The effect of low-volume high-intensity interval training on cardiometabolic health and psychological responses in overweight/obese middle-aged men
Bibliographic record
Abstract
High-intensity interval training (HIIT) has been proposed as a time-efficient exercise protocol to improve metabolic health, but direct comparisons with higher-volume moderate-intensity continuous training (MICT) under unsupervised settings are limited. This study compared low-volume HIIT and higher-volume MICT interventions on cardiometabolic and psychological responses in overweight/obese middle-aged men. Twenty-four participants (age: 48.1±5.2yr; BMI: 25.8±2.3kg·m−2) were randomly assigned to undertake either HIIT (10 X 1-min bouts of running at 80–90% HRmax separated by 1-min active recovery) or MICT (50-min continuous jogging/brisk walking at 65–70% HRmax) for 3 sessions/week for 8 weeks (2-week supervised + 6-week unsupervised training). Both groups showed similar cardiovascular fitness (VO2max) improvement (HIIT: 32.5±5.6 to 36.0±6.2; MICT: 34.3±6.0 to 38.2±5.1mL kg−1 min−1, p < 0.05) and %fat loss (HIIT: 24.5±3.4 to 23.2±3.5%; MICT: 23.0±4.3 to 21.5±4.1%, p< 0.05) over the 8-week intervention. Compared to baseline, MICT significantly decreased weight and waist circumference. No significant group differences were observed for blood pressure and cardiometabolic blood markers such as lipid profiles, fasting glucose and glycated haemoglobin. Both groups showed similar enjoyment levels and high unsupervised adherence rates (>90%). Our findings suggest that low-volume HIIT can elicit a similar improvement of cardiovascular fitness as traditional higher-volume MICT in overweight/obese middle-aged men.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".