Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person’s own observational time series data. In this paper, we estimate within-individual average treatment effects (WIATEs) of sleep duration on physical activity, and vice-versa. We introduce the model twin randomization (MoTR; “motor”) and propensity score twin (PSTn; “piston”) methods for analyzing one year of the authors’ Fitbit sleep duration and step count data. Formally, MoTR and PSTn are applications of the g-formula (i.e., standardization, back-door adjustment) and propensity score inverse probability weighting, respectively, under serial interference. They estimate stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approaches to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change.