Cooling During Exercise May Induce Benefits Linked to Improved Brain Perfusion
Bibliographic record
Abstract
The aim of this study was to evaluate the impact of using a cooling vest during physical exercise (per-cooling) in humid and temperate conditions (≈22°C, ≈80% relative humidity) on perceptual and physiological responses (tissue oxygenation and heart rate). 20 physically active men performed twice a 30-min cycling exercise at 70% of their theoretical maximum heart rate while using an activated (experimental condition) and a deactivated (control condition) cooling system in a randomized crossover study. Heart rate and tissue (cerebral and muscular) oxygenation were continuously measured during exercise and recovery, and skin temperature was measured every 10 min. Perception of temperature, humidity and comfort were assessed at the end of the recovery period. Results showed a decrease in trunk skin temperature (p<0.05), a faster heart rate recovery and an increase in the concentration of total hemoglobin at the brain level (p<0.05) compared with control condition. Moreover, an improved subjective rating of thermal sensations, wetness and comfort compared to control values (p<0.05) was noted. In conclusion, wearing a cooling vest during submaximal exercise improves perceptual and physiological responses in humid temperate conditions, which may be due to a better blood perfusion at the brain level and a better parasympathetic reactivation.
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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 | 0.000 |
| 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".