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 of physical activity on sleep duration, and vice-versa. We introduce the model twin randomization (MoTR; "motor") method for analyzing an individual's intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze 222 days of Fitbit sleep and steps data for one of the authors.

翻译：摘要：得益于移动应用和可穿戴传感器，时间密集的单人“小数据”已广泛可用。许多照护者和自我追踪者希望利用这些数据帮助特定个体改变其行为，以实现理想的健康结果。理想情况下，这涉及通过该个体自身的观测时间序列数据区分相关性与可能的因果关系。本文估计了身体活动对睡眠时长的个体内平均处理效应，反之亦然。我们提出了模型双随机化（MoTR；“发动机”）方法，用于分析个体的密集纵向数据。形式上，MoTR是在序列干扰条件下g公式（即标准化、后门调整）的一种应用。它估计稳定的重复效应，与n-of-1试验和单案例实验设计中的做法一致。我们将我们的方法与标准方法（可能存在混杂）进行比较，以展示如何利用因果推断为健康行为改变提供更好的个性化建议，并分析了作者之一222天的Fitbit睡眠和步数数据。