Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials
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.
KeywordsMicro-randomized trials Just-in-time adaptive interventions Mobile health Medical device data Data management
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.
- 1.Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA (2016) Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. https://doi.org/10.1007/s12160-016-9830-8
- 5.Smith SN, Lee AJ, Hall K, Seewald NJ, Boruvka A, Murphy SA, Klasnja P (2017) Design lessons from a micro-randomized pilot study in mobile health. In: Rehg JM, Murphy SA, Kumar S (eds) Mobile health, Springer, Cham, pp. 59–82. https://doi.org/10.1007/978-3-319-51394-2_4
- 6.Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA (2018) Efficacy of contextually tailored suggestions for physical activity: a micro-randomized optimization trial of HeartSteps. Ann Behav Med 1–10. https://doi.org/10.1093/abm/kay067
- 7.Seewald NJ, Sun J, Liao P (2016) MRT-SS calculator: an R shiny application for sample size calculation in micro-randomized trials. arXiv:1609.00695 [stat.ME]
- 9.Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, Riley WT, Shar A, Spring B, Spruijt-Metz D, Hedeker D, Honavar V, Kravitz R, Craig Lefebvre R, Mohr DC, Murphy SA, Quinn C, Shusterman V, Swendeman D (2013) Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med 45(2):228–236. https://doi.org/10.1016/j.amepre.2013.03.017 CrossRefGoogle Scholar
- 10.Modave F, Guo Y, Bian J, Gurka MJ, Parish A, Smith MD, Lee AM, Buford TW (2017) Mobile device accuracy for step counting across age groups. JMIR mHealth uHealth 5(6). https://doi.org/10.2196/mhealth.7870
- 12.Dempsey W, Liao P, Kumar S, Murphy SA (2017) The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments. arXiv:1711.03587
- 14.Kreuter M (2000) Tailoring health messages: customizing communication with computer technology. LEA’s communication series. Routledge, MahwahGoogle Scholar
- 16.Raij A, Ghosh A, Kumar S, Srivastava M (2011) Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. In: Proceeding of 2011 annual conference human factors computing system—CHI ’11, p 11. https://doi.org/10.1145/1978942.1978945
- 19.Kotz D (2011) A threat taxonomy for mHealth privacy. In: 2011 3rd international conference communication system networks, COMSNETS 2011. https://doi.org/10.1109/COMSNETS.2011.5716518