Low-Volume, High-Intensity Interval Training in Patients with CAD
Bibliographic record
Abstract
PURPOSE: Isocaloric interval exercise training programs have been shown to elicit improvements in numerous physiological indices in patients with CAD. Low-volume high-intensity interval exercise training (HIT) is effective in healthy populations; however, its effectiveness in cardiac rehabilitation has not been established. This study compared the effects of 12-wk of HIT and higher-volume moderate-intensity endurance exercise (END) on brachial artery flow-mediated dilation (FMD) and cardiorespiratory fitness (VO2 peak) in patients with CAD. METHODS: Twenty-two patients with documented CAD were randomized into HIT (n = 11) or END (n = 11) based on pretraining FMD. Both groups attended two supervised sessions per week for 12 wk. END performed 30-50 min of continuous cycling at 58% peak power output (PPO), whereas HIT performed ten 1-min intervals at 89% PPO separated by 1-min intervals at 10% PPO per session. RESULTS: Relative FMD was increased posttraining (END, 4.4% ± 2.6% vs 5.9% ± 3.6%; HIT, 4.6% ± 3.6% vs 6.1% ± 3.4%, P ≤ 0.001 pre- vs posttraining) with no differences between groups. A training effect was also observed for relative VO2 peak (END, 18.7 ± 5.7 vs 22.3 ± 6.1 mL · kg(-1) · min(-1); HIT, 19.8 ± 3.7 vs 24.5 ± 4.5 mL · kg(-1) · min(-1), P < 0.001 for pre- vs posttraining), with no group differences. CONCLUSIONS: Low-volume HIT provides an alternative to the current, more time-intensive prescription for cardiac rehabilitation. HIT elicited similar improvements in fitness and FMD as END, despite differences in exercise duration and intensity.
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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 | 0.000 |
| 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".