Evening light reduces fatigue and errors during night shifts: A randomised controlled trial
Bibliographic record
Abstract
Objective: Shift work causes circadian rhythms to misalign with the demands of the environment, which has been associated with sleep difficulties and cognitive impairments. Although bright light exposure during night shifts can improve circadian alignment, its implementation is often infeasible. Here, we tested whether light exposure in the evening, before the night shift, could improve fatigue, work performance, mood, and sleep. Methods: Fifty-seven healthy nurses who worked full-time rapidly rotating shift schedules completed the study. In a mixed-design randomised controlled trial, participants completed a baseline observation period before following one of two interventions. The experimental intervention aimed to improve circadian alignment using evening light exposure and morning light avoidance; the control intervention aimed to improve alertness and reduce sleep disturbances by modifying diet. Every morning and evening for 30 days, participants completed measures of fatigue, work-related errors, sleepiness, mood, sleep duration, and sleep quality. Results: Compared to the baseline observation period, the experimental intervention reduced errors by 67% while the control intervention reduced them by only 5%. This reduction was partially mediated by fatigue; experimental participants reported less fatigue on work days than control participants (d = 0.25 [0.12, 0.38]). The experimental group also showed a small improvement in mood. Both groups showed reductions in fatigue (d = .29 [.20, .35]) and sleepiness (d = 0.21 [0.13, 0.29]) as well as a small increase in sleep duration. Conclusion: Interventions based on evening light may thus be a feasible and effective strategy to reduce fatigue and errors in night shift 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".