Here you can find various examples of MRTs that are being used to build JITAIs that address a range of health problems.

For example, in the MARS (Mobile Assistance for Regulating Smoking) study, d3lab researchers are developing an app that encourages smokers attempting to quit to engage in brief self-regulatory activities. Participants are randomized six times per day, approximately every 2 hours, to one of three intervention options: (1) a prompt recommending a relatively high-effort self-regulatory activity (e.g., 3-min meditation exercise), (2) a prompt recommending a relatively low-effort self-regulatory activity (e.g., simple instruction for substitution activity), or (3) no message. Randomization probabilities are set so that an average of 3 prompts will be delivered each day. Lapse, Heart Rate Variability (HRV), and location are measured via wearable sensors and GPS. In addition, 1 hour following each randomization, participants receive an Ecological Momentary Assessment (EMA) to report engagement in self-regulatory activities in the last hour (primary proximal outcome). The EMA also includes questions about other key factors associated with lapse risk (e.g., emotions, cravings, cigarette availability, stressors). These factors will be used to investigate the conditions in which subsequent recommendations to engage in a self-regulatory activity increase proximal engagement in self-regulatory activities, as well as whether engagement is associated with changes in risk factors.

The goal of this MRT is to answer the following questions:

1. Does delivering a prompt recommending a self-regulatory activity (compared to no message) increase engagement in self-regulatory activities, on average across all individual states and circumstances?

2. Under what conditions delivering a prompt is most beneficial? For example, delivering a prompt to engage in a self-regulatory activity (compared to no prompt) may be especially beneficial when the individual experiences stress or craving.

3. Which type of prompt is most beneficial? Prompts recommending a low-effort self-regulatory activity may be (on average) more beneficial in promoting engagement than those recommending a high-effort activity, but high-effort activities may be (on average) more beneficial in building resiliency.

4. Under what conditions is one type of prompt better than another? For example, what time of day is best to deliver a prompt recommending a high-effort activity, compared to low-effort? Are smokers attempting to quit more likely to engage following a prompt recommending a low-effort activity (compared to high-effort) while at work?

Here you can find various examples of MRTs that are being used to build JITAIs that address a range of health problems.

References

Boruvka, A., Almirall, D., Witkiewitz, K., & Murphy, S. A. (2018). Assessing time-varying causal effect moderation in mobile health. Journal of the American Statistical Association, 113(523), 1112-1121.

Carpenter, S. M., Menictas, M., Nahum-Shani, I., Wetter, D. W., & Murphy, S. A. (2020). Developments in
Mobile Health Just-in-Time Adaptive Interventions for Addiction Science. Current Addiction Reports, 1-11.

Dempsey, W., Liao, P., Kumar, S., & Murphy, S. A. (2020). The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments. Annals of Applied Statistics, 14(2), 661-684.

Liao, P., Klasnja, P., Tewari, A., & Murphy, S. A. (2016). Sample size calculations for micro‐randomized trials in mHealth. Statistics in medicine, 35(12), 1944-1971.

Highlights

The Substance Abuse Research Assistant (SARA)

d3lab members collaborate on the development of SARA—a mobile app for promoting engagement in daily mobile-based self-reporting of substance use and related factors among adolescents and emerging adults. This includes the design and conduct of a MRT to optimize the delivery of just-in-time prompts that capitalize on behavioral economics principles to promote daily mobile-based self-reporting. This collaboration generated multiple publications, as well as documentation and code that our team made freely available online to guide scientists in curating MRT data. This work won the Michigan Institute for Data Science “2020 Reproducibility Challenge” for developing computer code and associated documentation that enables other analysts to verify and validate research findings.

2020 Reproducibility Challenge Award

Coughlin, L.N., Nahum-Shani, I., Kotov, M., Bonar, E.E., Rabbi, M., Klasnja, P., Murphy, S.A., & Walton, M.A., (in press). Developing an adaptive intervention for substance use prevention among adolescents and emerging adults: Feasibility and acceptability of a mobile health app. JMIR mHealth and uHealth.

Rabbi, M., Kotov, M. P., Cunningham, R., Bonar, E. E., Nahum-Shani, I., Klasnja, P., . . . Murphy, S. (2018). Toward increasing engagement in substance use data collection: development of the Substance Abuse Research Assistant app and protocol for a microrandomized trial using adolescents and emerging adults. JMIR Research Protocols, 7(7), e166.

Rabbi, M., Philyaw-Kotov, M., Klasnja, P., Bonar, E., Nahum-Shani, I., Walton, M., & Murphy, S. (2017). SARA – Substance Abuse Research Assistant. Retrieved from https://doi.org/10.17605/OSF.IO/VWZMD

Rabbi, M., Philyaw-Kotov, M., Li, J., Li, K., Rothman, B., Giragosian, L. Reyes, M., Gadway, H., Cunningham, R., Bonar, E., Nahum-Shani, I., Walton, M., Murphy, S.A., & Klasnja, P. (2020). Translating Behavioral Theory into Technological Interventions: Case Study of an mHealth App to Increase Self-reporting of Substance-Use Related Data. arXiv preprint arXiv:2003.13545.

Mobile Assistance for Regulating Smoking (MARS).

d3lab members collaborate on mDOT by MD2K– an NIH Big Data Center of Excellence (U54 EB020404, PI: Kumar) focusing on translating sensor data to knowledge. mDOT is a mHealth center focusing on the discovery, optimization and translation of temporally-precise interventions (https://mdot.md2k.org/). This collaboration has led to a funded U01 (CA229437 PIs: Nahum-Shani, Wetter), which includes the MARS study—a 10-day MRT designed to optimize the delivery of just-in-time prompts intended to engage smokers attempting to quit in self-regulatory activities. This work also includes the development of new methodologies for analyzing intensive longitudinal data from sensors and self-reports to identify the conditions in which the delivery of just-in-time interventions is most beneficial.

Nahum-Shani, B., Wetter, D. W. Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs. 2018. Grant Funded by NIH/NCI U01 CA229437

Nagesh, S., Moreno, A., Carpenter, S., Yap., J., Chatterjee, S., Lizotte, S.L., Neng, W., Kumar, S., Lam, C., Wetter, D., Nahum-Shani, I., & Rehg, J. (December, 2020) Deep forecasting of EMA compliance: Distinguishing non-compliance from non-response. 34th Conference on Neural Information Processing Systems (NeurIPS 2020).

Moreno, A., Wu, Z., Yap, J., Wetter, D. Lam, C., Nahum-Shani, I., Rehg, J. (December, 2020). A functional EM algorithm for panel count data with missing counts. arXiv preprint arXiv:2003.01169. 34th Conference on Neural Information Processing Systems (NeurIPS 2020).

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