Traffic-Related Air Pollution and Acute Changes in Heart Rate Variability and Respiratory Function in Urban Cyclists
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the acute health effects of air pollution exposures experienced while cycling in traffic. OBJECTIVES: We conducted a crossover study to examine the relationship between traffic pollution and acute changes in heart rate variability. We also collected spirometry and exhaled nitric oxide measures. METHODS: Forty-two healthy adults cycled for 1 hr on high- and low-traffic routes as well as indoors. Health measures were collected before cycling and 1-4 hr after the start of cycling. Ultrafine particles (UFPs; ≤ 0.1 μm in aerodynamic diameter), particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5), black carbon, and volatile organic compounds were measured along each cycling route, and ambient nitrogen dioxide (NO2) and ozone (O3) levels were recorded from a fixed-site monitor. Mixed-effects models were used to estimate associations between air pollutants and changes in health outcome measures relative to precycling baseline values. RESULTS: An interquartile range increase in UFP levels (18,200/cm3) was associated with a significant decrease in high-frequency power 4 hr after the start of cycling [β = -224 msec2; 95% confidence interval (CI), -386 to -63 msec2]. Ambient NO2 levels were inversely associated with the standard deviation of normal-to-normal (NN) intervals (β = -10 msec; 95% CI, -20 to -0.34 msec) and positively associated with the ratio of low-frequency to high-frequency power (β = 1.4; 95% CI, 0.35 to 2.5) 2 hr after the start of cycling. We also observed significant inverse associations between ambient O3 levels and the root mean square of successive differences in adjacent NN intervals 3 hr after the start of cycling. CONCLUSIONS: Short-term exposures to traffic pollution may contribute to altered autonomic modulation of the heart in the hours immediately after cycling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".