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  Text-messaging (SMS, WeChat...

  Feb 20, 2026

  WeChat-Based Intervention for Glycemic Control in Patients With Type 2 Diabetes Mellitus: Multicenter Randomized Controlled Trial

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  Authors List:

  • Chuanfen Zheng  
  • Xiong Dou  
  • Xiaotao Xiong  
  • En-Yu Lei  
  • Ming Jiang  
  • Yulin Wu  
  • Jing Yu  
  • Xianjun Wang  
  • Ling Zhang  
  • Honghui Rong  
  • Lu Lu  
  • Fengju Li  
  • Ting Luo  
  • Xiangyu Ma  
  • Ji-An Chen  

  Abstract:

  Background: China’s diabetes epidemic faces critical gaps in glycemic control, with only 50.1% of treated patients achieving hemoglobin A (HbA) targets in 2021. Conventional interventions struggle with scalability in primary care, particularly for vulnerable populations. Objective: This study aimed to evaluate the use of a WeChat-based health education tool (the WeWalk mini program, the Bayu Health public account, and a WeChat group) for improving glycemic control in community-dwelling patients with type 2 diabetes mellitus. Methods: This multicenter randomized controlled trial enrolled 600 adults with type 2 diabetes from 3 communities in Chongqing, randomly allocating participants 1:1 to either a 12-week WeChat-based intervention (n=300, 50%) or a control group (n=300, 50%) in September 2020. The control group received 4 face-to-face traditional health education sessions, whereas the intervention group participated in a digital program: a 4-week course followed by an 8-week practical implementation. At baseline and 12 weeks after the intervention began, both groups were examined in terms of HbA and fasting blood glucose (FBG) as the primary outcomes, as well as variables such as blood lipid profile, blood pressure, and physical fitness–related indexes as secondary outcomes. Longitudinal glycemic control was assessed through triplicate FBG measurements extracted from standardized electronic health records at the 2-year follow-up. Independent tests or Mann-Whitney tests were used to assess changes from baseline to follow-up between groups. Results: A total of 92.7% (556/600) of the participants completed the 12-week follow-up visit. The WeChat-based intervention demonstrated superior glycemic control outcomes, with intervention participants achieving a 0.59% greater HbA reduction than controls (−0.03% vs 0.56%; <.001) and significant improvements in FBG levels (−0.69 vs 0.00 mmol/L; Δ=0.69; =.001). Subgroup analysis revealed that WeChat-based health education was significantly effective in patients with diabetes with a disease duration of <10 years, educational level of junior high school or lower, and annual family income of

  • JMIR Mhealth Uhealth 2026;14(1):e80738

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  Usability of Apps and User...

  Feb 20, 2026

  Adapting and Validating Tools to Assess the Usability and Acceptability of mHealth Tools Among Community Health Workers in Rural Settings: Development and...

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  Authors List:

  • Jonathan Nkurunziza  
  • Sarah Nuss  
  • Eve Hiyori Estrada  
  • Marthe Kubwimana  
  • Adeline Adwoa Boatin  
  • Laban Bikorimana  
  • Richard Ribon Fletcher  
  • Nissi Byiringiro  
  • Bethany Hedt-Gauthier  
  • Vincent Kalumire Cubaka  

  Abstract:

  Background: Mobile health (mHealth) apps are increasingly leveraged to support community health workers (CHWs) in delivering high-quality care, particularly in low- and middle-income countries. However, despite the proliferation of mHealth tools, few have been implemented at scale, partly due to limited attention to usability and acceptability among end users. In sub-Saharan Africa, mHealth tools designed for CHWs often lack systematic evaluation using validated instruments tailored to local contexts. Without such assessments, it is difficult to ensure that these tools can be integrated effectively into CHW workflows and scaled sustainably. Objective: This study aimed to adapt and validate existing mHealth usability and acceptability assessment tools to be contextually appropriate for CHWs in Rwanda. Specifically, we sought to ensure contextual appropriateness for CHWs supporting postoperative home follow-up for women after cesarean delivery. The resulting tool was designed for use in an implementation study of a novel CHW-led mHealth app. Methods: This study was conducted in the Kirehe district, Rwanda, from October 2022 to March 2023. We adapted 2 established tools—the mHealth App Usability Questionnaire and selected items from the Practitioner Opinion (Acceptability) Scale—and added new items that reflect core functions of the CHW-focused mHealth app. All items were translated into Kinyarwanda and simplified to align with CHWs’ educational levels. We conducted a three-stage validation that consisted of (1) content validity testing with 8 local and international experts using a recommended content validity index threshold of >0.78; (2) face validity testing with 10 CHWs using a recommended face validity index threshold of ≥0.60; and (3) reliability testing using responses from 30 CHWs, with a Cronbach α coefficient of ≥0.70 indicating acceptable internal consistency. Results: Of the 25 items assessed, 22 (88%) achieved a content validity index score of >0.78 for both clarity and relevance. The face validity index across all 22 items was 0.991, indicating strong comprehensibility and relevance to CHWs. Internal consistency was high: the Cronbach α was 0.86 for the mHealth App Usability Questionnaire items, 0.73 for the Practitioner Opinion (Acceptability) Scale items, and 0.87 for the newly developed questions. The final tool—named the Community Health Worker mHealth Usability and Acceptability Assessment Tool—included 22 items with strong content validity, face validity, and internal reliability. Conclusions: This study presents a rigorously adapted and validated tool for assessing mHealth usability and acceptability among CHWs in Rwanda. The Community Health Worker mHealth Usability and Acceptability Assessment Tool can guide future evaluations of mHealth interventions in similar contexts and serve as a model for localizing mHealth assessment tools in low- and middle-income country settings to ensure fit-for-purpose implementation.

  • JMIR Mhealth Uhealth 2026;14(1):e64916

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  Wearables and MHealth Reviews

  Feb 19, 2026

  Video-Based Motion Capture Smartphone Apps for Testing Human Motor Performance Skills: Scoping Review

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  Authors List:

  • Clara Sophia Zoeller  
  • Claudia Niessner  
  • Manuel Fleps  
  • Thorsten Klein  
  • Anke Hanssen-Doose  
  • Alexander Burchartz  
  • Alexander Woll  
  • Thorsten Stein  

  Abstract:

  Background: Good motor performance skills (MPS) are relevant in all stages of life. Higher MPS are associated with enhanced cognitive abilities and physical and mental health. The assessment of MPS is important to identify deficits in MPS at an early stage and to implement interventions to address these deficits. One method to assess MPS is through marker-based motion capture in a laboratory setting with multiple cameras. However, this approach is expensive and time-consuming, making it impractical, for example, in large-scale studies for MPS assessment. Recent advancements (eg, artificial intelligence) in technology (eg, smartphone cameras) have opened up innovative solutions for various challenges (eg, testing large sample sizes). A potential solution is using video-based smartphone apps to assess MPS through markerless motion capture with a single camera. Objective: The objectives of this scoping review were to summarize existing smartphone apps designed to digitally assess MPS through motion capture, identify the target population of the apps, determine whether the apps have been validated, and summarize the specific MPS that were assessed. Methods: The scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines. The search was conducted in March 2024 using PubMed, Scopus, SPORTDiscus, Web of Science, Education Resources Information Centre, and SAGE Publications. All included studies investigated video-based motion capture smartphone apps to assess MPS. Results: A total of 10 studies met the inclusion criteria. Seven different smartphone apps were used within the studies, 6 of which have already been validated. The MPS assessed through the apps were gait, breaststroke, running, countermovement jump, and shoulder mobility, and 1 study assessed a functional movement test battery. The studied populations were healthy adults, older adults, athletes, or individuals with neurological illnesses. Conclusions: The assessment of MPS through smartphone apps represents a promising tool, which can be used in a variety of fields, such as health and performance monitoring, coaching, and scientific research. In the future, more studies should focus on developing new smartphone apps to assess different MPS and validate these apps.

  • JMIR Mhealth Uhealth 2026;14(1):e65474

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  mHealth for Wellness, Behavior Change...

  Feb 18, 2026

  Effectiveness of Step Goal Personalization Strategies on Physical Activity in a Mobile Health App: A Field Study

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  Authors List:

  • Xia Liu  
  • Tammo H A Bijmolt  
  • Marijke C Leliveld  
  • Ernst H Noppers  

  Abstract:

  Background: Goal personalization features integrated into mobile health apps have the potential to enhance physical activity, as some evidence shows that the personalized goals generated by algorithms are more effective than default or fixed goals. However, it remains unclear whether goals set by users are more effective than fixed goals and which personalization strategy is more effective for different user segments. Objective: This field study aimed to evaluate (1) the efficacy of 2 step goal personalization strategies—personalized-by-you and personalized-by-the-algorithm—and (2) which strategy is more effective among users with different activity levels. Methods: All users of SamenGezond, a Dutch mobile health app, have a default goal of 2000 steps per day, 5 days a week. For this study, 2 random groups were selected, totaling 5800 users. Subsequently, an email was sent to 3800 users in group 1, asking whether they were satisfied with their current goal. Those who were not satisfied were offered 2 personalization options: to set a goal themselves or to have the algorithm integrated in the app set goals for them. In total, 1399 users responded: 230 chose to set their own goals (personalized-by-you group), 236 opted for setting the goal by the algorithm (personalized-by-the-algorithm group), and 933 chose to keep the default goal (not-changed group). The algorithm used a moving-window percentile rank method based on step data from the previous 4 weeks. Users who did not personalize retained the default goal. The remaining 2000 users in group 2 did not receive the email and also retained the default goal. To evaluate the effectiveness of step goal personalization strategies, we used propensity score matching and difference-in-difference analysis. Results: Users in the personalized-by-you group increased weekly step count by 3793 a week, while those in the personalized-by-the-algorithm group increased by 4315 steps a week, compared with the not-changed group (users with default goals). The 2 strategies appear to have a similar effect. Interestingly, users in the not-changed group also increased their weekly steps by 1759. Furthermore, the effectiveness of each strategy varied by baseline activity level. The personalized-by-you strategy was effective for medium- (increase of 5842 steps) and high-active users (increase of 4266 steps) but not for low-active users (increase of 384 steps; =.82). Conversely, the personalized-by-the-algorithm strategy was effective for low- (increase of 5095 steps) and medium-active users (increase of 5278 steps) but not for high-active users (increase of 1446 steps; =.51). Conclusions: Step goal personalization demonstrates short-term effectiveness. However, their impact varies by users’ baseline activity levels, indicating the need for a tailored approach for different user segments. Future studies should examine the long-term effects of such interventions to design sustainable health behavior change strategies.

  • JMIR Mhealth Uhealth 2026;14(1):e81779

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  mHealth for Telemedicine and Homecare

  Feb 18, 2026

  Clinical Improvements From Telemedicine Interventions for Managing Type 2 Diabetes Compared With Usual Care: Systematic Review, Meta-Analysis, and...

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  Authors List:

  • Shujie Jiang  
  • Xianru Gao  
  • Haiqing Diao  
  • Yang Zhang  
  • Guangyu Lu  
  • Xiaoguang Liu  
  • Yuping Li  

  Abstract:

  Background: Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder that poses substantial challenges to global health care systems and patient management. Telemedicine, defined as the use of information and communication technologies to enhance health care delivery, has emerged as a potential tool to improve access to care and facilitate the management of T2DM. Objective: This systematic review and meta-analysis aimed to evaluate the clinical effectiveness of various telemedicine interventions compared with usual care in glycemic control, and cardiovascular health in adults with T2DM. Methods: A comprehensive literature search was conducted across databases such as PubMed, Cochrane Library, and Web of Science for randomized controlled trials (RCTs) published up to August 23, 2024. Eligible RCTs compared telemedicine interventions with usual care in adults with T2DM. The primary outcome assessed was hemoglobin A_1c (HbA_1c) levels, while the secondary outcomes included mean glucose, fasting blood glucose, BMI, weight, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The quality of the included studies was examined via the Cochrane risk-of-bias tool. Data were extracted and analyzed using a random-effects model, and meta-regression was performed to explore potential moderators. The quality of the evidence was assessed via the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: A total of 58 RCTs, encompassing 13,942 participants, were included in the analysis. Our findings showed that telemedicine interventions significantly improved HbA_1c levels compared with usual care (mean difference [MD] –0.38, 95% CI –0.49 to –0.27; Z=6.94; P<.001), despite high heterogeneity (I²=96%). Significant effects were also found for fasting blood glucose (MD –11.29, 95% CI –17.65 to –4.93; Z=3.48; P<.001), weight (MD –1.33, 95% CI –2.23 to –0.44; Z=2.91; P=.004), BMI (MD –0.43, 95% CI –0.72 to –0.13; Z=2.84; P=.004), systolic blood pressure (MD –2.14, 95% CI –3.02 to –1.26; Z=4.76; P<.001), and diastolic blood pressure (MD –1.24, 95% CI –2.02 to –0.46; Z=1.10; P=.002). No significant between-group differences were found in high-density lipoprotein cholesterol and low-density lipoprotein cholesterol improvement. Subgroup analyses revealed that telemedicine delivered by physicians, dietitians, and researchers achieved the most significant reductions in HbA_1c levels. Short-term and long-term interventions showed significant HbA_1c improvements, while medium-term interventions did not achieve statistical significance. Meta-regression analysis did not identify any statistically significant moderators. Conclusions: This review highlights telemedicine’s superior effectiveness over usual care in improving HbA_1c levels in patients with T2DM, regardless of the type of intervention. Telemedicine led by physicians, dietitians, and researchers showed the greatest efficacy in managing blood glucose levels. Furthermore, telemedicine interventions show promise for monitoring weight and cardiovascular health in patients with T2DM. Trial Registration: PROSPERO CRD42024608130; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=608130

  • JMIR Mhealth Uhealth 2026;14(1):e70429

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  Quality Evaluation and Descriptive...

  Feb 17, 2026

  Mobile App–Supported Self-Management for Chronic Low Back Pain: Realist Evaluation

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  Authors List:

  • Rebecca Hunter  
  • Trish Gorely  
  • Michelle Beattie  

  Abstract:

  Background: As the world’s population ages, the prevalence of chronic low back pain (CLBP) is increasing, placing a substantial burden on individuals and healthcare systems. Mobile health (mHealth) apps offer a potentially scalable solution to support self-management, but little is known about how, why, for whom, and under what circumstances such tools work in real-world settings. Objective: This study aimed to test and refine three programme theories—developed through a prior realist review—on how mobile apps support CLBP self-management. The goal was to understand the key contextual factors and mechanisms that influence when and why a digital self-management intervention may succeed or fail. Methods: A realist evaluation was conducted using one-on-one telephone interviews with nine participants who had used the Curable app for three months to self-manage their CLBP. Realist interviews followed a teacher–learner cycle to explore, test, and refine the programme theories. Abductive and retroductive analysis was used to develop context–mechanism–outcome configurations (CMOCs), which were synthesised into refined theories of digital self-management in chronic pain. Results: Twenty CMOCs were constructed, supporting three overarching programme theories centred on empowerment, self-management burden, and timing. First, the app was empowering when it offered credible and accessible knowledge that helped participants understand their pain, build confidence, and reduce reliance on healthcare providers. However, engagement depended on individual beliefs and expectations: those with strong biomedical views struggled to connect with the app’s psychosocial framing. Second, while the app could ease the burden of self-management by offering support between appointments, it could also increase burden during flare-ups, when users lacked the capacity to engage. Features such as proactive content delivery and low-demand interfaces were viewed as essential for continued use. Third, timing emerged as a key factor. Early introduction was beneficial for some, but others needed to first accept the chronicity of their condition before they were ready to engage with self-management tools. Trust in the source recommending the app also influenced engagement. While clinician endorsement was often valued—especially early in the self-management journey—participants who had experienced unmet needs or disillusionment in clinical encounters reported that peer recommendations or non-clinical sources held greater weight. This highlights the importance of aligning recommendations with individuals' evolving relationships with authority and trust. Conclusions: Mobile apps like Curable can support empowerment and continuity of care in CLBP, but their success depends on personalisation, timing, and relational dynamics. To prevent feelings of abandonment, such tools should be introduced as an adjunct to—rather than a replacement for—ongoing clinical support.

  • JMIR Mhealth Uhealth 2026;14(1):e66435

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  Usability of Apps and User...

  Feb 13, 2026

  Reluctance to Use a Psycho-Oncology Mobile App Among Patients With Primary Breast Cancer: Retrospective Cross-Sectional Survey

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  Authors List:

  • Marta Pawełczak-Szastok  
  • Anna Syska-Bielak  
  • Aleksandra Krzywon  
  • Michał Jarząb  

  Abstract:

  Background: E-health is an increasingly utilized method of healthcare in the field of psycho-oncology. While many reports highlight the positive impact of psychological e-health tools, there are patients who refuse to use them. Objective: Our goal was to expand our knowledge of the motivation and psycho-emotional functioning of patients who consciously refuse to use e-health technology in the form of a mobile psycho-oncology app for their phone as part of a clinical trial. To our knowledge, this is the first study of its kind. Methods: A retrospective study was conducted between December 2022 and February 2023 to investigate the reasons why 56 breast cancer patients refused to use the psycho-oncology mobile app offered as part of a clinical trial by the Breast Cancer Unit. The study aimed to analyze their psycho-emotional functioning, including stress levels (measured using the Distress Thermometer), personality traits (measured using the TIPI), coping strategies (measured using the Mini-Cope), and self-efficacy (measured using the GSES) and reasons for refusal to participate in the clinical trial. Results: The patients experienced a clinically meaningful elevation in stress levels (5 ± 2.1 points) and self-efficacy (32.1 ± 5.1 points). Among five dimensions of personality treats patients scores highest in Agreeableness (6.5 ± 0.8 stens) and Conscientiousness (6.4 ± 0.9) and lowest in Neuroticism (3.4 ± 1.8) (other dimension: Extraversion - 5.8 ± 1.6 and Openness to experiences 4.4 ± 1.5). In terms of coping with stress, patients most frequently used the strategies of Active coping (2.6 ± 0.5 points), Acceptance (2.6 ± 0.6) and Seeking emotional suport (2.6 ± 0.6), and least frequently used the strategies of Psychoactive substance use (0,2 ± 0.6) and Resistant (0,5 ± 0.7). Patient responses regarding refusal to participate in app testing were divided into four categories: 1) Focus on life outside the disease, 2) Focus on disease and treatment, 3) Denial mechanism, 4) Technical issues. Statistically significant differences were found between the groups. Focus on life outside the disease group of patients had higher levels of self-efficacy, lower neuroticism and more frequent use of Positive re-evaluation strategy compared to the other groups. Conclusions: Our patients' decision not to use the eHealth psycho-oncology app was mainly influenced by characteristics suggesting their better emotional coping with the disease and treatment. These were significantly more influential than other factors studied, particularly those related to technology. In the light of our study, assessing the reason for opting out of e-health and the associated psycho-emotional functioning is crucial for patients' adoption of e-health solutions.

  • JMIR Mhealth Uhealth 2026;14(1):e71412

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  Usability of Apps and User...

  Feb 11, 2026

  Designing mHealth Apps for Substance Use Recovery Through Real-World Co-Design and Deployment: Mixed Methods Study

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  Authors List:

  • Teale Masrani  
  • David C Hodgins  
  • Hyoun S Kim  
  • Katherine Rittenbach  
  • Erika Johnson  
  • Geoffrey Messier  

  Abstract:

  Background: Mobile health (mHealth) apps have shown promise to support recovery from substance use disorders. However, evidence on engagement and efficacy is still inconclusive. Objective: This study aims to identify design considerations for optimizing engagement in mHealth apps for those recovering from problematic substance use, by analyzing real-world experiences with co-designed app features. Methods: We co-designed, deployed, and evaluated an mHealth app. Initial co-design interviews with 14 individuals in recovery led to 3 new features integrated into an existing mHealth app. The app was deployed for a 6-week trial with 53 participants using it during their daily routines without researcher supervision. Usage patterns were analyzed throughout the trial period, and follow-up interviews with 12 app users foregrounded subjective usage experiences and considerations for future design. Results: We developed 3 new features following co-design interviews: a goal-setting feature, a craving tracker, and a meetings log. Usage metrics revealed mixed engagement, with 45.3% (24/53) of users actively engaging with the app throughout the trial. These active users opened the app 27.1 unique times on average, with a retention rate after 30 days among active users of 45.8% (11/24), exceeding the typical mobile app retention benchmark of 7% after 30 days. Interviews revealed that participants preferred app functionality to extend beyond substance use domains to support other dimensions of their lives not directly pertaining to substance use, such as general goals and daily routines. Participants further suggested that recovery apps should act as private digital journals while also providing a sense of community and connection to broader recovery ecosystems. Additionally, mHealth designs that allow users to configure their own personalized recovery pathways in the app can benefit some users who appreciate increased autonomy, while others may become overwhelmed by a lack of prescriptive guidance. Conclusions: It is valuable to incorporate iterative co-design methodologies into digital health and recovery app research to help optimize engagement. Furthermore, recovery apps can benefit from flexible designs with customizable degrees of user autonomy. Future designers can better cater to individual user preferences by personalizing mHealth designs so that they strike a balance between system control and user control over digital recovery pathways.

  • JMIR Mhealth Uhealth 2026;14(1):e83984

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  Quality Evaluation and Descriptive...

  Feb 11, 2026

  Mobile Apps for Oncology Health Care Professionals: Mapping and Assessment Study

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  Authors List:

  • David Liñares  
  • Iolie Nicolaidou  
  • Andreas Charalambous  
  • Daniela Cabutto  
  • Deborah Moreno-Alonso  
  • Clara Madrid Alejos  
  • Norbert Couespel  
  • Noemí López-Rey  
  • María José Fernández-Domínguez  
  • Carme Carrion  
  • Ana Clavería  

  Abstract:

  Background: The use of mobile apps in oncology has been expanding rapidly, encompassing prevention, treatment, and patient support. These technologies hold significant potential to improve care delivery and enhance the efficiency of health care services. However, their integration into clinical practice faces important challenges. A key issue lies in the difficulties health care professionals (HCPs) encounter when selecting apps that adequately meet their specific needs and comply with appropriate standards of quality and clinical effectiveness. This lack of robust evidence on the availability, adoption, and evaluation of mobile apps designed for cancer care professionals not only hinders their wider adoption but also restricts their potential to serve as reliable tools in oncology practice. Objective: This study aims to map the landscape of free mobile apps for cancer prevention, treatment, therapy, or support for HCPs, and assess the quality of the apps identified. Methods: A systematic search was conducted on Google Play and Apple App Store in June 2023 and December 2024 using predefined oncology- and professional-related keywords, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent reviewers (DL and AC) assessed the selected apps using the Mobile App Rating Scale (MARS), which evaluates engagement, functionality, aesthetics, information quality, and subjective quality on a 5-point Likert scale. Discrepancies in ratings were resolved by a third reviewer. Descriptive statistics summarized the app quality and characteristics. Results: Out of 221 apps initially identified, 20 met the inclusion criteria and were evaluated. Most apps (15/20, 75%) supported both Android and iOS platforms, with 90% (18/20) commercially developed. The mean overall MARS score was 3.51 (SD 0.54), indicating moderate quality but with room for improvement. Only 2 apps, ONCOassist (Portable Medical Technology Ltd.) (mean 4.25, SD 0.26) and Oncology Board Review (mean 4.03, SD 0.39), surpassed the threshold of 4.0, considered good quality. ONCOassist stood out for its comprehensive functionality and high information quality, offering clinical decision support tools such as treatment protocols, prognostic calculators, and toxicity grading aligned with professional oncology practice. Prevention and support apps generally scored lower, particularly in engagement and interactive features. No app achieved a high score across all MARS domains. Conclusions: The study highlights a fragmented landscape of free mobile apps for cancer care professionals, with predominantly low to moderate quality and limited evidence to support clinical effectiveness. ONCOassist emerges as a promising tool warranting further investigation. This underscores the urgent need for standardized evaluation frameworks, regulatory oversight, and sustainable development strategies to ensure the creation and adoption of reliable, evidence-based digital health tools in oncology.

  • JMIR Mhealth Uhealth 2026;14(1):e71203

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  Security and Privacy of mHealth and...

  Feb 11, 2026

  Privacy Policy Compliance of Mobile Sports and Health Apps in China: Scale Development, Data Analysis, and Prospects for Regulatory Reform

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  Authors List:

  • Rengui Guo  
  • Fanhong Chen  

  Abstract:

  Background: Driven by technological advancements, the proliferation of mobile sports and health applications (apps) has revolutionized health management by improving efficiency, cost-effectiveness, and accessibility. While the widespread adoption of these platforms has transformed public health practices and social well-being in China, emerging evidence suggests that inadequacies in their privacy policies may compromise personal information (PI) protection. Objective: This study conducts a systematic evaluation of privacy policy compliance among 100 leading mobile sports and health apps in mainland China, benchmarking them against the Personal Information Protection Law (PIPL) and associated regulatory guidelines. Methods: This study develops a privacy policy compliance indicator scale based on the information life cycle and the legal framework for PI protection in the Mainland of China. This scale consists of 5 level 1 indicators and 37 level 2 indicators that assess the privacy policy compliance. Results: The mean compliance score of the 100 sports and health apps is 58.375. 42% of the 100 apps score below the average, with 57.14% (n=24) scoring less than 40 points and 20% (n=20) scoring more than 80 points. The 5 level 1 indicators have the following scores: 75.22% for the collection of PI, 52% for the storage of PI, 52.21% for the use of PI, 61% for the entrusted processing, the sharing, the transfer, and the disclosure of PI, and 60.6% for the consultation and feedback on PI. The compliance level among Apple system apps surpasses that of Android system apps. Despite identical qualified rates for Apple and Android apps, both at 17 out of 50, the proportion of Apple apps rated as excellent (12%) and good (12%) markedly surpasses that of Android apps, which stand at 6% for excellent and 10% for good. The compliance evaluations for these 37 level 2 indicators, 18 show a mean compliance rate below 60%. Three indicators exhibit compliance rates below 20%: de-identified display and use of PI at 16%, storage of sensitive PI at 13%, and processing rules of deceased users at 11%. While the majority of 100 apps indicate that the collected PI will be retained in the Mainland of China for a reasonably necessary duration (mean 71%[SD 45.60%]), and that separate authorization will be required otherwise (mean 80%[SD 40.20%]), in accordance with the principle of necessity outlined in Article 19 and the principle of domestic storage in Article 39 of the PIPL, fewer than half (mean 44%[SD 49.89%]) of the evaluated apps will de-identify the data promptly through security measures after PI collection. Conclusions: Although some apps establish commendable policies, there are some shortcomings that compromise the efficacy of PI protection. In light of this, this paper puts forward suggestions from the perspective of privacy policy formulation, regulatory reform, and legislative improvement.

  • JMIR Mhealth Uhealth 2026;14(1):e73651

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  Wearables and MHealth Reviews

  Feb 11, 2026

  Wearable Devices for Remote Monitoring of Chronic Diseases: Systematic Review

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  Authors List:

  • Masresha Derese Tegegne  
  • Sharareh Rostam Niakan Kalhori  
  • Paulo Haas  
  • Viktor MG Sobotta  
  • Joana Warnecke  
  • Thomas M Deserno  

  Abstract:

  Background: Wearable devices enable the remote collection of health parameters, supporting the outpatient plans recommended by the World Health Organization (WHO) to manage chronic diseases. While disease-specific monitoring is accurate, a comprehensive analysis of wearables across various chronic diseases helps to standardize remote patient monitoring (RPM) systems. Objective: This review aims to identify wearables for remote monitoring of chronic diseases, focusing on (i) wearable devices, (ii) sensor types, (iii) health parameters, (iv) body locations, and (v) medical applications. Methods: We develop a search strategy and conduct searches across three databases: PubMed, Web of Science, and Scopus. After reviewing 1,160 articles, we selected 61 that address cardiovascular, cancer, neurological, metabolic, respiratory, and other diseases. We create a data analysis method based on our five objectives to organize the articles for a comprehensive analysis. Results: From the 61 articles, 39 use wearable bands such as smartwatches, wristbands, armbands, and straps to monitor chronic diseases. Wearable devices commonly include various sensor types, such as accelerometers (39/61), photoplethysmographic (PPG) sensors (18/61), biopotential meters (17/61), pressure meters (11/61), and thermometers (9/61). These sensors collect diverse health parameters, including acceleration (39/61), heart rate (24/61), body temperature (9/61), blood pressure (8/61), and peripheral oxygen saturation (SpO2) (7/61). Common sensor body locations are the wrist, followed by the upper arm, and the chest. The medical applications of wearable devices are neurological (21/61) and cardiovascular diseases (15/61). Additionally, researchers apply wearable devices for wellness and lifestyle monitoring (39/61), mainly for activity (39/39) and sleep (10/39). Conclusions: This review underscores that wearable devices primarily function as bands, commonly worn on the wrist, to monitor chronic diseases. These devices collect data on acceleration, heart rate, body temperature, blood pressure, and SpO2, with a focus on neurological and cardiovascular diseases. Our findings provide a foundational roadmap for designing generalized RPM systems to manage multimorbidity and support standardized terminology for interoperability across digital health systems. To translate this into practice, we recommend that future research prioritizes pragmatic clinical trials with medically certified devices. Clinical Trial: PROSPERO (CRD42023460873); https://www.crd.york.ac.uk/PROSPERO/view/CRD42023460873

  • JMIR Mhealth Uhealth 2026;14(1):e74071

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  Text-messaging (SMS, WeChat...

  Feb 11, 2026

  Individualized Treatment Effects of a Digital Smoking Cessation Intervention Among Individuals Looking Online for Help: Secondary Analysis of a Randomized...

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  Authors List:

  • Joel Crawford  
  • Jenny Blomqvist  
  • Katarina Ulfsdotter Gunnarsson  
  • Preben Bendtsen  
  • Marcus Bendtsen  

  Abstract:

  Background: Smoking cessation trials typically report the average treatment effects, in which causal inference is made regarding the average effect of a treatment on a heterogeneous sample. Nonetheless, individual factors such as age, gender, and genetics can impact the effectiveness of a treatment on outcomes. Objective: This study aimed to estimate the individualised effects of a text-message based smoking cessation intervention. Methods: Data from a randomised controlled trial including 1012 adults from the Swedish general population were used. The trial assessed the effects of a text-messaging intervention that aimed to change behaviour by increasing the importance for change, boosting knowledge on how to change, and instilling confidence for change. Outcomes were prolonged abstinence and point-prevalence of smoking cessation. Individualised treatment effects were modelled using baseline factors to study who benefitted the most from the intervention. Results: There was evidence of heterogenous effects with those benefitting the most being older individuals, those with planned surgery, those who smoked less and had done so for a shorter duration, those with high confidence in their ability to quit, and those who believed that quitting was important. Conclusions: The results demonstrate how individuals respond differently to a text-message smoking cessation intervention. This provides an insight into who benefits the most and least from the intervention and highlights who needs to be targeted in future interventions to further the reduce the prevalence of smoking. Clinical Trial: The trial was registered in the ISRCTN registry on 03/12/20 (ISRCTN13455271).

  • JMIR Mhealth Uhealth 2026;14(1):e63578

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