Improvement in exercise capacity in obese obstructive sleep apnea (OSA): Respective impact of ventilatory support during exercise and respiratory muscle training in a randomized controlled trial
Bibliographic record
Abstract
Background : Obesity and OSA are interconnected conditions both leading to reduced exercise tolerance. Nocturnal continue positive airway pressure (CPAP) treatment alone fails to alter physical activity. We investigated the respective effect of three modalities of exercise training programs on exercise tolerance in obese OSA. Methods : 38 Obese CPAP-treated OSA (age= 53±3 years; BMI= 38±3 kg/m²) were randomly assigned to a 3-month exercise training program (Ergocycle -ERGO- vs. Ergocycle supported by non-invasive ventilation -ERGONIV- vs. Ergocyle + respiratory muscle training -ERGOSPIRO) after a 6-week control period. Exercise tolerance was assessed by 6-minute walking distance (6MWD) and maximal incremental exercise test on cycloergometer. Results : 6MWD increased after training as compared with control and baseline in patients grouped as a whole (585 ± 92 vs. 562 ± 91 and 538 ± 102 m, respectively, p = 0.0004). Peak oxygen consumption improved after training with higher improvement in ERGONIV (+0.3±0.7 L/min), as compared to ERGO (+0.1±0.6 L/min) and ERGOSPIRO (+0.2±0.9 L/min) respectively (p = 0.004). Maximal ventilation at end of incremental test significantly increased in ERGOSPIRO (+17±34 l/min) as compared with ERGO (-2±19 L/min) and ERGONIV (-2±26 L/min) (p = 0.01). Conclusion : Exercise training improved functional and exercise capacity in obese OSA patients treated by CPAP. Improvement in maximal aerobic capacity was higher when ventilatory assistance or respiratory muscle training ergocycle was associated to ergocycle although walking distance was similarly improved between groups.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".