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

Nicholas J. Seewald1, Shawna N. Smith2, Andy Jinseok Lee3, Predrag Klasnja3, Susan A. Murphy4
1Department of Statistics, University of Michigan, 311 West Hall, 1085 South University Ave, Ann Arbor, MI, 48109, USA
2Departments of Psychiatry and General Medicine, University of Michigan, Ann Arbor, MI, USA
3School of Information, University of Michigan, Ann Arbor, MI, USA
4Departments of Statistics and Computer Science, Harvard University, Cambridge, MA, USA

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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

Spruijt-Metz D, Wen CKF, O’Reilly G, Li M, Lee S, Emken BA, Mitra U, Annavaram M, Ragusa G, Narayanan S (2015) Innovations in the use of interactive technology to support weight management. Curr Obes Rep 4(4):510–519. https://doi.org/10.1007/s13679-015-0183-6

Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA (2015) Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol 34(Suppl):1220–1228. https://doi.org/10.1037/hea0000305

Liao P, Klasnja P, Tewari A, Murphy SA (2016) Sample size calculations for micro-randomized trials in mHealth. Stat Med 35(12):1944–1971. https://doi.org/10.1002/sim.6847

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

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

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]

Price M, Yuen EK, Goetter EM, Herbert JD, Forman EM, Acierno R, Ruggiero KJ (2014) mHealth: a mechanism to deliver more accessible, more effective mental health care. Clin Psychol Psychother 21(5):427–436. https://doi.org/10.1002/cpp.1855

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

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

Boruvka A, Almirall D, Witkiewitz K, Murphy SA (2017) Assessing time-varying causal effect moderation in mobile health. J Am Stat Assoc. https://doi.org/10.1080/01621459.2017.1305274 . arXiv:1601.00237

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

Kumar S, Nilsen W, Pavel M, Srivastava M (2013) Mobile health: revolutionizing healthcare through transdisciplinary research. Computer 46(1):28–35. https://doi.org/10.1109/MC.2012.392

Kreuter M (2000) Tailoring health messages: customizing communication with computer technology. LEA’s communication series. Routledge, Mahwah

Noar SM, Harrington NG, Stee SKV, Aldrich RS (2011) Tailored health communication to change lifestyle behaviors. Am. J. Lifestyle Med. 5(2):112–122. https://doi.org/10.1177/1559827610387255

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

Seidl DE, Paulus G, Jankowski P, Regenfelder M (2015) Spatial obfuscation methods for privacy protection of household-level data. Appl Geogr 63:253–263. https://doi.org/10.1016/j.apgeog.2015.07.001

Saczynski JS, McManus DD, Goldberg RJ (2013) Commonly used data-collection approaches in clinical research. Am J Med 126(11):946–950. https://doi.org/10.1016/j.amjmed.2013.04.016

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

Martinez-Perez B, de la Torre-Diez I, Lopez-Coronado M (2015) Privacy and security in mobile health apps: a review and recommendations. J Med Syst 39(1):181. https://doi.org/10.1007/s10916-014-0181-3

Cassa CA, Wieland SC, Mandl KD (2008) Re-identification of home addresses from spatial locations anonymized by Gaussian skew. Int J Health Geogr 7:45. https://doi.org/10.1186/1476-072X-7-45

Rubin DB (1976) Inference and missing data. Biometrika 63(3):581. https://doi.org/10.2307/2335739

Rosenbaum PR (1984) The consquences of adjustment for a concomitant variable that has been affected by the treatment. J R Stat Soc A 147(5):656. https://doi.org/10.2307/2981697