Impact of sleep chronotype on in‐laboratory polysomnography parameters
Bibliographic record
Abstract
Morningness-eveningness preference, also known as chronotype, is the tendency for a person to sleep during certain hours of the day and is broadly categorised into morning and evening types. In-laboratory polysomnography (iPSG) is the gold-standard to assess sleep, however, an individual's chronotype is not accounted for in current protocols, which may confound collected sleep data. The objective of our study was to assess if chronotype had an association with sleep physiology. Patients who completed the diagnostic iPSG and the Morningness-Eveningness Questionnaire (MEQ), which categorises patients into morning type, neither or evening type, were assessed. Multivariable linear regression models were used to assess if chronotype was associated with sleep quality, duration, and physiology during iPSG. The study sample included 2612 patients (mean age of 53.6 years, 48% male) recruited during 2010-2015. Morning type, compared with neither type, was significantly associated with an increase in total sleep time and rapid eye movement (REM) sleep, and a decrease in sleep onset latency and the arousal index. Evening type, compared with neither type, was significantly associated with a decrease in total sleep time, sleep efficiency, and REM sleep, and an increase in sleep onset latency and wake after sleep onset. Additionally, iPSG lights out time was significantly different between the different chronotypes. Overall, a morningness chronotype was associated with favourable sleep quality and duration while an eveningness chronotype was associated with reduced sleep quality. Our study quantifies the association of chronotype with iPSG metrics and suggests that laboratory protocols should consider chronotype in their evaluations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".