to intervention adaptability, team-based communication, and multidimensional outcome tracking. Following three iterations of prototype development, cognitive walkthroughs were completed with 10 school nurses (n=10, 100% female; mean age 48.5, SD 9.5 years) representing different districts and years of experience. Nurses identified 16 discrete usability issues (each reported by 10%-60% of participants). Two issues receiving the highest priority (3.0): ability to access a virtual platform (n=3, 30% of participants) and data-sharing mechanisms between nurses and providers (n=6, 60% of participants). There was a moderate correlation between priority rating and the percentage of nurses reporting each issue (ρ=0.63; P=.01). Average IUS ratings (77.8, SD 11.1; 100-point scale) indicated appropriate usability. There was no difference in IUS ratings by school nurse experience (P=.54), student caseload (P=.12), number of schools covered (P=.90), or prior experience with type 1 diabetes (P=.83), suggesting that other factors may influence usability. The design team recommended strategies for SPACE implementation to overcome high-priority issues, including training users ","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e64096"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11982762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732665","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":"Examining How Adults With Diabetes Use Technologies to Support Diabetes Self-Management: Mixed Methods Study.","authors":"Timothy Bober, Sophia Garvin, Jodi Krall, Margaret Zupa, Carissa Low, Ann-Marie Rosland","doi":"10.2196/64505","DOIUrl":"10.2196/64505","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Technologies such as mobile apps, continuous glucose monitors (CGMs), and activity trackers are available to support adults with diabetes, but it is not clear how they are used together for diabetes self-management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to understand how adults with diabetes with differing clinical profiles and digital health literacy levels integrate data from multiple behavior tracking technologies for diabetes self-management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Adults with type 1 or 2 diabetes who used ≥1 diabetes medications responded to a web-based survey about health app and activity tracker use in 6 categories: blood glucose level, diet, exercise and activity, weight, sleep, and stress. Digital health literacy was assessed using the Digital Health Care Literacy Scale, and general health literacy was assessed using the Brief Health Literacy Screen. We analyzed descriptive statistics among respondents and compared health technology use using independent 2-tailed t tests for continuous variables, chi-square for categorical variables, and Fisher exact tests for digital health literacy levels. Semistructured interviews examined how these technologies were and could be used to support daily diabetes self-management. We summarized interview themes using content analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 61 survey respondents, 21 (34%) were Black, 23 (38%) were female, and 29 (48%) were aged ≥45 years; moreover, 44 (72%) had type 2 diabetes, 36 (59%) used insulin, and 34 (56%) currently or previously used a CGM. Respondents had high levels of digital and general health literacy: 87% (46/53) used at least 1 health app, 59% (36/61) had used an activity tracker, and 62% (33/53) used apps to track ≥1 health behaviors. CGM users and nonusers used non-CGM health apps at similar rates (16/28, 57% vs 12/20, 60%; P=.84). Activity tracker use was also similar between CGM users and nonusers (20/33, 61% vs 14/22, 64%; P=.82). Respondents reported sharing self-monitor data with health care providers at similar rates across age groups (17/32, 53% for those aged 18-44 y vs 16/29, 55% for those aged 45-70 y; P=.87). Combined activity tracker and health app use was higher among those with higher Digital Health Care Literacy Scale scores, but this difference was not statistically significant (P=.09). Interviewees (18/61, 30%) described using blood glucose level tracking apps to personalize dietary choices but less frequently used data from apps or activity trackers to meet other self-management goals. Interviewees desired data that were passively collected, easily integrated across data sources, visually presented, and tailorable to self-management priorities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Adults with diabetes commonly used apps and activity trackers, often alongside CGMs, to track multiple behaviors that impact diabetes self-management but found it challenging to link tracked behaviors to glycem","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e64505"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702181","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}