Effects of Respiratory Muscle Training on Performance in Athletes
Bibliographic record
Abstract
The purpose of this study was to perform a systematic review to determine if respiratory muscle training (RMT) improves sport performance and respiratory muscle strength and endurance. Methodology followed the Cochrane Collaboration protocol. MEDLINE, CINAHL, SPORTDiscus, PEDro, EMBASE, EBM reviews, and COCHRANE electronic databases were searched until July 2011. Articles were included if: (a) participants were athletes; (b) RMT was compared with sham or control in a randomized controlled design and included outcomes of respiratory muscle and sport performance; and (d) published in English. Quality assessment using PEDro and data abstraction was performed by 2 authors. Outcomes evaluated were measures of sport performance, exercise capacity, spirometry, and respiratory muscle strength and endurance. Meta-analyses were performed on outcomes reported in 2 or more papers. Results of this systematic review revealed that of the 6,923 citations retrieved from the search strategy, 21 met the inclusion criteria. Meta-analyses demonstrated a significant positive effect of RMT on sport performance outcomes of time trials, exercise endurance time, and repetitions on Yo-Yo tests. Inspiratory muscle strength and endurance improved in most studies, which in part, was dependent on the type of RMT employed. Determination of the type of athlete that may benefit most from RMT was limited by small sample sizes, differing RMT protocols, and differences in outcome measures across studies. In conclusion, RMT can improve sport performance. Closer attention to matching the ventilatory demands of RMT to those required during athletic competition and more aggressive progression of training intensity may show greater improvements in future studies.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".