Just-in-time Adaptive Mechanisms of Popular Mobile Applications for Individuals with Depression: Systematic Review

Background: There is an increasing number of smartphone applications (apps) focusing on prevention, treatment, and diagnosis of depression. A promising approach to increase the effectiveness while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. Objective: With this work, we systematically assess the use of JITAI mechanisms in apps for individuals with depression. Methods: We systematically searched for apps addressing depression in the Apple App Store, the Google Play Store, and in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. Relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, two authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, and Google Scholar), publications cited on the app’s website, information on the app’s website, and the app itself. Results: None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations or individuals. Three apps did not use any measurements, 20 apps exclusively used self-reports that are insufficient to leverage the full potential of JITAIs, and the five apps employing self-reports and passive measurements used them as progress or task indicators only. While 23 of the 68 reviewed publications investigated the effectiveness and 14 publications investigated the efficacy of the apps, not one publication mentioned or evaluated JITAI mechanisms. Conclusions: Promising JITAI mechanisms have not yet been translated into mainstream depression apps. The lack of publications investigating whether JITAI mechanisms lead to an increase of the apps’ effectiveness or efficacy highlights the need for further research, especially in real-world apps.