The Effect of Pre-exposure to Diluted Diesel Exhaust on 20km Cycling Time Trial Performance
Bibliographic record
Abstract
Particulate matter air pollution (PM) causes lung inflammation, systemic inflammation and endothelial dysfunction, however the effect of PM on endurance performance is unknown. With international sports competitions being held in cities with high levels of PM, it is important to understand the physiological and performance implications of this. PURPOSE: Determine the effect of pre-exposure to dilute diesel exhaust (DE) on 20km time trial (TT) performance, cardio-respiratory responses during exercise and lung function. METHODS: Seven endurance-trained males (mean ± SD; VO2max of 68.0 ± 9.0 mL·kg-1·min-1, age: 28.3 ± 5.9 yr, height: 178.8 ± 10.2 cm, weight: 71.0 ± 3.0 kg) participated. They attended on 3 separate occasions. Day 1 consisted of a maximal exercise test; days 2 and 3 included a 60-minute exposure followed be a 20km TT. Days 2 and 3 exposures were blinded and randomized to filtered air (FA) or DE containing PM2.5 (particles with a diameter of <2.5 microns) at a concentration of 300ug/m3. Pulmonary function was assessed immediately prior to and 30 minutes post-exposure, and 30 minutes post-exercise. Minute ventilation (VE) tidal volume (Vt) and breathing frequency (Fb,) were collected during the TTs. Duration, mean and peak power were analysed using paired T-tests; repeated measures ANOVA were used to assess the effect of exposure on physiological measures: pulmonary function was analysed using condition (DE vs. FA) and time (pre-exposure, post-exposure, post-TT); ventilatory data and heart rate during time trial performance were analysed using condition (DE vs. FA) and time (quartile 1, 2, 3 and 4 of the time trial). RESULTS: There were no significant differences in time trial duration (33.2 ± 1.4 min vs. 32.6 ± 0.9 min), mean power output (253.1 ± 29.1w vs. 262.9 ± 20.34w), peak power output (436.4 ± 167.6w vs. 467.1 ± 139.6w) between the FA and DE condition respectively. There were no significant differences in VE, Vt and Fb between tests. There was a trend for heart rate to be higher following DE compared to FA (p=0.057; Q1: 150.0 ± 11.1 vs. 155.1 ± 15.1, Q2: 157.0 ± 11.1 vs. 164.9 ± 12.6, Q3: 158.9 ± 14.7 vs. 167.4 ± 13.2, Q4: 169.6 ± 18.2 vs, 171.9 ± 12.6 bpm for FA and DE respectively). CONCLUSION: A 60-minute pre-exposure to 300ug/m3 PM does not significantly impair 20km time trial cycling performance in endurance athletes.
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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 | 0.000 | 0.001 |
| 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".