Zheng Li

Background: Exploring user satisfaction is crucial for enhancing and ensuring the sustainable development of mobile health (mHealth) apps, particularly in the fitness and weight management sectors. Analyzing user types and developing user profiles are valuable for understanding differences in satisfaction. However, prior research lacks a classification of user types based on self-management characteristics and an analysis of satisfaction disparities among these types. Objective: This study analyzes user heterogeneity from a self-management perspective among fitness and weight management app users by identifying user types and constructing profiles. It further explores differences in satisfaction with the functional design of these mHealth apps across user types. Methods: First, 8 feature indicators were selected based on the Health Belief Model and the Behavior Change Wheel to evaluate users’ levels of health knowledge and beliefs, as well as self-regulation related to self-management. Existing research was integrated to categorize mHealth app functional design into 5 categories: health guidance, health education, health monitoring, social features, and gamification. Second, a questionnaire survey was used to collect data on users’ 8 health management characteristics and their satisfaction with the 5 functional design categories. A total of 2518 responses were collected, of which 1025 were included in the analysis. Cluster analysis was conducted to classify users into distinct types based on the 8 health management characteristics, and user profiles were constructed according to the distribution of these characteristics within each type. Finally, the Kruskal-Wallis test was used to analyze differences in satisfaction across user types with respect to the 5 functional design categories of mHealth apps. Results: Cluster analysis revealed that users could be categorized into 6 types based on the 8 self-management characteristics: positively proactive energizers, proactive intenders, negatively proactive energizers, low health management demanders, potential health management demanders, and passive attitude holders. Significant differences were observed across all 8 health management characteristics among the 6 user types (all P<.001). The Kruskal-Wallis test indicated significant variations in user satisfaction with the 5 functional designs of mHealth apps: H(4)=445.388, (P<.001). Overall, users reported the highest satisfaction with health guidance and health monitoring (median 4.00, IQR 1.00) and the lowest satisfaction with gamification (median 3.00, IQR 1.00). Positively proactive energizers, proactive intenders, and negatively proactive energizers demonstrated the highest satisfaction with health education and health guidance (median 4.00). Potential health management demanders, proactive intenders, positively proactive energizers, and negatively proactive energizers reported the highest satisfaction with health monitoring (median 4.00). Proactive intenders reported the highest satisfaction with social features and gamification (median 4.00). Conclusions: Users of mHealth apps exhibit diverse types, with significant differences in health management characteristics and satisfaction with the 5 functional designs of fitness and weight management apps. This study clarifies individual-level differences in user satisfaction with mHealth apps.

Background: Stroke is the second leading cause of death and third leading cause of disability globally. The dose–response relationship between physical activity (PA), particularly moderate-to-vigorous physical activity (MVPA), and stroke risk remains unclear, with limited sex-specific evidence. Aims: To examine the dose–response associations of total PA and MVPA with stroke risk, considering sex and subtype differences. Methods: A systematic review and dose–response meta-analysis of prospective cohort studies published between 2013 and 2024, with follow-up durations ranging from 4.9 to 17.9 years, were conducted. PA exposures were standardized to MET-hours per week (MET-h/wk), and incident stroke was the primary outcome. Study-specific hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled using random-effects models. Dose–response associations were assessed using restricted cubic spline models. Analyses stratified by sex and subtype were performed when available. Results: Fourteen cohorts (n = 2,639,086) were included. Total PA showed a nonlinear inverse association with stroke risk: each 10 MET-h/wk increment reduced risk by 1% up to 130 MET-h/wk, corresponding to a 13% maximum reduction, after which benefits plateaued. MVPA exhibited an L-shaped association (P < 0.001), with the greatest benefit (19% reduction) at 19 MET-h/wk, followed by a gradual increase in risk. Sex-stratified analysis revealed a J-shaped pattern in females (optimal 10–15 MET-h/wk; 18% reduction). For males, the HR was 0.89 (95% CI: 0.70–1.13), and a nonlinear model could not be established due to limited data. In ischemic stroke, dose–response patterns paralleled those for total stroke. Conclusions: The study found a significant dose–response relationship between total PA and MVPA with stroke risk. Optimal prevention was observed at 130 MET-h/wk for total PA and 19 MET-h/wk for MVPA. Evidence in males and for hemorrhagic stroke remains limited and warrants further study.

BACKGROUND Exploring user satisfaction is crucial for enhancing and ensuring the sustainable development of mobile health (mHealth) apps, particularly in the fitness and weight management sectors. Analyzing user types and developing user profiles are valuable for understanding differences in satisfaction. However, prior research lacks a classification of user types based on self-management characteristics and an analysis of satisfaction disparities among these types. OBJECTIVE This study analyzes user heterogeneity from a self-management perspective among fitness and weight management app users by identifying user types and constructing profiles. It further explores differences in satisfaction with the functional design of these mHealth apps across user types. METHODS First, 8 feature indicators were selected based on the Health Belief Model and the Behavior Change Wheel to evaluate users’ levels of health knowledge and beliefs, as well as self-regulation related to self-management. Existing research was integrated to categorize mHealth app functional design into 5 categories: health guidance, health education, health monitoring, social features, and gamification. Second, a questionnaire survey was used to collect data on users’ 8 health management characteristics and their satisfaction with the 5 functional design categories. A total of 2518 responses were collected, of which 1025 were included in the analysis. Cluster analysis was conducted to classify users into distinct types based on the 8 health management characteristics, and user profiles were constructed according to the distribution of these characteristics within each type. Finally, the Kruskal-Wallis test was used to analyze differences in satisfaction across user types with respect to the 5 functional design categories of mHealth apps. RESULTS Cluster analysis revealed that users could be categorized into 6 types based on the 8 self-management characteristics: positively proactive energizers, proactive intenders, negatively proactive energizers, low health management demanders, potential health management demanders, and passive attitude holders. Significant differences were observed across all 8 health management characteristics among the 6 user types (all P<.001). The Kruskal-Wallis test indicated significant variations in user satisfaction with the 5 functional designs of mHealth apps: H(4)=445.388, (P<.001). Overall, users reported the highest satisfaction with health guidance and health monitoring (median 4.00, IQR 1.00) and the lowest satisfaction with gamification (median 3.00, IQR 1.00). Positively proactive energizers, proactive intenders, and negatively proactive energizers demonstrated the highest satisfaction with health education and health guidance (median 4.00). Potential health management demanders, proactive intenders, positively proactive energizers, and negatively proactive energizers reported the highest satisfaction with health monitoring (median 4.00). Proactive intenders reported the highest satisfaction with social features and gamification (median 4.00). CONCLUSIONS Users of mHealth apps exhibit diverse types, with significant differences in health management characteristics and satisfaction with the 5 functional designs of fitness and weight management apps. This study clarifies individual-level differences in user satisfaction with mHealth apps.