Interval Training with Different Intensities in Overweight/Obese Adolescent Females
Bibliographic record
Abstract
To compare the effects of 12 weeks of high-intensity interval training (HIIT) versus moderate-intensity interval training (MIIT) on hematological and inflammatory markers in youth females, 38 overweight/obese females (16.4±1.0 yrs.) were randomly assigned to HIIT (2×6-8 repetitions of 30 s at 100-110% maximal aerobic speed (MAS), with 30 s active recovery between repetitions at 50% MAS; n=13), MIIT (2×6-8 repetitions of 30 s at 70-80% MAS, with 30 s active recovery between repetitions at 50% MAS; n=13), and a control group (CG, no intervention; n=12). Body composition, blood pressure, maximal heart rate, hematological and inflammatory markers (C-reactive protein [CRP], and erythrocyte sedimentation rate [ESR]) markers were assessed in all groups before and following the training program. Results revealed a significant (P<0.05) interaction effect for body composition, systolic blood pressure (SBP), MAS, maximal heart rate, and CRP. Within-group analyses for the HIIT and MIIT groups showed significant improvements in body mass (P=0.009 and P=0.025, respectively), BMI Z-score (P=0.011 and P=0.028, respectively), and MAS (P<0.001 and P=0.011, respectively). The HIIT program showed a significant decrease in body fat (P=0.002), waist circumference (P=0.002), maximal heart rate (P=0.003), SBP (P=0.001), and plasma CRP (P=0.004). In both groups, no significant changes were observed in ESR and hematological markers after intervention. No variable changed in CG. HIIT was the effective method to manage cardiometabolic health and inflammatory disorders in obese youth.
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 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) | 0.000 | 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) | 0.001 | 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".