Effect of sportswear on performance and physiological heat strain during prolonged running in moderately hot conditions
Bibliographic record
Abstract
Abstract Introduction This study examined the impact of different upper‐torso sportswear technologies on the performance and physiological heat strain of well‐trained and national‐level athletes during prolonged running in moderately hot conditions. Methods A randomized crossover design was employed in which 20 well‐trained (n = 16) and national‐level (n = 4) athletes completed four experimental trials in moderately hot conditions (35°C, 30% relative humidity). In each trial, participants ran at 70% of their peak oxygen uptake (70% V̇O2peak) for 60 min, while wearing a different upper‐body garment: cotton t‐shirt, t‐shirt with sweat‐wicking fabric, compression t‐shirt, and t‐shirt with aluminum dots lining the inside of the upper back of the garment. Running speed was adjusted to elicit the predetermined oxygen consumption associated with 70% V̇O2peak. Physiological (core and skin temperatures, total body water loss, and urine specific gravity) and perceptual (thermal comfort and sensation, ratings of perceived exertion, and garment cooling functionality) parameters along with running speed at 70% V̇O2peak were continuously recorded. Results No significant differences were observed between the four garments for running speed at 70% V̇O2peak, physiological heat strain, and perceptual responses (all p > 0.05). The tested athletes reported larger areas of perceived suboptimal cooling functionality in the cotton t‐shirt and the t‐shirt with aluminum dots relative to the sweat‐wicking and compression t‐shirts (d: 0.43–0.52). Conclusion There were not differences among the tested garments regarding running speed at 70% V̇O2peak, physiological heat strain, and perceptual responses in well‐trained and national‐level endurance athletes exercising in moderate heat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".