A0527
Title: Innovative trial designs in mobile and digital health using reinforcement learning
Authors: Bibhas Chakraborty - Duke-NUS Medical School, National University of Singapore (Singapore) [presenting]
Abstract: Mobile health (mHealth) or, more broadly, digital health interventions (e.g., motivational text messages or nudges to promote healthy behaviors) are becoming increasingly common in tandem with advances in mobile and wearable sensor technologies. An innovative trial design arising in mHealth, namely, the micro-randomized trial (MRT), is discussed, which involves sequential, within-person randomization over many instances. The basic MRT design can be further improved to make it adaptive, thereby enabling it to learn from accumulated data as the trial progresses. This is appealing from an ethical perspective since adaptive learning tends to make better interventions available to the trial participants. Adaptive learning in such trial designs is often operationalized via reinforcement learning algorithms. Specifically, the role of a particular algorithm called Thompson sampling in designing adaptive MRTs is discussed. Theoretical and simulation results are shown to validate the proposed approach. mHealth clinical trials are discussed as case studies.
