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 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 two 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.

Objective To examine the frequency of evidence‐based treatment elements in popular smartphone apps for eating disorders (EDs), and to characterize the extent to which real‐world users encounter different elements. Method We searched the Apple App Store and Google Play Store for apps offering treatment or support to individuals with EDs. Then, we created a codebook of 47 elements found in evidence‐based treatments for EDs. We examined the presence or absence of each element within each ED app. We also acquired estimates of the monthly active users (MAU) of each app. Results The ED apps (n = 28) included a median of nine elements of empirically supported treatments (mean = 9.46, SD = 6.28). Four apps accounted for 96% of all MAU. MAU‐adjusted analyses revealed that several elements are reaching more users than raw frequency tallies would suggest. For example, assessments were included in 32% of apps, but 84% of users used an app with assessments. Similar trends were found for cognitive restructuring (21% of apps, 56% of MAU), activity scheduling (39%, 57%), and self‐monitoring (14%, 46%). Problem solving, exposure, and relapse prevention strategies, elements that are prominent in face‐to‐face empirically supported treatments, were rarely included in the apps. Discussion Evidence‐based content is commonly included in ED apps, with certain elements reaching more users than others. Additionally, the top four apps are responsible for nearly all active users. We recommend that ED clinicians and researchers familiarize themselves with these apps—those that patients are most likely to encounter.