Effects of Resistance Exercises on Body Composition and Some Biochemical Parameters
Bibliographic record
Abstract
Exercise has many positive effects on the human organism. In this study, the effects of resistance exercise program, which is applied regularly for eight weeks, were investigated on body composition and some biochemical values. Twenty-four male volunteers participated in the study group. The participants were randomly divided into two equal groups: control and resistance exercise groups. While the participants in the control group attended only practice lessons in the faculty, the exercise group participated in the resistance exercise program 2 days a week for eight weeks in addition to the practice lessons. Body compositions of the participants were measured before and after the program and blood samples were taken. Thus, body weight, body fat percentage, mass body fat, Body mass index (BMI), AST, ALT, GGT, cholesterol, triglyceride, HDL, LDL and VLDL cholesterol levels of the participants were determined. The data obtained were analyzed using SPSS software. As a result of statistical analysis; there was difference only GGT value of the control group (p<0.05). In the exercise group, body weight, body fat percentage, mass body fat, BMI, AST, ALT, GGT values were found to be significant differences (p<0.05). In conclusion, it was determined that resistance exercise program applied for eight weeks had significant effects on body composition and liver enzymes. However, although there are some minor changes in blood lipids, these changes are not statistically significant. It can be said that resistance exercises can be beneficial on liver enzymes and body composition but eight weeks resistance training may not be enough to change the blood lipid profile.
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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".