Effects of Functional Strength Training Combined with Aerobic Training on Body Composition, Physical Fitness, and Movement Quality in Obese Adolescents
Bibliographic record
Abstract
This study aimed to compare the effects of 12 weeks of functional strength training combined with aerobic training (TG) and traditional resistance training combined with aerobic training (CG) on the body composition, physical fitness, and movement quality of obese adolescents. Forty participants were randomly assigned to either the TG group (n = 20) or the CG group (n = 20). Each group underwent training five times per week, lasting 120 min each time, over a total period of 12 weeks. All participants followed a strict dietary program. Anthropometric parameters, body composition, physical fitness, and movement quality were evaluated at baseline and after intervention. A two-way repeated measures ANOVA observed a significant interaction between time and group for body mass (p = 0.043), body fat percentage (p = 0.045), body mass index (p = 0.025), neck circumference (p = 0.01), chest circumference (p = 0.027), left-hand grip strength (p = 0.043), right-hand grip strength (p = 0.048), standing broad jump (p = 0.044), and total Functional Movement Screen score (p = 0.003), and the improvement was greater for TG in comparison to CG. TG was found to be more effective than CG in enhancing body composition, physical fitness, and movement quality in obese 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) | 0.001 | 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".