Evaluation of a recommender app for apps for the treatment of depression and anxiety: an analysis of longitudinal user engagement

Abstract

Abstract Objective While depression and anxiety are common mental health issues, only a small segment of the population has access to standard one-on-one treatment. The use of smartphone apps can fill this gap. An app recommender system may help improve user engagement of these apps and eventually symptoms. Methods IntelliCare was a suite of apps for depression and anxiety, with a Hub app that provided app recommendations aiming to increase user engagement. This study captured the records of 8057 users of 12 apps. We measured overall engagement and app-specific usage longitudinally by the number of weekly app sessions (“loyalty”) and the number of days with app usage (“regularity”) over 16 weeks. Hub and non-Hub users were compared using zero-inflated Poisson regression for loyalty, linear regression for regularity, and Cox regression for engagement duration. Adjusted analyses were performed in 4561 users for whom we had baseline characteristics. Impact of Hub recommendations was assessed using the same approach. Results When compared to non-Hub users in adjusted analyses, Hub users had a lower risk of discontinuing IntelliCare (hazard ratio = 0.67, 95% CI, 0.62-0.71), higher loyalty (2- to 5-fold), and higher regularity (0.1–0.4 day/week greater). Among Hub users, Hub recommendations increased app-specific loyalty and regularity in all 12 apps. Discussion/Conclusion Centralized app recommendations increase overall user engagement of the apps, as well as app-specific usage. Further studies relating app usage to symptoms can validate that such a recommender improves clinical benefits and does so at scale.