The effect of concurrent endurance-resistance training on serum testosterone levels, body composition, muscular strength and international index of erectile function in older men
Bibliographic record
Abstract
Abstract Introduction The aim of this study was to evaluate the effect of 12 week concurrent endurance-resistance training on serum testosterone levels and sexual function indices in men over 50 years of age. Material and methods In this quasi-experimental study, the statistical sample consisted of 29 men with average weight of 81.1 ± 6.7 kg and body mass index of 26.4 ± 1.4 kg/m 2 , randomly divided into two control group (n = 12) and training group (n = 17). The training group performed concurrent training (endurance-resistance) for 12 weeks. Serum testosterone levels, cardiopulmonary endurance (VO 2 max), muscle strength and body composition were measured before and after training. Data were analyzed using covariance analysis (ANCOVA) (p < 0.05). Results Concurrent training in the training group significantly increased serum testosterone levels compared to the control group (p = 0.001). Concurrent training also increased sexual function in the areas of orgasmic performance (p = 0.010) and total score (p = 0.004) in the concurrent training group compared to the control group. Training significantly decreased fat mass (p = 0.046) and the ratio of waist to hip circumference (p = 0.024) also significantly increased VO 2 max (p = 0.001), mean relative muscle strength (p = 0.001) and lean body mass (p = 0.001) in the training group compared to the control group. Conclusions In general, based on our findings, it seems that training along with increasing serum testosterone levels increases sexual function in the areas of orgasm function and the total score in men over 50 years.
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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 | 0.001 |
| 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".