The impact of respiratory muscle training on respiratory function in patients with neuromuscular disease: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Neuromuscular diseases (NMDs) can impair respiratory muscle function, leading to increased morbidity and mortality. Respiratory muscle training (RMT) is widely used to manage these respiratory complications, but its efficacy across different NMDs remains unclear. This systematic review and meta-analysis evaluated the impact of physiotherapy interventions, specifically RMT, on respiratory muscle function in NMD patients. METHODS: A systematic search of multiple databases, including MEDLINE, EMBASE, Web of Science, Cochrane, CRS-Web, PEDro, LILACS, ICTPR, the China National Knowledge Infrastructure database, and ClinicalTrials.gov, was conducted up to February 2025. Randomized controlled trials (RCTs) and cohort studies evaluating RMT's effect on lung volumes and respiratory muscle strength in NMD patients were included. Risk of bias assessment was performed using Cochrane Risk of bias tool for RCTs and Newcastle-Ottawa Scale for cohorts. Meta-analyses were performed using a random-effects model, and heterogeneity was assessed with I² statistics. RESULTS: Sixteen studies were analyzed from 9,626 screened articles. The meta-analysis demonstrated significant improvements in respiratory muscle strength, particularly in maximal inspiratory pressure (MD: 6.83 cmH₂O, 95% CI: 2.08 to 11.58, p < 0.01, I² = 3.8%) and maximal expiratory pressure (MD: 13.05 cmH₂O, 95% CI: 3.65to 22.42, p < 0.01, I² = 43%). No significant improvements were observed in forced vital capacity (MD: 3.13%, 95% CI: -8.06 to 14.34, p = 0.58), sniff nasal inspiratory pressure (MD: 1.47 cmH₂O, 95% CI: -15.45 to 18.39, p = 0.86), forced expiratory volume in one second (MD: -0.02 L, 95% CI: -0.17 to 0.13, p = 0.78), and vital capacity (MD: -0.10 L, 95% CI: -0.31 to 0.11, p = 0.33). CONCLUSION: This review supports the role of respiratory muscle training in improving inspiratory and expiratory muscle strength in patients with neuromuscular diseases. However, variability in study methodologies and patient populations limits the statistical significance of some respiratory parameters. Future studies should aim to standardize interventions and outcome measures to provide more conclusive evidence on the efficacy of RMT.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.047 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".