EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning

TL;DR

This paper introduces EngageTriBoost, an explainable machine learning model that predicts user engagement in digital mental health interventions, providing insights to enhance adoption and mental health outcomes.

Contribution

The study develops an ensemble ML model with interpretability to predict engagement and identify key factors influencing user participation in DMHIs.

Findings

EngageTriBoost achieved 84% accuracy in predicting engagement.

SHAP analysis identified emotional dysregulation and stigma as key engagement factors.

The model offers actionable insights to improve DMHI adoption.

Abstract

Mental health challenges among young adults, are on the rise, necessitating effective solutions such as digital mental health interventions (DMHIs). Despite their promise, DMHIs face significant adoption barriers, including low initial uptake and high dropout rates. This study leverages machine learning (ML) to analyze behavioral patterns of users of a DMHI, eBridge, designed to increase the utilization of professional mental health services among at-risk college students through motivational interviewing-based online counseling. Our ensemble model, EngageTriBoost, achieved up to 84% accuracy in predicting engagement, measured by sign-ins and counselor interactions. We then applied the Shapley Additive exPlanations (SHAP) analysis which provided clear, interpretable insights into key factors influencing user engagement such as emotional dysregulation and perceived stigma, highlighting their critical effect on DMHI adoption. This study demonstrates the power of explainable ML for better understanding user engagement with DMHI to improve their adoption and achievable impact on mental health outcomes.