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

Nicholas J. Seewald (), Shawna N. Smith, Andy Jinseok Lee, Predrag Klasnja and Susan A. Murphy
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Nicholas J. Seewald: University of Michigan
Shawna N. Smith: University of Michigan
Andy Jinseok Lee: University of Michigan
Predrag Klasnja: University of Michigan
Susan A. Murphy: Harvard University

Statistics in Biosciences, 2019, vol. 11, issue 2, No 8, 355-370

Abstract: 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, these data are 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.

Keywords: Micro-randomized trials; Just-in-time adaptive interventions; Mobile health; Medical device data; Data management (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-018-09228-w

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