Reducing Muscle Temperature Drop after Warm-up Improves Sprint Cycling Performance
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Bibliographic record
Abstract
PURPOSE: This study aimed to determine the effect of passive insulation versus external heating during recovery after a sprint-specific warm-up on thigh muscle temperature and subsequent maximal sprint performance. METHODS: On three separate occasions, 11 male cyclists (age = 24.7 ± 4.2 yr, height = 1.82 ± 0.72 m, body mass = 77.9 ± 9.8 kg; mean ± SD) completed a standardized 15-min intermittent warm-up on a cycle ergometer, followed by a 30-min passive recovery period before completing a 30-s maximal sprint test. Muscle temperature was measured in the vastus lateralis at 1, 2, and 3 cm depth before and after the warm-up and immediately before the sprint test. Absolute and relative peak power output was determined and blood lactate concentration was measured immediately after exercise. During the recovery period, participants wore a tracksuit top and (i) standard tracksuit pants (CONT), (ii) insulated athletic pants (INS), or (iii) insulated athletic pants with integrated electric heating elements (HEAT). RESULTS: Warm-up increased Tm by approximately 2.5 °C at all depths, with no differences between conditions. During recovery, Tm remained elevated in HEAT compared with INS and CONT at all depths (P < 0.001). Both peak and relative power output were elevated by 9.6% and 9.1%, respectively, in HEAT compared with CONT (both P < 0.05). The increase in blood lactate concentration was greater (P < 0.05) after sprint in HEAT (6.3 ± 1.8 mmol·L(-1)) but not INS (4.0 ± 1.8 mmol·L(-1)) versus CONT (4.1 ± 1.9 mmol·L(-1)). CONCLUSIONS: Passive heating of the thighs between warm-up completion and performance execution using pants incorporating electrically heated pads can attenuate the decline in Tm and improve sprint cycling performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it