Leveraging Machine Learning to Personalize Depression Treatment: A Preregistered Study of 828 Adults Randomly Assigned to a Digital Single-Session Intervention or Waitlist

Abstract

Some digital single-session interventions (SSI) for depression appear effective, at least in youths, but not everyone benefits. In the present study, we use machine-learning methods to develop a treatment-matching algorithm for a digital SSI, the Common Elements Toolbox (COMET), versus a waitlist control. Eight hundred twenty-eight adults with a current or past mental-health problem were randomly assigned 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 posttreatment depressive symptoms. In the 20% held-out test data, PAI did not interact with treatment to predict depression severity after 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.