Endurance training intensity has greater effects than volume on heart rate variability and arterial stiffness adaptations in sedentary adult men: A Randomized Controlled Trial.
Bibliographic record
Abstract
Abstract Background: The current study investigated the chronic effects of high-volume moderate-intensity training and low-volume high-intensity training on heart rate variability (HRV) and arterial stiffness in sedentary adult men. Materials and methods: Forty-five males (age: 42± 5.7 yrs.) were randomly assigned into control group (n=15), high-volume moderate-intensity training (HVMIT) (n=15), and low-volume high-intensity training (LVHIT) (n=15). The HVMIT group ran three times per week, on the treadmill at 50% to 60% of VO2max for 45 to 60 minutes, while the LVHIT trained at 70% to 85% of VO2max for 25 to 40 minutes. Both training programs were equated by caloric expenditure. HRV, Pulse Wave Velocity (PWV), hemodynamic variables, and body composition were measured before and after 12 weeks.Results: Both protocols (HVMIT and LVHIT) significantly increased the Standard deviation of NN intervals (SDNN) and High-frequency (HF) bands after 12 weeks (p ˂ 0.05). The LF/HF ratio decreased significantly in both training groups (p ˂ 0.05). However, these changes were significantly greater in the LVHIT protocol (p ˂ 0.05). Furthermore, the Root mean square of successive RR interval differences (RMSSD) significantly increased only in the LVHIT protocol (P ˂ 0.05). Moreover, a significant decrease in low-frequency (LF) and PWV was only observed following the LVHIT protocol (P ˂ 0.05). Conclusion: This study indicates that the LVHIT protocol is more effective and efficient for improving HRV variables and PWV than the HVMIT protocol.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".