Inspiratory muscle training in patients with obesity: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Obesity is a chronic medical condition that affects, among others, the cardiovascular and respiratory systems. Interventions for its treatment focus on sustained weight reduction and general health improvement, leaving respiratory management aside. Our objective was to determine the effects of inspiratory muscle training (IMT) in patients with obesity. Methods A systematic review was performed in Embase, Cochrane Library (CENTRAL), CINAHL, Web of Science, and PubMed/MEDLINE on June 26, 2023. Randomized clinical trials (RCTs), and quasi-randomized clinical trials investigating the effects of IMT in people with obesity were included. Selected studies were screened by two independent reviewers who extracted data and assessed the quality of the evidence. Results The initial search returned 705 potential studies were included. Ultimately, eight studies met the criteria for eligibility and were included in the review. IMT improves physical capacity [6-minute walk test (6MWT): 44.5 m, 95% CI: 30.5 to 58.5; p < 0.0001] and the strength of the inspiratory muscles [maximal inspiratory pressure (MIP): −28.4 cm H2O, 95% CI: −41.9 to −14.8; p < 0.0001] compared to the controls, without differences in the pulmonary function, body mass index (BMI) and metabolic parameters. Conclusion Inspiratory muscle training improves physical capacity and inspiratory muscle strength without significant changes in lung function, BMI, and metabolic parameters. Systematic review registration: PROSPERO, identifier CRD42023439625, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023439625 .
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".