JMIR mHealth and uHealth

Tobacco Use Trajectories and Associated Changes in Biometrics and Sleep During the First 72 Weeks of Wearable Membership: Observational Cohort Study

2026-07-03T17:01:34-04:00

Background: Tobacco use remains a leading preventable cause of morbidity and mortality. Digital health tools and wearable technologies offer scalable opportunities for behavioral self-monitoring. However, real-world evidence characterizing long-term tobacco use trajectories and associated physiological changes during wearable adoption is limited. Objective: This study aims to characterize longitudinal trajectories of self-reported tobacco use during the first 72 weeks of wearable adoption and to examine associations between tobacco use and wearable-derived cardiopulmonary and sleep measures. Methods: We analyzed data from 12,678 new wearable members (18-79 years) who contributed up to 72 weeks of daily self-reported tobacco use and wearable-derived biometric data. Longitudinal trajectories of tobacco use were examined across prespecified 12-week quarters (Q1-Q6) using generalized linear mixed-effects models. Associations between tobacco use and wearable-derived nocturnal resting heart rate (RHR), heart rate variability (HRV), respiratory rate (RR), and sleep duration were evaluated using linear mixed-effects models that accounted for within- and between-person variation and adjusted for demographic and temporal covariates. Results: Across 3,765,573 person-days, the estimated daily probability of tobacco use declined from 55.1% (95% CI 53.8-56.4) during Q1 to 27.2% (95% CI 26.1-28.3) during Q6, representing an absolute reduction of 27.9 percentage points (95% CI –28.4 to –27.4). Among tobacco users with end-of-follow-up data, over one-quarter (1404/4975, 28.22%) reported no tobacco use during Q6. Greater logging engagement was associated with larger reductions in tobacco use; each 10-percentage-point increase in engagement corresponded to a 0.92-percentage-point greater decline from Q1 to Q6 (95% CI –1.58 to –0.26). Following tobacco use days, RHR was 1.71 beats/minute higher (95% CI 1.70-1.73), HRV was 3.54 ms lower (95% CI –3.59 to –3.49), RR was 0.19 breaths/minute higher (95% CI 0.19-0.20), and sleep duration was 9.78 minutes shorter (95% CI –10.08 to –9.49) relative to nonuse days. Reductions in tobacco use over time were associated with directionally favorable changes in RHR (P=.001), RR (P=.002), and sleep duration (P=.02). Conclusions: In this large-scale, observational, real-world study of wearable users, the probability of tobacco use declined over the first 72 weeks of adoption. Among participants with end-of-follow-up data, more than one-quarter reported no tobacco use during Q6. Tobacco use was consistently associated with less favorable cardiopulmonary and sleep measures, while reductions in tobacco use over time co-occurred with directionally favorable changes in these measures, although such changes may also reflect broader lifestyle or health changes. Overall, this study provides large-scale, longitudinal evidence that sustained reductions in tobacco use co-occur with favorable changes in physiological markers within a digital self-monitoring environment. As the findings derive from a single commercial ecosystem, independent replication in noncommercial, multiplatform settings will be needed to establish generalizability.

Alleviating Nurse Burnout With an Artificial Intelligence–Selected Mobile Cognitive Behavioral Therapy–Based Intervention: Mixed Methods Randomized Controlled Trial

2026-07-03T15:30:05-04:00

Background: Nurse burnout is a pervasive global problem. Cognitive behavioral therapy (CBT) has been shown to reduce burnout; however, most digital CBT programs use standardized approaches that overlook individual differences in burnout profiles. With advances in artificial intelligence (AI), algorithm-based recommendation systems now enable personalized intervention delivery by matching specific CBT modules to users. Objective: This study aimed to test the effects of an AI-selected mobile CBT-based intervention on nurse burnout and to describe participants’ experiences with the intervention. Specifically, it evaluated whether an AI-selected CBT-based intervention differentially reduced burnout subdomains compared with an information-only control group and explored how nurses perceived and engaged with the AI-selected program. Methods: This study adopted a mixed methods design, integrating a 2-group randomized controlled trial and qualitative content analysis exploring participants’ experiences. For this randomized controlled trial, a total of 125 nurses were enrolled and randomly assigned to either the experimental group (n=62) or the control group (n=63) between October 2024 and December 2024. The experimental group received an AI-selected mobile CBT-based intervention, in which an AI algorithm assigned CBT modules based on participants’ burnout profiles (client-related, personal, and work-related), job stress, and coping characteristics. The control group received information related to burnout management. Primary outcomes, client-related, personal, and work-related burnout, were assessed at baseline, 2 weeks, and 4 weeks. Secondary outcomes, including coping strategies, job stress, and stress response, were assessed at baseline and 4 weeks. Between-group differences in burnout over time were examined using repeated measures analysis of variance, with adjustment for job stress and stress response. Within-group changes and postintervention group differences were analyzed using tests. Open-ended survey responses and follow-up interviews (n=5 in the experimental group) were analyzed using thematic content analysis. Results: Follow-up completion rates were 84.6% (137/162) at both 2 and 4 weeks. The experimental group showed a greater reduction in client-related (=7.548; =.007), personal (=6.533; =.01), and work-related burnout (=38.194; <.001) than the control group, reflecting more pronounced within-group improvements over time. No significant between-group differences were observed for coping strategies, job stress, or stress response. Qualitative findings suggested that some participants were receptive to the AI-selected CBT-based intervention and reported increased self-awareness and reflective engagement with coping strategies that they might not have selected independently. Conclusions: The findings suggest that participants were receptive to AI-selected CBT-based interventions, suggesting the potential of such interventions as a supportive approach for alleviating nurse burnout. Future research should explore the sustainability of these effects and optimize the intervention duration to enhance engagement and impact. Trial Registration: Clinical Research Information Service (CRIS) KCT0009853; https://tinyurl.com/3b5pesd7

Efficacy of a Cognitive Behavioral Therapy–Based Online Self-Help Group for Depression and Suicide Ideation: Randomized Controlled Trial

2026-07-03T14:15:16-04:00

Background: Despite the high prevalence of depressive disorders, access to effective treatment remains limited due to financial, geographic, and social barriers. Online self-help groups offer a promising and scalable form of peer-based support beyond traditional clinical settings. Integrating cognitive behavioral therapy (CBT) techniques such as cognitive restructuring and behavioral activation into self-help groups may enhance their effectiveness. Objective: This study evaluated the efficacy of a cognitive behavioral therapy–based online self-help group (COS) that integrates structured CBT techniques with peer-led group support as a low-intensity intervention for individuals with depressive symptoms. A randomized controlled trial (RCT) comparing COS with a CBT-based mobile application was conducted. Additionally, a separately recruited waitlist control group was included as a supplementary comparison condition. Methods: Participants were recruited online. After eligibility screening via a structured clinical interview, participants were randomly assigned to a COS group (n=79) or a CBT-based mobile application group (n=39). An additional waitlist control group (n=48) was recruited separately during the second phase of the study. The COS intervention involved 7 videoconferencing sessions that incorporated peer-led group discussions, sharing lived experiences, and core CBT techniques such as cognitive restructuring. The primary outcome measure was depressive symptoms, assessed using the Beck Depression Inventory-II, and the secondary outcome was suicidal ideation, estimated using the Beck Scale for Suicide Ideation, measured at baseline, postintervention, and 3-month follow-up. Linear mixed models were used to evaluate group × time interaction effects. Reliable change indices were also calculated to assess clinical significance. All statistical tests were 2-tailed. Results: Among participants assigned to the COS group, 61% (48/79) completed all 7 sessions, and 84% (66/79) attended 5 or more sessions. A significant time × group interaction was observed for depressive symptoms (=7.23, <.001). The COS group exhibited a substantial reduction in depressive symptoms from baseline to postintervention (=10.77, two-tailed; <.001), with a large within-group effect size (=1.38); this improvement was maintained at the 3-month follow-up. Suicidal ideation also significantly decreased in the COS group (=4.55, two-tailed; <.001), with sustained effects at follow-up. Clinically meaningful improvement in depressive symptoms, as defined by the reliable change index, was observed in 75% (56/75) of COS participants. While both the COS and app-based CBT groups achieved comparable reductions in depressive symptoms, only the COS group demonstrated a significant reduction in suicidal ideation. Conclusions: This RCT provides evidence that a structured, CBT-informed online self-help group can reduce depressive symptoms and suicidal ideation. The COS program offers a scalable, accessible alternative to traditional therapy, particularly in settings with limited access to mental health professionals. Trial Registration: Clinical Research Information Service (CRiS) KCT0007673; https://tinyurl.com/328madxe

Inclusive Contactless Monitoring for Older Adults From Diverse Backgrounds: Mixed Methods Study

2026-07-03T10:45:15-04:00

Background: As Canada’s population ages, accessible tools for chronic health monitoring are increasingly needed. Traditional and contact-based devices pose barriers for underserved populations due to cost, maintenance, and usability. Contactless sensing technologies offer a promising alternative, but equitable development requires inclusive engagement and diverse data collection. Objective: This study aimed, first, to gather perspectives from culturally and health-diverse older adults on innovative contactless vital sign monitoring technology and, second, to build on a diverse dataset for developing and testing the contactless sensing software VitalSeer by the National Research Council of Canada. Methods: A mixed methods study was conducted with older adults from diverse backgrounds across 3 sites. Participants were asked to respond to a questionnaire that had closed- and open-ended questions regarding their perspectives on contactless sensing technology. Video and reference vital sign data were collected from each participant using a portable system designed specifically for gathering controlled data. Results: We collected data from 48 participants (mean age 70, SD 8 years), of whom 98% (n=47) expressed a positive perception of the usefulness of a contactless sensing system. We also identified four themes from the qualitative analysis of the open-ended questions: (1) perceived value—system potential and clinical relevance; (2) ease of use—noninvasiveness and comfort; (3) trust and transparency—data security and clarity of design; and (4) inclusion and improvement—accessibility, functionality, and feature expansion. Finally, the collected data, 288 minutes of concurrent video and reference vital sign data, will be used to test and enhance contactless sensing software for diverse older adult populations. Conclusions: This work advances the goal of inclusive medical device research and development. It highlights the potential for contactless sensing to be adopted to support independent living for older, diverse adults. Research is ongoing to adapt the technology for widespread adoption.

Systematic and Collaborative Approach to Learning and Educational Content Development (SCALED) for Health Apps: An Experience-Informed Conceptual Framework

2026-07-02T13:30:18-04:00

Mobile health (mHealth) apps are widely used for noncommunicable disease prevention and self-management. However, their effectiveness and safety are undermined by substantial variation in content quality. Existing guiding frameworks primarily focus on user interface, functionality, and intervention delivery, with limited emphasis on content creation. In this viewpoint, we introduce Systematic and Collaborative Approach to Learning and Educational Content Development (SCALED), a conceptual framework designed to guide a systematic, collaborative, and evidence-based approach to mHealth educational content development, intended for app developers, researchers, health care providers, and the wider mHealth community. Developed and refined across 3 phases, the SCALED framework consists of 11 components organized into 3 sequential stages: planning and conceptualization, development of textual content, and finalization into delivery format. We discuss the rationale behind each component and illustrate its applicability through 2 mHealth use cases. The framework integrates real-world experience from the development of 3 mHealth apps, qualitative findings from 2 studies, and insights from key stakeholders. By offering a structured and replicable methodology for content development, SCALED addresses a critical gap in current mHealth frameworks and provides a practical guide to improve content veracity, with potential for adaptation across a range of medical conditions.

Effects of Telerehabilitation Based on Motion Recognition Technology on Exercise Endurance of Patients With Non–Small Cell Lung Cancer After Surgery: Single-Center, Prospective, Open-Label, Randomized Controlled Trial

2026-06-29T17:00:03-04:00

Background: Non–small cell lung cancer (NSCLC) accounts for approximately 85% of primary pulmonary neoplasms. Complete surgical removal remains the cornerstone of curative therapy, yet it frequently diminishes residual lung function and exercise tolerance. Structured, center-based rehabilitation hastens physiological recovery, but conventional schemes rarely deliver continuous, patient-specific monitoring. Remote, digitally delivered exercise overcomes logistical obstacles; however, the lack of real-time quality assurance curtails effectiveness. Wearable motion capture platforms that provide millimeter-precise kinematic data and instantaneous biomechanical feedback can close this supervisory void by confirming movement accuracy and issuing immediate corrective prompts. Whether this technologically augmented telerehabilitation yields clinically relevant improvements in postoperative exercise capacity after NSCLC resection remains inadequately established. Objective: This study aims to find out whether motion capture–enabled telerehabilitation can enhance exercise endurance and functional recovery following NSCLC resection, thereby filling a critical void in postoperative care pathways. Methods: We performed a single-center, parallel-arm randomized trial in individuals who had completed curative lung resection for NSCLC and satisfied every enrollment criterion. Following randomization, eligible patients were assigned either to a technology-enhanced telerehabilitation protocol incorporating live motion sensing or to a WeChat-based regimen without that feedback layer. Each program spanned 4 weeks. Assessments—comprising exercise endurance, spirometric variables, health-related quality of life, and daily physical activity—were collected at discharge and repeated 4 weeks after intervention. Results: No exercise-related adverse events occurred during the 4-week intervention. Compared with conventional video-guided training, the motion recognition group demonstrated significantly greater improvements in exercise tolerance, pulmonary function, selected functional mobility outcomes, and quality of life. The intervention group achieved a greater increase in 6-minute walk distance (mean difference 32 meters, 95% CI 5.40-58.60; P=.02). Significant between-group differences were also observed in forced vital capacity (mean difference 476.4 mL; P=.02), forced expiratory volume in 1 second (mean difference 346.0 mL; P=.04), FEV₁/FVC ratio (mean difference 12.8%; P=.03), and peak expiratory flow (mean difference 70.8 L/minute; P=.002). For functional mobility, the intervention group showed superior improvement in the Timed Up and Go test (mean difference −1.63 seconds; P=.04), whereas no significant between-group differences were found in other mobility measures. Quality of life outcomes favored the motion recognition group, with greater improvements in physical well-being, functional well-being, additional concerns–lung subscale, and total Functional Assessment of Cancer Therapy–Lung score (mean difference 13.00; P<.001). Program adherence was higher in the intervention group (72% vs 40%; P=.02). Conclusions: Four-week motion capture–guided telerehabilitation yielded clinically meaningful gains in aerobic endurance, spirometric indices, ambulatory capacity, and global health–related quality of life for patients recovering from NSCLC surgery, underscoring its usefulness as a safe and effective remote care strategy. Clinical Trial: Chinese Clinical Trial Registry ChiCTR2500113139; https://www.chictr.org.cn/showprojEN.html?proj=270991

Feasibility and Acceptability of mPallCare, a Digital Health Intervention for People Living With Advanced Cancer in a Refugee Settlement in Uganda: Mixed Methods Study

2026-06-26T12:00:23-04:00

Background: Palliative care is a key component of comprehensive humanitarian health; yet, access and service capacity remain limited in displacement settings, where fragile health systems struggle to meet the complex needs of people living with advanced illness. Digital health technologies have the potential to enhance the reach and delivery of palliative care; yet, their feasibility and acceptability in humanitarian settings remain underexplored. Objective: We evaluated the feasibility and acceptability of mPallCare, a mobile health intervention integrating patient-reported symptom and outcome monitoring with a clinician dashboard, to support palliative care delivery in the Bidibidi Refugee Settlement, Uganda. Methods: A 6-week, uncontrolled, exploratory concurrent mixed methods feasibility study was conducted, involving 32 participants with advanced cancer. Community health workers (ie, village health teams) used the mobile app to document patient-reported symptoms and multidimensional outcomes, which were accessible to clinical teams via a dashboard. Following the use of mPallCare, patient and clinical team participants participated in face-to-face interviews. Data collected via mPallCare were analyzed using descriptive statistics to assess feasibility (ie, compliance with reporting, with a feasibility threshold of ≥65% of scheduled reports), and interview data from a subsample of patient and clinical team participants were analyzed using framework analysis to assess acceptability. Results: Participants completed 84.9% (163/192) of symptom reports and 59.4% (266/448) of outcome reports, with a combined 67% (429/640) of all scheduled reports completed. A modest decline in engagement with report submissions occurred across the 6-week study period. Commonly reported symptoms included headache (27/32, 84.4%), muscle pain (27/32, 84.4%), and dizziness (26/32, 81.3%). Interview findings indicated strong acceptability among patients and clinicians, who described improved communication, enhanced symptom management, and greater continuity of care. Reported challenges included initial navigation difficulties, limited translation accuracy, and technical synchronization issues. Participants and clinical leaders identified the potential for integrating mPallCare within Uganda’s district health information system to strengthen data use and visibility of palliative care within health reporting structures. Conclusions: mPallCare is a feasible and acceptable digital health intervention for palliative care in a humanitarian setting. While initial uptake was high, sustaining engagement over time may require simplified reporting processes, enhanced language accessibility, and optimizing the mobile app’s connectivity and usability. This feasibility phase highlights key priorities for scale-up, including integration with existing health information systems and adaptation for sustained, equitable use across low-resource and displaced populations.

Long-Term Effectiveness of Unguided Internet-Based Cognitive Behavioral Therapy on Major Depressive Disorder in Chinese Adults: Randomized Controlled Trial With a 12-Month Follow-Up

2026-06-24T17:30:17-04:00

Background: Unguided internet-based cognitive behavioral therapy (ICBT) is a low-cost and scalable treatment for major depressive disorder (MDD), but its long-term effects in Chinese populations remain unclear. Objective: This study aimed (1) to explore the short- and long-term effectiveness of unguided ICBT in treating adults with MDD; (2) to investigate the short- and long-term effects on disease-related symptoms, individual and social functioning, and quality of life; and (3) to assess the acceptability and satisfaction with the ICBT. Methods: An 8-week randomized controlled trial (ChiCTR2100046425) was conducted between August 2021 and June 2023 in Shenzhen, China, with 159 participants in the immediate ICBT group (7-module ICBT course plus usual care) and 158 in the waitlist control (WLC) group (usual care). The WLC group later completed the same ICBT course and follow-up assessments. Outcome measures (depressive and anxiety symptoms, psychological distress, social functioning, self-efficacy, quality of life, and stigma) were assessed before and after treatment and at 3-, 6-, and 12-month follow-ups for ICBT participants. Remission and response, adherence, and satisfaction were evaluated by predefined standards. Results: Among 300 participants analyzed (mean age 28.49, SD 7.0 years; female: n=225, 75%), dropout rates were 22.4% (34/152) in the immediate ICBT group versus 6.3% (10/158) in the WLC group. At posttreatment, the immediate ICBT group showed greater reduction in depressive symptoms versus WLC (mean difference −3.65, SE 0.60; <.001; =0.50), with higher remission (80/121, 66.1% vs 58/148, 39.2%; <.001) and response rates (50/121, 41.3% vs 27/148, 18.2%; <.001). At 12-month follow-up, the depressive symptoms were improved compared with that at pretreatment (mean difference −3.90, SE 0.32; <.001; =0.70), and no significant change was observed in comparison with the outcomes at posttreatment (mean difference −0.81, SE 0.33; =.33; =−0.15). ICBT treatment also exhibited similar short- and long-term effects on secondary outcomes, with significant improvement of disease-related symptoms, individual and social functioning, and quality of life. Moreover, the majority of the participants treated with ICBT reported high acceptability of and satisfaction with the ICBT course. Conclusions: Unguided ICBT effectively reduces depressive symptoms and enhances functioning in Chinese patients with MDD, with sustained benefits over 12 months. Its scalability and low-cost nature make it a promising option for resource-limited settings. Trial Registration: Chinese Clinical Trial Registry ChiCTR2100046425; https://tinyurl.com/bdcrj4zv

Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study

2026-06-24T16:30:03-04:00

Background: Patients with Parkinson disease (PD) along with subjective cognitive decline (PD-SCD) are considered an intermediate status between those with normal cognition (PD-NC) and those with mild cognitive impairment (PD-MCI). Wearable digital monitoring technologies and machine learning models offer significant potential for assessing cognitive impairment in patients with PD. Objective: We aimed to evaluate the utility of wearable technology and machine learning for identifying ordinal cognitive stages (OCS) in PD based on timed up and go (TUG) tests (including single-task TUG [TUGst] and dual-task TUG [TUGdt]). Methods: Patients with PD along with SCD, MCI, and NC were recruited in a movement disorder clinic. Patients performed TUGst and TUGdt gait trials wearing a motor function and motor symptom quantitative assessment system. In total, 209 kinematic parameters were synthesized for individual TUG to illustrate patients’ motion profiles. We constructed dual-task cost parameters (DTC), describing the magnitude of the effect of the cognitive challenge on motion performance. Covariate-adjusted ordered logistic regression was used to compare parameter differences among 3 groups. Multiple machine learning models were used to classify the participants into 3 cognitive impairment levels, with features being selected based on P values from intergroup statistical tests. The total population was randomly divided into a training set and an independent validation set in a 7:3 ratio, and 10-fold cross-validation was used in the training set. Furthermore, this study used permutation importance and Shapley Additive Explanations (SHAP) analysis (including summary plots, bar plots, and waterfall plots) to explain the feature importance of the final model. Results: The study included 65 age-matched patients (PD-NC: PD-SCD: PD-MCI= 14:21:30). Forty-five kinematic parameters were significantly different (P<.05) among the 3 groups, distributed across TUGst (n=25), TUGdt (n=12), and DTC (n=8) paradigms. Gait phase analysis revealed 35 parameters from walking phases, 9 from stand-to-sit transitions, and 1 from sit-to-stand transitions. Feature type distribution demonstrated predominance of variability features (n=20), followed by pace (n=12) and axial (n=8) characteristics. TUGdt paradigm analysis revealed pronounced movement differences between PD-MCI and both PD-NC and PD-SCD groups, particularly in variability, amplitude, pace, and axial domains. Cross-paradigm analysis identified consistent significant differences in specific features. These findings provide objective kinematic biomarkers for early cognitive state identification in Parkinson disease, with TUGdt parameters demonstrating superior discriminative capacity. Conclusions: This suggests patients with PD-SCD could have early kinetic signs of cognitive impairment, positioning them between PD-NC and PD-MCI, and our multiclass support vector machine classification model with kinematic parameters achieved a recall rate above 0.70 in both training and validation datasets. The feature importance analysis revealed that DTC_Trunk-Right Rotation Max, DTC_Trunk-Max Transverse Angular Velocity, and dTUG_Lumbar-Right Sway Max Std were the most critical features for distinguishing cognitive states, providing scientific evidence for cognitive function screening based on kinematic parameters. Trial Registration:

Enhancing Cognitive Functions of Older Adults With Software Robot: Longitudinal Exploratory Field Study

2026-06-24T16:15:13-04:00

Background: The global prevalence of dementia continues to rise and demands scalable, nonpharmacological interventions. Digital cognitive training has expanded, but many older adults, who have limited digital literacy, struggle to sustain use. We designed an intervention that integrates social interaction, reward-based engagement, and an artificial intelligence (AI) conversational agent, which aims to reduce these barriers and support continuous participation. Objective: This study examined whether a 12-week digital cognitive training program improves cognitive function in older adults. It also tested whether a group chat service, which enables interaction among participants and with an AI agent, increases engagement and social support. Methods: We recruited 133 participants (mean age 64.75, SD 6.76; range 55-75 years) who had no diagnosis of dementia. All participants used the program for 12 weeks. The program includes an AI chatbot () and a group chat service (), which supports peer interaction. We measured cognitive function using the Korean Mini-Mental State Examination—Version 2 (K-MMSE-2). We also assessed degrees of social support (Medical Outcomes Study—Social Support Survey), depression (Short Form Geriatric Depression Scale—Korean Version), and engagement (Twente Engagement with eHealth Technologies Scale), and analyzed usage logs to examine participation patterns. Results: Participants showed improved cognitive function after the intervention (Hedges =0.350, <.001). Active users (n=67), who engaged more frequently with the program, showed greater improvement than nonactive users (n=66), especially among those who had lower baseline cognitive scores (Hedges =0.523, <.001). Social support increased, particularly emotional and informational support (=−6.509, <.001). Participants reported higher engagement (=2.008, <.05) and lower depression scores (=3.093, <.01). Regression results showed that group chat participation, which promotes interaction with the AI agent, increased engagement in cognitive training (=12.395, <.001). Increased engagement was associated with higher social support (=4.102, <.001) and improved cognitive function (=2.467, <.05). Cognitive training alone did not produce a significant effect. Participants showed low adherence, which indicates a need for strategies that sustain long-term use. Conclusions: The program improved cognitive function and strengthened social support in older adults. Social interaction, which increases engagement, played a central role. These findings suggest that digital cognitive interventions should incorporate social mechanisms to achieve meaningful effects.