Effect of different intensities of active recovery on sprint swimming performance
Bibliographic record
Abstract
Active recovery reduces blood lactate concentration faster than passive recovery and, when the proper intensity is applied, a positive effect on performance is expected. The purpose of the study was to investigate the effect of different intensities of active recovery on performance during repeated sprint swimming. Nine male well-trained swimmers performed 8 repetitions of 25 m sprints (8 x 25 m) interspersed with 45 s intervals, followed by a 50 m sprint test 6 min later. During the 45 s and 6 min interval periods, swimmers either rested passively (PAS) or swam at an intensity corresponding to 50% (ACT50) and 60% (ACT60) of their individual 100 m velocity. Blood lactate was higher during PAS compared with ACT50 and ACT60 trials (p < 0.05), whereas plasma ammonia and glycerol concentration were not different between trials (p > 0.05). Mean performance time for the 8 x 25 m sprints was better in the PAS compared with the ACT50 and ACT60 trials (PAS: 13.10 +/- 0.07 vs. ACT50: 13.43 +/- 0.10 and ACT60: 13.47 +/- 0.10s, p < 0.05). The first 25 m sprint was not different across trials (p > 0.05), but performance decreased after sprint 2 during active recovery trials (ACT50 and ACT60) compared with the passive recovery (PAS) trial (p < 0.05). Performance time for the 50 m sprint performed 6 min after the 8 x 25 m sprints was no different between trials (p > 0.05). These results indicate that active recovery at intensities corresponding to 50% and 60% of the 100 m velocity during repeated swimming sprints decreases performance. Active recovery reduces blood lactate concentration, but does not affect performance on a 50 m sprint when 6 min recovery is provided. Passive recovery is advised during short-interval repeated sprint training in well-trained swimmers.
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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) | 0.001 | 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".