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 (rrm) 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 (rrm=0.17, P<.001) as well as the duration of rapid eye movement (rrm=0.12, P<.001) and light sleep (rrm=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 (rrm=–0.13, P<.001). Both measures show circadian variations, but only alertness showed a decline (rrm=–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.

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中文翻译：

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睡眠和生物行为方式对多维认知能力的影响：纵向，野外研究

背景：由于近20％的美国成年人使用健身追踪器，因此越来越关注这些设备的生理数据如何提供有关工作场所绩效的可行见解。然而，缺乏了解这些指标如何与不同人群的认知表现指标相关联的野外研究，并且设备制造商的说法还不清楚。尽管已经进行了广泛的研究，导致了关于生理测量如何影响认知表现的各种理论，但实际上所有此类研究都是在高度受控的环境中进行的，人们对它们在现实世界中的有效性了解甚少。目标：我们通过评估有关各种睡眠，活动，和心率参数对现实环境中收集的数据的认知表现的影响。方法：作为一项为期6周的野外研究的一部分，我们分别使用Fitbit Charge 3和智能手机应用程序收集了不同的生理和神经行为任务数据。我们收集了来自24个参与者的数据，这些参与者来自多个人群（轮班工人，正规工人和研究生），这些数据涉及不同的绩效指标（注意注意力和认知能力）。同时，我们使用健身追踪器毫不费力地获得了可能影响这些绩效指标的生理指标，包括900多个晚上的睡眠以及超过100万分钟的心率和体育锻炼指标。我们进行了重复测量相关性（rrm）分析，以调查哪些睡眠和生理指标与每种性能测量指标相关。我们还报告了我们的发现如何与现有理论和来自对照研究的先前观察结果相关。结果：发现白天的警觉性与前一天的总睡眠时间（rrm = 0.17，P <.001）以及快速眼球运动的持续时间（rrm = 0.12，P <.001）和轻度睡眠显着相关。 （rrm ＝ 0.15，P ＜.001）。相比之下，认知能力并没有发现与睡眠时间显着相关，而是与睡眠时间有显着相关性。昼夜节律向晚些睡眠时间的相移对应于第二天较低的认知能力（rrm = –0.13，P <.001）。 。两项指标均显示出昼夜节律变化，但由于稳态压力，只有机敏性下降（rrm = –0.1，P <.001）。心率和体力活动与警觉性和认知通量呈正相关。结论：我们的发现表明，与睡眠相关的生理指标影响这两种绩效指标中的每一项都有显着差异。这使得针对性更强的野外研究成为必要，以研究来自自我跟踪数据的生理指标如何影响或可用于预测认知表现的特定方面。我们的发现表明，与睡眠相关的生理指标会影响两种绩效指标中的每一项，存在显着差异。这使得针对性更强的野外研究成为必要，以研究来自自我跟踪数据的生理测量如何影响或可用于预测认知表现的特定方面。我们的发现表明，与睡眠相关的生理指标会影响两种绩效指标中的每一项，存在显着差异。这使得针对性更强的野外研究成为必要，以研究来自自我跟踪数据的生理测量如何影响或可用于预测认知表现的特定方面。

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