Effect of 12 Weeks of Hatha Balance Flow Yoga on Fall and Injury Risk in Postmenopausal Women
Bibliographic record
Abstract
Postmenopausal women are at an increased risk of falls and fractures. Upper-extremity fractures because of forward falls are common in women in their 50s and 60s, but little research has focused on the potential fall and injury risk factors within this age bracket and ways to mitigate the risks. Yoga is a promising intervention for older women to improve balance, muscle strength, and upper-body reactions for a safer fall landing. The purpose of the present study was to evaluate the effect of 12 weeks of a hatha Balance Flow Yoga class on forward fall and injury risk factors in postmenopausal women. Thirty-six women between the ages of 50 and 70 participated in an intervention study where they were tested at baseline (base), 12 weeks after a control period (pre), and again 12 weeks after participating in the group-based yoga intervention offered twice per week (post). Outcome measures included fall risk factors (balance, balance confidence, lower-body strength, and dual task) and forward fall injury risk factors (upper-body strength, range of motion, and response time). Repeated-measures multivariate analysis of variance found a significant time improvement in fall risk factors and forward fall injury risk factors (p = 0.004). Specifically, there was significant improvement in balance (one-leg stand), lower-body strength (30-second sit to stand), and upper-body response time after the intervention. Tailoring yoga classes for older women to focus on improving fall-related risk factors may help to provide effective options to improve their ability to prevent serious consequences of fall-related injury.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".