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.