The impact of long‐haul travel and 13 h time change on sleep and rest activity circadian rhythm in speed skaters during World Cup competitions
Bibliographic record
Abstract
Athletes frequently compete only a few days after long-haul travel. Longitudinal real-world data on athletes' sleep and sleep-wake cycle in competitive settings remain scarce. This study assessed the impact of a long-haul travel across ∼13 time zones on sleep patterns, rest-activity circadian rhythms (RAR), and their subsequent effects on neuromuscular function and race performance in the Canadian Short-Track Speed Skating Team. Nineteen athletes (24 ± 4 years, 11 women) travelled from Montréal (UTC-5) to Asia (UTC+8, UTC+9) for World Cup races between 2017 and 2019. Actigraphy data were collected before (Baseline) and during travel, during the stay in Asia (SIA), and during competition days. RAR were computed using cosinor analyses on accelerometry data with 24 h phase periods. Countermovement jump height (CMJ) was measured in a subsample (n = 10). Compared to baseline (7:08 ± 0:53), athletes obtained less sleep during travel (6:16 ± 1:27) and competition days (6:35 ± 1:10), and more during SIA (7:32 ± 0:46; time effect P < 0.0001). Sleep efficiency and CMJ were greater in SIA than baseline (P = 0.007 and P = 0.0004, respectively). During SIA, sleep time increased by 9 min per night until the fifth day (P < 0.0001), with a slight decrease in sleep efficiency (P = 0.005) and an increase in CMJ (P < 0.0001). For RAR, mean activity peaked on day 2, shifting from late evening to ∼15:00. Race performance was not different from other races of the same season (P > 0.254). Our results demonstrated that, despite the possible sleep debt from the long-haul travel, athletes recovered within 5 days, highlighting their adaptability to manage sleep debt and jetlag without impacting competitive outcomes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".