Effect of Clothing Fabric on 20-km Cycling Performance in Endurance Athletes
Bibliographic record
Abstract
Purpose: Examine the effect of synthetic fabrics (SYN, 60% polyester: 40% nylon) vs. 100% cotton fabric (CTN) on the 20-km cycling time trial (20 kmCTT) performance of competitive cyclists and triathletes. Methods: In this randomized controlled crossover study, 15 adults (5 women) aged 29.6 ± 2.7 years (mean ± SE) with a peak rate of O2 consumption of 60.0 ± 2.0 ml/kg/min completed a 20 kmCTT under ambient laboratory conditions (24.3 ± 0.7°C and 17 ± 7% relative humidity) with a simulated wind of ~3 m/s while wearing SYN or CTN clothing ensembles. Both ensembles were of snowflake mesh bi-layer construction and consisted of a loose-fitting long-sleeved shirt with full-length trousers. Results: Participants maintained a significantly (p < 0.05) higher cycling speed and power output over the last 6-km of the 20 kmCTT while wearing the SYN vs. CTN ensemble (e.g., by 0.98 km/h and 18.4 watts at the 20-km mark). Consequently, 20 kmCTT duration was significantly reduced by 15.7 ± 6.8 sec or 0.8 ± 0.3% during SYN vs. CTN trials (p < 0.05). Improved 20 kmCTT performance with SYN vs. CTN clothing could not be explained by concurrent differences in esophageal temperature, sweat rate, ratings of perceived exertion and/or cardiometabolic responses to exercise. However, it was accompanied by significantly lower mean skin temperatures (~1°C) and more favorable ratings of perceived clothing comfort and thermal sensation during exercise. Conclusion: Under the experimental conditions of the current study, athletic clothing made of synthetic fabrics significantly improved the 20 kmCTT performance of endurance-trained athletes by optimizing selected thermoregulatory and perceptual responses to exercise.
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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 | 0.000 |
| 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) | 0.001 | 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".