Psychometric properties of volitional tests used to measure respiratory muscle strength and endurance: A systematic review
Bibliographic record
Abstract
Introduction: It is essential that diagnostic tests for evaluating respiratory muscles have proven reliability and validity. This study aims to synthesize studies that evaluated the psychometric properties of volitional tests used to measure respiratory muscle strength and endurance. Methods: A systematic literature search was conducted in MEDLINE/PubMed, LILACS, Cochrane Central Register of Controlled Trials, Scopus and SciELO. Primary studies that evaluated the reliability and validity of volitional tests to measure respiratory muscle strength and endurance were included. The quality of the included studies was assessed using the Critical Appraisal Tool (CAT). Results: Twenty-eight studies were included in this review, describing the psychometric properties of eight different approaches to measuring respiratory muscle strength and endurance. Respiratory muscle strength and endurance were assessed using static maximal inspiratory pressure, static maximal expiratory pressure, dynamic maximal inspiratory pressure, sustained maximal inspiratory pressure, nasal inspiratory pressure, manual respiratory muscle measurements, and maximal incremental inspiratory muscle performance. Overall, the studies included were of good methodological quality. Data related to validity and reliability showed excellent results for the maximum inspiratory pressure and maximum expiratory pressure, with maximum ICC values of 0.979 (CI 0.947-0.991) and 0.989 (CI 0.022-0.001), respectively. Other tests evaluated did not present high reliability and validity. Conclusion: This review concluded that volitional tests vary in reliability for measures of respiratory muscle strength and endurance. The more traditional ones, such as maximum inspiratory pressure and maximum expiratory pressure, presented higher validity and reliability values compared to the other tests.
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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 | 0.035 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".