{"id":355691,"date":"2019-01-17T09:47:12","date_gmt":"2019-01-17T17:47:12","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=355691"},"modified":"2019-01-17T09:47:12","modified_gmt":"2019-01-17T17:47:12","slug":"harnessing-web-population-scale-physiological-sensing-case-study-sleep-performance","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/harnessing-web-population-scale-physiological-sensing-case-study-sleep-performance\/","title":{"rendered":"Harnessing the Web for Population-Scale Physiological Sensing: A Case Study of Sleep and Performance"},"content":{"rendered":"<p>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 [39].<\/p>\n<p>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-de\ufb01ned sleep measures over time. We demonstrate that real-world performance varies throughout the day and is in\ufb02uenced by both circadian rhythms, chronotype (morning\/evening preference), and prior sleep duration and timing.<\/p>\n<p>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 insuf\ufb01cient 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 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