Effects of Varying Post-Warm-Up Recovery Time on 200-m Time-Trial Swim Performance
Bibliographic record
Abstract
CONTEXT: Warm-up before athletic competition might enhance performance by affecting various physiological parameters. There are few quantitative data available on physiological responses to the warm-up, and the data that have been reported are inconclusive. Similarly, it has been suggested that varying the recovery period after a standardized warm-up might affect subsequent performance. PURPOSE: To determine the effects of varying post-warm-up recovery time on a subsequent 200-m swimming time trial. METHODS: Ten national-caliber swimmers (5 male, 5 female) each swam a 1500-m warm-up and performed a 200-m time trial of their specialty stroke after either 10 or 45 min of passive recovery. Subjects completed 1 time trial in each condition separated by 1 wk in a counterbalanced order. Blood lactate and heart rate were measured immediately after warm-up and 3 min before, immediately after, and 3 min after the time trial. Rating of perceived exertion was measured immediately after the warm-up and time trial. RESULTS: Time-trial performance was significantly improved after 10 min as opposed to 45 min recovery (136.80 +/- 20.38 s vs 138.69 +/- 20.32 s, P < .05). There were no significant differences between conditions for heart rate and blood lactate after the warm-up. Pre-time-trial heart rate, however, was higher in the 10-min than in the 45-min rest condition (109 +/- 14 beats/min vs 94 +/- 21 beats/min, P < .05). CONCLUSIONS: A post-warm-up recovery time of 10 min rather than 45 min is more beneficial to 200-m swimming time-trial 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".