Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly prescribed for weight management, while physical activity remains a central component of lifestyle management. Evidence on their comparative and combined effects on weight in real-world populations is limited. Methods: We analyzed data from All of Us Research Program (v8), containing electronic health record and Fitbit step counts. We included 316 adults (age ≥18) with obesity (BMI ≥ 30 kg/m 2 ) without type 2 diabetes (T2D), weight available at baseline and 1-year follow-up, and ≥3 months of persistent GLP-1 use. Comparative analyses used a covariate-adjusted regression model (2,279 non–GLP-1 users) and a propensity score matching (293 samples per group). The primary outcome was percent change in body weight, with GLP-1 use (yes/no) and physical activity (average daily steps >5,000) as main predictors. We further examined four prespecified contrasts to evaluate the main effects of GLP-1 use and step counts, as well as their interaction. Additional analyses included step counts as a continuous variable, subgroup analyses by obesity class, and assessment of Fitbit-related selection bias by comparing the cohort to those without Fitbit data. Results: Mean age was 53.3 years (SD 12.6); 73% female, 70% white, and 88.4% non-Hispanic. Mean one-year weight loss was 5% (SD = 8.7%); 47% achieved ≥ 5% loss and 25% achieved ≥ 10%. The greatest reduction was observed among individuals with higher step counts in addition to GLP-1 therapy (mean: 6.1%, 95% CI: 4.3-8.0) after propensity score matching on age, sex, race and baseline BMI. GLP-1 use and step counts were independently associated with weight loss, with no significant interaction effect. Higher step counts, higher baseline BMI and White race (vs. Black) were significant predictors of weight loss. In contrast to clinical trials, real-world data show higher BMI is linked to less weight loss, possibly due to lower activity or unmeasured lifestyle factors. No major selection bias was detected from Fitbit data availability. Conclusions: Both GLP-1 therapy and physical activity were independently associated with clinically meaningful weight reduction at one year. Physical activity may add further benefit to GLP-1 use. This would support the complementary roles of lifestyle and pharmacological strategies in obesity management.

PURPOSE:As the prevalence of diabetes continues to rise, innovative strategies are essential to optimize patient outcomes while curbing the economic burden on healthcare systems. This study aims to describe the costs of implementing a team-based mobile health intervention for diabetes management (the mDAS intervention) that successfully lowered glycated hemoglobin (HbA1c) in African American and Latinx adults with type 2 diabetes from a health-system perspective, and to compare healthcare resource utilization (HCRU) and associated costs between the intervention and usual care groups over one year. METHODS:Intervention delivery costs were described by providing a breakdown of start-up and operating costs. Frequencies of outpatient, inpatient, and emergency department (ED) visits and associated charges were obtained using health system billing data. Negative binomial regression models were employed to compare frequencies of visits, and gamma regression models to assess differences in total healthcare costs. RESULTS:The total cost of implementing the mDAS intervention for 1 year was $56,032. This included $11,627 in start-up costs and $44,660 in annual operational costs. The intervention (n = 108) and usual care (n = 112) groups exhibited similar rates of outpatient visits (rate ratio [RR], 1.07; P = 0.52), ED visits (RR, 0.82; P = 0.68), and total healthcare costs (cost ratio, 1.03; P = 0.86) over 1 year. CONCLUSION:The mDAS intervention incurred modest implementation costs. It did not result in significant differences in short-term HCRU and associated costs when compared to usual care. Future research should explore specific mechanisms impacting HCRU and the long-term cost-effectiveness of implementing such interventions more broadly in nonacademic clinical environments.

Chronic disease management requires sustained engagement, trust, and collaboration among patients, families, and primary care teams. In underserved rural areas with aging populations, mobile health (mHealth) services offer opportunities to support remote monitoring and self-management; however, real-world user experiences and determinants of sustained adoption remain underexplored. This study explored the lived experiences and acceptance of community-based mHealth services among residents of an underserved rural area in South Korea, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. A convergent mixed-methods design was employed. Twenty-four participants using community-based mHealth services were recruited through public health centers. In-depth semi-structured interviews were analyzed using directed content analysis guided by UTAUT2 constructs, with inductive coding for emergent themes. Quantitative survey data on usability, service use, and willingness to pay were analyzed descriptively to complement qualitative findings. Participants were predominantly older adults (mean age 71.3 years) with multimorbidity, most commonly hypertension, hyperlipidemia, and diabetes. Performance expectancy, social influence, facilitating conditions, and habit emerged as the strongest drivers of acceptance and sustained use. Real-time visualization of health data motivated lifestyle changes and reinforced perceived health benefits. Nurse-involved, human-in-the-loop support-including troubleshooting, interpretation of data, and group-based activities-was critical in overcoming early usability challenges and fostering trust. Habit formation was evidenced by the integration of monitoring activities into daily routines. Perceived ease of use varied by age and prior digital experience, with older participants requiring longer adaptation periods. Nineteen participants expressed willingness to pay for continued access, whereas others cited sufficient self-management confidence or device-related issues as reasons for discontinuation. Community-based mHealth services embedded within nurse-involved and relationship-centered care models can be successfully integrated into daily life among rural older adults. Sustained adoption depends not only on technology usability but also on continuous human support, social engagement, and habit formation. Digital health strategies for underserved rural communities should prioritize intuitive design, structured onboarding, and long-term investment in primary care teams to promote equity, sustainability, and effective chronic disease self-management.

Background:Community-based mobile health (mHealth) services are increasingly used to support chronic disease management in underserved rural populations facing workforce shortages, geographic isolation, and rapid aging. South Korea entered a super-aged society in December 2024, intensifying pressures in rural regions where multiple mHealth programs are embedded within primary care and public health systems. However, evidence on sustained use in real-world settings remains limited. Objective:This study aimed to explore user experiences and acceptance of community-based mHealth services in an underserved rural area of South Korea and identify facilitators and barriers to sustained engagement, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods:A convergent mixed methods design was used, with qualitative and quantitative data collected in parallel, analyzed separately, and integrated at the interpretation stage. Overall, 24 participants with ≥6 months of experience using 1 of 4 publicly funded mHealth services in Pyeongchang County, Gangwon State, were purposively recruited. Semistructured interviews guided by the UTAUT2 were analyzed using directed content analysis, combining deductive and inductive coding. Structured questionnaires assessing usability and behavioral intention were analyzed using descriptive statistics. Findings were integrated through joint interpretation. Results:Participants had a mean age of 71.3 (SD 9.2) years, and 70.8% (17/24) were female; hypertension (18/24, 75%) and hyperlipidemia (15/24, 58.3%) were the most common. Perceived difficulty was low (mean 2.54, SD 2.06, on a 0-10 scale), intention for continued use was high (23/24, 95.8%), and recommendation intention was unanimous (24/24, 100%). Willingness to pay was reported by 79.2% (19/24), most commonly KRW 1000-5000 (US $1-3) per month. Qualitative findings identified performance expectancy, social influence, facilitating conditions, and habit as the most salient determinants of sustained use. Real-time monitoring enhanced health awareness, motivated dietary modification, and increased physical activity. Public health center nurses served as human-in-the-loop facilitators, providing continuous training, troubleshooting, and emotional support, while family and peers reinforced engagement. Habit formation emerged as a central mechanism, with 91.7% (22/24) integrating mHealth use into routines anchored to waking, exercise, and bedtime. Effort expectancy barriers among older participants were mitigated through nurse-led training, and hedonic motivation was driven by intrinsic satisfaction and peer interaction. Integrated analysis showed convergence for ease of use and behavioral intention, and partial divergence for willingness to pay. Conclusions:Community-based mHealth services were successfully integrated into daily life and supported chronic disease self-management among older adults in an underserved rural setting. Sustained engagement was driven by perceived health benefits, continuous human support, and habit formation rather than technology features alone, underscoring the importance of relationship-centered, human-in-the-loop implementation models. Strengthening intuitive design, hands-on onboarding, multidisciplinary primary care teams, and stable financing will be essential for equitable digital health adoption in rural and aging communities.

Type 2 diabetes (T2D) management requires individualized pharmacotherapy that balances glycemic control with cardiovascular, renal, and metabolic risk. However, translating rapidly evolving clinical guidelines and medication recommendations into patient-specific treatment decisions remains a persistent challenge. We present a medication-focused digital twin framework designed to generate personalized medication recommendations for patients with T2D, i.e., T2D-Med-DT. The system integrates electronic health records (EHR), remote glucose monitoring streams, and real-world evidence with counterfactual simulation to support clinical decision-making. The core is a structured patient Twin State that encodes key attributes, including demographics, renal function, cardiovascular phenotype, metabolic parameters, and current medications. A curated knowledge base incorporates guideline-derived indication rules, safety constraints, and findings from meta-analyses. A deterministic reasoning engine uses this knowledge through constraint enforcement, guideline-based prioritization, and counterfactual simulation across multiple treatment scenarios. Preliminary results from a prototype demonstration case suggest potential reductions in projected 3-year major adverse cardiovascular event (MACE) risk under optimized treatment strategies.

Background: Home test-to-treat (HTTT) programs deliver timely antiviral treatment for acute respiratory infections, including COVID-19 and influenza, through at-home testing and telehealth. Because access is often measured by visit occurrence, variation in how and when care is delivered may be overlooked. We hypothesized that telehealth access follows distinct process-based patterns. Methods: We analyzed de-identified encounters from the national HTTT program (September 2023-July 2024); 6,213 of 8,160 eligible individuals remained after exclusions for missing data. Phenotypes were derived by k-means clustering of standardized variables capturing encounter timing, modality preference, process duration, and sociodemographic and digital access attributes. Ten-day surveys assessed symptom duration and health care utilization. Results: Three phenotypes emerged: Delayed/Disrupted Access (n = 1,537; 24.7%), Digitally Engaged but Socioeconomically Vulnerable (n = 1,460; 23.5%), and Mainstream Access and Efficient Utilization (n = 3,216; 51.8%). Mean process duration differed (15.93 [SD 3.84] vs. 3.69 [3.31] vs. 2.87 [2.41] h; p < 0.001). Synchronous preference was lowest in the Digitally Engaged group (22.9%); antiviral prescribing was high (88.6-91.9%). Among 10-day respondents (n = 1,023), symptom duration did not differ. Emergency department visits were most frequent in the Digitally Engaged group (2.3% vs. 0.0% and 0.5%; p = 0.02) and urgent care in the Delayed/Disrupted group (5.8% vs. 4.1% vs. 2.0%; p = 0.02). Conclusions: Telehealth use in a national HTTT program formed distinct phenotypes defined by timing, modality, and care-process efficiency. Evaluating equity requires attention to how and when care is delivered, not simply whether it occurred.

Health-related quality of life (HRQL) assessment provides insights into the lived experiences of diverse populations. This study evaluated the convergent validity and responsiveness of the EQ-5D-3L in racial/ethnic minority populations with type 2 diabetes and elevated hemoglobin A1c (HbA1c). Secondary data from a clinical trial of a diabetes adherence support intervention for African-American and Latinx patients with type 2 diabetes (NCT02990299) were analyzed. Clinical (HbA1c, systolic blood pressure [SBP], body mass index [BMI]), psychological (brief 4-item diabetes distress scale [DDS4] for diabetes-related distress, 9-item patient health questionnaire [PHQ-9] for depressive symptoms), and HRQL (EQ-5D-3L) data were collected at baseline and every 6 months for 2 years. Convergent validity was assessed by examining the strength of associations between clinical/psychological measures and EQ-5D scores. A responder analysis using generalized estimating equation models was used to evaluate the EQ-5D's sensitivity to changes in clinical/psychological factors over time. Among 221 individuals analyzed, HbA1c and SBP levels did not differ across those reporting varying levels of problems in any EQ-5D dimension. Individuals with higher BMI were more likely to report problems with mobility, usual activities, pain/discomfort, and anxiety/depression. DDS4 scores were moderately correlated with EQ-5D anxiety/depression dimension and index score, while PHQ-9 scores were strongly correlated with both. EQ-5D index score was insensitive to improvements in clinical measures but sensitive to improvements in psychological measures over time. The EQ-5D-3L captured variations in BMI and psychological measures but not in HbA1c and SBP. This study provides HRQL estimates that can be compared with other populations or studies.

Backround: Self-care is essential for managing heart failure (HF), yet many patients struggle with adherence to prescribed medications, low sodium diet, and daily self-monitoring of vital signs and symptoms. Commercial mobile health (mHealth) technologies offer unique opportunities for improving HF self-care, but their efficacy is underexplored. Aim: This study aimed to assess the feasibility and preliminary efficacy of a patient-centered intervention (iCardia4HF) that integrates multiple commercial mHealth apps and devices with individually tailored text messages (TM) to promote HF self-care. Methods: We conducted a two-arm, pilot randomized trial (NCT04262544) with allocation concealment and masking of outcome assessors. Eligible patients (adults with Stage C HF) were randomly assigned to either the control or intervention group. The control group (CG) received usual care enhanced with the provision of three consumer mHealth devices: Fitbit activity tracker and Withings Body Cardio scale and blood pressure (BP) monitor. The intervention group (IG) received the iCardia4HF program which synergistically integrates these devices with three commercial mHealth apps (Health Storylines, Withings, and Fitbit) and a program of individually tailored TM promoting HF self-care adherence. The primary outcome was medication adherence (MEMS bottle) and daily weighing adherence (Withings scale) over 12 weeks. Secondary outcomes included daily BP monitoring (Withings BP cuff) and change in self-reported self-care (SCHFI v7.2). Timestamped adherence data were transmitted from the devices to a secure digital health platform. Two-sample t-tests were performed to assess intervention effects. Results: A total of 81 patients completed the 12-week follow up (IG=42, CG=39). Participants’ mean age was 54.7 years (SD=11.0), 81% were African American, 45% were female, 64% had HFrEF, and 85% had NYHA class II or III. There were significant differences between the IG and CG in medication adherence (85% vs 75%; d=0.5, 95% CI: 0.04, 0.96; p=0.03) and daily self-monitoring of weight (72% vs 54%; d=0.6, 95% CI: 0.15, 1.04; p=0.007) and BP (67% vs 50%; d=0.54, 95% CI: 0.08, 1.0; p=0.02) over 12 weeks. There were no significant differences between the two groups in self-reported HF self-care. Conclusion: This pilot study provides preliminary evidence of feasibility and potential efficacy warranting further evaluation of the iCardia4HF intervention in a larger, fully powered trial with longer follow-up.

BACKGROUND:Comorbidities worsen cancer survival, but patterns of preexisting and new-onset comorbidities among cancer survivors are unknown. METHODS:We investigated self-reported and clinically diagnosed comorbidity among cancer survivors in the All-of-Us program's national database. Eight highly prevalent comorbidities were identified using self-reported data from the personal health history survey among cancer survivors (n = 20 534) and noncancer adults (n = 113 628) and validated among cancer survivors (n = 26 978) using data from electronic health records (EHRs). Among 5-year survivors (n = 9174) documented in EHR, we further estimated the incidence of new-onset comorbidities. RESULTS:The most prevalent comorbidities identified in personal health history data were hypertension (40.5%), osteoarthritis (28.4%), depression (28.0%), and obesity (23.2%). EHR data identified preexisting comorbidities: hypertension (43.3%), osteoarthritis (29.4%), depression (19.4%), and obesity (19.1%). During 5-year survival, more than 50% of cancer survivors developed at least one new comorbidity, and more than 25% developed two or more. The onset of new comorbidities showed a sharp increase in the first-year postdiagnosis. Incidence rates varied by age, race, and ethnicity. CONCLUSION:Future research is needed to develop effective strategies to prevent new-onset comorbidities during and after cancer treatment.

BackgroundHeart failure (HF) is one of the most common causes of hospital readmission in the United States. These hospitalizations are often driven by insufficient self-care. Commercial mobile health (mHealth) technologies, such as consumer-grade apps and wearable devices, offer opportunities for improving HF self-care, but their efficacy remains largely underexplored. ObjectiveThe objective of this study was to examine the feasibility, acceptability, safety, and preliminary efficacy of a patient-centered mHealth intervention (iCardia4HF) that integrates 3 consumer mHealth apps and devices (Heart Failure Health Storylines, Fitbit, and Withings) with a program of individually tailored SMS text messages to improve HF self-care. MethodsWe conducted a phase 1 randomized controlled trial. Eligible patients had stage C HF, were aged ≥40 years, and had New York Heart Association (NYHA) class I, II, or III HF. Patients were randomly assigned to either iCardia4HF plus usual care or to usual care only and were observed for 8 weeks. Key feasibility measures were recruitment and retention rates. The primary efficacy outcome was change in HF self-care subscale scores (maintenance, symptom perception, and self-care management) at 8 weeks, assessed with the Self-Care Heart Failure Index (SCHFI; version 7.2). Key secondary outcomes were modifiable behaviors targeted by the intervention (health beliefs, self-efficacy, and HF knowledge), health status, and adherence to daily self-monitoring of 2 core vital signs (body weight and blood pressure). ResultsA total of 27 patients were enrolled in the study and randomly assigned to iCardia4HF (n=13, 48%) or usual care (n=14, 52%). Of these 27 patients, 11 (41%) in the intervention group (iCardia4HF) and 14 (52%) in the usual care group started their assigned care and were included in the full analysis. Patients’ mean age was 56 (SD 8.3) years, 44% (11/25) were female, 92% (23/25) self-reported race as Black, 76% (19/25) had NYHA class II or III HF, and 60% (15/25) had HF with reduced left ventricular ejection fraction. Participant retention, completion of study visits, and adherence to using the mHealth apps and devices for daily self-monitoring were high (>80%). At 8 weeks, the mean group differences in changes in the SCHFI subscale scores favored the intervention over the control group: maintenance (Cohen d=0.19, 95% CI –0.65 to 1.02), symptom perception (Cohen d=0.33, 95% CI –0.51 to 1.17), and self-care management (Cohen d=0.25, 95% CI –0.55 to 1.04). The greatest improvements in terms of effect size were observed in self-efficacy (Cohen d=0.68) and health beliefs about medication adherence (Cohen d=0.63) and self-monitoring adherence (Cohen d=0.94). There were no adverse events due to the intervention. ConclusionsiCardia4HF was found to be feasible, acceptable, and safe. A larger trial with a longer follow-up duration is warranted to examine its efficacy among patients with HF. Trial RegistrationClinicalTrials.gov NCT03642275; https://clinicaltrials.gov/study/NCT03642275

Background:The COVID-19 pandemic led to increased demand for remote management of type 2 diabetes using secure messaging, or patient-provider text-based communication. Prior research on secure messaging has described the content of messages sent for type 2 diabetes management and demonstrated its impact on clinical outcomes. However, there is a gap in knowledge about how secure messaging performs as a communication medium for specific tasks in clinical care (eg, prescription management and discussing medical questions). Additional research is needed to understand physicians' experiences using secure messaging to communicate with patients about clinical tasks that support diabetes management. Objective:This study aims to investigate physicians' experience using secure messaging to communicate with patients about specific clinical tasks for type 2 diabetes management. Methods:We interviewed a sample of endocrinologists and internists from 2 different medical facilities who have used secure messaging to communicate with adult patients about type 2 diabetes management. Semistructured interviews were used to solicit physicians' experience using secure messaging for 6 specific tasks that support diabetes management: refilling prescriptions, answering nonurgent medical questions, scheduling appointments, discussing test results, making referral requests, and discussing visit follow-up. Interviews were conducted until we achieved saturation of themes for these tasks. Interview data were collected between 2021 and 2023. Qualitative data were analyzed using the framework method for thematic analysis. Results:We interviewed 6 internists and 4 endocrinologists (n=10). Physicians reported spending between 2 and 5 hours per day messaging with patients. They observed that secure messaging increased the frequency and timeliness of communication, which improved care coordination and facilitated care delivery between visits. This served as a time-efficient way to iterate specific components of treatment plans, including discussing test results, visit follow-up, scheduling, and prescription refill. Physicians were frustrated with the unstructured nature of secure messages. Patients wrote messages that were often disorganized, confusing, or did not have enough information for the provider to take action. This often made answering nonurgent medical questions difficult. In many cases, poorly structured secure messages resulted in lengthy back-and-forth communications between patients and physicians, which sometimes required a phone call or an office visit to resolve. Conclusions:Physicians reported that secure messaging supports a longitudinal model of care, where patients can iterate their treatment plan between visits. For tasks with well-defined information boundaries, such as scheduling and prescription refill, physicians reported that secure messaging improved the time efficiency of care delivery. Providers experienced challenges using secure messaging for more complex tasks and often reported not receiving sufficient clinical information. We identified a demand for workflow technologies to process incoming secure messages to improve clarity and ensure that messages have sufficient information to inform decisions on the best course of action.

Background: Acute ischemic stroke is a leading cause of death in the United States. Identifying patients with stroke at high risk of mortality is crucial for timely intervention and optimal resource allocation. This study aims to develop and validate machine learning-based models to predict in-hospital mortality risk for intensive care unit (ICU) patients with acute ischemic stroke and identify important associated factors. Methods: Our data include 3,489 acute ischemic stroke admissions to the ICU for patients not discharged or dead within 48 h from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Demographic, hospitalization type, procedure, medication, intake (intravenous and oral), laboratory, vital signs, and clinical assessment [e.g., Glasgow Coma Scale Scores (GCS)] during the initial 48 h of admissions were used to predict in-hospital mortality after 48 h of ICU admission. We explored 3 machine learning models (random forests, logistic regression, and XGBoost) and applied Bayesian optimization for hyperparameter tuning. Important features were identified using learned coefficients. Results: Experiments show that XGBoost tuned for area under the receiver operating characteristic curve (AUC ROC) was the best performing model (AUC ROC 0.86, F1 0.52), compared to random forests (AUC ROC 0.85, F1 0.47) and logistic regression (AUC ROC 0.75, F1 0.40). Top features include GCS, blood urea nitrogen, and Richmond RASS score. The model also demonstrates good fairness for males versus females and across racial/ethnic groups. Conclusions: Machine learning has shown great potential in predicting in-hospital mortality risk for people with acute ischemic stroke in the ICU setting. However, more ethical considerations need to be applied to ensure that performance differences across different racial/ethnic groups will not exacerbate existing health disparities and will not harm historically marginalized populations.

Despite the known benefits of physical activity, cancer survivors remain insufficiently active. Prior trials have adopted digital health methods, though several have been pedometer-based and enrolled mainly female, non-Hispanic White, and more highly educated survivors of breast cancer. The objective of this study was to test a previously developed mobile health system consisting of a Fitbit activity tracker and the MyDataHelps smartphone app for feasibility in a diverse group of cancer survivors, with the goal of refining the program and setting the stage for a larger future trial. Participants were identified from one academic medical center’s electronic health record, referred by a clinician, or self-referred to participate in the study. Participants were screened for eligibility, enrolled, provided a Fitbit activity tracker, and instructed to download the Fitbit: Health & Wellness and MyDataHelps apps. They completed usability surveys at baseline, 1-, and 3-months. Interviews were conducted at the end of the 3-month intervention with participants and cancer care clinicians to assess acceptability of the intervention and implementation of the intervention into clinical practice, respectively. Descriptive statistics were calculated for demographics, usability surveys, and Fitbit adherence and step counts. Rapid qualitative analysis was used to identify key findings from interview transcriptions. Of the 100 patients with cancer who were screened for eligibility, 31 enrolled in the trial, achieving a response rate of 31%. The mean (SD) age of participants was 64.8 (11.1) years. The two most frequent cancer diagnoses were prostate and breast cancer. Participants provided positive feedback on the MyDataHelps app usability; the overall app quality received a mean score of 3.79 (SD 0.82) on a Likert scale from 1 to 5, (1 = worst, 5 = best). Overall, participants felt the Fitbit activity tracker and MyDataHelps app were easy to use, but also benefited from the technical assistance of the research team. Clinicians appreciated the idea of having an objective measure of physical activity data but expressed a desire to receive training on using Fitbit data, as well as having a standard workflow in place for prescribing the Fitbit activity tracker for physical activity. Implementing a remotely-delivered, light-intensity physical activity program was feasible and acceptable in a population of diverse cancer survivors. Future studies should consider registry-based methods and work with clinicians to engage hard-to-reach survivor populations who have low physical activity levels and disproportionately high adverse health outcomes.