Sleep is critical to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night's rest is going to be. For an activity that humans spend around a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is important to create a personalized sleep model. This research proposes a sleep model that can identify causal relationships between daily activities and sleep quality and present the user with specific feedback about how their lifestyle affects their sleep. Our method uses N-of-1 experiments on longitudinal user data and event mining to generate understanding between lifestyle choices (exercise, eating, circadian rhythm) and their impact on sleep quality. Our experimental results identified and quantified relationships while extracting confounding variables through a causal framework. These insights can be used by the user or a personal health navigator to provide guidance in improving sleep.

中文翻译：

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N=1 生活方式对睡眠表现影响的建模

睡眠对于过上健康的生活方式至关重要。每天，大多数人睡觉时都不知道他们晚上的休息情况如何。对于人类花费大约三分之一的生命进行的活动，它周围存在着惊人的谜团。尽管目前有研究，但在现实环境中创建个性化睡眠模型一直具有挑战性。现有文献提供了日常活动与睡眠质量之间的多种联系。不幸的是，这些见解在许多人中并没有很好地概括。由于这些原因，创建个性化的睡眠模型很重要。这项研究提出了一种睡眠模型，可以识别日常活动和睡眠质量之间的因果关系，并向用户提供有关他们的生活方式如何影响睡眠的具体反馈。我们的方法对纵向用户数据和事件挖掘使用 N-of-1 实验，以了解生活方式选择（运动、饮食、昼夜节律）及其对睡眠质量的影响。我们的实验结果在通过因果框架提取混杂变量的同时识别并量化了关系。用户或个人健康导航员可以使用这些见解来提供改善睡眠的指导。