Effect of Yogic Asanas on Autonomic Functions Tests in Premenstrual Syndrome Medical Students
Bibliographic record
Abstract
Background: Premenstrual Syndrome (PMS) affects a significant number of menstruating women, including medical students who face unique stressors. The autonomic nervous system (ANS) is often disrupted in PMS, contributing to various symptoms. Yoga, particularly yogic asanas, may offer a non-pharmacological intervention to improve autonomic function and alleviate PMS symptoms. Objective: To evaluate the effect of yogic asanas on autonomic function tests and PMS symptoms in medical students. Methods: A randomized controlled trial was conducted with 60 medical students diagnosed with PMS, randomly assigned to an intervention group (yoga) or a control group. The yoga intervention included 60-minute sessions three times a week for 8 weeks. Autonomic function was assessed using heart rate variability (HRV) and blood pressure variability (BPV). PMS symptoms and quality of life were measured using the Premenstrual Symptoms Screening Tool (PSST) and WHOQOL-BREF scale. Results: The intervention group showed significant improvements in HRV (SDNN and RMSSD), reductions in BPV, and a notable decrease in PMS symptom severity compared to the control group. Quality of life scores also improved significantly in the intervention group. Conclusion: A structured yoga intervention effectively enhances autonomic function and reduces PMS symptoms in medical students. Integrating yoga into wellness programs could provide a valuable, non-pharmacological approach for managing PMS and improving overall quality of life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".