Predictors of adherence to a publicly available self-guided digital mental health intervention

ABSTRACT

Low adherence to self-guided digital mental health interventions (DMHIs) have raised concerns about their real-world effectiveness. Naturalistic data from self-guided DMHIs are often not available, hindering our ability to assess adherence among real-world users. This study aimed to analyze 3 years of user data from the public launch of an empirically supported 12-session self-guided DMHI, to assess overall program adherence rates and explore predictors of adherence. Data from 984 registered users were analyzed. Results showed that only 14.8% of users completed all 12 modules and 68.6% completed less than half of the modules. Users who were younger, had milder depression, had never seen a mental health provider, and who rejected signing-up for weekly program emails completed significantly more modules. Results add to concerns about the generalizability of controlled research on DMHIs due to lower adherence outside of research trials. This study highlights the potential of user data in identifying key factors that may be related to adherence. By examining adherence patterns among different sub-sets of users, we can pinpoint and focus on individuals who may adhere and benefit more from self-guided programs. Findings could also have implications for guiding intervention personalization for individuals who struggle to complete DMHIs.

KEYWORDS:

• Digital mental health
• public sample
• adherence
• engagement
• acceptance and commitment therapy

Disclosure statement

No potential conflict of interest was reported by the author(s).