High-intensity interval training vs. moderate-intensity continuous exercise training in heart failure with preserved ejection fraction: a pilot study
Bibliographic record
Abstract
Heart failure with preserved ejection fraction (HFpEF) is a major cause of morbidity and mortality. Exercise training is an established adjuvant therapy in heart failure; however, the effects of high-intensity interval training (HIIT) in HFpEF are unknown. We compared the effects of HIIT vs. moderate-intensity aerobic continuous training (MI-ACT) on peak oxygen uptake (V̇o₂peak), left ventricular diastolic dysfunction, and endothelial function in patients with HFpEF. Nineteen patients with HFpEF (age 70 ± 8.3 yr) were randomized to either HIIT (4 × 4 min at 85-90% peak heart rate, with 3 min active recovery) or MI-ACT (30 min at 70% peak heart rate). Fifteen patients completed exercise training (HIIT: n = 9; MI-ACT: n = 6). Patients trained 3 days/wk for 4 wk. Before and after training patients underwent a treadmill test for V̇o₂peak determination, 2D-echocardiography for assessment of left ventricular diastolic dysfunction, and brachial artery flow-mediated dilation (FMD) for assessment of endothelial function. HIIT improved V̇o₂peak (pre = 19.2 ± 5.2 ml·kg(-1)·min(-1); post = 21.0 ± 5.2 ml·kg(-1)·min(-1); P = 0.04) and left ventricular diastolic dysfunction grade (pre = 2.1 ± 0.3; post = 1.3 ± 0.7; P = 0.02), but FMD was unchanged (pre = 6.9 ± 3.7%; post = 7.0 ± 4.2%). No changes were observed following MI-ACT. A trend for reduced left atrial volume index was observed following HIIT compared with MI-ACT (-3.3 ± 6.6 vs. +5.8 ± 10.7 ml/m(2); P = 0.06). In HFpEF patients 4 wk of HIIT significantly improved V̇o₂peak and left ventricular diastolic dysfunction. HIIT may provide a more robust stimulus than MI-ACT for early exercise training adaptations in HFpEF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".