JMIR mHealth and uHealth

Application of Mobile Health Technologies to Prevent Unintentional Childhood Injuries: Scoping Review

2026-08-14T16:30:02-04:00

Background: Unintentional childhood injuries represent a major public health issue affecting the lives and health of children worldwide, with the risk and injury type dynamically changing with age and developmental stage. To effectively prevent unintentional injuries, intervention measures must be adjusted based on actual circumstances and kept up to date in real time. Mobile health (mHealth) technologies can meet this need, but their specific functions and intervention methods remain unclear at present. Objective: This study aimed to conduct a scoping review of research on the application of mHealth technologies in the prevention and emergency management of unintentional injuries among children, with a focus on identifying the types of mHealth technologies used, intervention contents, and outcome measures, thereby providing a reference for related studies. Methods: We systematically searched 5 databases—PubMed, Web of Science, Embase, Cochrane Library, and CINAHL—from their inception to December 31, 2025. Data extraction and synthesis were performed on the included studies. Results: Of 1085 articles, 15 (1.3%) met the inclusion criteria, and 1 additional reference was added during full-text review. Thus, 16 studies were included. The included studies comprised 9 (56.2%) randomized controlled trials, 2 (12.5%) quasi-experimental studies, 3 (18.8%) mixed methods studies, 1 (6.2%) qualitative study, and 1 (6.2%) descriptive study. mHealth technologies included apps, WeChat, mobile-based e-learning programs, and virtual reality combined with wearable devices. Their functions included health education, interactive communication, monitoring and reminders, behavioral training, questionnaires and record-keeping, and emergency response. Outcome measures included the incidence of unintentional injuries among children; knowledge, attitudes, and behaviors regarding injuries; other psychological and social indicators; and usability of mHealth technology. Conclusions: mHealth technologies have shown some progress in reducing unintentional injuries among children. Content based on this technology has demonstrated positive effects in reducing injury incidence, enhancing caregivers’ awareness, and improving related attitudes and behavioral capabilities. However, only 2 mobile apps are currently available to the public. Future research should optimize the functional design of mHealth, strengthen sustainability, explore long-term implementation methods, and conduct large-scale, long-term studies to further evaluate its impact on the incidence of unintentional injuries among children. Trial Registration: Open Science Framework 8QPR6; https://osf.io/8qpr6

Correction: Short-Term Effects of an mHealth Intervention on Healthy Behaviors and Cardiometabolic Health in Sedentary Employees: Quasi-Experimental Study

2026-08-14T15:15:11-04:00

Mobile Health in Home-Based Palliative Care: Realist Review of Contextual Factors and Mechanisms Influencing Outcomes

2026-08-14T14:45:11-04:00

Background: The increasing prevalence of chronic illness presents a significant global health challenge due to growing end-of-life suffering. Palliative care is now an essential health service under Universal Health Coverage. Its integration into primary health care and use of mobile health (mHealth) have been recommended to improve access. Objective: This study aimed to synthesize evidence on how mHealth interventions in home-based palliative care work, for whom, and in what context. Methods: This realist review identified global evidence relevant to mHealth interventions for home-based palliative care through searches of 9 electronic databases (ie, MEDLINE, Embase, PsycINFO, Global Health, CINAHL, Web of Science, Cochrane Database of Systematic Reviews, Scopus, and Global Index Medicus), and forward citation tracking of included articles conducted in March 2026. The review followed the 5 key stages of a realist review: scoping the literature to assess what is important about the context of mHealth intervention in home-based palliative care and what mechanisms might be important in how such interventions result in their intended outcomes; articulating the underlying program theory and refining the review scope through consultation with international palliative care experts; conducting iterative searches for and appraisal of relevant evidence; extracting data; and narratively synthesizing the data, prioritized by relevance and rigor to generate conclusions and recommendations. The Framework of Complexity in Palliative Care Context, adapted from Bronfenbrenner’s Ecological Systems Theory, was applied to examine the contextual influences and interactions among factors within the multilayered system. Context-mechanism-outcome configurations were developed and iteratively tested to refine the program theory for mHealth in home-based palliative care. Results: A total of 4134 records were identified, of which 422 (10.21%) articles were retained for full-text screening, and 126 (3.05%) studies were included in the final synthesis. The contextual factors and mechanisms that positively influence the intended outcomes include (1) alleviating concerns and mitigating perceived threats about mHealth’s suitability in palliative care through proper orientation for patients and carers, along with clear guidelines for health care professionals; (2) minimizing infrastructural and technological barriers through user-friendly designs and investment in sustainable models of mHealth for home-based palliative care; (3) engaging diverse stakeholders to ensure mHealth aligns with priority health care needs, user preferences, and the operations of implementing organizations and the wider health care system; (4) streamlining health management information systems through interoperable mHealth systems implemented at different levels of care; (5) enabling access to and regular communication with relevant health care teams; and (6) facilitating access to adequate information to empower users in palliative care services. Conclusions: mHealth interventions can enhance home-based palliative care but must align with local contexts. It is recommended that mHealth interventions ensure safety and comfort, technology competence, tailored communication, empowerment of end users, family involvement, health care worker motivation, and system integration. Trial Registration: PROSPERO CRD42022369443; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022369443

Robust Assessment of Free-Living Physical Behaviors and Activity Intensity Using Dual-Wearable Multitask Learning: Development and Evaluation Study From the Multicenter WEALTH Project

2026-08-14T14:45:11-04:00

Background: Accurate assessment of physical behaviors (PBs) and activity intensity is essential for public health research and digital health monitoring. Wearable accelerometers combined with machine learning (ML) or deep learning (DL) enable objective behavior assessment, but most existing models are trained on laboratory data, limiting generalizability to free-living conditions. Objective: This study aimed to develop and evaluate multitask ML and DL models for PB classification across 7 categories (sitting, standing, walking, running, sports, cycling, and lying) and activity intensity categories (AIC) across 3 levels (sedentary, light, and moderate-to-vigorous physical activity) using thigh-worn (activPAL) and waist-worn (ActiGraph) wearable accelerometers. A second objective was to compare model-derived estimates of daily time spent in PB and AIC across single- and dual-sensor (activPAL + ActiGraph) configurations, and to evaluate agreement between the best-performing model and corresponding estimates obtained from the proprietary activPAL classification of real-world everyday activities (CREA) algorithm using free-living data collected over a 9-day monitoring period. Methods: Data were obtained from 590 adults in the multicenter WEALTH study (627 recruited) and included up to 9 days of concurrent activPAL and ActiGraph free-living recordings. Sparse accelerometer-labeled data were obtained using ecological momentary assessment and refined by retaining instances with ≥75% agreement with the CREA algorithm. Resulting labeled data of 583 participants were used to develop ML models for single-sensor (activPAL or ActiGraph) and combined (dual-sensor) configurations. A random forest (RF) model using engineered features and a multihead convolutional neural network (MH-CNN) were trained within a multitask learning framework to jointly predict PB (task 1) and AIC (task 2) using a subject-independent hold-out split. The test subset (n=87) was used to estimate daily time spent in PB and AIC over 9 days, which were compared with CREA-derived estimates using Pearson coefficients and intraclass correlation coefficients (ICCs). Results: The dual-sensor configuration consistently outperformed single-sensor models. For PB classification, the MH-CNN achieved the highest performance (-score=0.750). For AIC, the RF model performed best (-score=0.741). Dual-sensor free-living estimates showed epidemiologically plausible distributions across the 24-hour period, including sitting 37% (538/1440 min), lying 34% (496/1440 min), walking 9% (131/1440 min), and moderate-to-vigorous physical activity (MVPA) 2% (31/1440 min). Agreement with CREA was strongest for standing, walking, and cycling (≥0.86; ICC ≥0.72), while lying showed modest reliability (ICC=0.48). For AIC, agreement was highest for light physical activity (LPA) and MVPA (ICC 0.72‐0.75). Conclusions: Multitask models combining thigh- and waist-worn accelerometers provide consistent estimates of PB and AIC under free-living conditions. The dual-sensor approach yielded more stable and epidemiologically coherent estimates than single-sensor methods, supporting its potential for large-scale population monitoring and mobile health apps. International Registered Report Identifier (IRRID): RR2-10.2196/preprints.70186

Conceptualization From the Sensors to Suicide-Related Outcomes: Scoping Review Based on Layered Hierarchical Sensemaking Framework

2026-08-13T16:30:05-04:00

Background: Suicide is a leading cause of preventable mortality worldwide, with more than 700,000 deaths annually. Although suicidal ideation fluctuates rapidly, conventional risk assessments rely on retrospective self-report collected infrequently, and the detection of short-term suicide risk remains limited. Passive digital sensing using smartphones and wearable devices enables continuous monitoring of behavioral and physiological signals associated with suicide-related outcomes. However, current evidence remains fragmented, without a clear framework for translation into clinically interpretable risk indicators. Objective: This scoping review synthesized and mapped passive digital markers associated with suicide-related outcomes via the layered hierarchical sensemaking framework (LHSF), which structures information from raw sensor data to high-level behavioral markers. We aimed to illustrate a clinically interpretable mapping of digital markers for suicide-specific digital phenotyping. Methods: Following Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this scoping review was conducted using the population-concept-context framework (population: not restricted; concept: passively collected digital data from smartphones or wearable devices; and context: suicide-related outcomes). PubMed, CINAHL, PsycINFO, and IEEE Xplore were searched for studies published between 2015 and 2025. Studies were included if they (1) collected passive digital data from smartphones or wearable devices, and (2) measured suicide-related outcomes. Narrative mapping was conducted using LHSF to distinguish between low-level features (ie, measurable properties extracted from sensors) and high-level behavioral markers (ie, clinically meaningful constructs interpreted from low-level features). Results: Of 626 studies identified, 14 (2.2%) met inclusion criteria. Six (42.9%) used predictive modeling, and 8 (57.1%) conducted correlational analyses. Among predictive studies (area under the curve [AUC]=0.56-0.89), a lower heart rate variability predicted an elevated suicide risk in 1 study (AUC=0.89). Of correlational studies, 7 (87.5%) of 8 reported at least one significant association between passive sensor data and suicide-related outcomes. Mapped to the LHSF, low-level features spanned 7 domains, linked to high-level markers, such as autonomic dysregulation, sleep disturbance, social withdrawal, smartphone use patterns, and suicide-related expression. Physiological indicators of autonomic regulation were associated with suicide-related outcomes in all 4 studies examining them and achieved the highest predictive performance (AUC=0.89). Smartphone use metrics were significantly associated in both studies, whereas linguistic (2/3 studies, 66.7%) and location-based features (2/2 studies, 100%) were associated with at least one outcome, with nonsignificant findings for some indicators or studies. Sleep parameters and movement intensity showed few significant associations. Conclusions: Physiological indicators were associated with suicide-related outcomes across all relevant studies and showed the highest predictive performance (AUC=0.89), followed by smartphone-derived behavioral features. Linguistic and location-based features showed mixed associations, whereas sleep- and activity-related indicators showed few significant associations. Future research should prioritize multimodal data integration, algorithmic refinement, and external validation to strengthen clinical utility in digital suicide phenotyping based on the LHSF.

Long-Term Engagement With a Physical Activity App Among Gynecologic Cancer Survivors in the LETSGO Trial: 12-Month Prospective, Multicenter, Quasi-Experimental Study

2026-08-13T14:15:08-04:00

Background: Physical activity (PA) alleviates many treatment-related side effects in gynecologic cancer survivors, yet long-term PA levels remain low. Mobile health interventions can support self-management and increase PA levels; however, evidence from real-world, year-long engagement with smartphone apps in this population is still limited. Objective: The aim of this study is to describe 12-month user engagement with the PA component of a smartphone app implemented within a partially nurse-led routine follow-up in a real-world cohort of gynecologic cancer survivors. Methods: This descriptive study analyzed server-generated log data from the intervention arm of the prospective, multicenter, quasi-experimental LETSGO (Lifestyle and Empowerment Techniques in Survivorship of Gynecologic Oncology) trial (NCT04122235). Between December 2019 and July 2022, 378 cancer survivors (ovarian, endometrial, cervical, vulvar, or vaginal cancer) from 5 Norwegian hospitals were enrolled in the intervention arm and were offered the app plus a Garmin Vivofit 4 activity tracker alongside standard consultations. Primary outcomes for this study were (1) weekly PA registrations (objective step counts from the activity tracker and self-reported PAs) and (2) temporal patterns of step logging during each cancer survivor’s first 52 weeks postenrollment. Secondary analyses compared baseline characteristics of app users (≥2 wk of PA logging) and nonusers. Results: Of 378 eligible participants (mean age 63, SD 13 y; BMI 28.5, SD 6.4 kg/m²), 272 (72%) logged at least 2 weeks of PA, and 225 (60%) synchronized objective step data. Mean daily steps were 5657 (SD 2799; median 5533, IQR 3499‐7498). Step tracking dominated app use (mean 21, SD 16 logged wk), followed by self-reported walking (mean 23, SD 19 wk) and resistance training (mean 15, SD 16 wk). Weekly step-logger counts fell around study weeks 16, 32, and 44, but rebounded by 30 to 50 participants within 3 weeks, indicating episodic rather than permanent disengagement. App users were younger (mean difference –6.7 y, 95% CI –9.6 to –3.9; <.001), more often employed (²=12.7; <.001), and more likely to have higher education (²=13.6; <.001) than nonusers. Tumor type and treatment modality were not associated with engagement. Conclusions: In a routine-care setting, nearly three-quarters of gynecologic cancer survivors engaged repeatedly with an app-supported PA module over 12 months, although mean PA levels were modest and participation was more prevalent among younger, employed, and more highly educated participants. Engagement followed an ebb-and-flow pattern, suggesting that built-in re-engagement prompts and equity-focused onboarding are needed to sustain and broaden participation. These findings support the feasibility of blended mobile health follow-up while highlighting the importance of adaptive strategies to promote long-term PA adherence and bridge the digital divide among cancer survivors. Trial Registration: ClinicalTrials.gov NCT04122235; https://clinicaltrials.gov/study/NCT04122235 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-050930

Design Preferences for Mental Health Apps Among Nondigitally Native Adults With Chronic Pain: Qualitative Analysis

2026-08-11T17:30:19-04:00

Background: On a population level, mental health apps are accessible and effective. However, nondigitally native adults with chronic pain are a large and growing population who have been neglected during the development process of these interventions. Although technology use is rapidly growing among this population, their engagement with mobile health–related apps is lagging because usability is often not optimized for their needs and preferences. Objective: This study aimed to identify design preferences and determinants of engagement with mental health apps by nondigitally native adults who have chronic pain and coexisting symptoms of depression or anxiety. Methods: In this qualitative study, participants completed a semistructured interview regarding their experience with, and perceptions of, mobile devices, apps, and digital health interventions. Participants were 45 years or older; scored ≥10 on the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, or both; endorsed pain on most days or every day in the past 3 months; and were living in the United States. The interview guide was informed by the Consolidated Framework for Implementation Research and the Behavioral Intervention Technology model. Codes were organized into themes. Recruitment continued until thematic saturation was achieved. Results: A total of 42 participants were interviewed (mean age 57, SD 8 years; n=32, 76% women). Participants strongly preferred apps that are free, describe strong privacy policies, and add functional value to their lives. They were more motivated by “real-life” goal achievement and tangible health improvements than by gamification within an app, and many were wary of allowing apps to passively collect certain types of data, especially to make inferences about their mental health. Most participants were unaware of apps designed to address chronic pain, but they were interested in the concept, particularly to help track their mood and pain and then identify associations between their symptoms, app activity, and other life events. Despite their daily use of apps, many participants described frequent challenges related to app navigation. While on-demand access to app tools was preferred, most participants appreciated the potential value of occasional push notifications if their timing was thoughtful and customizable. Participants cautioned against an overly cheerful, “infantile,” or informal tone for an app that addresses serious issues such as mental health and chronic pain. Conclusions: Mental health apps for nondigitally native adults should highlight tangible health improvements that can be achieved from app engagement (more so than gamification), potentially using a multidomain tracking feature, if appropriate. This population is available to receive just-in-time adaptive interventions, but the frequency and timing should be thoughtful, customizable, and not wholly reliant on passively collected personal data. Health-related apps designed to address conditions that are more common with increasing age should account for these preferences.

Characteristics of Tailored Text Messages Associated With Increased Physical Activity Among Cardiac Rehabilitation Enrollees: Secondary Analysis of a Microrandomized Trial

2026-08-11T17:30:03-04:00

Background: Emerging data suggest that text message–based mobile health interventions may enhance physical activity levels in patients with cardiovascular disease enrolled in cardiac rehabilitation. The optimal characteristics of texts that lead to maximal patient engagement and drive meaningful behavioral change are not well understood. Objective: This study aimed to understand how text- and participant-level characteristics impact physical activity levels after text delivery. Methods: The VALENTINE (Virtual Application-Supported Environment to Increase Exercise) study was a randomized controlled trial designed to evaluate a mobile health intervention delivered to low- and moderate-risk adults enrolled in cardiac rehabilitation. Embedded within this study was a microrandomized trial focused on the effect of texts on physical activity levels among intervention participants. Participants in the intervention group received texts through a smartwatch (Apple Watch or Fitbit Versa) that were tailored to the time of day, day of the week (weekday vs weekend), weather, and time since enrollment in cardiac rehabilitation. Texts also differed in content type (walking vs antisedentary) and in the level of personalization (inclusion of the participant’s name or not). Delivery was randomized at 4 user-selected time points daily, with participants having a 25% probability of receiving a text at any time point. The primary outcome was step count 60 minutes after a decision point. This analysis focuses on the text- and participant-level factors that moderated the intervention’s effect on the primary outcome. Given potential measurement differences determined a priori, analyses were stratified by device type and phase of cardiac rehabilitation and adjusted for age, sex, and baseline activity status using a generalization of regression analysis. Results: More than 70,552 randomizations occurred in 108 participants (mean age 59.5, SD 10.7 years; n=36, 33.3% female; n=19, 17.6% non-White; n=68, 63% Apple Watch users) over 6 months. Overall, no text characteristics (including personalization with the participant’s name) or participant characteristics (including baseline physical activity) consistently impacted text responsiveness for either device type. Although the findings were not consistently significant between device types and across phases of the trial, there was a trend toward increased responsiveness to texts that promoted walking (compared to antisedentary texts) and that were delivered to younger (aged <65 years) and male participants. Conclusions: In this randomized clinical trial, we found that tailored texts improved physical activity levels among cardiac rehabilitation enrollees in the initiation phase, but this effect was not explained by text- or participant-level moderators. Additional work is needed to explore the impact of tailoring based on an extended set of personal and environmental factors to optimize the delivery and efficacy of text message–based interventions. Trial Registration: ClinicalTrials.gov NCT04587882; https://clinicaltrials.gov/study/NCT04587882

Functionality Review of Mobile Apps for the Tracking and Self-Management of Fatigue: Systematic Search in App Stores and Content Analysis

2026-08-11T17:15:11-04:00

Background: Fatigue and chronic fatigue syndrome (CFS) have a considerable impact on quality of life, thus motivating people to develop skills for better management of their fatigue. While the number of commercial apps in this domain has increased, there has been limited exploration of their functionalities. Objective: This paper aims to address this research gap through a functionality review of 17 top-rated iOS and Android apps for fatigue, with the aim to articulate design implications for technologies focused on supporting the management of fatigue. Methods: We conducted a systematic search on the 2 most common app marketplaces, which resulted in the initial identification of 427 Apple apps and 1218 Google apps. From these, 17 apps were selected for review after applying a screening process to shortlist the top-rated apps. The functionalities of these apps were then coded through a week-long usage of each app for an expert evaluation leveraging authors’ human-computer interaction (HCI) expertise. We looked for functionalities such as tracking and visualization seen in previous research on functionality reviews, in addition to interventional functionalities, which were informed by research on fatigue. Results: Findings reveal the prevalence of functionalities for tracking fatigue (8/17, 47%), related symptoms (8/17, 47%), for visualizing tracked content (10/17, 59%), for assessing the user’s condition (2/17, 12%), and for providing interventions for the management of fatigue (12/17, 71%). Functionalities providing interventions for self-management of fatigue are surprisingly limited, with the most relevant ones including pacing (2/17, 12%) alongside energy estimation (2/17, 12%). Conclusions: The top-ranked apps for fatigue in the major marketplaces support 3 main functionalities under the scope of tracking fatigue along with related data, and visualizing such data, with limited provision of self-management interventions. Drawing from these findings, we articulate implications for the sensitive design of technologies to support the management of fatigue, including supporting hybrid tracking, combined visualizations to support sense-making of fatigue data with related factors, and supporting energy estimates and pacing interventions.

Patient-Facing AI-Enabled Digital Health Technologies and Quality of Life in Cancer: Systematic Review and Exploratory Meta-Analysis

2026-08-10T16:00:21-04:00

Background: Cancer affects multiple physical, psychological, and social aspects of an individual’s life. Cancer survivors frequently report unmet needs long after diagnosis and require ongoing support. AI is increasingly embedded in patient-facing digital health technologies (DHTs) in oncology, yet its impact on different domains of patients’ and survivors’ health-related quality of life (HRQOL) remains unclear. Objective: This systematic review aims to (1) examine how AI has been integrated into patient-facing DHTs designed to support cancer survivors, (2) narratively synthesize the potential effects of these technologies on HRQOL and provide preliminary quantitative estimates through an exploratory meta-analysis, and (3) explore broader changes in additional patient-reported outcomes (PROs; secondary aim). Methods: PubMed, PsycINFO, Embase, Scopus, CINAHL, and the Cochrane Library were searched for articles published between January 2020 and August 2025. Reference lists of included articles were hand-searched for additional eligible studies. Eligible studies enrolled cancer survivors of any age and disease stage, evaluated a patient-facing DHT with AI components, and assessed HRQOL. Nonoriginal research and non-English reports were excluded. Risk of bias was assessed in all controlled studies using RoB 2 (revised Cochrane risk of bias 2) or ROBINS-I V2 (Risk of Bias in Non-Randomized Studies—of Interventions, Version 2), according to study design. Data on HRQOL and other PROs were synthesized narratively, and exploratory random-effects meta-analyses were conducted for HRQOL domains. Results: Eight reports from 7 studies from China and the United States (N=2867 participants) met the inclusion criteria, and 3 (n=292 participants) contributed to the exploratory meta-analysis. All studies included adults with various cancers at different stages and times since diagnosis. Most studies showed low risk of bias or some concerns (RoB 2), but one was evaluated as having a serious risk of bias (ROBINS-I V2). AI applications ranged from symptom monitoring to targeted education. The narrative synthesis suggested positive effects on selected HRQOL domains, particularly general health, with more pronounced effects in studies conducted in China. Exploratory meta-analyses demonstrated provisional moderate positive effects on global health (Hedges =0.77, 95% CI 0.15‐1.40) and social functioning (Hedges =0.75, 95% CI 0.08‐1.42), but no effects on physical functioning, role functioning, or emotional well-being. Other PROs indicated generally high user satisfaction and adherence, improved mental health outcomes, and reductions in physical symptoms. Only minor and mild adverse events were reported. Conclusions: Current evidence, although limited, suggests that AI-enabled patient-facing DHTs may benefit survivors’ HRQOL and other PROs, particularly in early survivorship. However, our findings are based on small and heterogeneous studies and should therefore be interpreted with caution. Robust trials with adequate sample sizes, longer follow-up, and appropriate control conditions, including DHTs without AI components, are needed to determine the specific contribution of AI. Trial Registration: PROSPERO CRD420251021466; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251021466