Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.

BACKGROUND

Although mindfulness ecological momentary interventions (MEMI) appear effective in alleviating worry symptoms, treatment engagement remains suboptimal. Determining baseline variables of MEMI over self-monitoring placebo (SM) can inform tailored interventions for individuals with generalized anxiety disorder (GAD).

METHOD: Machine meta-learning methods (ML) were applied to predict two-week engagement (log-transformed number of prompts completed) among individuals randomized to MEMI or SM (N = 110). Sixteen baseline variables comprised the predictor set: clinical, demographic, process, and executive functioning (EF) factors. Random forest using a five-fold nested cross-validation approach mitigated overfitting. X-learner meta-algorithms estimated conditional average treatment engagement (CATE). Shapley additive explanations evaluated relative importance.

RESULTS

The 16-predictor model displayed strong predictive performance (R-squared [R2] = 82.7%; root mean squared error [RMSE] = 0.780; mean absolute error [MAE] = 0.512). The top-10 predictor model also yielded good predictive performance (R2 = 82.1%; RMSE = 0.547; MAE = 0.307). As predicted by the CATE analysiscate, participants had the highest treatment engagement when assigned to MEMI instead of SM (d = 1.447, p < .001). The following baseline variables predicted more engagement with MEMI over SM: lower GAD severity, inhibition response time (RT), and EF errors, higher attentional control, empathy, and verbal fluency (capitalization theory); lower mindfulness, and treatment expectancy, poorer working memory, and higher set-shifting RT (compensation model).

LIMITATIONS: The small sample size, single engagement metric, and brief duration might constrain generalizability.

DISCUSSION: Integrating robust ML approaches could optimally identify prescriptive predictors of engagement to brief digital mental health interventions to inform targeted treatments.

TRIAL REGISTRATION: ClinicalTrials.gov ID (NCT04846777).