The National Diabetes Prevention Program (DPP) is an evidence-based intervention proven to delay or prevent progression to type 2 diabetes, yet most at-risk people do not enroll. In Hawai'i, Native Hawaiian and Other Pacific Islander (NHOPI) and Filipino adults experience disproportionately high rates of prediabetes and diabetes but have low DPP enrollment. From July to October 2024, the Hawai'i State Department of Health launched Beat Diabetes, a statewide media campaign encouraging DPP enrollment among at-risk adults, with a focus on NHOPI and Filipino communities. This evaluation assessed whether campaign exposure was associated with self-reported likelihood of joining a DPP among Hawai'i adults at risk for diabetes, particularly NHOPI or Filipino adults. A postcampaign cross-sectional online survey was conducted from October to December 2024, with Hawai'i residents aged 35-64 years who reported at least 1 diabetes risk factor. NHOPI or Filipino adults were oversampled to determine campaign effectiveness among the target audience. The survey measured self-reported likelihood of joining a lifestyle change program (main outcome), campaign recall (main exposure), demographic characteristics, diabetes risk factors, and beliefs that could affect DPP enrollment likelihood, including intrinsic motivation, perceived inevitability of developing diabetes, and perceived health benefits of DPP participation. Three general linear regression models examined the association between campaign exposure and DPP enrollment likelihood ratings, adjusted for demographic characteristics, diabetes risk factors, and belief variables. A sensitivity analysis among just those diagnosed with prediabetes was conducted. A total of 860 adults completed the survey, with 34.7% (298/860) and 12.2% (105/860) self-identifying as NHOPI and Filipino, respectively. In total, 40% (346/860) reported campaign exposure. Exposed individuals had higher mean DPP enrollment likelihood ratings and higher inevitability belief scores than those not exposed. A large proportion of exposed respondents reported that enrolling in a DPP would "improve their health a lot." No significant differences in campaign exposure were observed across ethnicities. All 3 regression models showed a significant positive association between campaign exposure and DPP enrollment likelihood ratings. In the final adjusted model controlling for all covariates, significant predictors included campaign exposure (β=.52, P<.001), male gender (β=.34, P=.01), residence outside Honolulu County (β=.31, P=.02), motivation index scores (β=.38, P<.001), inevitability belief (β=.20, P<.001), and the belief that DPP improves health "a little" (β=.76, P<.001) or "a lot" (β=1.63, P<.001). The sensitivity analysis among those diagnosed showed exposure was not associated with likelihood ratings (β=.30, P=.27). Campaign exposure was associated with higher ratings of likelihood to join a DPP among at-risk adults with no prediabetes diagnosis. Perceived positive health impact of DPP participation was the strongest contributor to likelihood ratings. Campaigns aiming to increase awareness of DPP and intentions to join should promote DPP effectiveness and the urgency of preventative actions.
Problem-solving is essential for the self-management of type 2 diabetes but remains challenging for underserved individuals. Although mobile health (mHealth) interventions can improve diabetes self-management, few focus on problem-solving. This study evaluates the efficacy of Mobile Diabetes Detective (MoDD), a fully automated web-based intervention with SMS text messaging that provides problem-solving support tailored to self-monitoring data, for improving glycemic control among medically underserved adults with type 2 diabetes. This open-label, 1:1 cluster-randomized controlled trial was conducted in 2013-2018. Participants were adults with type 2 diabetes (glycated hemoglobin [HbA1c] >7.5%) receiving care at 8 Federally Qualified Health Centers serving medically underserved communities in the New York metropolitan area. The centers served as clusters and were randomized using computer-generated allocation. Recruitment and study sessions were conducted either in person or in a hybrid format. The intervention arm used MoDD for 12 months, whereas the control arm received standard diabetes education and routine care. The primary outcome was the change in HbA1c from baseline to 12 months, recorded from medical chart data. We hypothesized greater improvement in the intervention arm than in the control arm. Secondary outcomes included psychosocial measures. Outcomes were compared between groups using intention-to-treat analyses. This report presents the final analysis of the outcomes. This trial randomized 248 participants (intervention arm: n=126; control arm: n=122); 219 were included in the final analysis (intervention arm: n=111; control arm: n=108). Participants were predominantly female (147/219, 67.1%) and ethnically and racially diverse (112/219, 51.1%, Hispanic and 92/219, 42%, African American), with a mean baseline HbA1c of 9.9%. Overall, of the 111 participants, 44 (39.6%) engaged with MoDD at least once weekly in the first 30 days, and 22 (19.8%) engaged at least once weekly in the first 90 days. HbA1c did not differ significantly between groups at baseline (intervention: 9.81%, 95% CI 9.42%-10.20%; control: 9.95%, 95% CI 9.55%-10.34%; difference=0.14%, P=.63) or at 12 months (intervention: 9.36%, 95% CI 8.95%-9.78%; control: 9.58%, 95% CI 9.15%-10.01%; difference=-0.22%, P=.47). Both groups demonstrated reductions in HbA1c from baseline to 3 months. Sustained within-group improvement at 12 months was observed in the intervention group but not in the control group. No intervention-related adverse events were reported. This study evaluated the impact of a mobile intervention for problem-solving in diabetes. MoDD is innovative because it operates autonomously and tailors support to individuals' self-monitoring data. Although there was no significant between-group difference in HbA1c, the intervention group showed sustained within-group improvement at 12 months. These findings highlight the potential long-term benefits of autonomous mHealth interventions for problem-solving. The study observed an increase in diabetes distress, possibly reflecting heightened awareness of uncontrolled blood glucose levels. If implemented in clinical practice, MoDD could complement diabetes education and help improve glycemic control. ClinicalTrials.gov NCT02021591; https://clinicaltrials.gov/ct2/show/NCT02021591.
Online health information seeking (OHIS) has become a central component of chronic disease management within an increasingly interactive, algorithm-mediated digital ecosystem. For individuals with diabetes, ongoing self-management demands create sustained needs for accessible, actionable health information. Although prior reviews have described general information-seeking behaviors, few have integrated technological evolution, multilevel determinants, and equity considerations specific to diabetes. This scoping review maps patterns of OHIS among individuals with diabetes, identifies the types of information sought, synthesizes the multilevel determinants of OHIS, and explores temporal shifts across major phases of digital health development. This scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension (PRISMA-S) reporting guidelines and was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework. Five electronic databases (PubMed, Scopus, Web of Science, CINAHL, and Embase) were systematically searched for English-language empirical studies from inception to May 4, 2026. Eligible studies included empirical research investigating OHIS behaviors among individuals with type 1 diabetes, type 2 diabetes, or gestational diabetes. Data were extracted using a standardized charting form and synthesized descriptively. Determinants were organized according to the Social Ecological Model, and qualitative findings were analyzed using content analysis. Studies were stratified into 3 periods reflecting shifts in digital infrastructure: early web environments (2002-2010), expansion of social media and mobile technologies (2011-2018), and integrated digital and artificial intelligence (AI)-enabled ecosystems (2019-2025). Eighty-one studies from 32 countries met the inclusion criteria. The use of digital sources diversified over time. Early studies emphasized search engines and institutional websites, whereas later studies increasingly reported engagement with social media platforms and online communities. Mobile health apps and generative AI chatbots appeared in recent publications, although evidence on AI use remained limited. The most frequently sought content included self-management and lifestyle guidance, general diabetes knowledge, and treatment-related information. Determinants of OHIS operated across multiple levels. At the individual level, younger age, greater educational attainment, higher income, and better eHealth literacy were associated with increased engagement, while psychological factors such as perceived knowledge gaps and a desire for autonomy motivated searching. Interpersonal influences included peer support and clinician communication. Organizational and environmental factors encompassed health care access, digital infrastructure, information quality, and platform characteristics. Persistent disparities were observed among older adults and socioeconomically disadvantaged groups. This review synthesizes OHIS among individuals with diabetes through the lenses of technological evolution, multilevel determinants, and digital health equity. Unlike previous reviews that focused on specific platforms or general information-seeking behaviors, it maps the transition from web-based resources to social media and emerging AI-enabled ecosystems. This temporally informed synthesis advances understanding of digital engagement in diabetes self-management, identifies key evidence gaps, and informs clinical, organizational, and policy strategies to promote equitable access to trustworthy online health information.
Suboptimal glycemic control remains a significant public health challenge among adults with type 2 diabetes and prediabetes, with 47.4% of US adults with diagnosed diabetes having HbA1c ≥7.0%. Remote glucose monitoring programs have shown promise for supporting self-management, but real-world evidence on the causal impact of varying patient engagement levels on glycemic outcomes remains limited. To estimate the causal dose-response relationship between patient engagement, operationalized as weekly glucose monitoring frequency, and glycemic control measured by hemoglobin A1c (HbA1c), among adults enrolled in a comprehensive primary care-integrated remote monitoring program. We conducted a retrospective cohort study of 1,436 adults with type 2 diabetes or prediabetes enrolled in the iHealth Unified Care program between 2019 and 2024. The program integrated Bluetooth-connected glucose meters, a mobile application, structured lifestyle coaching, and primary care coordination across 74 physician practices. Engagement was defined as mean weekly glucose monitoring frequency during the first 6 months. The causal effect of monitoring frequency on 6-month HbA1c was estimated using marginal structural models (MSMs) with inverse probability weighting (IPW) to address time-varying confounding. Covariates included age, sex, BMI, baseline HbA1c, comorbidities, medication status, and physical activity level. The cohort was predominantly older (95.4% aged ≥46 years), with 82.6% having hypertension and 45.9% classified as obese. Overall, HbA1c decreased by 0.54 percentage points (95% CI 0.47-0.62; P < .001) over 6 months. Cluster analysis identified three engagement tiers: low (n=835; mean 2.55 measurements/week), medium (n=493; 6.19/week), and high (n=108; 12.59/week). A monotonic dose-response was observed, with HbA1c reductions of 0.38 (95% CI 0.29-0.48), 0.71 (95% CI 0.58-0.85), and 1.01 (95% CI 0.69-1.32) percentage points for the low, medium, and high tiers, respectively (all P < .001 by paired t-test). In weighted MSMs, each additional weekly measurement was associated with a 0.05 percentage point greater HbA1c reduction (95% CI 0.03-0.07; P < .001). Among patients with high baseline HbA1c (≥9.0%), the high-engagement group achieved a mean reduction of 3.12 percentage points (95% CI 2.29-3.94). Findings were consistent in sensitivity analyses at 3 months (β = -0.04; P < .01) and 12 months (β = -0.03; P < .01) and across alternative weighting specifications. Higher engagement with a digitally enabled, primary care-integrated remote glucose monitoring program was causally associated with significantly greater HbA1c reductions in adults with type 2 diabetes and prediabetes. These findings support scalable remote patient monitoring strategies that actively foster sustained patient engagement as an effective approach to improving glycemic control and reducing the burden of diabetes-related complications at a population level.
Digital twin (DT) systems have emerged as a promising approach in health care, enabling real-time, patient-specific virtual modeling and personalized interventions. In diabetes care, DTs offer the potential to revolutionize glucose management, decision support, and therapy personalization through integration of real-time and longitudinal patient data. This scoping review mapped the current landscape of DT applications in diabetes and synthesized evidence across 13 research questions organized into 7 thematic domains: system design, target conditions, data sources, personalization strategies, intelligence and adaptability, validation methods, and implementation considerations. This scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and JBI methodological guidance for scoping reviews. A literature search was performed in PubMed, IEEE Xplore, Scopus, and Web of Science for studies published up to April 2025; all databases were last searched on June 23, 2025. Eligible studies were original empirical articles in English that described patient-specific DT systems or closely related individualized virtual models applied to diabetes diagnosis, monitoring, management, treatment, or complication-related care. Reviews, editorials, commentaries, theoretical papers without original data, and studies not focused on diabetes were excluded. Furthermore, FSR, MJ, and KK independently screened records and assessed full texts, with disagreements resolved through discussion and, when needed, by EB. Data were charted using a structured framework based on 13 predefined research questions, and were synthesized descriptively and thematically. Of 208 records identified, 123 underwent title and abstract screening, 39 full texts were assessed for eligibility, and 28 studies were included. Most studies focused on type 1 or type 2 diabetes and used data-driven, hybrid, or simulation-based DT approaches. Common clinical applications included therapeutic control, glucose prediction, decision support, and disease management. Lifestyle data, wearables, continuous glucose monitoring, and electronic health records were the dominant inputs, while personalization relied on adaptive feedback, insulin optimization, and behavior-driven tools. Intelligent features, such as adaptive learning, explainable artificial intelligence, and real-time synchronization, enhanced adaptability, although human oversight was rare. Validation was mainly retrospective or simulation-based, with few clinical trials; reported outcomes included improved hemoglobin A1c, time-in-range, and reduced hypoglycemia. Ethical discussions focused on data privacy, while implementation barriers centered on validation gaps, data quality, and workflow integration. DT research in diabetes is expanding and shows strong potential for personalized and data-driven care; however, the evidence base remains heterogeneous, inconsistently reported, and limited in prospective clinical validation. Key gaps include standardized definitions, robust real-world evaluation, fairness and governance considerations, and integration into clinical workflows. Future work should prioritize clinically grounded validation, regulatory readiness, and interoperable architectures to support safe, equitable, and scalable implementation.
Continuous glucose monitoring (CGM) is central to diabetes care, but explaining CGM patterns consistently and empathetically remains time-intensive in clinical practice. Large language model (LLM)-based systems may support patient-facing interpretation of CGM data, but evidence remains limited for retrieval-grounded tools evaluated against clinician-authored responses in counseling scenarios. The system was intended for CGM interpretation and communication support rather than autonomous therapeutic decision-making. This study aimed to evaluate whether a retrieval-grounded LLM-based conversational agent (CA) could support patient understanding of CGM data and preparation for diabetes consultations by generating responses to questions arising during CGM-informed diabetes counseling, with quality comparable to clinician-authored responses. We developed a scaffolded LLM-based CA for CGM interpretation and diabetes counseling support. The system was designed to provide plain-language explanations of CGM patterns and responses to diabetes management questions while avoiding directive or individualized medical advice, such as recommending medication initiation, dose adjustment, or regimen changes. Around 12 CGM-informed cases, each comprising a deidentified CGM trace, a synthetic patient vignette, and accompanying CGM visual materials, were constructed from using available clinical datasets. Between October 2025 and February 2026, 6 senior UK diabetes clinicians each reviewed 2 assigned cases and answered 24 questions (12 per case). In a source-masked multirater evaluation, each CA-generated and clinician-authored response was independently rated by 3 clinicians on 6 quality dimensions, including clinical accuracy, guideline adherence, actionability, personalization, communication clarity, and empathy. Safety flags and perceived source labels were also recorded. The primary analysis used linear mixed effects models with random intercepts for case and rater. A total of 288 unique responses (144 CA and 144 clinician responses) were evaluated, generating 864 ratings. CA-generated responses received higher quality scores than clinician-authored responses under controlled vignette-based conditions, with mean scores of 4.37 (SD 0.57) versus 3.58 (SD 0.90) and an estimated mean difference of 0.782 points on a 5-point scale (95% CI 0.692-0.872; P<.001). This pattern was observed across all 6 categories of patient questions. The largest estimated differences were for empathy (mean difference 1.062, 95% CI 0.948-1.177) and actionability (0.992, 95% CI 0.877-1.106). Safety flag distributions were similar between CA and clinician responses, with major concerns rare in both groups (n=3, 0.7% each). Although CA responses were longer, additional analyses adjusting for word count did not indicate that response length explained the overall quality difference. Scaffolded LLM-based systems may have value as adjunct tools for CGM review, patient education, and preconsultation preparation by supporting standardized explanatory tasks. However, these findings should be interpreted in light of the vignette-based design, restricted datasets, and a small clinician panel, and they do not establish suitability for autonomous therapeutic decision-making, medication adjustment, or unsupervised real-world use. Prospective validation in clinical workflows is needed before implementation.
Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes mellitus (T2DM) post partum, with up to half of affected women progressing within a decade. Early identification of high-risk individuals is critical for implementing preventive interventions. Artificial intelligence (AI) offers enhanced predictive capabilities that can substantially enhance the prevention of postpartum diabetes. This systematic review and meta-analysis aimed to evaluate the performance of AI models in predicting the progression from GDM to T2DM or prediabetes. A total of 7 databases (MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) were systematically searched from inception through September 12, 2025, supplemented by backward and forward reference screening and biweekly alerts to capture newly published studies. This review included peer-reviewed English-language studies that applied AI algorithms to predict T2DM or prediabetes among women with previous GDM. Eligible studies focused on human participants; reported performance metrics (eg, accuracy, sensitivity, and specificity); and excluded non-AI models, animal studies, reviews, protocols, abstracts, and non-English publications. Moreover, 2 reviewers independently conducted study selection, data extraction, and risk of bias assessment using the PROBAST (Prediction Model Risk of Bias Assessment Tool)+AI tool. Pooled estimates were computed using random-effects meta-analysis models. In total, 10 studies met the inclusion criteria, of which 8 were eligible for meta-analysis. The reviewed studies spanned from 2011 to 2025 and were conducted across 7 countries, predominantly in the United States (3/10, 30%). Most publications were journal articles (9/10, 90%), and retrospective designs (6/10, 60%) were slightly more common than prospective designs (4/10, 40%). AI models demonstrated high predictive performance for T2DM, with pooled accuracy of 0.85 (95% CI 0.79-0.90; prediction interval [PI] 0.64-0.98), sensitivity of 0.89 (95% CI 0.81-0.95; PI 0.63-1.00), specificity of 0.88 (95% CI 0.81-0.93; PI 0.67-0.99), F1-score of 0.80 (95% CI 0.75-0.85; PI 0.68-0.93), and area under the curve of 0.86 (95% CI 0.77-0.91; PI 0.54-0.97). However, AI performance for prediabetes prediction was modest (area under the curve=0.69, 95% CI 0.60-0.77). Subgroup analyses showed that random forest, decision tree, logistic regression, and naïve Bayes models performed comparably. Fasting plasma glucose and BMI were the most identified significant predictors in the included studies. AI models show potential in predicting T2DM after GDM. However, evidence remains limited by small sample sizes, high heterogeneity, lack of external validation, and high risk of bias. Our findings have important implications for digital health, supporting the integration of AI-driven risk prediction into electronic health record systems and postpartum care pathways to enable early identification, targeted prevention, and improved long-term outcomes. Future research should use large, diverse cohorts, integrate multidimensional data, adopt standardized reporting frameworks, and encourage open-access data sharing.
Diabetes mellitus management requires considerable patient self-efficacy, knowledge, and support for social determinants of health. These needs become particularly acute during pregnancy. Mobile health (mHealth) tools are a promising approach to enhance patient engagement with the health care system, education, and health promotion and may be particularly helpful during the period of rapid skills acquisition, which is a hallmark of experiencing diabetes during pregnancy. Therefore, we developed SweetMama, a web-based mHealth app designed to support and provide information to low-income pregnant individuals with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM). This study aimed to understand the user experiences of low-income pregnant people who were randomized to use SweetMama during a feasibility trial. This mixed methods secondary analysis of data from a feasibility randomized controlled trial (RCT) included participants randomized to SweetMama, an interactive, web-based mHealth app with multiple motivational and educational features that help reduce barriers to care, offer health education, and aim to improve diabetes self-care for low-income pregnant people. In the parent trial, English-speaking pregnant individuals with GDM or T2DM were randomized to use SweetMama during pregnancy or usual care. SweetMama users experienced an individualized curriculum from enrollment through 6 weeks postpartum. Upon exit, users completed 2 qualitative interviews (during the delivery hospitalization and at the postpartum visit) and surveys assessing standardized usability metrics. The surveys included the System Usability Scale (SUS), the Usefulness, Satisfaction, Ease of Use (USE) scale, and the mHealth App Usability Questionnaire (MAUQ) to assess usability. Qualitative data were analyzed using constant comparative techniques. Of 30 SweetMama users, 60% (n=18) had GDM, 83.3% (n=25) had publicly funded prenatal care, and the majority identified as non-Hispanic Black (n=17, 56.7%) or Hispanic (n=11, 36.7%). Scores on the SUS (median 85.0/100, IQR 70.0-88.8; ≥71% indicates acceptable or higher usability), USE (overall median 84.5/100, IQR 81.0-91.4), and MAUQ (median 84.1/100, IQR 79.0-91.3) indicated favorable usability assessments, particularly for the "ease of learning" domain. Qualitative interviews supported these findings: participants described the app as easy to navigate, well organized, and helpful for staying on track, citing features such as clear visual design, timely text reminders, and actionable tips. Users valued motivational elements and content specificity, while recommending increased customization and enhanced esthetics. In this user experience evaluation of a web-based mHealth app for low-income pregnant individuals with diabetes, participants found the tool to be user-friendly, visually appealing, informative, and motivating. Constructive feedback for application improvement for use in a future larger trial of clinical effectiveness was collected.
Adolescents and young adults (AYAs) with type 1 diabetes (T1D) face a heightened risk of care gaps and preventable complications during the transition from pediatric to adult care. AYAs from low-income families are more likely to experience poor outcomes. Care guidelines for T1D now recommend a transition program beginning several years prior to anticipated independence. However, existing transition programs often require substantial resources, limiting their scalability and accessibility. We used a human-centered design approach to develop and refine a low-cost, virtual intervention to support T1D transitions among publicly insured AYAs. Guided by the "3I" model (inspiration, ideation, implementation) of IDEO, we conducted semistructured interviews with 26 providers and staff across 4 University of California medical centers to identify challenges and opportunities for improving transition care. We then completed customer discovery interviews with 36 adopters and influencers, including AYAs, endocrinologists, and diabetes educators, to identify priorities and needs and create value propositions. The resulting intervention was tested with 3 cohorts (n=25) of publicly insured AYAs aged 17 to 21 years receiving pediatric diabetes care at an academic medical center. The intervention consisted of two 75-minute virtual group sessions led by a diabetes educator and peer mentors with lived experience. Feasibility, acceptability, and appropriateness were assessed via validated surveys and qualitative interviews. Core functions of the intervention included education and discussion on "adulting with diabetes" and navigating the health care system, interactive creation of a transition plan, mentors sharing their experiences with diabetes, open discussion among peers, opportunities to ask questions, and provision of resources based on individual needs. Facilitators and mentors reported high feasibility (mean 4.25, SD 0.52) and appropriateness (mean 4.43, SD 0.53). Qualitative interviews highlighted peer support and lived-experience mentorship as core drivers of engagement. Attendance was highest among participants enrolled closer to session dates. Participant feedback informed ongoing adaptations to content and delivery across cohorts, including timing of the intervention, use of the chat function, multiple text reminders before intervention sessions, and specific topics of interest. This human-centered virtual transition intervention addressed priorities identified by AYAs, peer mentors, and clinicians, including peer support, judgment-free discussion, and self-advocacy. While engagement and attendance varied across cohorts, findings support further refinement and evaluation of the intervention in diverse settings.
Patient-reported outcomes in digital health solutions can offer patients with type 1 diabetes an opportunity to voice their needs in outpatient care, enabling clinicians to tailor support. Evidence on long-term health impact and routine integration of such digital solutions outside controlled settings is limited. This study aimed to compare a flexible digital supplement to outpatient care for type 1 diabetes (DigiDiaS) with usual care over 1 year, with self-management as the primary outcome and glycemic control and well-being as secondary outcomes. This longitudinal real-world observational pre-post study was conducted at the Endocrinology Department of Akershus University Hospital in Norway. Adults with type 1 diabetes were recruited consecutively from October 2022 to October 2023 and could choose either the digital mobile health (mHealth)-based outpatient care model-"DigiDiaS" care-or usual care. DigiDiaS care is delivered via a smartphone app and includes a messaging service; patient-reported outcome-based preconsultation questionnaires; video, telephone, and in-person consultations; and an individually tailored information section. Outcome data comprised self-reported measures, clinical data extracted from electronic medical records, the national diabetes registry, and the digital platform. Data were collected at baseline and at the 1-year follow-up. Change in self-management (Patient Activation Measure short version [PAM-13]; primary outcome) and the secondary outcomes well-being (Five Well-Being Index [WHO-5]) and glycemic control (glycated hemoglobin [HbA1c]) were analyzed using a generalized linear model. Utilization of the digital solution and health care services was also explored. We included 237 patients, with 185 (78%) opting for DigiDiaS care and 52 (22%) for usual care. At the follow-up, most participants (145/178, 81%) in the DigiDiaS care group used the digital solution. The messaging service was the most utilized feature with 592 messages sent collectively (median 1, min-max 0-57). There were no statistically significant between-group differences in change from baseline to follow-up in self-management (mean difference 1.18, 95% CI -5.2 to 7.6; P=.72), glycemic control (HbA1c mean difference -2.1, 95% CI -6.6 to 2.4; P=.36), or well-being (mean difference 1.9, 95% CI -3.1 to 6.9; P=.46). Health care utilization did not differ between the groups, including participation in one or more individual consultations during the 1-year follow-up (DigiDiaS care: 159/178, 89%; usual care: 42/50, 84%; P=.30) or appointment cancellations (DigiDiaS care: 64/91, 70%; usual care: 13/18, 72%; P=.20). This real-world pragmatic observational comparison under routine conditions demonstrates that reorganizing outpatient care for patients with type 1 diabetes into a flexible digital model, DigiDiaS care, did not result in statistically significant between-group differences in health outcomes, including self-management, glycemic control, and well-being, compared with usual care. Over 80% of participants in DigiDiaS care utilized the digital solution, with patient-initiated asynchronous messaging as the most frequently used feature. RR2-10.2196/52766.
Serious digital games have been proposed as a novel approach to support diabetes education and self-management, but evidence regarding their effectiveness remains limited. This study aimed to evaluate the effects of SugarVita, a serious game for people with type 2 diabetes, on diabetes-related knowledge, self-confidence, and self-management. Secondary outcomes included hemoglobin A1c (HbA1c), engagement, and user evaluation. In this pilot randomized controlled trial, 30 adults with type 2 diabetes were randomized to SugarVita plus standard care or standard care alone for 8 weeks. Outcomes were assessed before and after the intervention using validated questionnaires and laboratory HbA1c values. Within-group changes were analyzed using Wilcoxon signed-rank tests and between-group differences using Mann-Whitney U tests. Bonferroni correction was applied for multiple primary outcomes. No statistically significant between-group differences were observed for diabetes-related knowledge, self-confidence, or self-management after correction for multiple testing. Both groups showed numerical improvements over time. HbA1c decreased significantly within the intervention group (median 73.0, IQR 70.8-81.5 to median 64.5, IQR 60.8-72.0 mmol/mol; P=.007), whereas no significant change was observed in the control group. Greater total playtime was moderately associated with HbA1c reduction. User evaluations indicated high perceived educational value. Participants reported positive experiences with SugarVita and perceived the game as educational and user-friendly. No statistically significant between-group differences were observed for the primary outcomes. These findings support the feasibility and acceptability of SugarVita as a digital educational intervention and warrant further evaluation in larger studies.
Type 1 diabetes remains an underrecognized and challenging condition across Southeast Asia, where many countries face limited health care infrastructure, shortages of trained health care professionals, inconsistent access to diabetes education, and a lack of culturally appropriate resources in local languages. These barriers contribute to delayed diagnosis, suboptimal self-management, high rates of diabetic ketoacidosis, and inequities in care. During the COVID-19 pandemic, Action4Diabetes, a nonprofit organization working with local health care professionals and diabetes associations across Southeast Asia, developed HelloType1, a multilingual digital educational platform designed to improve awareness, education, and access to credible type 1 diabetes information. The platform was launched sequentially in Cambodia in 2021, Vietnam and Thailand in 2022, and Malaysia in 2023 through formal memorandums of understanding with local partners. This study aimed to evaluate the reach, platform usage, and online engagement of the HelloType1 digital educational platform across Southeast Asia between 2021 and 2024. Website analytics from Google Analytics 4 and Meta Business Suite metrics were descriptively analyzed to assess digital reach and engagement across countries. Metrics were compared over time and by country to examine patterns of platform uptake and user engagement. HelloType1 demonstrated substantial growth between 2021 and 2024. Total unique website users increased from 1178 in 2021 to 40,361 in 2024, representing a marked expansion in regional reach. Pageviews rose from 4644 in 2021 to 83,689 in 2024, suggesting increasing content use and user platform engagement. By 2024, most website visits originated from organic search engines. Platform use was predominantly mobile-based, particularly in Vietnam, Thailand, and Malaysia, with the strongest engagement among adults aged 25 to 54 years. Facebook followers increased from 940 to 4553, and average engagement rates rose between 2022 and 2024. Cambodia achieved the highest number of Facebook interactions, whereas Thailand demonstrated the highest engagement rate. Content-level analysis showed that practical self-management topics, including blood glucose monitoring, insulin treatment, nutrition and exercise, complications, and emotional support, generated high levels of reach and interaction. HelloType1 demonstrates strong growth, mobile-first use, search-driven visibility, and engagement with practical self-management content. These findings support the feasibility and potential utility of a low-cost, multilingual digital education model, while future studies should evaluate its effects on knowledge, behavior, and clinical outcomes.
Self-care plays an important role in improving health symptoms in patients diagnosed with chronic illnesses such as diabetes. Gamification is among the most effective methods for enhancing health monitoring by applying principles of behavioral economics and motivation. This study aimed to develop an innovative self-care system using a virtual reality (VR)-gamified intervention and to evaluate its effects on patients diagnosed with diabetes metabolic symptoms and health-related behaviors compared to other standard interventions. This study was a randomized controlled trial with a parallel-group design, conducted between December 2024 and February 2025. A total number of 78 patients diagnosed with diabetes were recruited from The Children's Medical Center clinic in a closed, offline setting. Briefing, tutorials, and interventions were partly face-to-face. Eligible participants (n=68) were randomly assigned to the 4 groups using stratified block randomization based on age and sex, with allocation concealment ensured through a sequentially numbered process. Each group received a different type of self-care intervention for 6 weeks, which was health care provider-assisted. One group received the VR gamified intervention, while the other 3 groups received comparator interventions, including the MySugr (mySugr GmbH) monitoring app, traditional counseling, and medication only, respectively. The primary outcomes were fasting blood sugar and health-related behaviors, including physical activity and food intake, while secondary outcomes were long-term metabolic indicators, including hemoglobin A1c (HbA1c) and BMI. All outcomes were measured by self-assessed questionnaires. No blinding was implemented for participants or care providers. However, data analysis was conducted using anonymized group labels. Eight participants were lost to follow-up. Minor missing daily entries were handled by weekly data aggregation. Data from 60 participants were included in the final analysis (VR-gamified group n=16, application group n=14, counseling group n=15, and control group n=15). Linear mixed model showed significant time×group interaction effects for fasting blood sugar (F 15,280=2.046; P=.01; partial η²=.099), physical activity (F 15,280.61=2.544; P=.001; partial η²=.120), and food intake (F 15,336 =2.794; P<.001; partial η2=.111), indicating greater improvement over time in the VR gamification group compared with the other parallel groups. No statistically significant differences were observed for BMI or glycated hemoglobin. No serious adverse events related to the VR intervention were reported. VR gamified self-care interventions may potentially contribute to better management of diabetes-related symptoms and behaviors compared with conventional self-care approaches. Such gamified VR systems show promise as alternative or complementary tools for enhancing health outcomes among children and adolescents diagnosed with diabetes.
Type 2 diabetes mellitus (T2DM) affects approximately 590 million people worldwide, and its management relies heavily on patient education. With the emergence of online health information and artificial intelligence (AI) large language models, patients are increasingly sourcing medical information independently. This study compared the quality, readability, and transparency of websites and AI-generated leaflets (AIGLs) related to T2DM. Four predefined search terms ("type 2 diabetes," "type 2 diabetes mellitus," "T2DM," and "adult diabetes") were entered into 3 major search engines (Google, Yahoo, and Bing), and the top 20 search results were retrieved. AIGLs with patient information about T2DM were produced using a standardized prompt in 4 AI large language models (ChatGPT, Gemini, DeepSeek, and Grok). Information quality was assessed using the DISCERN score, calculated by 3 independent raters and ChatGPT. The Journal of the American Medical Association (JAMA) benchmarks were used to measure reliability and transparency. The Flesch-Kincaid Grade Level was used to determine readability. Seventy-five websites and 4 AIGLs were evaluated. Mean author-rated DISCERN scores were 42.6 (SD 11.3) for websites and 43.9 (SD 1.74) for AIGLs, corresponding to fair quality (DISCERN 41-51). In contrast, ChatGPT-rated mean DISCERN scores were higher, with 58.5 (SD 11.5) for websites and 61.0 (SD 2.94) for AIGLs, corresponding to good quality (DISCERN 52-63). Mean JAMA benchmark scores were 2.74 (SD 0.965) for websites, indicating moderate reliability (2-3 out of 4 points), whereas all AIGLs scored 0 out of 4 points. Mean Flesch-Kincaid Grade Level scores for websites were 8.67 (SD 2.23) and 8.30 (SD 1.92) for AIGLs, corresponding to an eighth- to ninth-grade comprehension level. Spearman rank correlation demonstrated minimal variability among the 3 independent raters but showed a significant difference between ChatGPT and the 3 independent raters. Given the high prevalence of T2DM, both websites and AIGLs demonstrated suboptimal quality, readability, and transparency. Increasing patient reliance on digital health information calls for improved readability standards and stronger safeguards for AI-generated content. Both websites and AIGLs require an eighth- to ninth-grade comprehension level, far above the average reading age of 9 years in the United Kingdom (fourth- to fifth-grade level). This reduces the accessibility of online health information. The landscape of medical consultations is evolving, with patients increasingly presenting with preconceived notions based on online health information; hence, health care professionals should adapt to this shift.
Despite advancements in digital health coaching (DHC) for type 2 diabetes self-management, a persistent digital divide limits access among underserved populations, necessitating low-tech, telephone-based alternatives. This formative qualitative study explored the lived experiences of type 2 diabetes mellitus (T2DM) self-management challenges among adults in Alabama and elicited preferences for telephone-based DHC to inform intervention optimization. This study was part of a larger National Institutes of Health clinical trial (5R01DK129378) that examined the feasibility and needs of a telephone-based DHC intervention for self-management of T2DM. The study employed a qualitative, phenomenological approach to assess patients' needs surrounding digital coaching. Twelve patients diagnosed with T2DM were recruited between August and December 2022. Data collection involved in-depth, semistructured interviews conducted via telephone calls or secure Zoom sessions. All interviews were audio-recorded, professionally transcribed, and verified for accuracy. Two independent coders analyzed the data using NVivo software version 14 Plus. Two themes emerged: (1) multifaceted barriers to diabetes self-management spanning behavioral (dietary adherence and nutritional gaps), physical/environmental (mobility limitations and weather constraints), and structural domains (medication shortages, insurance coverage limiting continuous glucose monitoring); (2) expectations for telephone-based DHC, including targeted education (meal planning, exercise adaptations, and medication effects), health coaches as accountability partners, daily reminders and check-ins via texts/calls, and incentives as extrinsic motivators to adherence. Insights from the study directly shaped the intervention components of a pilot feasibility trial (NCT05344859). Key challenges in day-to-day T2DM management included diet, physical activity, medication unavailability, and lack of insurance coverage for continuous glucose monitors. Expectations for a potential telephone-based DHC intervention included targeted education on diet, exercise, and medication effects; perceptions of health coaches as accountability partners; reminders via texts/calls as beneficial; and monetary incentives as extrinsic motivators for adherence to the intervention.
Population health management requires tools that transform complex clinical data into actionable insights to guide care coordination, community outreach, and system-level planning. The objective of this study is to develop and apply a population health intelligence dashboard that integrates inpatient utilization, process indicators, and health status data for patients with diabetes mellitus, using a clinically meaningful classification system and geospatial visualization. We used data from the SingHealth Diabetes Registry (SDR; 2019-2024) to build an interactive dashboard using R Shiny (Posit Software). A semiautomated mapping algorithm was developed to map ICD-10-AM (International Classification of Diseases, 10th Revision, Australian Modification) principal diagnosis codes into CCSR (Clinical Classifications Software Refined) categories. We built an interactive dashboard in R Shiny incorporating 3 analytic domains: inpatient utilization (by admission count, length of stay, and prolonged stays), diabetes care process indicators, and health status indicators (eg, comorbidities, laboratory results, and diabetes-related complications). Geographic information system mapping enabled spatial visualization by patients' residential locations. Diabetes mellitus with complication (END003) was the leading cause of admission (7.0%-8.1% annually), followed by pneumonia (RSP002, 3.7%-5.1%), fluid and electrolyte disorders (END011, 3.4%-4.1%), and skin infections (SKN001, 2.8%-3.1%). In 2024, top ICD-10-AM diagnoses under END003 included E1122-type 2 diabetes mellitus with established diabetic nephropathy, E1173-type 2 diabetes mellitus with foot ulcer due to multiple causes, and E1172-type 2 diabetes mellitus with features of insulin resistance. For END011, the most frequent diagnosis codes were E877-fluid overload, R18-ascites, E875-hyperkalemia, and hypo-osmolality and hyponatremia. In total, SingHealth Diabetes Registry patients accounted for 687,062 inpatient bed days in 2024. Circulatory conditions (eg, cerebral infarction and heart failure) contributed 124,417 (17.7%) bed days, while injuries (eg, hip fractures and surgical complications) accounted for 86,541 (12.6%) bed days. CCSR-based analyses revealed distinct patterns when comparing conditions driving admission frequency versus prolonged length of stay. GIS mapping identified residential clusters with high inpatient utilization, unmet care processes, and poor cardiometabolic control, supporting region-specific intervention planning. The dashboard demonstrates a novel, interactive approach to visualizing inpatient utilization, care gaps, and health status, enabling targeted, place-based interventions. It represents a scalable framework for operationalizing population health intelligence across other chronic disease areas and health care systems.
Despite the growing amount of patients who underwent coronary artery bypass grafting (CABG) in low- and middle-income countries like China, their glucose control was suboptimal, likely due to poor adherence to healthy lifestyles and preventive medications. Mobile health tools facilitating secondary prevention seem promising, but evidence focusing on this high-risk population is scarce. This study aimed to evaluate the significance of mobile health tools in long-term glycemic management for post-CABG patients with comorbid diabetes mellitus. GUIDEME (glycemic control using mini program-based intervention in patients with diabetes undergoing coronary artery bypass to promote self-management) is a multicenter, open-label, closed-user group, randomized controlled trial, in which 1066 patients with diabetes who had recently undergone CABG were enrolled and allocated into 2 groups. Patients in the control group received conventional health education before discharge, whereas those in the intervention group additionally received automatic delivery of bite-sized health education and medication reminders through a smartphone app during the 6 months after discharge. The primary end point was a change in glycosylated hemoglobin (HbA1c) from baseline to 6 months. Among the 1066 eligible participants enrolled, a total of 1038 (97.4%) had completed the follow-up, while 1000 (93.8%) had 6-month HbA1c results available. Although only 79 (14.9%) patients in the intervention group were defined as active users, a greater reduction of HbA1c in the intervention group was observed (adjusted between-group mean difference -0.13, 95% CI -0.25 to -0.01; P=.04). The intervention group also had a high proportion of good medication adherence (96.1% vs 93.2%, P=.04). There was no difference between the 2 groups regarding the secondary end points. Health education and medication reminders based on smartphone app achieved a statistically significant but modest between-group difference in HbA1c, the clinical relevance of which remains uncertain.
Type 1 diabetes mellitus (T1DM) is one of the most common chronic diseases among adolescents and is posing a threat to health and potentially endangering life. It can have a significant impact on the physical, social, and emotional development of adolescents. Understanding the lived experiences of adolescents with T1DM is crucial for improving their health outcomes and future aspirations. However, there is currently limited research, and existing studies on psychosocial and self-care perspectives do not highlight their lived experiences in Ethiopia. This study aimed to explore the lived experiences of adolescents living with T1DM in Bahir Dar, Ethiopia. A descriptive phenomenological design was used. The study was conducted at Felege Hiwot Comprehensive Specialized Hospital in Bahir Dar from March to May 2023. Ten adolescents aged 10-19 years receiving follow-up care at the hospital were selected using purposive sampling. Data were collected through face-to-face, in-depth interviews using a semistructured guide translated into Amharic. All interviews were conducted by the principal investigator, audio-recorded, transcribed verbatim, and translated conceptually. Thematic analysis was conducted using ATLAS.ti (8.4.24; ATLAS.ti Scientific Software Development GmbH) software, guided by the Colaizzi phenomenological method. The study included 10 participants (7 female and 3 male), aged 13-18 years, with the duration of living with T1DM ranging from 6 months to 11 years. It identified 4 key themes in adolescents managing insulin dependence. First, psychoemotional factors were significant, with participants experiencing stress, anxiety, low self-esteem, and negative emotions such as fear and hopelessness, although some also reported positive feelings from strong social support. Second, supportive factors were crucial, as family, friends, teachers, health care providers, and community health insurance offered essential emotional and practical help. Third, significant challenges included insulin unavailability, financial issues, fasting-related nonadherence, fear of hypoglycemia, and sleep disturbances, all impacting their well-being. Finally, adolescents used various coping strategies, such as avoidance, acceptance, prayer, and social engagement, to manage emotional distress and maintain daily functioning. Adolescents with T1DM in Bahir Dar face multifaceted challenges that affect their emotional well-being and diabetes management. Despite these difficulties, they demonstrate resilience through various coping strategies and benefit from strong support systems. These findings underscore the need for adolescent-centered diabetes care, improved insulin access, and psychosocial support in both clinical and community settings.
The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The PROTEIN project aimed to facilitate users in choosing healthy living, thereby improving their metabolism and T2D management. This study aims to assess the efficacy of a personalized mobile app to achieve a 5% time in range (TIR) improvement over a 12-week intervention in adults with prediabetes or T2D. We conducted an exploratory pilot randomized controlled trial with 21 individuals with T2D or prediabetes who used a continuous glucose monitoring system and the PROTEIN mobile app for personalized meals and exercise recommendations based on their glucose levels and physical activity. The TIR of the participants increased (P<.05; from 71.8%, SD 27.3% to 76%, SD 28.1%) with individual use of the PROTEIN app but did not achieve a 5% improvement overall; however, given the exploratory design and small sample size, this finding should be interpreted with caution. Glycated hemoglobin, fasting blood glucose, and body weight did not fluctuate throughout the 12-week intervention. The dropout rate was high, and the average duration of use of the PROTEIN app was 42 (range 5-84) days. Our results showed a modest increase in TIR with the use of the PROTEIN app; however, considering the exploratory design and small sample size, this finding should be interpreted as preliminary. Integrating wearables and automated personalization for well-being is an innovative approach that must keep pace with the accelerated development of ever-evolving technologies. The COVID-19 pandemic was a major obstacle to recruitment in our clinical trial. ClinicalTrials.gov NCT05951140; https://clinicaltrials.gov/study/NCT05951140.
Chronic illness disrupts everyday routines, social roles, and sense of self, particularly among young individuals undergoing identity formation. With the expansion of digital media, social platforms have become key sites where patients narrate illness experiences, negotiate stigma, and seek support. However, such processes remain underexplored in non-Western, collectivist contexts. This study examines how young Chinese individuals with diabetes construct illness narratives and negotiate identity in digital environments. This study uses a narrative analysis approach, combining inductive thematic coding with culturally and critically informed interpretation. A total of 303 narrative posts were collected from RedNote, a Chinese social media platform characterized by diary-like user-generated content. The dataset includes both text-based and video-based posts, capturing longitudinal and first-person accounts of living with diabetes. In total, 4 distinct narrative types were identified. The chaos narrative captures experiences of cognitive dissonance, emotional breakdown, and disruption of daily routines following diagnosis, often accompanied by guilt toward family members and anxiety over future uncertainty. The stigma narrative reflects social withdrawal, concealment of illness, and perceived discrimination in intimate relationships and employment contexts, highlighting the role of externally imposed social judgment. The resilience narrative illustrates processes of self-acceptance, disciplined self-management, and the integration of illness into everyday life through routinized practices such as blood glucose monitoring and dietary regulation. The solidarity narrative emphasizes the importance of familial care and digitally mediated peer support, where users exchange practical knowledge, emotional encouragement, and collective identity markers, transforming isolation into shared experience. Across these narratives, illness is not only experienced as disruption but also actively reinterpreted through culturally embedded values such as familial responsibility and collective belonging. This study advances illness narrative research by demonstrating how digital platforms mediate culturally specific forms of meaning-making among young patients with chronic illness. It extends the concept of biographical disruption by conceptualizing it as a dynamic and relational process shaped by digital storytelling, familial expectations, and peer interaction. The findings highlight the importance of culturally sensitive and platform-aware approaches to health communication and digital patient support.