Age differences in upper extremity joint moments and strength during a laboratory-based tether-release forward fall arrest in older women
Bibliographic record
Abstract
Age-related declines in upper extremity muscle strength may affect an older adult’s ability to land and control a simulated forward fall impact. The role of individual upper extremity joints during a forward fall impact has not been examined. The purpose was to evaluate the age differences in upper extremity joint moment contributions during a simulated forward fall and upper extremity muscle strength in older women. A convenience sample of 68 older women (70 (8) yrs) performed three trials of a simulated forward fall. Percentage joint moments of the upper extremity were recorded. Upper extremity muscle strength was collected via handgrip, hand-held dynamometry of the shoulder and elbow and a custom multi-joint concentric and eccentric strength isokinetic dynamometer protocol. Percentage joint moment contributions differed between women in their sixties and seventies with significantly greater relative shoulder joint involvement (P =.008), coupled with lower elbow joint contributions (P =.004) in comparison to 80 year olds. An increase in each year of age was associated with a 4% increase in elbow contribution (Beta = -0.421, r2 = 17.9, P = 0.0001) and a 3.7% decrease in shoulder contribution (Beta = 0.373, r2 = 14.6, P = 0.002). Older women exhibit different landing strategies as they age. Fall injury prevention research should consider interventions focused on these differences taking into account the contributions of upper extremity strength.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".