Controlled‐frequency breath swimming improves swimming performance and running economy
Bibliographic record
Abstract
Respiratory muscle fatigue can negatively impact athletic performance, but swimming has beneficial effects on the respiratory system and may reduce susceptibility to fatigue. Limiting breath frequency during swimming further stresses the respiratory system through hypercapnia and mechanical loading and may lead to appreciable improvements in respiratory muscle strength. This study assessed the effects of controlled-frequency breath (CFB) swimming on pulmonary function. Eighteen subjects (10 men), average (standard deviation) age 25 (6) years, body mass index 24.4 (3.7) kg/m(2), underwent baseline testing to assess pulmonary function, running economy, aerobic capacity, and swimming performance. Subjects were then randomized to either CFB or stroke-matched (SM) condition. Subjects completed 12 training sessions, in which CFB subjects took two breaths per length and SM subjects took seven. Post-training, maximum expiratory pressure improved by 11% (15) for all 18 subjects (P < 0.05) while maximum inspiratory pressure was unchanged. Running economy improved by 6 (9)% in CFB following training (P < 0.05). Forced vital capacity increased by 4% (4) in SM (P < 0.05) and was unchanged in CFB. These findings suggest that limiting breath frequency during swimming may improve muscular oxygen utilization during terrestrial exercise in novice 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".