The Longevity of Mental Health Apps: A Survival Analysis of the Largest Mental Health App Database.

Authors

Julian Herpertz, Bridget Dwyer, Matthew Flathers, Nils Opel, Christian Otte, Stefan M Gold, Ulrich W Ebner-Priemer, Julian Schwarz, Jake Linardon, Sean Ryan, John Torous

Published in

The American journal of psychiatry. Pages appiajp20260155. Sep 09, 2026. Epub Sep 09, 2026.

Abstract

Despite over 10,000 mental health apps available, app marketplaces are unstable, characterized by high attrition rates and technological obsolescence. This study analyzed factors associated with app survival to help patients and clinicians in selecting more durable mental health apps and inform developers in creating more stable ones.
Using data from the M-Health Index and Navigation Database (MIND; MindApps.org), the authors performed a 5-year longitudinal survival analysis on 865 apps. App longevity was evaluated using Kaplan-Meier estimates, and Cox regression was used to assess the impact of 54 clinical, technical, and commercial predictors of attrition. Additionally, a random forest classifier was used to identify app characteristics associated with 2-year survival status.
Of 865 apps identified, 464 (53.6%) were removed from MindApps.org during the 2,142-day study period. Disparities in longevity were observed across clinical targets; aside from the small cohort of apps for schizophrenia, smoking cessation apps presented the highest risk for attrition. Sleep-related apps demonstrated the greatest longevity. Cox regression (C-index=0.77) and an exploratory random forest analysis (area under the receiver operating characteristic curve=0.82) identified platform exclusivity (not working on both Android and iOS) as the most prominent characteristic associated with removal from MindApps.org, with Android-only apps exhibiting a significantly elevated risk, carrying more than double the hazard of attrition (Gini feature importance=0.12; hazard ratio=2.32).
The findings provide a roadmap for identifying durable mental health apps, thereby minimizing the risk of treatment attrition. The predictors of removal from MindApps.org provide critical lessons for the new era of AI-based tools.

PMID:
42711750
Bibliographic data and abstract were imported from PubMed on 09 Sep 2026.

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