Aerobic training, resistance training, or their combination as a means to fight against excess weight and metabolic syndrome in obese students — which is the most effective modality? A randomized controlled trial
Bibliographic record
Abstract
This study aimed to determine the effects of either aerobic training (AT) or resistance training (RT) or both (A+RT) on obesity and its comorbidities in young adults. A total of 61 participants, aged 21.74 ± 1.42 years and with a body mass (BM) index (BMI) of 36.21 ± 2.43 kg/m2, were randomized for 12 weeks into control (CONT, n = 15), AT (n = 15), RT (n = 16), and A+RT (n = 15) groups. BM, body composition, and cardiovascular disease risk factors were assessed before and after intervention. BM did not change in the CONT and RT groups but decreased significantly by 7.5 kg in the AT (p ≤ 0.05) and 8.82 kg in the A+RT (p ≤ 0.05) groups, respectively. Significant reductions were also noted in waist circumference, BMI, and body fat percentage in the exercising groups. The most significant variations were in the A+RT group. High-density lipoprotein cholesterol (HDL-C) concentrations were increased after A+RT by 2.39 mg/dL. Significant reductions were also noted in very-low-density lipoprotein cholesterol (VLDL-C) concentrations (−2.84 mg/dL) in the A+RT group. AT alone is effective in improving BM and body composition, while RT alone improves the body composition and A+RT ensures better outcomes concerning BM, body composition, HDL-C, and VLDL-C. Novelty: Aerobic training alone is effective in improving BM and body composition. Resistance training alone improves the body composition. The combination of aerobic and resistance exercises ensures better outcomes for BM, body composition, HDL-C, and VLDL-C.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".