High-intensity interval training in overweight and obese children and adolescents: systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: While High Intensity Interval Training is praised in many populations for its beneficial effects on body composition and cardiometabolic health, its use among obese youth remain uncertain. This study aimed at determining whether HIIT is effective to improve aerobic fitness and reduce cardiometabolic risk factors in overweight and obese youth. EVIDENCE ACQUISITION: A systematic search was conducted and articles reporting studies that investigated the effects of HIIT in 6 to 18-year-old youth were eligible. Meta-analyses were performed when appropriate. EVIDENCE SYNTHESIS: Fifteen studies were included for the systematic review and meta-analyses. HIIT significantly improves maximal oxygen uptake (1.117 [95% CI: 0.528 to 1.706], P<0.001), and reduces body mass (-0.295 [95% CI: -0.525 to -0.066], P<0.05), body fat (-0.786 [95% CI: -1.452 to -0.120], P<0.05), systolic and diastolic blood pressure (-1.026 [95% CI: -1.370 to -0.683], P<0.001; -0.966 [95% CI: -1.628 to -0.304], P<0.01 respectively), and the HOMA-IR (-1.589 [95% CI: -2.528 to -0.650], P<0.01). However, there is significant heterogeneity, and low to high inconsistency for most cardiometabolic risk factors and aerobic fitness. CONCLUSIONS: Although few studies have reported cardiometabolic risks, HIIT may also be as effective as traditional endurance continuous training to decrease blood pressure and insulin resistance. HIIT is effective to improve aerobic fitness, body composition, and cardiometabolic risk factors in obese youth, but data are insufficient to determine whether it is more effective than traditional continuous submaximal intensity exercise training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 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) | 0.000 | 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".