Assessing Time-Varying Causal Effect Moderation in Mobile Health

TL;DR

This paper develops a formal framework and methods for analyzing how time-varying factors influence the effectiveness of mobile health interventions, demonstrated through a study on college students' drinking and smoking behaviors.

Contribution

It introduces a new formal definition of moderated effects in mobile health, tailored for real-time, dynamic treatment settings, and compares estimation methods within this framework.

Findings

Proposed a formal potential outcomes-based definition for moderated effects.

Developed and compared methods for estimating time-varying moderation effects.

Applied the approach to real data from a mobile intervention targeting college students.

Abstract

In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators-i.e., individual characteristics, time-varying context or past treatment response that moderate the effect of current treatment on a subsequent response. This paper introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.