Prevention Science

, Volume 20, Issue 1, pp 100–109 | Cite as

Standardized Effect Sizes for Preventive Mobile Health Interventions in Micro-randomized Trials

  • Brook LuersEmail author
  • Predrag Klasnja
  • Susan Murphy


Mobile Health (mHealth) interventions are behavioral interventions that are accessible to individuals in their daily lives via a mobile device. Most mHealth interventions consist of multiple intervention components. Some of the components are “pull” components, which require individuals to access the component on their mobile device at moments when they decide they need help. Other intervention components are “push” components, which are initiated by the intervention, not the individual, and are delivered via notifications or text messages. Micro-randomized trials (MRTs) have been developed to provide data to assess the effects of push intervention components on subsequent emotions and behavior. In this paper, we review the micro-randomized trial design and provide an approach to computing a standardized effect size for these intervention components. This effect size can be used to compare different push intervention components that may be included in an mHealth intervention. In addition, a standardized effect size can be used to inform sample size calculations for future MRTs. Here, the standardized effect size is a function of time because the push notifications can occur repeatedly over time. We illustrate this methodology using data from an MRT involving HeartSteps, an mHealth intervention for physical activity as part of the secondary prevention of heart disease.


Micro-randomized trials Precision behavioral science Standardized effect size 



Research presented in this paper was supported by the National Heart, Lung and Blood Institute under award number R01HL125440; the National Institute on Alcohol Abuse and Alcoholism under award number R01AA023187; the National Institute on Drug Abuse under award number P50DA039838; and the National Institute of Biomedical Imaging and Bioengineering under award number U54EB020404.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Human Participants and/or Animals

This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study of HeartSteps.

Supplementary material

11121_2017_862_MOESM1_ESM.pdf (259 kb)
(PDF 258 KB)


  1. Bowen, S., & Marlatt, A. (2009). Surfing the urge: Brief mindfulness-based intervention for college student smokers. Psychology of Addictive Behaviors, 23, 666.CrossRefGoogle Scholar
  2. Cleveland, W.S., & Devlin, S.J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83, 596–610.CrossRefGoogle Scholar
  3. Cohen, J. (1988). Statistical power analysis for the social sciences. Hillsdale: Erlbaum.Google Scholar
  4. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155.CrossRefGoogle Scholar
  5. Dempsey, W., Liao, P., Klasnja, P., Nahum-Shani, I., Murphy, S.A. (2015). Randomised trials for the fitbit generation. Significance, 12, 20–23.CrossRefGoogle Scholar
  6. Efron, B., & Tibshirani, R. (1994). An introduction to the bootstrap. Boca Raton: Taylor & Francis.Google Scholar
  7. Friedman, J., Hastie, T., Tibshirani, R. (2009). The elements of statistical learning. New York: Springer.Google Scholar
  8. Gollwitzer, P.M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54, 493.CrossRefGoogle Scholar
  9. Hovsepian, K., al’Absi, M., Ertin, E., Kamarck, T., Nakajima, M., Kumar, S. (2015). cstress: Towards a gold standard for continuous stress assessment in the mobile environmentt. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (pp. 493–504).Google Scholar
  10. Klasnja, P., Hekler, E.B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., Murphy, S.A. (2015). Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychology, 34, 1220.CrossRefGoogle Scholar
  11. Kumar, S., Abowd, G., Abraham, W.T., al’Absi, M., Chau, D.H., Ertin, E., Wetter, D. (2017). Center of excellence for mobile sensor data-to-knowledge (md2k). IEEE Pervasive Computing, 16, 18–22.CrossRefGoogle Scholar
  12. Liao, P., Klasnja, P., Tewari, A., Murphy, S.A. (2016). Sample size calculations for micro-randomized trials in mHealth. Statistics in Medicine, 35, 1944–1971.CrossRefGoogle Scholar
  13. Luszczynska, A. (2006). An implementation intentions intervention, the use of a planning strategy, and physical activity after myocardial infarction. Social Science & Medicine, 62, 900– 908.CrossRefGoogle Scholar
  14. Nahum-Shani, I., Smith, S.N., Spring, B.J., Collins, L.M., Witkiewitz, K., Tewari, A., Murphy, S.A. (2016). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 1–17.
  15. Olejnik, S., & Algina, J. (2000). Measures of effect size for comparative studies: Applications, interpretations, and limitations. Contemporary Educational Psychology, 25, 241–286.CrossRefGoogle Scholar
  16. Seewald, N.J., Sun, J., Liao, P. (2016). MRT-SS Calculator: An R shiny application for sample size calculation in micro-randomized trials. arXiv:1609.00695. [February 18, 2017].
  17. Smith, S.N., Lee, A., Kelly, H., Seewald, N., Boruvka, A., Murphy, S.A., Klasnja, P. (2017). Design lessons from a micro-randomized pilot study in mobile health. In J. M. Rehg, S. A. Murphyand, & S. Kumar (Eds.), Mobile health: Sensors, analytic methods, and applications. Switzerland: Springer International Publishing. In press.Google Scholar
  18. Wilkinson, L. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594.CrossRefGoogle Scholar
  19. Witkiewitz, K., Desai, S.A., Bowen, S., Leigh, B.C., Kirouac, M., Larimer, M.E. (2014). Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychology of Addictive Behaviors, 28, 639.CrossRefGoogle Scholar

Copyright information

© Society for Prevention Research 2018

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of MichiganAnn ArborUSA
  2. 2.School of InformationUniversity of MichiganAnn ArborUSA
  3. 3.Kaiser Permanente Washington Health Research InstituteSeattleUSA
  4. 4.Institute for Social ResearchUniversity of MichiganAnn ArborUSA

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