06. Modeling Engagement in Mobile Health: Considerations for Multi-Treatment Designs

Mobile health interventions have become increasingly prominent in recent years as tools for supporting health behavior change. Recent studies have focused on improving medication adherence and self-care among individuals managing chronic conditions such as diabetes and hypertension. A key component of these mobile health interventions is designing them in a way that encourages engagement. However, because differential engagement with the intervention can translate to differential benefit, the sustained impact of the intervention faces challenges due to declining engagement patterns.
Understanding and quantifying the role of engagement is therefore critical for evaluating the effectiveness of mobile health interventions on outcomes of interest. Prior methodological development has leveraged causal inference tools, notably modifications to instrumental variable approaches via sensitivity analysis, to characterize how engagement drives improvements in outcomes when the exclusion restriction cannot reasonably be assumed to hold. These contributions, however, have been limited to settings with two treatment arms and a continuous measure of engagement. There is an opportunity to extend these methods more broadly to enhance their applicability to additional real-world interventions.
This work advances the methodological foundation for characterizing engagement by expanding upon prior research to consider multiple treatment groups and alternative distributions for the engagement variable. In this presentation, we focus on the proper interpretation and methodology of these approaches and demonstrate their utility through applications to recent studies of mobile health interventions.

Mobile Health

Engagement

Instrumental variables

Causal inference 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025