The type of training program affects appetite-regulating hormones and body weight in overweight sedentary men
Bibliographic record
Abstract
) and glucagon-like peptide-1 (GLP-1), as well as weight and body mass index (BMI) changes. Forty-four overweight men were randomly assigned into 4 groups of 11 individuals, which included (i) endurance group (3 sets of 10 min with 80%-90% of maximum heart rate), (ii) resistance group (4 sets of 8 repetitions with 80% of 1-repetition maximum), (iii) concurrent group (combination of programs of endurance and resistance groups in an alternate manner), and (iv) control group. Training protocols were conducted for 12 weeks for 3 sessions per week. Results showed that all 3 types of training programs resulted in weight loss (p = 0.000, p = 0.000, and p = 0.036 for resistance, concurrent, and endurance groups, respectively), BMI reduction (p = 0.000, p = 0.000, and p = 0.034), decreased serum acylated ghrelin (p = 0.000, p = 0.000, and p = 0.004), and increased PYY hormone levels (p = 0.028, p = 0.035, and p = 0.036). However, the effect of resistance training on these changes was more pronounced. Moreover, none of the exercise programs had any effect on serum levels of GLP-1. In addition, there was a significant positive correlation between weight (p = 0.003) and BMI (p = 0.009) changes with ghrelin while a negative correlation was observed between weight (p = 0.003) and BMI (p = 0.03) changes with PYY. The findings suggest that regular exercise training, in particular resistance training, is likely to reduce body weight and improve body composition of overweight inactive people by suppressing orexigenic hormones and stimulating the anorexigenic hormones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".