Is active recovery during cold water immersion better than active or passive recovery in thermoneutral water for postrecovery high-intensity sprint interval performance?
Bibliographic record
Abstract
High-intensity exercise is impaired by increased esophageal temperature (Tes) above 38 °C and/or decreased muscle temperature. We compared the effects of three 30-min recovery strategies following a first set of three 30-s Wingate tests (set 1), on a similar postrecovery set of Wingate tests (set 2). Recovery conditions were passive recovery in thermoneutral (34 °C) water (Passive-TN) and active recovery (underwater cycling; ∼33% maximum power) in thermoneutral (Active-TN) or cold (15 °C) water (Active-C). Tes rose for all conditions by the end of set 1 (∼1.0 °C). After recovery, Tes returned to baseline in both Active-C and Passive-TN but remained elevated in Active-TN (p < 0.05). At the end of set 2, Tes was lower in Active-C (37.2 °C) than both Passive-TN (38.1 °C) and Active-TN (38.8 °C) (p < 0.05). From set 1 to 2 mean power did not change with Passive-TN (+0.2%), increased with Active-TN (+2.4%; p < 0.05), and decreased with Active-C (–3.2%; p < 0.05). Heart rate was similar between conditions throughout, except at end-recovery; it was lower in Passive-TN (92 beats·min−1) than both exercise conditions (Active-TN, 126 beats·min−1; Active-C, 116 beats·min−1) (p < 0.05). Although Active-C significantly reduced Tes, the best postrecovery performance occurred with Active-TN. Novelty An initial set of 3 Wingates increased Tes to ∼38 °C. Thirty minutes of Active-C was well tolerated, and decreased Tes and blood lactate to baseline values, but decreased subsequent Wingate 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".