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Identifying Screening-Level Predictors of Enrollment in a Mobile Mindfulness Meditation Trial: A Bayesian Model-Averaged Logistic Regression Approach
ABSTRACT
Background:
Mindfulness meditation apps are increasingly used in clinical and non-clinical contexts, with growing evidence of effectiveness. Although app-based programs are scalable and accessible, limited research has examined who chooses to enroll in such interventions. Identifying predictors of enrollment is important to understand selection effects, address barriers, and ensure equitable dissemination.
Objective:
We examined demographic and motivational predictors of enrollment in an app-based mindfulness meditation randomized controlled trial using Headspace.
Methods:
Participants were non-faculty staff at a U.S. public university recruited for a trial testing the effects of Headspace on workplace stress. At screening (N=262), participants provided information on demographic characteristics, prior meditation experience, and willingness to be randomized into either the meditation or waitlist groups. Bayesian model-averaged logistic regression was used to evaluate predictors of enrollment, testing all possible subsets of predictors with posterior model probabilities and Bayes factors used to assess evidence.
Results:
Among screened participants, 53.4% (n=140) enrolled. Willingness to be in the meditation group emerged as the sole robust predictor, with these participants being 4.5 times more likely to enroll. Age, gender, prior meditation practice, and willingness to be in the waitlist group showed evidence against inclusion.
Conclusions:
Willingness to join the active treatment condition strongly predicted enrollment, underscoring the role of motivation and acceptability. Future research should test whether providing pre-enrollment education or addressing concerns about randomization increases enrollment across diverse populations. Clinical Trial: Registration: ClinicalTrials.gov NCT03652168; http://clinicaltrials.gov/ct2/show/NCT03652168
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Copyright
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