PROJECT SUMMARY Smart devices (e.g., smartphones, smartwatches) and other wearables are increasingly used to deliver personalized, digital interventions to patients in real time to assist with management of chronic diseases. Digital interventions provide accessible, low-cost intervention delivery in unlimited amount and duration whenever and wherever needed, even for reticent or difficult-to-reach populations. Mobile health systems are excellent tools for the delivery of just-in-time adaptive interventions (JITAI), a general intervention design that aims to provide the right type/amount of support, at the right time, by adapting to an individual’s changing context. Many JITAIs have been developed with minimal use of empirical evidence. Micro-randomized trials (MRTs) are an increasingly common experimental design that are the main source for data-driven evidence of JITAI effectiveness. Causal moderation analysis is the cornerstone data analytic method for building effective JITAIs, allowing investigators to answer scientific questions about when and how to deliver digital interventions. Current data analytic methods for moderation analysis of MRT data require investigators to pre-specify features of the intensively collected longitudinal data (ILD) and high frequency sensor and mobile data (SMD) that are used in a linear model for outcome prediction. Machine learning (ML) algorithms are better suited to generate these outcome predictions but their use to estimate causal effects can lead to bias under model misspecification and do not allow for proper uncertainty quantification of treatment effect estimates. Moreover, current methods are designed to handle data coming from a single MRT; however, it is increasingly common for researchers to employ designs in which only a subset of study participants are included in the MRT or to have access to summary-statistics from external mHealth studies. Combining information from participants in other study arms or external studies with information from a smaller but more detailed MRT may significantly improve health scientists’ ability to answer important scientific questions about how intervention effectiveness may change over time or may be moderated by individual characteristics, time-varying context, or past responses. We propose to combine ML algorithms and data integration techniques with the standard statistical tools used to assess causal effect moderation to improve tailoring strategies for prevention and treatment of chronic diseases. At its conclusion, the proposal will provide comprehensive statistical tools for efficient inference of time-varying causal effects. These methods are essential for building effective intervention packages to improve health outcomes in populations managing chronic diseases.