  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.
