Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 4, 2022
Open Peer Review Period: May 4, 2022 - Jun 29, 2022
Date Accepted: Jan 16, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited.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.
Digital phenotyping data to predict symptom improvement and mental health app personalization in college students
ABSTRACT
Background:
Mental health apps offer a transformative means to increase access to scalable evidence-based care for college students. Yet low rates of engagement currently preclude the effectiveness of these apps. One promising solution is to make these apps more responsive and personalized through digital phenotyping methods able to predict symptoms and offer tailored interventions.
Objective:
Following our protocol and utilizing the exact model shared in that paper, in this work we assess the prospective validity of mental health symptom prediction using the mindLAMP app. We also explore secondary aims around app intervention personalization and correlations of engagement with the Technology Acceptance Model (TAM) and Digital Working Alliance Inventory (D-WAI) scale.
Methods:
The study was 28 days in duration and followed the published protocol with participants collecting digital phenotyping data and being offered optional scheduled as well as algorithm recommend app interventions. Study compensation was tied to the completion of Weekly Surveys and was not otherwise tied to engagement or use of the app.
Results:
170 college student participants completed informed consent, of which 108 passed the study trial period, and 74 completed the study. The area under the curve values for the symptom prediction model ranged from 0.58 for the UCLA Loneliness Scale to 0.71 for the Patient Health Questionaire-9. Engagement with the app interventions was high with a study mean of 73% but few participants engaged with the optional recommended interventions. The perceived utility of the app in the TAM was higher among those completing at least one recommended intervention
Conclusions:
Our results suggest how digital phenotyping methods can be used to create generalizable models that may help create more personalized and engaging mental health apps.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.