Impact of long‐haul airline travel on athletic performance and recovery: A critical review of the literature
Bibliographic record
Abstract
Participation in many important sport events (e.g., World championships, Olympics) requires athletes to fly >4 h and to cross several time zones. This transmeridian travel results in a transient desynchronization of the body's circadian rhythms due to a disconnect between the timing of the endogenous circadian oscillator and the external stimuli, manifested as 'jet lag'. While recent reviews highlight the importance of managing jet lag, the time required for resynchronization of the internal clock and dissipation of jet lag symptoms has not yet been summarized. Further, although the literature reports that rapid transmeridian travel is detrimental for athletes' performance, empirical evidence from studies involving athletes is equivocal. Herein, we summarize the evidence that the variability in responses to transmeridian travel can be attributed to differences in (i) travel (real vs. simulated, westbound vs. eastbound, time zones crossed, during normal waking hours vs. normal sleep time), (ii) testing (assessment of performance vs. factors related to performance), and (iii) timing of the testing (destination time vs. 'body time'), and we offer the possibility that differences in (iv) teams, (v) traits, and (vi) tournaments may also be implicated. We focus on (i) aerobic power/endurance, (ii) anaerobic power and capacity, (iii) strength, and (iv) mood state, sleep quantity and quality, and jet lag symptoms in this literature review, which is limited to athletes or physically active participants, travelling west or east crossing four or more time zones.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".