Effects of Aerobic Fitness and Pulmonary Function on Mechanical Ventilatory Constraints During Exercise in Healthy Women
Bibliographic record
Abstract
Females with a higher maximal aerobic capacity (Vo2max) have higher ventilatory requirements during exercise relative to their untrained counterparts. The higher ventilatory loads in trained females might make them more susceptible to developing expiratory flow limitation (EFL) during exercise. However, the interrelationship between aerobic fitness, pulmonary function and EFL has not been well characterized in women. PURPOSE: To determine the effect of Vo2max and maximal expiratory flow-volume (MEFV) curve characteristics on EFL in healthy women. METHODS: A progressive stepwise cycle test to exhaustion was completed by 22 healthy females (age=28±5; Vo2max=49±8, range 31-62 ml·kg-1·min-1). A MEFV curve was generated by having subjects perform several expirations from total lung capacity to residual volume at different efforts before and after exercise. Tidal breaths at each exercise stage were superimposed within the MEFV curve by having subjects perform inspiratory capacity maneuvers. The magnitude of EFL was calculated as the percent of the tidal breath that met or exceeded the outer boundary of the MEFV curve. Subjects were partitioned into those with EFL (n=10) and those with no EFL (NEFL; n=12). RESULTS: There was no difference in Vo2max between the EFL group and the NEFL group (47±5 vs. 51±10 ml·kg-1·min-1, respectively, P >0.05). There was no relationship between the degree of EFL and Vo2max (r=0.18, P>0.05). Absolute values were different between the EFL and NEFL groups for: peak expiratory flow (PEF) (6.9±0.9 vs. 8.5±1.0 l·sec-1, P < 0.05), and for forced expiratory flows at 75% of vital capacity (FEF75%) (6.0±0.6 vs. 7.6±0.9 l·sec-1, P<0.05), 50% of vital capacity (FEF50%) (3.5±0.4 vs. 5.6±1.0 l·sec-1, P<0.05) and 25% of vital capacity (FEF25%) (1.8±0.2 vs. 3.0±0.9 l·sec-1, P<0.05). The forced vital capacity was significantly larger in the NEFL group compared to the EFL group (4.4±0.5 vs. 3.8±0.4 l, P<0.05). When expressed as a percentage of PEF, FEF75% was not different between groups (87±7 vs. 90±6%, P>0.05) while FEF50% (52± 8 vs. 66±6%, P<0.05) and FEF25% (26±6 vs. 35±7.39%, P<0.05) were different. CONCLUSION: The degree of EFL experienced by women appears to be more associated with lung size and the characteristics of their MEFV curve rather than aerobic fitness. Funding: BC Lung Association and NSERC
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".