Taking the sleep lab to the field: Biometric techniques for quantifying sleep and circadian rhythms in humans
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".