Impact of airflow on body cooling in exercise: an exploratory study
Bibliographic record
Abstract
Objective: To analyze the body heat dissipation by thermography during indoor running treadmill with different airflow conditions. Materials and method: Nine male participants (23.0±2.5 years old) underwent three 45-minute moderate-intensity running sessions (60-70% reserve heart rate) on a treadmill. At each session, a different experimental condition was applied in a crossover design: without airflow (NF), and with low (LF) and high airflow (HF) generated by a fun. Thermograms were obtained with a thermal camera before exercise, during (every 10 minutes), and after exercise. Skin temperature (Tsk) was measured on regions of interest of the upper body: pectoral, brachial biceps, and upper back. A repeated measures ANOVA was used to compare Tsk over time and between conditions, considering p<0.05 as statistically significant. Results: In pectoral and brachial biceps, LF and HF conditions provided greater reductions in Tsk at all moments when compared to the NF (p<0.05). There was a higher reduction in Tsk to the HF vs LF in biceps at 30, 40, and 45 min during exercise (p<0.05). In the upper back, Tsk remained below baseline at all moments during exercise only in the HF condition (p<0.05). In NF and LF conditions, Tsk returned to baseline at 30 min during exercise (p>0.05). Conclusion: The frontal wind flow enhances body heat dissipation during moderate-intensity running in the pectoral, brachial biceps, and upper back, with a direct relationship of flow speed and Tsk reduction during 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) | 0.002 | 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".