Effect of Yoga Exercise on Premenstrual Symptoms among Female Employees in Taiwan
Bibliographic record
Abstract
Yoga classes designed for women with premenstrual syndrome are available, but their efficacy is unclear. We investigated the effects of 12 weeks' yoga exercise (yoga intervention) on premenstrual symptoms in menstruating females in Taiwan. Sixty-four subjects completed the yoga intervention, and before and after the intervention filled out a structured self-report questionnaire about their demographics, personal lifestyle, menstrual status, baseline menstrual pain scores, premenstrual symptoms, and health-related quality of life. Of 64 subjects, 90.6% reported experiencing menstrual pain during menstruation. After the yoga intervention, subjects reported decreased use of analgesics during menstruation (p = 0.0290) and decreased moderate or severe effects of menstrual pain on work (p = 0.0011). The yoga exercise intervention was associated with the improvement of the scale of physical function (p = 0.0340) and bodily pain (p = 0.0087) of the SF-36, and significantly decreased abdominal swelling (p = 0.0011), breast tenderness (p = 0.0348), abdominal cramps (p = 0.0016), and cold sweats (p = 0.0143). Menstrual pain mitigation after yoga exercise correlated with improvement in six scales of the SF-36 (physical function, bodily pain, general health perception, vitality/energy, social function, mental health). Employers can educate female employees about the benefits of regular exercise such as yoga, which may decrease premenstrual distress and improve female employee health.
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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".