Leg Strength or Velocity of Movement
Bibliographic record
Abstract
OBJECTIVE: To determine which component of leg power (maximal limb strength or limb velocity) is more influential on balance performance in mobility limited elders. DESIGN: In this cross-sectional analysis we evaluated 138 community-dwelling older adults with mobility limitation. Balance was measured using the Unipedal Stance Test, the Berg Balance Test (BERG), the Dynamic Gait Index, and the performance-oriented mobility assessment. We measured one repetition maximum strength and power at 40% one repetition maximum strength, from which velocity was calculated. The associations between maximal estimated leg strength and velocity with balance performance were examined using separate multivariate logistic regression models. RESULTS: Strength was found to be associated [odds ratio of 1.06 (95% confidence interval, 1.01-1.11)] with performance on the Unipedal Stance Test, whereas velocity showed no statistically significant association. In contrast, velocity was consistently associated with performance on all composite measures of balance (BERG 14.23 [1.84-109.72], performance-oriented mobility assessment 33.92 [3.69-312.03], and Dynamic Gait Index 35.80 [4.77-268.71]). Strength was only associated with the BERG 1.08 (1.01-1.14). CONCLUSIONS: Higher leg press velocity is associated with better performance on the BERG, performance-oriented mobility assessment, and Dynamic Gait Index, whereas greater leg strength is associated with better performance on the Unipedal Stance Test and the BERG. These findings are likely related to the intrinsic qualities of each test and emphasize the relevance of limb velocity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".