Plyometric exercise combined with high-intensity interval training improves metabolic abnormalities in young obese females more so than interval training alone
Bibliographic record
Abstract
The aim of this study was to compare the effects of 12 weeks of high-intensity interval training (HIIT) with the effects of 12 weeks of plyometric exercise combined with HIIT (P+HIIT) on anthropometric, biochemical, and physical fitness data in young obese females. Sixty-eight participants (age, 16.6 ± 1.3 y; body mass, 82.8 ± 5.0 kg; body fat, 39.4% ± 3.3%; body mass index z score, 2.9 ± 0.4) were assigned to 1 of 3 groups: HIIT (2 blocks per session of 6-8 bouts of 30-s runs at 100% velocity at peak oxygen uptake, with 30-s active recovery between bouts at 50%velocity at peak oxygen uptake (n = 23)); P+HIIT (2 blocks per session of 3 different 15-s plyometric exercises with 15-s passive recoveries, totaling 2 min for each plyometric exercise + the same HIIT program (n = 26)); or control (no exercise (n = 19)). Anthropometric (body mass, body mass index z score, body fat, lean body mass, and waist circumference), biochemical (plasma glucose, insulin, leptin and adiponectin concentrations, leptin/adiponectin ratio, and homeostasis model assessment of insulin resistance (HOMA-IR)), physical fitness (peak oxygen uptake, velocity at peak oxygen uptake, squat jump, and countermovement jump performances), and energy intake data were collected. Both training programs improved the anthropometric, biochemical, and physical fitness variables. However, the P+HIIT program induced greater improvements than did the HIIT program in lean body mass (+3.0% ± 1.7%), plasma glucose and leptin concentrations (-11.0% ± 4.7% and -23.8% ± 5.8%, respectively), plasma leptin/adiponectin ratio (-40.9% ± 10.9%), HOMA-IR (-37.3% ± 6.2%), and squat jump performance (22.2% ± 7.5%). Taken together, these findings suggest that adding plyometric exercises to a HIIT program may be more beneficial than HIIT alone in obese female adolescents.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".