Publication Date

2021

Journal or Book Title

JOURNAL OF MEDICAL INTERNET RESEARCH

Abstract

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

ISSN

1438-8871

ORCID

Kalanadhabhatta, Manasa/0000-0002-9520-6129; Rahman, Tauhidur/0000-0003-1981-6395

DOI

https://doi.org/10.2196/23936

Volume

23

Issue

2

License

UMass Amherst Open Access Policy

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Funder

National Science FoundationNational Science Foundation (NSF) [SBE 1839999]; College of Information and Computer Sciences; Institute for Applied Life Sciences at the University of Massachusetts Amherst

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