This study (1R01HD107002-01A1) protocol describes the planned creation and use of an agent-based model (ABM) of early childhood physical activity (PA). Successful early childhood PA interventions can potentially play an important role in both increasing overall population health as well as closing health disparities across subpopulations. At present, effective strategies for doing so are currently unknown. In large part, this is because PA determinants operate across levels dynamically, interact with one another, and can differ substantially across children. A complex systems approach-specifically, ABM-can be used to provide important insights about effect pathways driving child PA. Design of the proposed ABM will be based on high-quality extant research on childhood physical activity while allowing for the testing of hypotheses that extend beyond this body of literature. Its primary source of input data will be participants in GROW (NCT01316653), a completed cohort-based randomized controlled trial (RCT) that includes extensive longitudinal PA data collected from accelerometer observations of children from ages 3-9. We will iteratively test and improve upon an etiologic ABM of childhood PA, ensuring that it can satisfactorily reproduce micro- and macro-level influences and trends comparable to those seen in GROW. The tested ABM will then be used to extrapolate beyond the context of the GROW RCT, experimentally identifying potentially efficacious intervention strategies to improve childhood physical activity through program implementation or changes in policies and practices. We will use expert input to identify promising intervention approaches. We will use the model to systematically experiment with a wide array of different hypothetical combinations of intervention specifications and combinations. At the end of the model experimentation step, we expect to generate insights of broad applicability to the field of PA science regarding what might work, and for whom, in promoting PA and reducing disparities in these behaviors.