Low- and High-Volume of Intensive Endurance Training Significantly Improves Maximal Oxygen Uptake after 10-Weeks of Training in Healthy Men
Bibliographic record
Abstract
UNLABELLED: Regular exercise training improves maximal oxygen uptake (VO2max), but the optimal intensity and volume necessary to obtain maximal benefit remains to be defined. A growing body of evidence suggests that exercise training with low-volume but high-intensity may be a time-efficient means to achieve health benefits. In the present study, we measured changes in VO2max and traditional cardiovascular risk factors after a 10 wk. training protocol that involved three weekly high-intensity interval sessions. One group followed a protocol which consisted of 4×4 min at 90% of maximal heart rate (HRmax) interspersed with 3 min active recovery at 70% HRmax (4-AIT), the other group performed a single bout protocol that consisted of 1×4 min at 90% HRmax (1-AIT). Twenty-six inactive but otherwise healthy overweight men (BMI: 25-30, age: 35-45 y) were randomized to either 1-AIT (n = 11) or 4-AIT (n = 13). After training, VO2max increased by 10% (∼5.0 mL⋅kg(-1)⋅min(-1)) and 13% (∼6.5 mL⋅kg(-1)⋅min(-1)) after 1-AIT and 4-AIT, respectively (group difference, p = 0.08). Oxygen cost during running at a sub-maximal workload was reduced by 14% and 13% after 1-AIT and 4-AIT, respectively. Systolic blood pressure decreased by 7.1 and 2.6 mmHg after 1-AIT and 4-AIT respectively, while diastolic pressure decreased by 7.7 and 6.1 mmHg (group difference, p = 0.84). Both groups had a similar ∼5% decrease in fasting glucose. Body fat, total cholesterol, LDL-cholesterol, and ox-LDL cholesterol only were significantly reduced after 4-AIT. Our data suggest that a single bout of AIT performed three times per week may be a time-efficient strategy to improve VO2max and reduce blood pressure and fasting glucose in previously inactive but otherwise healthy middle-aged individuals. The 1-AIT type of exercise training may be readily implemented as part of activities of daily living and could easily be translated into programs designed to improve public health. TRIAL REGISTRATION: ClinicalTrials.govNCT00839579.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".