Evaluation of the HeartSteps Online Sampling Algorithm

TL;DR

This paper evaluates an online sampling algorithm for micro-randomized trials, specifically HeartSteps V2V3, demonstrating its effectiveness in meeting key design constraints and providing insights for future MRT design improvements.

Contribution

It introduces and assesses an online sampling algorithm tailored for MRTs with specific constraints, addressing a challenge in trial design.

Findings

Algorithm effectively met the constraints in the case study.

Performance evaluation identified strengths and areas for improvement.

Provides practical recommendations for MRT designers.

Abstract

Micro-randomized trials (MRTs), which sequentially randomize participants at multiple decision times, have gained prominence in digital intervention development. These sequential randomizations are often subject to certain constraints. In the MRT called HeartSteps V2V3, where an intervention is designed to interrupt sedentary behavior, two core design constraints need to be managed: an average of 1.5 interventions across days and the uniform delivery of interventions across decision times. Meeting both constraints, especially when the times allowed for randomization are not determined beforehand, is challenging. An online algorithm was implemented to meet these constraints in the HeartSteps V2V3 MRT. We present a case study using data from the HeartSteps V2V3 MRT, where we select appropriate metrics, discuss issues in making an accurate evaluation, and assess the algorithm's performance. Our evaluation shows that the algorithm performed well in meeting the two constraints. Furthermore, we identify areas for improvement and provide recommendations for designers of MRTs that need to satisfy these core design constraints.