Inspiratory muscle training for enhancing repeated-sprint ability: A pilot study
Bibliographic record
Abstract
Abstract This pilot study examined the effect of inspiratory muscle training (IMT) on repeated-sprint ability and vastus lateralis reoxygenation. Ten recreationally trained subjects were randomly divided into two groups to complete 4 weeks of IMT or Sham (placebo) training. Pre- and post-intervention, a repeated-sprint ability (RSA) test was performed in both normoxia and hypoxia (F i O 2 ≈ 14.5%). Vastus lateralis reoxygenation (VL reoxy ), defined as peak to minimum amplitude deoxyhaemoglobin for each sprint/recovery cycle, was assessed during all trials using near-infrared spectroscopy. For total work performed, power analysis revealed that for small, medium and large effects (Cohen’s f ), sample sizes of n = 8, 16 and 90 respectively, are required to achieve a power of 80% at an α level of 0.05. Maximal inspiratory mouth pressure increased in IMT by 36.5%, 95% CI [20.9, 61.6] and by 2.7%, 95% CI [−4.46, 8.8] in Sham. No clear difference in the change of work completed during the sprints between groups were observed in normoxia (Sham −0.805 kJ, 95% CI [−3.92, 0.39]; IMT −2.06 kJ, 95% CI [−11.5, 4.96]; P = 0.802), or hypoxia (Sham −3.09 kJ, 95% CI [−7, 0.396]; IMT 0.354 kJ, 95% CI [−1.49, 2.1]; P = 0.802). VL reoxy in IMT increased by 9.34%, 95% CI [5.15, 13.7] in normoxia only. In conclusion, despite a large increase in IMT, this was only associated with a small effect on RSA in our pilot study cohort. Owing to a potentially relevant impact of training the inspiratory musculature, future studies should include a sample size of at least 16-20 to detect moderate to large effects on RSA.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".