Does Frequency Of Resistance Training Effect Proximal Femur Bone Mineral Density In Community-dwelling Senior Women?
Bibliographic record
Abstract
Longitudinal studies suggest proximal femur areal bone mineral density (aBMD) decreases with age; resistance training (RT) may offset this loss in older women. ACSM recommends older adults engage in RT 2-3 ×/week to maintain bone mass, but few studies investigated the effect of RT frequency on BMD. PURPOSE: To compare the effect of RT frequency (0, 1, or 2 ×/week) on total proximal femur aBMD in post-menopausal women. METHODS: We conducted an a priori secondary analysis of a 12 month randomized controlled trial whose primary outcome was cognitive function. We enrolled 155 community-dwelling women and randomized them to 1 of 3 group-based exercise interventions: (i) twice-weekly RT (RT2); once-weekly RT (RT1); or (iii) twice-weekly control group with balance and tone exercises (BT). Our primary outcome was aBMD (g/cm2) of the left total proximal femur by dual energy X-ray absorptiometry (DXA). We measured participants at baseline, midpoint, and 12 months. We assessed between-group differences for outcomes using ANCOVA. RESULTS: At baseline, 144 women were assessed by DXA; 111 women returned for DXA assessment at both follow-ups (n: BT= 33, RT1= 39, RT2= 39). At baseline, mean age was 69y for both RT1 and RT2 groups and 70y for BT; mean aBMD (g/cm2) for groups was: BT= 0.813± 0.105, RT1= 0.825± 0.125, and RT2= 0.809± 0.134; mean 6 minute walk test (meters) for BT= 532± 77, RT1= 527± 58, and RT2= 512± 103; median Functional Comorbidity Index score was 2 for all groups; and mean Physical Activity Scale for the Elderly (PASE) score for BT= 128± 52, RT1= 117± 60 and RT2= 130± 63. After controlling for baseline aBMD, there were no differences in aBMD or percent change in aBMD between groups at midpoint and final assessment. The greatest percent change in total proximal femur aBMD for all groups occurred between baseline and 6 months (BT= 1.64%, RT1= 1.74%, RT2= 1.33%). There were no significant between group differences in self-reported activity levels (PASE) outside of the interventions at either follow-up. CONCLUSION: Cross-sectional and cohort studies suggest that community-dwelling older women lose approximately 1% of their bone mass annually. Although there were no differences between groups in bone density at the proximal femur, it was maintained in all three groups; further study is needed to elucidate this effect.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".