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

  • Nicholas J. SeewaldEmail author
  • Shawna N. Smith
  • Andy Jinseok Lee
  • Predrag Klasnja
  • Susan A. Murphy
Article

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 

Notes

Acknowledgements

Research reported in this publication was supported by NIAAA, NIDA, NIBIB, and NHLBI of the National Institutes of Health under Award Numbers R01AA023187, P50DA039838, U54EB020404, and R01HL125440. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors wish to thank Dr. Audrey Boruvka for her work in managing data from HeartSteps, as well as the two anonymous reviewers for their thoughtful comments.

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Copyright information

© International Chinese Statistical Association 2019

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of MichiganAnn ArborUSA
  2. 2.Departments of Psychiatry and General MedicineUniversity of MichiganAnn ArborUSA
  3. 3.School of InformationUniversity of MichiganAnn ArborUSA
  4. 4.Departments of Statistics and Computer ScienceHarvard UniversityCambridgeUSA

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