Leveraging machine learning to personalize depression treatment: A pre-registered study of 828 adults randomized to a digital single-session intervention or waitlist

Abstract

Some digital single-session interventions (SSI) for depression appear effective, at least in youth, but not everyone benefits. The present study uses machine learning methods to develop a treatment matching algorithm for a digital SSI, the Common Elements Toolbox (COMET), versus a waitlist control. 828 adults with a current or past mental health problem were randomized to COMET or a waitlist control. Elastic net regularization models with 10-fold cross-validation were used to develop a Personalized Advantage Index (PAI) indicating the relative benefit of receiving COMET over the waitlist in 2-week post-treatment depressive symptoms. In the 20% held-out test data, PAI did not interact with treatment to predict depression severity post-treatment (β = 0.880, SE = 1.21, t = −0.72, p = .47), indicating that our treatment matching algorithm was not able to provide statistically significant recommendations. Even in a large sample, personalized treatment recommendations for digital SSIs are difficult to develop.