Impact of resistance training and melatonin consumption on sex hormone levels, PGF2α, FSH, LH, pain intensity, and sleep quality in girls with primary dysmenorrhea
Bibliographic record
Abstract
Background and Aim: Primary dysmenorrhea significantly affects the daily lives and performance of girls. This study aimed to investigate the effects of resistance training and melatonin consumption on sex hormone levels, PGF2α, FSH, LH, pain intensity, and sleep quality in girls with primary dysmenorrhea. Methods: This semi-experimental study included 60 girls with moderate primary dysmenorrhea randomly assigned to four groups: exercise + melatonin, exercise + placebo, melatonin, and control (15 girls in each group). Participants in the exercise + melatonin group received 10 mg of melatonin daily, while the placebo group received an equivalent amount of carbohydrates. The resistance training program involved 50-60 minutes of weight training daily, three days a week for eight weeks. Pain intensity was evaluated using the McGill questionnaire, and sleep quality was assessed using the Pittsburgh questionnaire. Serum levels of sex hormones, PGF2α, FSH, and LH were also measured. Results: The exercise + melatonin group exhibited a significant increase in estrogen (P<0.001), progesterone (P<0.001), LH (P<0.001), and FSH (P<0.001) levels compared to all other groups. Additionally, both the exercise + placebo group and the melatonin group showed higher levels of these hormones compared to the placebo group. The melatonin group demonstrated reductions in PGF2α (P<0.001), pain intensity (P<0.001), and sleep quality (P<0.001) compared to all other groups. Similar improvements were observed in the exercise + placebo group and the exercise + melatonin group compared to the placebo group. Conclusion: The findings reveal that hypothalamic and corticotropin hormone release may play a role in primary dysmenorrhea. Melatonin appears to modulate sex hormone fluctuations associated with primary dysmenorrhea, leading to improved sleep quality and reduced pain intensity by decreasing PGF2α levels.
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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) | 0.001 | 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) | 0.002 | 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".