Chronotype Modulates Sleep Duration, Sleep Quality, and Social Jet Lag in Shift-Workers
Bibliographic record
Abstract
This study explores chronotype-dependent tolerance to the demands of working morning, evening, and night shifts in terms of social jet lag, sleep duration, and sleep disturbance. A total of 238 shift-workers were chronotyped with the Munich ChronoType Questionnaire for shift-workers (MCTQ(Shift)), which collects information about shift-dependent sleep duration and sleep timing. Additionally, 94 shift-workers also completed those items of the Sleep Questionnaire from the Standard Shift-Work Index (SSI) that assess sleep disturbances. Although all participants worked morning, evening, and night shifts, subsamples differed in rotation direction and speed. Sleep duration, social jet lag, and sleep disturbance were all significantly modulated by the interaction of chronotype and shift (mixed-model ANOVAs). Earlier chronotypes showed shortened sleep duration during night shifts, high social jet lag, as well as higher levels of sleep disturbance. A similar pattern was observed for later chronotypes during early shifts. Age itself only influenced sleep duration and quality per se, without showing interactions with shifts. We found that workers slept longer in fast, rotating shift schedules. Since chronotype changes with age, investigations on sleep behavior and circadian misalignment in shift-workers have to consider chronotype to fully understand interindividual and intraindividual variability, especially in view of the current demographic changes. Given the impact of sleep on health, our results stress the importance of chronotype both in understanding the effects of shift-work on sleep and in devising solutions to reduce shift-work-related health problems.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".