Predicting individual differences in digital alcohol intervention effectiveness through multimodal data.

ABSTRACT: Digital interventions can change behaviors like alcohol use, but effectiveness varies widely across individuals. Accurately identifying non-responders-i.e., those least (vs. most) likely to change their behavior-before intervention delivery is difficult. Individual intervention effectiveness predictions from prior studies perform only slightly above chance (e.g., AUC ≈0.60; balanced accuracy ≈0.60). We present a novel approach integrating multimodal data across theory-driven domains-including psychological assessments, social network data, and neural responses to alcohol cues-to make ex-ante predictions about the effectiveness of smartphone-delivered alcohol interventions targeting psychological distancing in young adults (Study 1: N = 67; Study 2: N = 114). Demonstrating the feasibility o

SUBMITTER: Fuchs M 

PROVIDER: S-EPMC12913919 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature