A Bayesian Time-Varying Effect Model for Behavioral mHealth Data

TL;DR

This paper introduces a Bayesian time-varying effect model tailored for analyzing intensive longitudinal data from mobile health studies, enabling dynamic understanding of risk factors influencing behaviors like smoking cessation.

Contribution

It develops a novel Bayesian variable selection method with nonparametric priors to identify and cluster dynamic risk factors over time in behavioral health data.

Findings

Effective identification of time-varying risk factors

Clustering of similar effects across subjects

Enhanced evaluation of intervention strategies

Abstract

The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as well as deploy and evaluate intervention strategies. In these studies, researchers often collect intensive longitudinal data (ILD) using ecological momentary assessment methods, which aim to capture psychological, emotional, and environmental factors that may relate to a behavioral outcome in near real-time. In order to investigate ILD collected in a novel, smartphone-based smoking cessation study, we propose a Bayesian variable selection approach for time-varying effect models, designed to identify dynamic relations between potential risk factors and smoking behaviors in the critical moments around a quit attempt. We use parameter-expansion and data-augmentation techniques to efficiently explore how the underlying structure of these relations varies over time and across subjects. We achieve deeper insights into these relations by introducing nonparametric priors for regression coefficients that cluster similar effects for risk factors while simultaneously determining their inclusion. Results indicate that our approach is well-positioned to help researchers effectively evaluate, design, and deliver tailored intervention strategies in the critical moments surrounding a quit attempt.