Comparison of exercise training modalities and change in peak oxygen consumption in heart failure with preserved ejection fraction: a secondary analysis of the OptimEx-Clin trial
Bibliographic record
Abstract
AIMS: Exercise training (ET) is an effective therapy in heart failure with preserved ejection fraction (HFpEF), but the influence of different ET characteristics is unclear. We aimed to evaluate the associations between ET frequency, duration, intensity [% heart rate reserve (%HRR)] and estimated energy expenditure (EEE) with the change in peak oxygen consumption (V̇O2) over 3 months of moderate continuous training (MCT, 5×/week) or high-intensity interval training (HIIT, 3×/week) in HFpEF. METHODS AND RESULTS: ET duration and heart rate (HR) were recorded with a smartphone application. EEE was calculated using the HR data during ET and the individual HR-V̇O2 relationships during cardiopulmonary exercise testing. Differences between groups and associations between ET characteristics and peak V̇O2 change were assessed with linear regression analyses. Peak V̇O2 improved by 9.2 ± 13.2% after MCT and 8.7 ± 15.9% after HIIT (P = 0.67). The average EEE of 1 HIIT session was equivalent to ∼1.42 MCT sessions and when adjusted for EEE, the mean difference between MCT and HIIT was -0.1% (P = 0.98). For both MCT and HIIT, peak V̇O2 change was positively associated with ET frequency (MCT: R2 = 0.103; HIIT: R2 = 0.149) and duration/week (MCT: R2 = 0.120; HIIT: R2 = 0.125; all P < 0.05). Average %HRR was negatively associated with peak V̇O2 change in MCT (R2 = 0.101; P = 0.034), whereas no significant association was found in HIIT (P = 0.234). Multiple regression analyses explained ∼1/3 of the variance in peak V̇O2 change. CONCLUSION: In HFpEF, isocaloric HIIT and MCT seem to be equally effective over 3 months. Within each mode, increasing ET frequency or duration/week may be more effective to improve peak V̇O2 than increasing ET intensity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".