Abstract: Mobile health (mHealth) technologies are providing new promising ways to deliver interventions in both clinical and non-clinical settings. Wearable sensors and smartphones collect real-time data streams that provide information about an individual’s current health including both internal (e.g., mood, blood sugar level) and external (e.g., social, location) contexts. Both wearables and smartphones can be used to deliver interventions. mHealth interventions are in current use across a vast number of health-related fields including medication adherence, physical activity, weight loss, mental illness and addictions. This tutorial discusses the micro-randomized trial (MRT), an experimental trial design for use in optimizing real time delivery of sequences of treatment, with an emphasis on mHealth. We introduce the MRT design using HeartSteps, a physical activity study, as an example. We define the causal excursion effect and discuss reasons why this effect is often considered the primary causal effect of interest in MRT analysis. We introduce statistical methods for primary and secondary analyses for MRT with continuous binary outcomes. We discuss the sample size considerations for designing MRTs.

Bio: Tianchen Qian is an Assistant Professor in the Department of Statistics at University of California, Irvine. He completed his PhD at the Johns Hopkins University and was a postdoctoral fellow at Harvard University. His research is focused on the experimental design and statistical analysis methods for developing mobile health interventions. In particular, he has developed causal inference methods for analyzing micro-randomized trial data and sample size calculation approaches for designing micro-randomized trials.

Tianchen Qian, Ph.D., Assistant Professor, Department of Statistics, Donald Bren School of Information and Computer Sciences, UC Irvine | Email: t.qian@uci.edu | Website: https://sites.google.com/view/tianchen-qian