Objectives
Remarkably, the specifics of sleep along the human lineage have been slow to emerge, which is surprising given our distinct mental and behavioral capacity and the importance of sleep for individual health and cognitive performance. Largely due to difficultly of measuring sleep outside a controlled, clinical, and laboratory study in ambulatory individuals, human biologists have yet to undergo a thorough examination of sleep in ecologically diverse settings. Here, I outline the procedures and methods for generating sleep data in a broader ecological context with the goal of facilitating the integration of sleep and circadian analyses into human biology research.

Methods
I describe the steps involved in participant recruitment, screening by way of survey instruments, and sample collection. In addition to describing field use of the traditional (but invasive) equipment such as the gold‐standard application of electroencephalography (EEG), I demonstrate leading‐edge noninvasive techniques for biometric devices (ie, wrist‐worn actigraphy, ring worn arterial pulsometry) to generate sleep and circadian rhythms data.

Results
I outline best approaches to process and analyze data—including variables such as sleep duration, 24‐hour sleep time (ie, summation of night and day sleep), sleep efficiency, sleep fragmentation, and nonparametric circadian rhythms analysis to quantify circadian amplitude. Finally, I discuss comparative statistical methods that are optimized for the use of time‐series data.

Conclusions
This review serves as an introduction to the best practices for studying sleep‐wake patterns in humans—with the goal of standardizing tools for launching new human sleep biology research initiatives across the globe.

Direct Link: https://doi.org/10.1002/ajhb.23541

Journal: American Journal of Human Biology. 2020 Nov 30:e23541

Keywords: best practice, Circadian rhythm, field based, Sleep,

Applications: Sleep,

CamNtech Reference: M20056

Back to Search Results

UK & International customers

CamNtech Ltd.
Manor Farm
Fenstanton
Cambridgeshire
PE28 9JD, UK

US customers

CamNtech Inc.
630 Boerne Stage Airfield,
Boerne,
Texas 78006,
USA

Copyright

© 2024 CamNtech Ltd and CamNtech Inc

Company information

Registered in England No. 2221302
VAT No: GB486 3019 34


Privacy Policy