Influence of the combination of end-expiratory breath-hold training and inspiratory muscle training on performance in artistic swimmers
Bibliographic record
Abstract
This study explored the effects of combining repeated-sprint training with end-expiratory breath-hold (EBH) and inspiratory muscle training (IMT) on cardiorespiratory fitness and performance in artistic swimming. In a quasi-experimental study, 15 artistic swimmers (15.7 ± 1.5 years) completed, over 3 weeks, nine training sessions (each involving 32 15-m swim sprints) with EBH complemented with daily IMT at 50% of inspiratory muscle strength (EBH + IMT, n = 8) or the same EBH training with a placebo IMT procedure (EBH, n = 7). Laboratory outcomes and performance measures included inspiratory muscle pressure, peak power output, peak O 2 uptake ([Formula: see text]O 2peak ), muscle oxygenation, ventilatory thresholds markers, and a 275 m underwater swim test (UWST). Both groups increased maximal inspiratory muscle strength (∼21%, time effect p < 0.001, Cohen’s ES = 0.72). There was a significant interaction for [Formula: see text]O 2peak , which increased more in EBH + IMT than EBH alone (3.1 mL·kg −1 ·min −1 , interaction p = 0.036, ES = 0.73). The deoxygenation of the vastus lateralis muscle during the incremental test increased following EBH + IMT only (38%, interaction p = 0.01). No change occurred in ventilatory threshold markers nor in UWST performance in any groups. These results suggest that combining repeated-sprint training with EBH and daily IMT for 3 weeks led to greater peripheral muscle O 2 extraction, conducive to an increased [Formula: see text]O 2peak gain, but exerted no influence on a more specific swimming/breath-hold 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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".