Associations between jet lag and cortisol diurnal rhythms after domestic travel.
Bibliographic record
Abstract
OBJECTIVE: Millions of adults in the United States travel abruptly across time zones each year. Nevertheless, the impact of traveling over relatively short distances (across 3 or fewer time zones) on diurnal patterning of typical physiological response patterns has yet to be studied in a large, epidemiological sample. DESIGN: The current research focuses on 764 middle-aged men comparing variations in diurnal cortisol regulation based on number of time zones traveled eastward or westward the day before. MAIN OUTCOME MEASURE: Participants provided samples of salivary cortisol at waking, 30-min postwaking, 10 a.m., 3 p.m., and bedtime. RESULTS: Eastward travel was associated with a steeper salivary cortisol awakening response (p < .01) and lower peak (PEAK) levels of salivary cortisol the next morning (p < .05). Westward travel was associated with lower peak levels of cortisol the next morning (p < .05). Effect sizes for these differences ranged from Cohen's d = .29 to .47. Differences were not present for 2 days in their home environment. CONCLUSIONS: The results provide evidence that traveling across time zones is associated with diurnal cortisol regulation and should be studied further to understand the subsequent impacts on health and well-being in large national samples.
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 | 0.000 | 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 | 0.000 |
| 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".