Does exercising during peri- or early post-menopause prevent bone and muscle loss: A systematic review
Bibliographic record
Abstract
INTRODUCTION: The highest rate of bone and muscle loss occurs during the menopause transition. Yet, most clinical exercise trials have excluded peri- and early post-menopausal female participants. This systematic review aimed to determine (1) the effects of exercise on bone and muscle health during the menopause transition; and (2) which types of exercise are most effective for preventing bone and muscle mass loss during the menopause transition. METHODS: Articles were retrieved from five electronic databases (MEDLINE, Embase, CENTRAL, CINAHL, and SPORTDiscus). Inclusion criteria included: (1) randomized controlled trial (RCT); (2) 45-to 60-year-old and peri- or early post-menopausal females; (3) reported bone mineral density (BMD) or lean mass. RESULTS: Six studies met inclusion criteria; two evaluated peri-menopausal and four investigated early post-menopausal female participants. All studies had low quality of evidence, and high risk of bias. Strength training, endurance training, and Tai Chi did not improve areal BMD (aBMD) or lean mass during peri-menopause. Strength training and walking benefited total body, hip, spine, femoral neck, and trochanter aBMD and lean mass during early post-menopause. When grouped by exercise type, strength training improved aBMD at all sites but not all strength training studies showed improvements in lean mass. Walking improved total hip aBMD only. CONCLUSION: Due to the limited number of studies and variety of interventions, it remains inconclusive which training method is optimal to prevent bone and muscle loss during the menopause transition. Future strength training RCTs should include longer duration interventions that compare effects between peri- and early post-menopausal female participants.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".