Technological advances in mobile and digital health, such as wearable sensors and momentary self-reporting, have now made it possible to monitor treatment response in near real-time. This has led to significant scientific interest in developing technology-assisted adaptive interventions. An adaptive intervention is a protocolized sequence of decision rules used to guide an intervention across multiple stages of treatments contingent on the evolving status of the individual. We introduce a new class of adaptive interventions called event-triggered adaptive interventions, which leverage time-varying tailoring variables to determine when, if, and what treatment is needed at each stage. In such mobile monitoring environments, event-triggered adaptive interventions are more agile and address temporal treatment response heterogeneity to further improve individual outcomes. Sequential multiple-assignment randomized trial (SMART) designs can be used to develop optimized event-triggered adaptive interventions. We propose a two-stage regression algorithm based on the structural nested mean model to analyze data from a SMART with continuous, longitudinal outcomes and time-varying tailoring variables. This approach targets stage-level causal effects and allows the scientist to examine time-varying treatment moderators while avoiding causal collider bias. We illustrate our methodology on data from a SMART to develop an event-triggered adaptive intervention for digitally monitored weight loss.

Graduate Student Research Assistant

d3center

Institute for Social Research