Reducing Muscle Temperature Drop after Warm-up Improves Sprint Cycling Performance
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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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) | 0.001 | 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".