Health tracking via mobile apps for depression self-management: a qualitative content analysis of user reviews (Preprint)

BACKGROUND Tracking and visualizing health data through mobile health applications (apps) can be an effective self-management tool for mental health conditions. However, little evidence is available to guide the design of health tracking and data visualization mechanisms. OBJECTIVE Analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms in future apps. METHODS We systematically reviewed depression self-management apps on the Google Play and iOS app stores. English-language reviews of eligible apps published between January 1, 2018 and December 31, 2021 were extracted from the app stores, and reviews which referenced health tracking and data visualization were included in sentiment analysis as well as qualitative framework analysis. RESULTS Searches identified 130 unique apps, 26 of which were eligible for inclusion. We included 783 reviews in framework analysis. Five themes emerged: “impact of app-based health tracking,” “tracking mental health: data recording and data visualization,” “a larger health ecosystem,” “cost, finance, and paywalls,” and “technical issues.” Reviews that discussed previous experiences with health tracking apps, the responsiveness of the developer, and personalization had higher ratings and more positive sentiment scores, while those which discussed data accuracy, interoperability, limited ability to display multiple data streams, and technical issues had lower ratings and scores. CONCLUSIONS App-based health data tracking supports depression self-management when features align with users’ individual needs and goals. Heterogeneous preferences pose a challenge for app developers and further research should prioritize features based on importance and impact to users.