Assessing Time-Varying Causal Effect Moderation in Mobile Health

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-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.

Keywords: effect modification; mHealth; structural nested mean model.

Figures

Figure 1

A BASICS-Mobile participant’s data for two treatment occasions leading up to Yt+1, depicted in chronological order. Information is primarily collected via self-reports three times per day—morning, afternoon and evening. Treatment occasions take place after the afternoon and evening self-reports.