Air pollution and performances in outdoor sports: a systematic review and meta-analysis of short-terms associations
Bibliographic record
Abstract
Laboratory studies suggest that high concentrations of particles matter (diameter<2.5 mm [PM2.5]) and ozone (O3) may reduce sport performances at short term. However, a systematic review of literature examining whether air pollution has an effect on sport performances in real situations is missing.Seven databases were searched for articles which examined the association between particulate (PM2.5, PM10, PMCoarse), gaseous (O3, sulfur dioxide [SO2], nitrogen dioxide [NO2], carbon monoxide [CO], and index (Air Quality Index [AQI]) of air pollutants and sport performances in real context. We included observational studies with objective measures of air pollution and performances. They were categorized into endurance, power/speed, technical performances. Harvest plots were used to summarize the findings. When appropriate, meta-analyses were completed to evaluate the strength of the associations. Over 200 reported associations from 23 articles were examined. Participants were primarily elite athletes. The majority of studies examined football (n = 9), and endurance sports (n = 10). Overall, no consistent associations were observed for AQI, PM2.5, or NO₂ and performances. We found that exposure to O3 was associated with reduced sports performance, (d = -0.33, 95%CI: -0.65 –0.02 [all performances]; d = -0.54, 95%CI: -1.02 -0.04) [endurance performances]). An absence of significant association between AQI, PM2.5, or NO2 exposure and sports performance is clearly supported by our systematic review and meta-analyses. Higher O3 concentration could impair sport performances in real context. For short-term exposures to CO, PM10, and SO₂, no definitive conclusions can be drawn due to the limited number of studies.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| 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 | 0.002 |
| 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".