Effects of different exercise training program on post-exercise VO <sub>2</sub> kinetics and VO <sub>2</sub> recovery delay in stable patients with coronary heart disease
Bibliographic record
Abstract
Abstract Post-exercise V̇O 2 kinetics and V̇O 2 recovery delay (RD) are clinical prognostic markers in cardiac patients, but have not been studied after exercise training in patients with coronary heart disease (CHD). We aimed to compare the effects of 12-weeks moderate-intensity continuous exercise training (MICET), low volume high-intensity interval training (LV-HIIT), or combined MICET/HIIT on O 2 deficit, post-exercise V̇O 2 kinetics, O 2 debt and V̇O 2 recovery delay (RD) in patients with CHD. Methods Patients with CHD were randomised in MICET, LV-HIIT or combined MICET/HIIT group for 12 weeks. Cardiopulmonary exercise test (CPET) parameters were assessed, and key exercise variables were calculated during and after exercise. CPET post-exercise kinetics time constant (r) (for V̇O 2 , V̇CO 2 , V̇ E and HR), O 2 deficit, O 2 debt and V̇O 2 recovery delay (RD) were calculated before and after training. Results A significant time effect (training) for r V̇O 2 (min) (p<0.05) was shown for all groups. Shorter r V̇O 2 values with small effect size (ES: 0.21 to 0.4) were noted for the combined MICET/HIIT and MICET groups. A significant time effect (p<0.01) was noted for O 2 debt that was increased after training (ES: 0.1 to 0.47). No significant statistical effect was shown for V̇O 2 RD and r V̇CO 2 , r V̇ E, r HR and O 2 deficit in all groups. Conclusions In patients with CHD, exercise training improved post-exercise V̇O 2 kinetic and the O 2 debt, with a higher impact of exercise dose (combined MICET/HIIT). Exercise training did not improved the V̇O 2 RD or other τ CPET recovery variables in CHD patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".