Effects of partial circadian adjustments on sleep and vigilance quality during simulated night work
Bibliographic record
Abstract
In most situations, complete circadian adjustment is not recommended for night workers. With complete adjustment, workers experience circadian misalignment when returning on a day-active schedule, causing repeated circadian phase shifts and internal desynchrony. For this reason, partial circadian realignment was proposed as a good compromise to stabilize internal circadian rhythms in night shift workers. However, the extent of partial circadian adjustment necessary to improve sleep and vigilance quality is still a matter of debate. In this study, the effects of small but statistically significant partial circadian adjustments on sleep and vigilance quality were assessed in a laboratory simulation of night work to determine whether they were also of clinical significance. Partial adjustments obtained by phase delay or by phase advance were quantified not only by the phase shift of dim light salivary melatonin onset, but also by the overlap of the episode of melatonin production with the sleep-wake cycle adopted during simulated night work. The effects on daytime sleep and night-time vigilance quality were modest. However, they suggest that even small adjustments by phase delay may decrease the accumulation of sleep debt, whereas the advance strategy improves subjective alertness and mood during night work. Furthermore, absolute phase shifts, by advance or by delay, were associated with improved subjective alertness and mood during the night shift. These strategies need to be tested in the field, to determine whether they can be adapted to real-life situations and provide effective support to night workers.
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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 | 0.002 |
| 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 | 0.001 |
| 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".