N=1 Modelling of Lifestyle Impact on SleepPerformance
This work addresses the problem of poor sleep prediction for individuals by providing personalized insights, though it appears incremental as it builds on existing connections between lifestyle and sleep.
The research tackled the challenge of creating personalized sleep models by developing a method using N-of-1 experiments and event mining to identify causal relationships between daily activities and sleep quality, with experimental results quantifying these relationships and extracting confounding variables.
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.