The role of lifestyle medicine in menopausal health: a review of non-pharmacologic interventions
Bibliographic record
Abstract
OBJECTIVE: Menopause, typically occurring between ages 45 and 55 years, is a natural life stage marked by hormonal changes that can affect the symptom burden, quality of life and chronic disease risk. While not a disease, the transition often requires individualized, holistic care. Lifestyle medicine - encompassing healthy eating, physical activity, mental well-being, avoidance of risky substances, restorative sleep and healthy relationships - offers a promising non-pharmacological strategy to optimize health during this period. METHOD: A systematic literature search was conducted in PubMed, Embase, Scopus and Web of Science (January 2000-December 2024) using the following keywords and combinations: 'menopause', 'lifestyle medicine', 'healthy eating', 'physical activity', 'mental wellbeing', 'avoidance of risky substances', 'restorative sleep', 'healthy relationships', 'weight management', 'chronic disease prevention', 'health equity and access' and 'general health frameworks'. Peer-reviewed human studies in perimenopausal, menopausal or postmenopausal women evaluating one or more lifestyle medicine pillars were included. Data were extracted on study design, population, interventions, outcomes and main findings. RESULTS: Lifestyle medicine interventions were associated with reductions in vasomotor symptoms, improved sleep quality, enhanced mental well-being, healthier weight regulation, and reduced cardiometabolic and osteoporosis risk. Multidisciplinary, person-centered approaches improved adherence and patient-reported outcomes. Strategies were cost-effective, adaptable and beneficial for long-term disease prevention across diverse populations. CONCLUSION: Lifestyle medicine offers a foundational, evidence-based framework for equitable menopause care. Integrating these strategies into clinical guidelines and public health policy can improve quality of care, empower women to manage their health and reduce disparities in access. Collaborative action among healthcare providers, policymakers and communities is essential to maximize impact.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".