Effect of Sleep and Biobehavioral Patterns on Multidimensional Cognitive Performance: Longitudinal, In-the-Wild Study

Background With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand metrics correlate with cognitive performance measures across a diverse are lacking, and claims made by device manufacturers vague. While has been extensive research leading to variety theories affect performance, virtually all such have conducted in highly controlled settings their validity real world poorly understood. Objective We seek bridge this gap evaluating prevailing effects sleep, activity, heart rate parameters against collected real-world settings. Methods used Fitbit Charge 3 smartphone app collect different neurobehavioral task data, respectively, as part our 6-week-long study. 24 participants multiple groups (shift workers, regular graduate students) (vigilant attention throughput). Simultaneously, we tracker unobtrusively obtain could influence measures, including over 900 nights sleep 1 million minutes physical activity metrics. performed repeated correlation (rrm) analysis investigate which markers show association each measure. also report findings relate existing previous observations studies. Results Daytime alertness was found be significantly correlated total duration night (rrm=0.17, P<.001) well rapid eye movement (rrm=0.12, light (rrm=0.15, P<.001). Cognitive throughput, contrast, not but timing—a circadian phase shift toward later time corresponded lower throughput following day (rrm=–0.13, Both variations, only showed decline (rrm=–0.1, result homeostatic pressure. positively throughput. Conclusions Our reveal significant differences terms sleep-related 2 measures. This makes case for more targeted investigating self-tracking influence, or predict, specific aspects