Cold-water Immersion Impairs Power Earlier than Strength Through Time-Dependent Reductions in Intramuscular Temperature in Human Dorsiflexor Muscles
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate the time- and intramuscular temperature-dependent changes in neuromuscular function throughout 1 h of cold-water immersion (CWI) at 10°C. It was hypothesized that acute CWI (<30 min) would not affect neuromuscular function due to limited reductions in intramuscular temperature, whereas prolonged CWI (>30 min) would impair muscle contractility by drastically reducing intramuscular temperature. METHODS: Twelve healthy participants (nine males and three females) partook in a randomized crossover design study involving 1-h CWI at 10°C of their lower leg, with three experimental visits consisting of 1) 1-h CWI at 10°C (CWI-only), 2) nonfatiguing exercise followed by 1-h CWI at 10°C to mimic the use of postexercise CWI (Ex + CWI), and 3) passive muscle preheating followed by 1-h CWI at 10°C (Heat + CWI). Skin temperature, intramuscular temperature, and neuromuscular function were periodically assessed in the dorsiflexors throughout the 1 h of CWI. RESULTS: Decreased peak power was observed after 10 min of CWI, CWI-only (50.3 ± 16.0%, P < 0.05), Ex + CWI (55.0 ± 18.3%, P < 0.05), and Heat + CWI (62.0 ± 16.8%, P < 0.05), whereas maximal isometric torque decreased after ≥30 min of CWI, CWI-only (81.1 ± 9.1%, P < 0.05), Ex + CWI (86.6 ± 14.3%, P < 0.05), and Heat + CWI (88.7 ± 10.0%, P < 0.05). Decreases in M-wave peak-to-peak amplitude, 50-Hz torque, and postactivation potentiation were only evident following prolonged CWI (P < 0.05). CONCLUSIONS: These results highlight that peak power is more sensitive to reductions in intramuscular temperature than maximal isometric strength, reflecting a time- and temperature-dependent effect on skeletal muscle function.
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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".