The effect of inspiratory muscle training on the inspiratory muscle metaboreflex: A systematic review
Bibliographic record
Abstract
Background: Increases in respiratory work and, consequently, the development of respiratory muscle fatigue can result in activation of the inspiratory muscle metaboreflex (IMM). This central nervous system response includes redirection of blood flow from skeletal muscles to respiratory muscles, which can limit exercise capacity. Therefore, developing approaches that may minimize the need for IMM is important. Recent studies have suggested that inspiratory muscle training (IMT) may reduce the IMM response. This review aims to synthesize studies that examine the efficacy of IMT in reducing IMM. Methods: Databases were systematically searched for randomized controlled clinical trials (RCTs) and non-randomized controlled trials evaluating the effect of IMT on the IMM. Searches were performed in MEDLINE/PubMed, SCOPUS, Web of Science and LILACS from inception to December 2023. Assessment of the methodological quality of the included studies was guided by the PEDro scale. Results: Four studies met the inclusion criteria, two RCTs and two non-randomized controlled trials, collectively including 76 subjects. Three studies included healthy subjects, and one included patients with heart failure. In all studies, IMT demonstrated a significant attenuating effect on the IMM, increasing inspiratory muscle strength and time of exercise tolerance (Tlim). Conclusion: Current evidence suggests that IMT may provide an effective approach for attenuating the IMM and increasing inspiratory muscle strength and Tlim. However, the small collective sample size and heterogeneity across studies limit current recommendations.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".