The Impact of Ashtanga Yoga Training on Melatonin Hormone in Fibromyalgia Patients
Bibliographic record
Abstract
The Ashtanga Yoga program was applied for a period of (3) months, totaling (12) weeks and (24) training units. Measurements for the psychosomatic disorders scale (under investigation) were conducted after completing the application of the proposed program using Ashtanga Yoga exercises. The proposed Ashtanga Yoga training program led to an improvement in melatonin hormone levels, pain intensity, and the number of sleep hours in patients with fibromyalgia, in favor of the post-test measurements. Upon comparing and discussing the measurements, the following results were reached: • The proposed Ashtanga Yoga training program led to an improvement in pain intensity, with an improvement rate of (-35.50%) for the research sample. • The proposed Ashtanga Yoga training program led to a reduction in the number of body pain points, with an improvement rate of (-36.20%) for the research sample. • The proposed Ashtanga Yoga training program led to an improvement and increase in the number of sleep hours, with an improvement rate of (32.21%) for the research sample. Research Recommendations: In light of the obtained results, we recommend the following: • Utilizing the Ashtanga Yoga training program designed by the researcher on different variables and samples in the sports field and various other fields and activities. • Referring to the proposed program involving the use of Ashtanga Yoga exercises due to their positive impact on improving and increasing melatonin concentration levels, reducing pain intensity, reducing the number of pain points, and increasing sleep hours for women with fibromyalgia in hospitals, medical centers, and private clinics. • The necessity of practicing regulated physical activities in general to mitigate the symptoms of fibromyalgia.
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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) | 0.002 | 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".