Accepted for/Published in: JMIR Mental Health
Date Submitted: Jun 12, 2021
Date Accepted: Sep 16, 2021
(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Warning: This is an author submission that is not peer-reviewed or edited.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.
Testing multiple measures of adherence within a self-guided online intervention for depression and anxiety: Lessons learned and recommendations
ABSTRACT
Background:
Self-guided online interventions offer users the ability to participate in an intervention at their own pace and address some traditional service barriers (e.g., attending in person appointments, cost, etc.). However, these interventions suffer from high drop-out rates, and data are limited as to what types of treatment adherence might be associated with improvement in clinical outcomes. Further, existent literature provides little guidance for defining and measuring treatment adherence in online interventions.
Objective:
Guided by best practices identified in the literature, this study aimed to demonstrate a process of developing and testing multiple measures of adherence as applied to a specific self-guided online intervention. Given unique challenges associated with the investigation of online interventions, our purpose was to illustrate various methodological decision points that necessitate careful consideration.
Methods:
Data for this study come from a randomized control trial of an eight week online cognitive behavioral prevention program aimed at reducing depression and anxiety symptoms in a college student population. We generated multiple measures intended to capture behavioral and attitudinal adherence to the intervention at varying levels of effort (i.e., low, moderate and high effort) and tested the utility of these measures for predicting depression, stress and anxiety symptom change. Simple linear regressions were run to test relationship between measures of adherence and improvement on the DASS-21. Analyses were run twice, once with the intent to treat sample, and once with the initiators only sample.
Results:
Among the 947 participants, 747 initiated any activity, and 449 provided posttest data. Results from the intent-to-treat sample indicated that minimal levels of effort for both behavioral and attitudinal adherence significantly predicted symptom change (behavioral: F(3, 743) = 17.61, p < .001, β = -.306, p < .05; attitudinal: F(3, 743) = 16.82, p < .001, β = -.316, p <.05). In the same manner, moderate levels of effort for both behavioral and attitudinal adherence significantly predicted symptom change (behavioral adherence: F(3, 743) = 14.704, p < .001, β = -.290, p < .05; attitudinal adherence: F(3, 743) = 14.66, p < .001, β = -.337, p < .05 ). Results differed in the initiators-only sample, where none of the adherence measures significantly predicted symptom change.
Conclusions:
Our findings highlight the importance of carefully developing measures and clearly reporting methods as well as methodological decisions that influence outcomes. Such decisions include, but are not limited to: 1) defining initiator sample, 2) selecting multiple measures of adherence, 3) choosing method of dealing with missingness. We summarize our lessons learned into a recommendations table that any researcher aiming to test measures of adherence can utilize. Clinical Trial: This trial was retroactively registered with ClinicalTrials.gov (NCT04361045).
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