Can walking exercise programs improve health for women in menopause transition and postmenopausal? Findings from a scoping review
Bibliographic record
Abstract
OBJECTIVE: Our goal was to explore the range and characteristics of published papers on therapeutic walking programs for menopausal women and to identify program features that resulted in successful outcomes including reduced symptoms and improved long-term wellness. METHODS: We searched biomedical and exercise-related databases for articles published up to June 1, 2017, using keywords related to menopause and walking. Data were collected into EndNote X8 reference manager to identify and remove duplicates. The final selection included all articles that studied walking as a health intervention for women in menopause transition or postmenopausal. RESULTS: A total of 3,244 papers were collected from the six databases. After removing duplicates and applying inclusion and exclusion criteria, 96 articles were charted, including 77 different walking programs. Walking interventions ranged from 4 weeks to 3 years with an average weekly frequency of 3.8 ± 1.8 and were applied to a variety of symptoms and their biological markers and risk factors. Overall, 91% of the programs showed a beneficial outcome in at least one menopause-related medical issue. Information on menopause-specific symptoms, especially vasomotor symptoms and sleep problems, was scarce. CONCLUSION: The scoping review highlights the growing interest in walking programs as therapies for menopause and related symptoms and provides evidence of their possible benefit as a wellness option for women in menopause and beyond. Further research would be recommended to establish the therapeutic value of walking programs for women with specific focus on typical menopause symptoms at different stages of menopause. : Video Summary:http://links.lww.com/MENO/A587.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".