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：感谢移动应用和可穿戴传感器的普及，时间上密集的单人“小数据”变得广泛可用。许多护理人员和自我追踪者希望使用这些数据帮助特定个体改变他们的行为以达到期望的健康结果。理想情况下，这需要从该人自己的观察时间序列数据中辨别可能的原因和相关性。在本文中，我们估计身体活动对睡眠时长以及反之的个体平均治疗效应。我们引入了模型双重随机化（MoTR）方法，用于分析单个人的密集沿时数据。严格来说，MoTR是g-formula（即标准化、后门调整）在串行干扰下的应用。它估计稳定的周期性效应，就像n-of-1试验和单案例实验设计中所做的那样。我们将我们的方法与标准方法（可能存在混杂因素）进行比较，以展示如何使用因果推断为个性化行为变革提供更好的建议，并分析一位作者的222天Fitbit睡眠和步数数据。
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