Is low-volume high-intensity interval training a time-efficient strategy to improve cardiometabolic health and body composition? A meta-analysis
Bibliographic record
Abstract
The present meta-analysis aimed to assess the effects of low-volume high-intensity interval training (LV-HIIT; i.e., ≤5 min high-intensity exercise within a ≤15 min session) on cardiometabolic health and body composition. A systematic search was performed in accordance with PRISMA guidelines to assess the effect of LV-HIIT on cardiometabolic health and body composition. Twenty-one studies (moderate to high quality) with a total of 849 participants were included in this meta-analysis. LV-HIIT increased cardiorespiratory fitness (CRF, SMD = 1.19 [0.87, 1.50]) while lowering systolic blood pressure (SMD = −1.44 [−1.68, −1.20]), diastolic blood pressure (SMD = −1.51 [−1.75, −1.27]), mean arterial pressure (SMD = −1.55 [−1.80, −1.30]), MetS z-score (SMD = −0.76 [−1.02, −0.49]), fat mass (kg) (SMD = −0.22 [−0.44, 0.00]), fat mass (%) (SMD = −0.22 [−0.41, −0.02]), and waist circumference (SMD = −0.53 [−0.75, −0.31]) compared to untrained control (CONTROL). Despite a total time-commitment of LV-HIIT of only 14%–47% and 45%–94% compared to moderate-intensity continuous training and HV-HIIT, respectively, there were no statistically significant differences observed for any outcomes in comparisons between LV-HIIT and moderate-intensity continuous training (MICT) or high-volume HIIT. Significant inverse dose–responses were observed between the change in CRF with LV-HIIT and sprint repetitions ( β = −0.52 [−0.76, −0.28]), high-intensity duration ( β = −0.21 [−0.39, −0.02]), and total duration ( β = −0.19 [−0.36, −0.02]), while higher intensity significantly improved CRF gains. LV-HIIT can improve cardiometabolic health and body composition and represent a time-efficient alternative to MICT and HV-HIIT. Performing LV-HIIT at a higher intensity drives higher CRF gains. More repetitions, longer time at high intensity, and total session duration did not augment gains in CRF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".