Timing‐based strategies to minimize the impact of long‐haul travel on sleep: A pilot study in elite athletes traveling for competition
Bibliographic record
Abstract
Long-haul travel poses significant challenges to sleep in elite athletes, yet evidence-based interventions tested in competitive settings remain scarce. This study investigated the effects of timing-based interventions on sleep in 10 national-level Canadian speed skaters prior to a World Cup competition in Beijing (13 time zones crossed). Athletes followed a tailored sleep schedule upon arrival and for the days preceding the competition. Total sleep time in Beijing was not different from Canada (p = 0.254) or pre-season (p = 0.999) and was lower the night before travel (p < 0.001) due to the early flight to Beijing. When comparing data with a similar dataset presenting no intervention, bedtime was successfully delayed and resulted in later wake-up time and longer total sleep time. Total sleep time increased by ~10 min/night, suggesting adjustments in sleep-wake rhythm during the first days upon arrival were still present. Race performance was unaffected by travel, with no time effect on overall rank (p = 0.74). These preliminary findings suggest that individualized timing-based strategies might support sleep regulation and circadian re-synchronization in elite athletes following long-haul travel. Further studies are warranted to confirm these results in larger samples and explore the effectiveness of customized timing-based intervention on different time changes and on performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".