Air pollution and cognitive function: the potential protective effect of physical activity
Bibliographic record
Abstract
Physical activity (PA) may mitigate pollution‐related cognitive decline while concurrently increasing individuals’ exposure to harmful pollutants. Data were obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS), comprising 17,734 participants aged 45 years or older. Information regarding particulate matter (PM 1 , PM 2.5 , and PM 10 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ) and ozone (O 3 ) was sourced from the China High Air Pollutants (CHAP) database. The assessment of cognitive function was carried out following the approach utilized in the Health and Retirement Study (HRS). The impact of air pollutants on cognitive function was estimated using the two-stage least squares method, with the ventilation coefficient serving as an instrumental variable. The results indicated that all air pollutants were significantly associated with cognitive function. An interquartile range (IQR) increase in PM 1 , PM 2.5 , PM 10 , NO 2 , SO 2 , and O 3 corresponded to decreases in cognitive function of −0.45 (95% CI: −0.76, −0.13), −0.43 (95% CI: −0.74, −0.13), −0.53 (95% CI: −0.90, −0.16), −0.57 (95% CI: −0.96, −0.17), −0.47 (95% CI: −0.80, −0.14), and −1.06 (95% CI: −1.81, −0.31), respectively. Further stratified analyses revealed that higher levels of PA significantly moderated the association between air pollution and cognitive function, suggesting a potential protective effect. The level of PA was found to modify this association of pollution and cognitive function, with higher PA levels seemingly alleviating the adverse cognitive effects of air pollution. These findings underscore the importance of policies that simultaneously target pollution reduction and promote PA to safeguard cognitive health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".