Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning.

ABSTRACT:

Background

Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement.

Methods

We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful i

SUBMITTER: Ramos LA 

PROVIDER: S-EPMC8451420 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature