Cold-water immersion and other forms of cryotherapy: physiological changes potentially affecting recovery from high-intensity exercise
Bibliographic record
Abstract
High-intensity exercise is associated with mechanical and/or metabolic stresses that lead to reduced performance capacity of skeletal muscle, soreness and inflammation. Cold-water immersion and other forms of cryotherapy are commonly used following a high-intensity bout of exercise to speed recovery. Cryotherapy in its various forms has been used in this capacity for a number of years; however, the mechanisms underlying its recovery effects post-exercise remain elusive. The fundamental change induced by cold therapy is a reduction in tissue temperature, which subsequently exerts local effects on blood flow, cell swelling and metabolism and neural conductance velocity. Systemically, cold therapy causes core temperature reduction and cardiovascular and endocrine changes. A major hindrance to defining guidelines for best practice for the use of the various forms of cryotherapy is an incongruity between mechanistic studies investigating these physiological changes induced by cold and applied studies investigating the functional effects of cold for recovery from high-intensity exercise. When possible, studies investigating the functional recovery effects of cold therapy for recovery from exercise should concomitantly measure intramuscular temperature and relevant temperature-dependent physiological changes induced by this type of recovery strategy. This review will discuss the acute physiological changes induced by various cryotherapy modalities that may affect recovery in the hours to days (<5 days) that follow high-intensity 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".