Xiaoyi Jiao

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: China’s 3-child policy has increased the demand for scientific and personalized pregnancy health management. The convenience of mobile health has promoted the use of pregnancy management apps among pregnant women. User satisfaction has a significant impact on continued use intention. Systematically evaluating user satisfaction with pregnancy management apps is of great significance in promoting the digital transformation of maternal-infant health care. Objective: This study aimed to explore user satisfaction with pregnancy management apps by mining user-generated content and analyzing the differences in user satisfaction among different operating systems. Under the guidance of Herzberg two-factor theory, this study explored the demand structure in user experience and investigated the influencing factors on satisfaction from 2 aspects: user satisfaction and dissatisfaction, providing reference for the development of mobile health apps for pregnancy management. Methods: We screened pregnancy management apps from the app stores of 5 mobile phone manufacturers in mainland China and collected the app-based reviews and ratings posted by users. We performed topic clustering and semantic parsing using the latent Dirichlet allocation and DeepSeek-R1 models. Influencing factors for satisfaction and dissatisfaction were identified using the Tobit regression model. The Kano model was used to classify the factors as basic or attractive. The Wald test was applied to analyze the differences in the effects across various factors. Results: We examined 86 pregnancy management apps, amounting to 180,107 reviews in total. The overall satisfaction rate with pregnancy apps was relatively high (72.34%). Android users had higher satisfaction rates than iOS users (89.32% vs 60.75%). User reviews were clustered into 12 themes, categorized into 3 types: technical security support, basic service experience, and maternal-infant scenarios. The basic factors causing dissatisfaction included system login (β=2.829; P<.001) and privacy disclosure (β=1.955; P<.001). Attractive factors boosting satisfaction included storage optimization (β=0.220; P<.001), page design (β=0.223; P<.001), function provision (β=0.023; P=.001), platform feedback (β=0.222; P<.001), physicians’ inquiries (β=0.356; P<.001), menstrual management (β=0.209; P<.001), pregnancy guidelines (β=0.306; P<.001), parenting science popularization (β=0.238; P<.001), growth record (β=0.401; P<.001), and maternal-infant community (β=0.307; P<.001). Conclusions: There were significant differences in user satisfaction between the iOS and Android platforms, reflecting the heterogeneity of responses to user demands in different technological ecosystems. Twelve themes showed the multilevel needs of pregnant women for pregnancy management apps, forming a progressive service chain: security guarantee to demand satisfaction to experience improvement. Twelve factors had asymmetric correlations with user satisfaction and dissatisfaction with pregnancy management apps. Two factors were related to basic attributes of the apps and 10 factors to attractive attributes. By distinguishing between basic and attractive factors, the use of pregnancy management apps may be improved.

BACKGROUND China’s 3-child policy has increased the demand for scientific and personalized pregnancy health management. The convenience of mobile health has promoted the use of pregnancy management apps among pregnant women. User satisfaction has a significant impact on continued use intention. Systematically evaluating user satisfaction with pregnancy management apps is of great significance in promoting the digital transformation of maternal-infant health care. OBJECTIVE This study aimed to explore user satisfaction with pregnancy management apps by mining user-generated content and analyzing the differences in user satisfaction among different operating systems. Under the guidance of Herzberg two-factor theory, this study explored the demand structure in user experience and investigated the influencing factors on satisfaction from 2 aspects: user satisfaction and dissatisfaction, providing reference for the development of mobile health apps for pregnancy management. METHODS We screened pregnancy management apps from the app stores of 5 mobile phone manufacturers in mainland China and collected the app-based reviews and ratings posted by users. We performed topic clustering and semantic parsing using the latent Dirichlet allocation and DeepSeek-R1 models. Influencing factors for satisfaction and dissatisfaction were identified using the Tobit regression model. The Kano model was used to classify the factors as basic or attractive. The Wald test was applied to analyze the differences in the effects across various factors. RESULTS We examined 86 pregnancy management apps, amounting to 180,107 reviews in total. The overall satisfaction rate with pregnancy apps was relatively high (72.34%). Android users had higher satisfaction rates than iOS users (89.32% vs 60.75%). User reviews were clustered into 12 themes, categorized into 3 types: technical security support, basic service experience, and maternal-infant scenarios. The basic factors causing dissatisfaction included system login (β=2.829; P<.001) and privacy disclosure (β=1.955; P<.001). Attractive factors boosting satisfaction included storage optimization (β=0.220; P<.001), page design (β=0.223; P<.001), function provision (β=0.023; P=.001), platform feedback (β=0.222; P<.001), physicians’ inquiries (β=0.356; P<.001), menstrual management (β=0.209; P<.001), pregnancy guidelines (β=0.306; P<.001), parenting science popularization (β=0.238; P<.001), growth record (β=0.401; P<.001), and maternal-infant community (β=0.307; P<.001). CONCLUSIONS There were significant differences in user satisfaction between the iOS and Android platforms, reflecting the heterogeneity of responses to user demands in different technological ecosystems. Twelve themes showed the multilevel needs of pregnant women for pregnancy management apps, forming a progressive service chain: security guarantee to demand satisfaction to experience improvement. Twelve factors had asymmetric correlations with user satisfaction and dissatisfaction with pregnancy management apps. Two factors were related to basic attributes of the apps and 10 factors to attractive attributes. By distinguishing between basic and attractive factors, the use of pregnancy management apps may be improved.

Objective: There is currently a lack of in-depth understanding of patient satisfaction and usage of internet hospitals in real-world scenarios. This study aims to comprehensively collect internet hospital Applications (APPs) in China, investigate their patient satisfaction, identify influencing factors, and understand the differences in the factor attributes. Methods: This study was a cross-sectional observational study. We collected China's internet hospital APPs and their patient reviews from eight Chinese APP stores in October 2024. First, data preprocessing was conducted through deduplication, identification of bot accounts, sentiment analysis, and manual inspection. Second, based on the Two-Factor Theory, the Latent Dirichlet Allocation topic model and Tobit model were employed to identify influencing factors. Third, the Wald test was used to examine the effect differences of these factors. Finally, the factor attributes were identified using the Kano model. Results: A total of 148 internet hospital APPs in China and their 121,458 patient reviews were included. The number of these APPs and users showed an initial increase followed by a decrease, peaking in 2020. For influencing factors, 12 factors significantly affected patient satisfaction and dissatisfaction. The Wald test results indicated that there is a significant difference in the influencing effect between patient satisfaction and dissatisfaction. Twelve factors were further categorized into ten charm factors and two essential factors. Conclusion: In recent years, patient satisfaction and real-world usage effectiveness of internet hospital APPs have been suboptimal. Research has shown that influencing factors exhibit asymmetry and can be further classified into charm factors and essential factors. On the one hand, reliability and customer service are basic needs of patients. On the other hand, online diagnosis and treatment functions, doctor's professional level, easy to use, and compatibility can effectively improve patient compliance.

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.