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December 2020 A Bayesian time-varying effect model for behavioral mHealth data
Matthew D. Koslovsky, Emily T. Hébert, Michael S. Businelle, Marina Vannucci
Ann. Appl. Stat. 14(4): 1878-1902 (December 2020). DOI: 10.1214/20-AOAS1402


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.


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Matthew D. Koslovsky. Emily T. Hébert. Michael S. Businelle. Marina Vannucci. "A Bayesian time-varying effect model for behavioral mHealth data." Ann. Appl. Stat. 14 (4) 1878 - 1902, December 2020.


Received: 1 August 2020; Revised: 1 September 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194252
Digital Object Identifier: 10.1214/20-AOAS1402

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.14 • No. 4 • December 2020
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