Environmental Influences on Performance of Human and Animal Athletes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Environmental factors such as temperature, humidity, and altitude play a critical role in shaping athletic performance, influencing outcomes across both human and animal athletes. This systematic review and meta-analysis examined the impact of key environmental variables on endurance and strength-related performance. A comprehensive literature searches of PubMed, Scopus, and Web of Science (2000–2023) yielded 1,215 studies. Following rigorous screening, 45 studies met inclusion criteria, comprising 30 human and 15 animal studies. Inclusion criteria favored peer-reviewed studies reporting quantitative data on the performance effects of environmental exposures. Data were extracted and assessed independently by two reviewers using Cochrane Risk of Bias and Newcastle-Ottawa scales. Meta-analytic synthesis revealed that elevated temperature and humidity significantly reduced performance (temperature: SMD = -0.45, p < 0.001; humidity: SMD = -0.30, p < 0.01), while moderate altitude enhanced endurance performance (SMD = 0.25, p < 0.001) but reduced strength outcomes (SMD = -0.20, p < 0.05). Moderate heterogeneity was observed (I² = 45%). These findings highlight the need to consider environmental conditions in both training and competition planning. Future research should explore long-term adaptations to environmental stress, cross-species physiological comparisons, and the impact of extreme conditions across diverse athletic populations.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".