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Experimental Design: Novel Methodologies and Experimental Designs

Recent development of novel methodologies and experimental designs hold much promise for accelerating the pace of testing the efficacy of digital health interventions.

To promote, educate, and support the use of these innovative scientific tools and approaches, the CTBH has compiled a list of resources (e.g., websites with instructional materials and published manuscripts that review or outline novel experimental and analytic approaches) that can be accessed through the links below. In addition, a number of educational and instructional resources for open source platforms for app development and usability testing are listed.

Methodology Resources

Just-in-time Adaptive Interventions

Just-in-time Adaptive Interventions, by Design lab at the Institute for Social Research at the University of Michigan (

  • Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015). Building health behavior models to guide the development of  just-in-time adaptive interventions: A pragmatic framework. Health Psychology, 34(S), 1209.

Micro Randomized Trials

  • 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(S), 1220.

Sequential Multiple Assignment Randomized Trials (SMART)
Advancing Intervention Data Science, by Design lab at the Institute for Social Research at the University of Michigan ( – includes information on SMART designs (a design that can be used to test adaptive interventions) with resources, training modules and tech reports on adaptive interventions and SMARTs (including R and SAS code for analyzing data from SMARTs)

  • Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention science, 5(3), 185-196.
  • Nahum-Shani, I., Almirall, Yap, J.R., D. McKay, J., Lynch, K., Freiheit, E., & Dziak, J.J. (in press). SMART Longitudinal Analysis: A Tutorial for Using Repeated Outcome Measures from SMART Studies to Compare Adaptive Interventions. Psychological Methods.
  • Nahum-Shani, I., Qian, M., Almirall, D., Pelham, W., Gnagy, B., Fabiano, G., … Murphy, S. A. (2012). Experimental design and primary data analysis methods for comparing adaptive interventions. Psychological Methods, 17, 457-77.

Multiphase Optimization Strategy (MOST) – Framework for intervention development

Optimizing Behavioral and Biobehavioral Interventions, by Intervention Optimization Initiative at NYU School of Global Public Health (

  • Collins, L. M. (2018) Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST). Springer.

Factorial Experiments for optimizing multi-component interventions – Research method related to MOST

  • Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47, 498-504.

Digital Intervention Development and Human Coaching
Northwestern’s Center for Behavioral Intervention Technologies – includes a focus on the role of human coaching in supporting digital interventions – describes the Center’s research intervention development and coaching models and provides references:

  • Schueller SM, Tomasino KN, Mohr DC. Integrating Human Support into Behavioral Intervention Technologies: The Efficiency Model of Support. Clinical Psychology: Science and Practice. 2016(24):27-45.

Center for Digital Health Interventions in St. Gallen Switzerland Mobile Coach website – provides an open source behavioral intervention platform:

Single-Case Experimental Design and Analysis
The International Collaborative Network (ICN) for N-of-1 Clinical Trials and Single-Case Experimental Designs (SCEDs) provides resources on design and analysis, including links to online courses, effect size and analysis calculators, and other resources.

  • Dallery, J., Cassidy, R., Raiff, B. R. (2013). Single-case experimental designs to evaluate novel technology-based health interventions. Journal of Medical Internet Research. 15:e22.
Analysis of Longitudinal Data and Intensive Longitudinal Data
Penn State's QuantDev Methodology Core has a number of helpful advanced tutorials on analysis techniques for the use of longitudinal data and intensive longitudinal data (data with many time points; such as ecological momentary assessment data, among others).
Analysis of Timing Effects in Interventions
The Bennet Piece Center includes resources about analysis of the timing of interventions and how relationships change as a function of interventions ( time-varying effect model [TVEM]).


Other examples of available open source platforms and instructional methods for mHealth app development and testing which may help accelerate mHealth research: