Accepted for/Published in: JMIR Human Factors
Date Submitted: Feb 7, 2024
Date Accepted: Jun 17, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Application of an Adapted Health Action Process Approach Model to Predict Engagement with a Digital Mental Health Website
ABSTRACT
Background:
Digital Mental Health (DMH) tools are an effective, readily accessible, and affordable form of mental health support. However, sustained engagement with DMH is suboptimal, with limited research on DMH engagement. The Health Action Process Approach (HAPA) is an empirically supported theory of health behavior adoption and maintenance. Whether this model also explains DMH tool engagement remains unknown.
Objective:
This study examined if an adapted HAPA model predicted engagement with DMH via a self-guided website.
Methods:
Visitors to the [masked for review] website were invited to complete a brief survey measuring HAPA constructs. The adapted HAPA model was tested with data from 16,078 sessions on [masked for review] via structural equation modeling in predicting two engagement outcomes: (1) choice to engage with DMH (ie, spending 3 or more seconds on an [masked for review] page, excluding screening pages) and (2) level of engagement (ie, time spent on [masked for review] pages and number of pages visited, both excluding screening pages).
Results:
Participants chose to engage with the [masked for review] website in 94.3% (15,161/16,078) of the sessions. Perceived need (β=.66, P<.001), outcome expectancies (β=.49, P<.001), self-efficacy (β=.44, P<.001), and perceived risk (β=.17-.18, P<.001) significantly predicted intention, and intention (β=.77, P<.001) significantly predicted planning. Planning was not significantly associated with choice to engage (β=.03, P=.18). Within participants who chose to engage, the association between planning with level of engagement was statistically significant (β=.12, P<.001). Model fit indices for both engagement outcomes were poor, with the adapted HAPA model accounting for only 0.1% and 1.4% of the variance in choice to engage and level of engagement, respectively.
Conclusions:
There was limited support for the adapted HAPA model in a DMH context. Our data suggest that while motivational constructs fit the HAPA model (ie, perceived need, outcome expectancies, self-efficacy, and perceived risk predicting intention), this model was not appropriate for predicting engagement with DMH via a self-guided website. More research is needed to identify appropriate theoretical frameworks and practical strategies (eg, digital design) to optimize DMH tool engagement.
Citation
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Copyright
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