Functional Impact of Post-exercise Cooling and Heating on Recovery and Training Adaptations: Application to Resistance, Endurance, and Sprint Exercise
Bibliographic record
Abstract
The application of post-exercise cooling (e.g., cold water immersion) and post-exercise heating has become a popular intervention which is assumed to increase functional recovery and may improve chronic training adaptations. However, the effectiveness of such post-exercise temperature manipulations remains uncertain. The aim of this comprehensive review was to analyze the effects of post-exercise cooling and post-exercise heating on neuromuscular function (maximal strength and power), fatigue resistance, exercise performance, and training adaptations. We focused on three exercise types (resistance, endurance and sprint exercises) and included studies investigating (1) the early recovery phase, (2) the late recovery phase, and (3) repeated application of the treatment. We identified that the primary benefit of cooling was in the early recovery phase (< 1 h post-exercise) in improving fatigue resistance in hot ambient conditions following endurance exercise and possibly enhancing the recovery of maximal strength following resistance exercise. The primary negative impact of cooling was with chronic exposure which impaired strength adaptations and decreased fatigue resistance following resistance training intervention (12 weeks and 4-12 weeks, respectively). In the early recovery phase, cooling could also impair sprint performance following sprint exercise and could possibly reduce neuromuscular function immediately after endurance exercise. Generally, no benefits of acute cooling were observed during the 24-72-h recovery period following resistance and endurance exercises, while it could have some benefits on the recovery of neuromuscular function during the 24-48-h recovery period following sprint exercise. Most studies indicated that chronic cooling does not affect endurance training adaptations following 4-6 week training intervention. We identified limited data employing heating as a recovery intervention, but some indications suggest promise in its application to endurance and sprint 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".