  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.

Human-Computer Interaction;Computers and Society;Information Retrieval;Neurons and Cognition