The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations
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Abstract (arXiv)
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted--weekly, daily, or even many times a day. The micro-randomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2021.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 3; 2026: 2.
11 papers.
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- 2024The Digital Transformation in Health: How AI Can Improve the Performance of Health Systemscited by 1
- 2025Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensorscited by 1
- 2025Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamicscited by 1
- 2023A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderationcited by 0
- 2024Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRxcited by 0
- 2025N2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completioncited by 0
- 2026Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completioncited by 0
- 2026When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experimentscited by 0
- 2026When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments15 Sep 2026
- 2026Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion28 May 2026
- 2025Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics4 Nov 2025
- 2025Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors22 Sep 2025
- 2025N2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion4 Jun 2025
- 2024The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems24 Sep 2024
- 2024Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx15 Aug 2024
- 2024Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials26 Feb 2024
- 2023Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study24 Nov 2023
- 2023A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation28 Jun 2023
- 2022Testing Stationarity and Change Point Detection in Reinforcement Learning3 Mar 2022