# Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials

## TL;DR

This paper offers practical guidelines for designing and managing data collection systems in mobile health micro-randomized trials, emphasizing quality, feature selection, and handling missing data to improve causal inference.

## Contribution

It provides specific recommendations and a checklist for data collection and management in MRTs, based on experience with the HeartSteps study.

## Key findings

- Guidelines for feature selection and timing in data collection
- Strategies for identifying and handling missing data
- Advice on choosing randomization agents in MRTs

## Abstract

There is a growing interest in leveraging the prevalence of mobile technology to improve health by delivering momentary, contextualized interventions to individuals' smartphones. A just-in-time adaptive intervention (JITAI) adjusts to an individual's changing state and/or context to provide the right treatment, at the right time, in the right place. Micro-randomized trials (MRTs) allow for the collection of data which aid in the construction of an optimized JITAI by sequentially randomizing participants to different treatment options at each of many decision points throughout the study. Often, this data is collected passively using a mobile phone. To assess the causal effect of treatment on a near-term outcome, care must be taken when designing the data collection system to ensure it is of appropriately high quality. Here, we make several recommendations for collecting and managing data from an MRT. We provide advice on selecting which features to collect and when, choosing between "agents" to implement randomization, identifying sources of missing data, and overcoming other novel challenges. The recommendations are informed by our experience with HeartSteps, an MRT designed to test the effects of an intervention aimed at increasing physical activity in sedentary adults. We also provide a checklist which can be used in designing a data collection system so that scientists can focus more on their questions of interest, and less on cleaning data.