OBJECTIVE: To assess the dissemination of evidence-based content within smartphone apps for depression and anxiety by developing and applying user-adjusted analysis-a method for weighting app content based on each app's number of active users. METHOD: We searched the Apple App Store and Google Play Store and identified 27 apps within the top search hits, which real-world users are most likely to encounter. We developed a codebook of evidence-based treatment elements by reviewing past research on empirically supported treatments. We coded the apps to develop an initial tally of the frequency of treatment elements within the mental health (MH) apps. We then developed and applied user-adjusted analysis to refine the tallies based on each app's number of monthly active users. RESULTS: The 2 most popular apps were responsible for 90% of monthly active users, and user-adjusted analysis markedly altered conclusions of prior reports based on tallies alone. For example, mindfulness was present in 37% of apps but reached 96% of monthly active users, cognitive restructuring was present in 22% but reached only 2%, and exposure was present in 7% but reached only 0.0004%. CONCLUSIONS: The potential impact of MH apps on mental health may be best evaluated via assessment that combines tallies of evidence-based content with data on the content users are actually accessing. Given wide variation in the popularity of MH apps, findings weighted by usage data may differ markedly from findings based on raw tallies alone. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
OBJECTIVE: To assess the dissemination of evidence-based content within smartphone apps for depression and anxiety by developing and applying user-adjusted analysis-a method for weighting app content based on each app's number of active users. METHOD: We searched the Apple App Store and Google Play Store and identified 27 apps within the top search hits, which real-world users are most likely to encounter. We developed a codebook of evidence-based treatment elements by reviewing past research on empirically supported treatments. We coded the apps to develop an initial tally of the frequency of treatment elements within the mental health (MH) apps. We then developed and applied user-adjusted analysis to refine the tallies based on each app's number of monthly active users. RESULTS: The 2 most popular apps were responsible for 90% of monthly active users, and user-adjusted analysis markedly altered conclusions of prior reports based on tallies alone. For example, mindfulness was present in 37% of apps but reached 96% of monthly active users, cognitive restructuring was present in 22% but reached only 2%, and exposure was present in 7% but reached only 0.0004%. CONCLUSIONS: The potential impact of MH apps on mental health may be best evaluated via assessment that combines tallies of evidence-based content with data on the content users are actually accessing. Given wide variation in the popularity of MH apps, findings weighted by usage data may differ markedly from findings based on raw tallies alone. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Authors: Akash R Wasil; Sarah Gillespie; Tiffany Schell; Lorenzo Lorenzo-Luaces; Robert J DeRubeis Journal: World Psychiatry Date: 2021-02 Impact factor: 49.548
Authors: Simon B Goldberg; Scott A Baldwin; Kevin M Riordan; John Torous; Cortland J Dahl; Richard J Davidson; Matthew J Hirshberg Journal: Assessment Date: 2021-05-18