📢 New at #IJBNPA! Can a #mHealth self-managed intervention help patients with #Parkinsons improve #PhysicalActivity and #Nutrition? Learn more about the results of this RCT.
🔗 Read more:
link.springer.com/article/10.1186/s12966-0...

#mHealth-Enabled Stroke Screening for #Pediatric Sickle Cell Disease in Low-Resource Settings: Systematic Literature Review of Critical Barriers, Emerging Technologies, and AI-Driven Solutions Background: Sickle cell disease (SCD) is a genetic blood disorder affecting millions globally, with life-threatening complications, and most patients live in sub-Saharan Africa. Particularly, #Children with SCD have a high risk of stroke. Although early screening for stroke could help prevent many cases, access to effective stroke screening remains limited in low-resource settings (LRS). Existing traditional approaches are highly operator-dependent, costly, resource-intensive, or difficult to deploy at scale in #Pediatric care. These limitations highlight the urgent need for accessible, scalable, and #Child-appropriate stroke screening and assessment tools suitable for low-resource health care contexts. Objective: The aims of this systematic literature review are to (1) uncover system-level barriers affecting stroke screening accessibility for patients with #Pediatric sickle cell disease (PSCD) in LRS, including underserved contexts within high-income countries; (2) identify existing and emerging stroke screening and assessment technologies and their implementation characteristics, such as feasibility, scalability, portability, and training requirements; and (3) propose a user-centered mobile health (#mHealth) framework for stroke screening that improves accessibility and feasibility in resource-constrained health care settings. Methods: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to organize the search process. A systematic search was conducted using an advanced query and defined eligibility criteria in the academic databases of PubMed, IEEE Xplore, Wiley Online Library, and Google Scholar. Studies published in English between January 1, 2021, and October 31, 2025, were selected. Collected data were arranged in a preformatted Microsoft Excel spreadsheet for analysis. Risk-of-bias assessment was performed using various risk-of-bias assessment tools because of the heterogeneity of the included studies. Narrative synthesis was used for data synthesis. Results: The literature search initially identified 1465 studies, of which 28 (2%) were selected for analysis. Among the 28 studies, 10 (36%) focused on stroke screening accessibility for patients with PSCD in either low- and middle-income countries or other income-level countries for LRS, and 18 (64%) outlined key features and the feasibility of stroke screening technologies. Identified barriers were organized into 4 major categories (workforce and training constraints, health care system and infrastructure barriers, sociocultural and awareness factors, and economic and logistical constraints), emphasizing difficulties in accessing stroke screening in LRS. Additionally, existing and emerging stroke screening technologies were classified into 5 groups: nonimaging, imaging, light-based optical spectroscopy, biomarker-based, and artificial intelligence– and machine learning–based #mHealth wearable approaches. Finally, a comprehensive #mHealth app is proposed for an easy-to-use screening experience to address stroke screening challenges for patients with PSCD in LRS. Conclusions: This study contributes to identifying major barriers to stroke screening in LRS and highlights key characteristics of stroke screening solutions that can be used in the future. It also contributes to the design of a holistic #mHealth solution for implementing stroke screening clinical care for patients with PSCD in LRS. Trial Registration: PROSPERO 2025 CRD420251172487; https://tinyurl.com/3djampu6

JMIR Pediatrics: #mHealth-Enabled Stroke Screening for #Pediatric Sickle Cell Disease in Low-Resource Settings: Systematic Literature Review of Critical Barriers, Emerging Technologies, and AI-Driven Solutions

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Smart Technology–Assisted Patient-Centered Management in Venous Thromboembolism: Pilot Study on Anticoagulation Adherence Background: Achieving optimal adherence to anticoagulation therapy is a major challenge in the management of venous thromboembolism (VTE). Mobile health (mHealth) technologies may offer a scalable approach to supporting medication adherence and self-management. Objective: This pilot study aimed to assess the #feasibility and preliminary impact of a smart technology–assisted, patient-centered care mHealth app for managing VTE (mVTEA) on short-term anticoagulation adherence among patients with VTE or at moderate-to-high risk of VTE. Methods: Baseline medication adherence and beliefs were assessed using the Chinese versions of the 8-item Morisky Medication Adherence Scale and the Beliefs about Medicines Questionnaire–Specific to characterize baseline status only. The primary outcome was perfect adherence at 1 month, assessed through structured telephone interviews, outpatient visits, and the mVTEA physician-patient communication module. During follow-up, researchers verified current medication regimens, recorded missed doses, assessed therapy continuation, and whenever possible, confirmed adherence through pharmacy refill records or remaining medication packaging. Secondary outcomes included the mVTEA check-in rate and clinical safety events (VTE recurrence, major bleeding per International Society on Thrombosis and Haemostasis criteria, VTE-related hospitalizations, VTE-related rehospitalizations, all-cause mortality). Results: In total, 45 participants completed the study (mean age 60.80, SD 15.20 years; n=16, 36% female). Baseline 8-item Morisky Medication Adherence Scale scores indicated suboptimal adherence (mean 6.24, SD 1.80), with 29% (13/45) classified as good adherence and 71% (32/45) as moderate or poor adherence. The primary contributors to nonadherence were forgetting to take medication. Baseline Beliefs about Medicines Questionnaire–Specific scores showed stronger beliefs in medication necessity than concerns (17.58, SD 2.52 vs 14.56, SD 3.34;

JMIR Formative Res: Smart Technology–Assisted Patient-Centered Management in Venous Thromboembolism: Pilot Study on Anticoagulation Adherence #HealthTech #VenousThromboembolism #Anticoagulation #mHealth #MedicationAdherence

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The Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS) Framework: Holistic Approach to Pandemic mHealth Apps The #covid19 pandemic has highlighted the crucial role of smartphone apps in public health, but it has also revealed challenges in terms of user acceptance and trust, as well as the secure integration of medical data. To overcome these, the COMPASS initiative (Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing)—part of the German Network University Medicine (NUM) program—developed a structured framework for the coordinated development and delivery of pandemic apps, with a focus on #usability, accessibility, security, and scalability. By incorporating expertise from 9 university hospitals and external partners, COMPASS provided a modular approach to pandemic app development that balances technology, regulation, and public acceptance. The framework includes governance, best practices, compliance, research compatibility, interoperability, and a scalable technology platform. In addition, standardized app components and templates were created to support an effective pandemic response. Real-world validation was provided by study-specific apps such as the Mainz Gutenberg Study #covid19 app (University Medical Center Mainz) and the SentiSurv app (University Medical Center Mainz), which generated nearly 1 million data points from over 25,000 participants. COMPASS successfully developed study-specific apps, improved core functionalities, and contributed to larger digital health projects such as the InnovationHub CAEHR. Beyond its immediate applications, COMPASS serves as a scalable blueprint for future mobile health solutions, with a focus on data protection, user trust, and open-source collaboration. By integrating important technological, ethical, and user-oriented considerations, it sets a new standard for digital health innovation and ensures sustainable and widely accepted pandemic preparedness.

JMIR Formative Res: The Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS) Framework: Holistic Approach to Pandemic mHealth Apps #COVID19 #PublicHealth #mHealth #PandemicApps #DigitalHealth

User Testing an mHealth Behavioral Health App for Hopi/Tewa Youth During the #covid19 Pandemic: #usability Study Background: American Indian/Alaska Native (AI/AN) people represent a culturally diverse people group within the United States. AI/AN people experience some of the most severe health disparities in the United States, including behavioral health. A quarter of AI/AN people in the United States live on tribal lands, experiencing significant barriers to mental health resources and broadband infrastructure for telehealth. We developed Amplifying Resilience Over Restricted Internet Access (ARORA)—a mobile health (mHealth) smartphone app, promoting mindfulness practices and community building through AI/AN culture and values. Originally co-designed with both Hopi/Tewa and Navajo youth and adults, this study evaluated app resonance among Hopi/Tewa youth, supporting its iterative design. While we initially planned in-person user testing, this was moved online due to the #covid19 pandemic. Objective: This study assessed the potential and acceptability of an mHealth app supporting Hopi/Tewa youth practicing mindfulness inspired by their culture, values, and beliefs. This research served as preliminary work for an ongoing, iterative participatory action research study, identifying points of improvement to align with our partner community’s goals. Methods: After meeting with 6 community advisory board members and focus groups prior to this study, we developed a prototype for ARORA. This study evaluated intuitiveness and #usability through testing and interviews with Hopi/Tewa youth. All meetings with stakeholders were moved online due to the #covid19 pandemic. Using screen-sharing via Zoom (Zoom Communications, Inc) and Android emulators, we received feedback for the iterative design process. Results: This study involved 9 participants aged 16-24 years. Of these participants, 1 was male and 8 were female; all identified as Hopi/Tewa and/or Tewa. This study included a quantitative assessment using a modified version of the User Version of the Mobile Application Rating Scale. The mean score across all questions was 3.71 (SD 0.427), suggesting generally positive reception. Qualitative results from thematically analyzing open-ended focus group data produced 5 open codes and 12 axial themes, reaching thematic saturation after engaging with 9 participants. Qualitative feedback revealed that while its use was generally enjoyable, the ARORA app could be more specific to Hopi/Tewa culture. Finally, we reflect on adaptations made to our initial protocol in response to the #covid19 pandemic, offering guidelines for future mHealth work involving rural or hard-to-reach communities. Conclusions: In this evaluation and #usability testing of the ARORA prototype, participants expressed interest and engagement in the mindfulness activities. Participants also identified spaces in which the app could improve, both in #usability and in cultural groundedness, especially with the visual dimensions of the app. Reflecting on our experience in facilitating remote user testing, we encourage future work in rural mHealth to consider practices for conducting research when in-person meetings are not feasible.

JMIR Formative Res: User Testing an mHealth Behavioral Health App for Hopi/Tewa Youth During the #covid19 Pandemic: #usability Study #mHealth #BehavioralHealth #IndigenousHealth #MentalHealth #Telehealth

Assessing the Implementation and Potential Effects of the Nishauri #mHealth Intervention on HIV Care Among Men in Homa Bay County, Kenya: #Protocol for a Mixed Methods #Study Background: About 2.4% of Kenyan people (approximately 1.3 million people) are living with HIV. Despite advances in antiretroviral therapy, men continue to experience disproportionately poor engagement in HIV care due to entrenched masculine norms, stigma, and lack of tailored interventions. Mobile health (#mHealth) platforms offer a promising strategy to improve care engagement, but evidence on its implementation and impact among men living with HIV is limited. Objective: This #Study aims to assess the implementation and potential effects of the Nishauri #mHealth intervention on HIV care and treatment outcomes among men in Western Kenya. Specifically, it seeks to (1) analyze its effects on HIV care engagement and treatment outcomes, (2) explore the role of masculine identity in modifying acceptability and uptake, and (3) identify barriers and facilitators of adoption, use, and sustainment. Methods: We will use a mixed methods design combining a stepped-wedge cluster approach and a pre- and postimplementation assessment across 4 health facilities in Homa Bay County, Kenya. Approximately 347 men receiving HIV treatment who own a #Smartphone #mHealth will be enrolled. The stepped-wedge design will sequentially introduce the intervention across the 4 facilities in 2-month intervals following baseline assessments, allowing each site to serve as its own control. Surveys will collect data on sociodemographics, masculinity, intervention acceptability and uptake, and HIV clinical outcomes using validated measures. Intervention effects on pre- and postbinary outcomes will be assessed using the McNemar test, while generalized estimating equations (=.05; =.2; 95% CI) will account for clustering and repeated measures in the stepped-wedge analysis. Focus group discussions (n=5-6) will be conducted with men living with HIV, health care providers, and #App developers to explore barriers and facilitators of implementation and adoption. Focus group discussions will be audio-recorded, transcribed, coded, and analyzed thematically. Results: This #Study received institutional review board approval in July 2025 and was registered on ClinicalTrials.gov in August 2025. Recruitment began in September 2025 and concluded in November 2025. A total of 307 men living with HIV were recruited across the 4 clinics for the pre- and postquantitative assessment. Preliminary findings will describe implementation outcomes and early effects on HIV care engagement. Conclusions: This trial will use a stepped-wedge design to evaluate the implementation and effects of the Nishauri #mHealth intervention on antiretroviral therapy adherence and clinic attendance among men in Homa Bay County. By examining both clinical outcomes and the influence of masculine norms on intervention uptake, it will provide robust evidence on the effectiveness of #mHealth strategies tailored for men in low-resource, high–HIV-burden settings. Findings will inform the design, scalability, and optimization of similar interventions by identifying key implementation barriers and facilitators. Trial Registration: ClinicalTrials.gov NCT07116538; https://clinicaltrials.gov/#Study/NCT07116538 International Registered Report Identifier (IRRID): DERR1-10.2196/85279

JMIR Res Protocols: Assessing the Implementation and Potential Effects of the Nishauri #mHealth Intervention on HIV Care Among Men in Homa Bay County, Kenya: #Protocol for a Mixed Methods #Study

Effects of the #mHealth Supportive Care Program for Family Caregivers of Individuals With Dementia and #Diabetes: Pilot Randomized Controlled Trial Background: The comorbidity of dementia and type 2 #Diabetes mellitus exacerbates the burden on family caregivers (FCGs). #Mobile #Health (#mHealth) technology offers a promising alternative to overcome the spatiotemporal limitations of traditional interventions, but evidence for its efficacy in supporting dementia–type 2 #Diabetes mellitus caregivers remains scarce. Objective: This study aimed to evaluate the effectiveness of an #mHealth supportive care program for FCGs of individuals with dementia and #Diabetes, focusing on caregiver burden, social support, and dementia care knowledge. Methods: A 2-arm, parallel-group randomized controlled trial was conducted. Between September 2022 and January 2023, FCGs were recruited from 5 urban and 10 rural communities under a community #Health center in Xiamen, China. Eligible caregivers were legally related to the patient, providing care for more than 8 hours per day for at least 1 month, conscious adults with basic literacy, owning and able to use a smartphone, and willing to provide informed consent. Their care recipients met diagnostic criteria for both dementia and type 2 #Diabetes, aged more than 60 years. Participants were randomly allocated (1:1) to intervention (n=30) or wait-list control (n=30). The intervention group received a 12-week #mHealth supportive care program via the “Xiamen i-#Health” platform, comprising 6 core modules (updated biweekly) and on-demand #Teleconsultation, in addition to conventional offline #Health education. The control group received conventional monthly 1-hour home-visit #Health education only. The primary outcome was caregiver burden measured by the Caregiver Burden Inventory (CBI). Secondary outcomes included social support (Social Support Rating Scale; SSRS) and dementia care knowledge (Dementia Care Knowledge Scale; DCKS). Assessments were performed at baseline (T0) and 3-month postintervention (T1). Only data collectors and statistical analysts were blinded. Results: Of 108 potential participants, 60 were randomly assigned. Per-protocol analysis included 55 participants (intervention group n=28 and control n=27). Postintervention, the intervention group showed a significantly greater reduction in CBI scores compared to the control group (between-group difference, =−3.534,

New in JMIR mhealth: Effects of the #mHealth Supportive Care Program for Family Caregivers of Individuals With Dementia and #Diabetes: Pilot Randomized Controlled Trial

Factors Contributing to the Establishment of Out#Patient #Smartphone #mHealth-Based Barcode #Medication Administration: A Work-System Analysis Date Submitted: Mar 7, 2026. Open Peer Review Period: Mar 19, 2026 - May 14, 2026.

Reminder>> Factors Contributing to the Establishment of Out#Patient #Smartphone #mHealth-Based Barcode #Medication Administration: A Work-System Analysis (preprint) #openscience #PeerReviewMe #PlanP

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Integrating #Wearable-enabled #mHealth Reports into Recovery High School Care Planning: A Mixed Methods Pilot #Study Date Submitted: Mar 17, 2026. Open Peer Review Period: Mar 17, 2026 - May 12, 2026.

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Families Moving Forward Connect #mHealth Intervention for Caregivers of Children With Fetal Alcohol Spectrum Disorders: Randomized Controlled Trial Background: Fetal alcohol spectrum disorders (FASD) affect 1.1% to 5% of the general population. Yet, most children with FASD and their families cannot access evidence-based interventions. #Mobile #Health (#mHealth) interventions have the potential to increase access to care on a broad scale. While numerous self-directed parenting apps exist, none have been tested for FASD. The FMF (Families Moving Forward) Connect #App is a self-directed intervention derived from an empirically supported intervention for caregivers raising children with FASD. FMF Connect is the first self-directed parenting #App for FASD, and also one of the first parenting apps to be systematically developed and tested. Objective: This study aimed to test the efficacy of FMF Connect for caregivers raising children with FASD on targeted primary (child behavior, caregiver attributions, parenting efficacy and satisfaction, FASD knowledge, and family needs met) and secondary (child adaptive behavior, caregiver self-care, and #App satisfaction) outcomes. Methods: This study involved a 3-arm randomized controlled trial with equal allocation to groups (1) FMF Connect+coaching, (2) FMF Connect, or (3) waitlist control. Participants from the United States were recruited online through an open access website. Recruitment materials were distributed by the Collaborative Initiative on FASD, FASD listserves, and social media. In total, 129 caregivers of children (aged 3‐12 y) with FASD or prenatal alcohol exposure (PAE) were enrolled. Online surveys were administered at baseline, 6 weeks, and 12 weeks. Data were analyzed with linear mixed modeling, linear regressions, and structural equation modeling using SPSS (version 29.0; IBM) and Mplus 8 (Muthén & Muthén). Results: A total of 43 participants were randomized to each group. Caregivers were predominantly White adoptive mothers. Of the total, 64% (n=83) of participants were retained through the 12-week follow-up. Groups did not differ in terms of demographic characteristics, baseline levels of functioning, or attrition. Usage patterns were similar across groups, suggesting coaching did not increase engagement. Given a few differences, #App intervention groups were combined for analyses. Relative to the waitlist group, caregivers in the FMF Connect group evidenced greater improvements in FASD knowledge, child behavior attributions, family needs met, and self-care after 12 weeks (=.01-.048). After controlling for multiple comparisons, differences in FASD knowledge, self-care, and family needs met approached significance (=.06-.07). Groups did not differ in parenting satisfaction, child behavior problems, or adaptive functioning. More #App usage is related to greater changes in parenting efficacy. Caregiver behavior attributions at 6 weeks did not mediate intervention effects. Conclusions: This study demonstrated initial efficacy of the FMF Connect #App for targeted caregiver outcomes, with small to medium effect sizes. As an #mHealth #App, the FMF Connect intervention has potential for scalability and accessibility. This could lead to a substantial public #Health impact, particularly for families who face challenges accessing evidence-based resources or encounter other barriers to care. Trial Registration: ClinicalTrials.gov NCT05028517; https://clinicaltrials.gov/NCT05028517

New in JMIR mhealth: Families Moving Forward Connect #mHealth Intervention for Caregivers of Children With Fetal Alcohol Spectrum Disorders: Randomized Controlled Trial

Factors Contributing to the Establishment of Out#Patient #Smartphone #mHealth-Based Barcode #Medication Administration: A Work-System Analysis Date Submitted: Mar 7, 2026. Open Peer Review Period: Mar 19, 2026 - May 14, 2026.

Factors Contributing to the Establishment of Out#Patient #Smartphone #mHealth-Based Barcode #Medication Administration: A Work-System Analysis (preprint) #openscience #PeerReviewMe #PlanP

mHealth Intervention to Promote Nonexercise Physical Activity in Patients With Type 2 Diabetes: Secondary Analysis and Implementation Study Background: Physical activity (PA) has an important role in the prevention and treatment of type 2 diabetes (T2D). Interventions with mobile-based technology (mobile health [mHealth]) seem promising in PA promotion, but their behavioral framework is often vague, and the implementation is seldom reported. Objective: This paper examines perceived behavior change needs and implementation of an mHealth approach in increasing nonexercise PA in patients with T2D. Methods: A 3-arm mHealth intervention was conducted in primary care. Information on perceived behavior change needs was collected with a modified capability, opportunity, motivation—behavior (COM-B) questionnaire before the intervention from a separate sample of patients with T2D (n=25) and at the intervention baseline (n=119). Implementation evaluation focused on the fidelity and acceptability of the main arm of the intervention (n=39), which included 24-hour accelerometer use, a smartphone app with personal feedback, a PA leaflet, a YouTube video on walking, and individual counseling with 3 face-to-face sessions and 4 telephone contacts. Data on fidelity were accumulated during the intervention through counseling cards and cloud computing. Data on acceptability were collected with a questionnaire at the end of the intervention (Likert scale from 1 to 5). Data analysis was mainly descriptive. Results: The participants’ responses revealed 3 items in capability and 2 in motivation, which stood out as perceived behavior change needs. Moreover, the main intervention arm showed good fidelity (eg, face-to-face sessions: 112/117, 96% and telephone contacts completed: 145/156, 93%; mean weekly accelerometer use 54%; ranging from 80% to 17% during the intervention) and acceptability (mean score ranging from 3.8 to 4.8), although some challenges were also experienced, especially in cloud-computed feedback and accelerometer-app use. Conclusions: The findings on behavior change needs call for additional research since no comparable studies were found. In addition, the explanatory value of the COM-B model and the psychometric properties of the COM-B questionnaire deserve further attention. The main intervention arm seemed applicable to clinical practice. However, the challenges discovered underscore the importance of pretesting technology-based approaches in patients with T2D. Trial Registration: ClinicalTrials.gov NCT04587414; https://clinicaltrials.gov/study/NCT04587414

JMIR Formative Res: mHealth Intervention to Promote Nonexercise Physical Activity in Patients With Type 2 Diabetes: Secondary Analysis and Implementation Study #mHealth #Type2Diabetes #PhysicalActivity #HealthTech #DiabetesCare

Telemedicine not yet reaching potential for serving rural, disadvantaged communities, Brown study finds The retrospective study was conducted by Brown and Harvard-affiliated researchers.

Telemedicine not yet reaching potential for serving rural, disadvantaged communities, study finds #telemedicine #AI #LLM #healthcare #populationhealth #mHealth

www.browndailyherald.com/article/2026...

Integrating #Wearable-enabled #mHealth Reports into Recovery High School Care Planning: A Mixed Methods Pilot #Study Date Submitted: Mar 17, 2026. Open Peer Review Period: Mar 17, 2026 - May 12, 2026.

Integrating #Wearable-enabled #mHealth Reports into Recovery High School Care Planning: A Mixed Methods Pilot #Study (preprint) #openscience #PeerReviewMe #PlanP

Adherence to and Engagement With an #mHealth #PhysicalActivity Intervention After Mild Stroke or Transient Ischemic Attack: Secondary Analysis of a Feasibility Randomized Controlled Trial Background: Regular #PhysicalActivity is a crucial and an important modifiable lifestyle factor reducing the risk of recurrent incidents after stroke or transient ischemic attack (TIA). #Mobile #Health (#mHealth) has emerged as a promising approach for providing long-term support for #PhysicalActivity. However, little is known about how individuals poststroke or TIA adhere to and engage with #mHealth interventions. Objective: This study aimed to (1) describe adherence to supervised sessions in an #mHealth intervention targeting #PhysicalActivity, (2) describe engagement with self-managed #mHealth support for #PhysicalActivity during and after the intervention, (3) compare characteristics of participants with high and low adherence and #App engagement, and (4) examine whether high adherence and #App engagement were associated with maintained #PhysicalActivity after having completed the intervention and at a 12-month follow-up. Methods: In this study, a secondary analysis of data from the experimental arm of a feasibility randomized controlled trial was conducted. The experimental group received a 6-month #mHealth version of the i-REBOUND intervention, which included supervised #mHealth support for #PhysicalActivity and behavior change, followed by a 6-month postintervention period with access to self-managed #mHealth support. The control group received #mHealth consultations via video conferencing. Adherence measures included attendance at supervised exercise and counseling sessions, while #App engagement was measured by weekly interactions with self-managed #mHealth support during and after the intervention. Participants’ level of #PhysicalActivity (steps per day) was measured using accelerometers at baseline, and at 6- and 12-month postbaseline. Logistic regression analysis examined the associations between high adherence and #App engagement during the intervention and postintervention period and maintained #PhysicalActivity (ie, >7000 steps/day) across the 12-month study period. Results: Of the 57 participants enrolled, 51 (89%) completed the intervention; the average age was 71 years, 34/51 (67%) were female, and 47/51 (92%) had mild stroke symptoms. Adherence to supervised #mHealth support was high (supervised exercise sessions: 79%, counseling sessions: 98%), while engagement with self-managed #mHealth support was high during the intervention (83%) but declined postintervention (38%). A larger proportion of females (24/31, 77%) demonstrated high adherence to the intervention compared to males (7/31, 23%, ²=4.1; =.04). High adherence (≥80%) during the intervention was associated with maintained #PhysicalActivity between baseline and the 6-month follow-up (OR 12.07, 95% CI 2‐72.76; =.01), while high #App engagement (≥80%) during postintervention was associated with maintained #PhysicalActivity between the 6- and 12-month follow-up (OR 5.10, 95% CI 1.02‐25.52; =.05). Conclusions: Supervised #mHealth support was well received with high adherence, while modules for self-management of #PhysicalActivity faced challenges in engaging the participants. Future studies could benefit from qualitative and cocreative approaches to better understand and refine self-managed #mHealth support for individuals poststroke or TIA. Trial Registration: ClinicalTrials.gov NCT0511195; http://clinicaltrials.gov/ct2/show/NCT0511195

New in JMIR mhealth: Adherence to and Engagement With an #mHealth #PhysicalActivity Intervention After Mild Stroke or Transient Ischemic Attack: Secondary Analysis of a Feasibility Randomized Controlled Trial

📢 New in #IJBNPA! Learn more about the feasibility of the “LvL UP” trial, an holistic #mHealth intervention, aiming to support #PhysicalActivity, #Diet, and #EmotionalRegulation!
🔗 Read more:
link.springer.com/article/10.1186/s12966-0...

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Content and Quality Analysis of #mHealth Apps for Feeding Children with Autism Spectrum Disorder Date Submitted: Mar 8, 2026. Open Peer Review Period: Mar 10, 2026 - May 5, 2026.

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Features of mHealth Apps for Tobacco Cessation Important to Black Adults: Discrete Choice Experiment Background: Although mobile health (mHealth) apps for tobacco cessation augment traditional cessation methods and have contributed to increases in cessation rates, Black adults are underrepresented in mHealth app studies for tobacco cessation. As a result, their mHealth app preferences are not well-known. Objective: Our goal was to identify features of mHealth apps for cessation that are important to Black adults who use tobacco products. Methods: We developed an online discrete choice experiment with 12 pairs of hypothetical mHealth apps for tobacco cessation. Inclusion criteria included being 21 years or older, current use of any tobacco product, and identifying as Black or African American. Participants had to be interested in tobacco cessation and have a history of mHealth app use or be willing to use one in the future. From each pair of hypothetical apps within the survey, participants had to choose the app they preferred. Each hypothetical app was made up of 7 features developed from existing mHealth literature and prior qualitative work: graphics, marketing, strategies for quitting, connection with others, personalization, benefits of quitting, and health information. Each feature had up to 4‐5 levels (ie, variations of that attribute), and each hypothetical mHealth app was comprised of a random assortment of levels of features. Hierarchical Bayes estimation was used to determine the part-worth utility for each level within each feature for each participant, which was then used to calculate the importance score. Average importance scores across respondents were used to determine overall importance scores for each feature. Results: We had 901 adult participants. The mean age was 41 (SD 14.02) years, and about a third of participants (377/901, 42%) were female. Two-thirds of participants (549/901, 61%) had used an mHealth app in the past, and the great majority (786/901, 87%) indicated a willingness to use an app for health purposes in the future. The features had the following importance: graphics (16%), marketing (15%), strategies for quitting (15%), connection with others (14%), personalization (13%), benefits of quitting (13%), and health information (13%). Within features, strategies for quitting had the highest and third-highest levels of “making a step-by-step quit plan” and “recommendations to manage relapse or withdrawal,” respectively. Marketing had the second-highest level of “Historically Black Colleges and Universities–endorsed app.” Graphics had the fourth-highest level of “short video testimonials from people who successfully quit,” while connection with others had the fifth-highest level of “quit buddy program for support and accountability.” Conclusions: This study identified features of mHealth apps important to Black adult tobacco users. To enhance the appeal of mHealth apps to such adults, prioritizing inclusion of highly preferred levels in apps may lead to higher use and improved cessation.

JMIR Formative Res: Features of mHealth Apps for Tobacco Cessation Important to Black Adults: Discrete Choice Experiment #mHealth #TobaccoCessation #HealthApps #PublicHealth #BlackHealth

Designing an mHealth App to Encourage Uptake of Muscle-Strengthening Exercise in Older Adults: Co-Design Focus Group Study Background: Sarcopenia, the age-related decline in muscle mass and strength, poses a significant threat to functional independence in older adults. Despite strong evidence supporting resistance training as a preventive and therapeutic strategy, adherence to muscle-strengthening guidelines remains low. Mobile health (mHealth) technologies offer a promising avenue to bridge this gap; however, few apps are tailored to older adults or designed with their input. Objective: This study aimed to identify key features that a muscle-strengthening exercise app should include to enhance engagement and uptake among older adults. Secondary aims were to explore perceived barriers and facilitators to app use and to inform the development of an evidence-based, co-designed mHealth intervention. Methods: We used a qualitative co-design approach, involving 4 focus groups with 18 older adults (aged 60-83 years); each group comprised 3 to 6 older adults, stratified by experience with mHealth apps. Sessions were conducted online via Microsoft Teams and guided by a semistructured protocol informed by prior mHealth research and behavior change theory. Transcripts were analyzed using deductive thematic analysis, underpinned by the Technology Acceptance Model, focusing on perceived usefulness and perceived ease of use. Results: A total of 4 overarching themes and 10 subthemes were identified. Theme 1, mHealth as a tool for supporting health and well-being, highlighted participants’ recognition of digital tools in promoting activity and overcoming accessibility barriers. Theme 2, motivation and engagement through app features, revealed the importance of reminders, progress tracking, and feedback, although views on gamification were mixed. Theme 3, drawbacks of current mobile apps, captured concerns around complexity, poor usability, and lack of age-appropriate content, with skepticism regarding safety and evidence base. Theme 4, desired app elements and features, emphasized the need for customizable reminders, clear instructional videos, adaptable exercise options, and optional social features. Participants stressed the importance of simplicity, personalization, and relatable content to foster trust and sustained engagement. Conclusions: Older adults are receptive to mHealth interventions for muscle-strengthening when design is user centered and grounded in their lived experiences. This study provides a framework for future app development, highlighting the need for intuitive interfaces, personalized features, and credible educational content. By aligning design with Technology Acceptance Model constructs and co-design principles, mHealth apps can better support healthy aging and sarcopenia prevention. These findings offer actionable guidance for developers and researchers aiming to enhance digital health equity and effectiveness in older populations. Clinical Trial: Open Science Framework 10.17605/OSF.IO/J64ER; https://osf.io/j64er/overview

New in JMIR Aging: Designing an mHealth App to Encourage Uptake of Muscle-Strengthening Exercise in Older Adults: Co-Design Focus Group Study #mHealth #ElderlyFitness #Sarcopenia #MobileHealth #ResistanceTraining

Adolescents’ Engagement With an #mHealth Multiple #Health Behavior Change Intervention (LIFE4YOUth): Mixed Methods and Qualitative Comparative Analysis Background: Behavior change interventions delivered through #Mobile phones often have low engagement among end users. Objective: This study aimed to explore factors influencing engagement among Swedish high school students with access to LIFE4YOUth, a #Mobile-based multiple behavior change intervention targeting #PhysicalActivity, diet, alcohol consumption, and cigarette smoking. Special emphasis was placed on understanding low engagement. Methods: A sequential explanatory mixed methods design was used. Quantitative usage data from 377 students were analyzed to describe engagement patterns. This was followed by qualitative data collection through 3 focus groups and 2 individual interviews (n=20), analyzed using inductive content analysis. Finally, qualitative comparative analysis (QCA) was used to integrate findings and identify configurations of psychosocial and behavioral conditions associated with low engagement. The results from all phases were interpreted and discussed as a whole. Results: A majority (253/377, 67%) of participants showed low engagement, with 62% (158/253) never interacting with the intervention beyond receiving weekly SMS text messaging. Focus group discussions revealed 3 overarching categories influencing engagement: perceived importance of behavior change, user experiences, and environment of use. In total, 48% (121/253) of the low-engaged participants were represented by 1 of 3 configurations, which described participants’ characteristics as unmotivated high-needers, motivated low-needers, and dissatisfied needers. Robustness tests confirmed the stability of the unmotivated high-needers configuration. Conclusions: LIFE4YOUth (Linköping University) did not engage high school students with multiple risk behaviors who were content with their lives and did not consider healthy behaviors as very important. However, positive experiences of being both confirmed and encouraged may explain engagement among students engaged in a combination of #Health-risk and #Health-promoting behaviors. Future research could explore how tailoring the number of behaviors targeted by #mHealth interventions for adolescents might increase engagement and, in turn, behavioral outcomes.

New in JMIR mhealth: Adolescents’ Engagement With an #mHealth Multiple #Health Behavior Change Intervention (LIFE4YOUth): Mixed Methods and Qualitative Comparative Analysis