Sample Size Calculations for Micro-randomized Trials in mHealth

TL;DR

This paper introduces a micro-randomized trial design for mobile health interventions, providing methods for testing treatment effects, including a test statistic and sample size calculator, to advance data science in mHealth.

Contribution

It proposes a novel micro-randomized trial design and develops statistical tools for assessing proximal treatment effects in mobile health studies.

Findings

Developed a test statistic for proximal effect assessment.

Created a sample size calculator for micro-randomized trials.

Conducted simulation studies demonstrating the methods' effectiveness.

Abstract

The use and development of mobile interventions are experiencing rapid growth. In "just-in-time" mobile interventions, treatments are provided via a mobile device and they are intended to help an individual make healthy decisions "in the moment," and thus have a proximal, near future impact. Currently the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a "micro-randomized" trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity.