利用网络进行人口规模的生理感知：睡眠和表现的案例研究
Harnessing the Web for Population-Scale Physiological Sensing: A Case Study of Sleep and Performance
摘要：
人类的认知能力对生产力、学习和避免事故至关重要。认知表现每天都在变化，部分是由内在的，近24小时的昼夜节律驱动的。之前关于睡眠和昼夜节律对认知表现影响的研究通常仅限于小规模的实验室研究，这些研究无法捕捉真实世界条件的变化，例如环境因素，动机和真实世界环境中的睡眠模式。鉴于这些限制，领先的睡眠研究人员呼吁对睡眠和表现进行更大规模的原位监测。我们提出了迄今为止最大的研究，客观测量的现实世界的睡眠对性能的影响，使通过重构的日常互动与网络搜索引擎作为一系列的性能任务。我们的分析包括300万个夜晚的睡眠和7500万个交互任务。我们通过在网络搜索引擎上搜索和点击交互的速度来测量认知性能，并将它们与可穿戴设备定义的睡眠测量值相关联。我们证明，现实世界中的表现在一天中各不相同，并受到昼夜节律，时钟类型（早上/晚上的偏好）和先前的睡眠时间和时间的影响。我们开发了一个统计模型，将大量关于睡眠和表现的工作操作化，并证明我们对昼夜节律，稳态睡眠驱动和睡眠惯性的估计与基于实验室的睡眠研究的预期一致。此外，我们量化了睡眠不足对现实世界表现的影响，并表明连续两个晚上睡眠不足6小时与持续6天的表现下降有关。这项工作证明了使用在线交互进行大规模生理传感的可行性。
英文摘要：
Human cognitive performance is critical to productivity, learning, and accident avoidance. Cognitive performance varies throughout each day and is in part driven by intrinsic, near 24-hour circadian rhythms. Prior research on the impact of sleep and circadian rhythms on cognitive performance has typically been restricted to small-scale laboratory-based studies that do not capture the variability of real-world conditions, such as environmental factors, motivation, and sleep patterns in real-world settings. Given these limitations, leading sleep researchers have called for larger in situ monitoring of sleep and performance. We present the largest study to date on the impact of objectively measured real-world sleep on performance enabled through a reframing of everyday interactions with a web search engine as a series of performance tasks. Our analysis includes 3 million nights of sleep and 75 million interaction tasks. We measure cognitive performance through the speed of keystroke and click interactions on a web search engine and correlate them to wearable device-defined sleep measures over time. We demonstrate that real-world performance varies throughout the day and is influenced by both circadian rhythms, chronotype (morning/evening preference), and prior sleep duration and timing. We develop a statistical model that operationalizes a large body of work on sleep and performance and demonstrates that our estimates of circadian rhythms, homeostatic sleep drive, and sleep inertia align with expectations from laboratory-based sleep studies. Further, we quantify the impact of insufficient sleep on real-world performance and show that two consecutive nights with less than six hours of sleep are associated with decreases in performance which last for a period of six days. This work demonstrates the feasibility of using online interactions for large-scale physiological sensing.
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