Effect of exercise combined with phytoestrogens on quality of life in postmenopausal women
Bibliographic record
Abstract
BACKGROUND: Postmenopausal women seem to favor alternative therapies such as exercise and phytoestrogens as a substitute for potentially harmful hormone replacement therapy. Based on previous research, we hypothesized that phytoestrogens combined with exercise could have a synergic effect on women's health. OBJECTIVE: To verify whether phytoestrogens enhance the response to mixed training regarding menopausal symptoms and quality of life in postmenopausal women. METHODS: From a pool of women participating in a 6-month randomized, controlled exercise study, 21 received a placebo (mean age 58.3 ± 5.4 years, body mass index 29.8 ± 5.1 kg/m(2)) and 19 received phytoestrogen supplements (mean age 60.1 ± 3.4 years; body mass index 30.3 ± 4.6 kg/m(2)). Body weight, fat mass and lean body mass (dual-energy X-ray absorptiometry) were assessed. Quality of life was estimated by the Short Form-36 (SF-36) and Perceived Stress Scale-10 (PSS-10) questionnaires, and menopausal symptoms by the Kupperman index. All measurements were performed before and after the intervention. RESULTS: Although the Kupperman index and PSS-10 remained unchanged in both groups, the SF-36 Physical Component Summary and almost all the SF-36 subscales (except for role-emotional and mental health) increased only in the exercise group taking phytoestrogens (0.001 < p < 0.04). CONCLUSION: While phytoestrogens combined with mixed exercise were not sufficient to improve menopausal symptoms, it seemed to be a better strategy than exercise alone to improve the general quality of life in postmenopausal women.
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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 | 0.000 | 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".