Gait Variability Is Associated With Frailty in Community-dwelling Older Adults
Bibliographic record
Abstract
BACKGROUND: The relationship between frailty and gait characteristics other than velocity has received little attention. Gait variability quantifies the automaticity of gait with greater variability usually indicating an irregular and unstable gait. High gait variability reflects the loss of gait regulation and predicts mobility decline and falls, which may reveal systemic vulnerability. Thus, we hypothesize that high gait variability may be associated with frailty phenotype. METHODS: Cross-sectional study including 100 community-dwelling women and men 75 years and older. Frailty was defined using validated phenotypic criteria and two additional frailty indexes that omit gait velocity criterion were used to verify associations between frailty and quantitative gait parameters. Gait was assessed under usual and fast pace using an electronic walkway. RESULTS: Frailty phenotype was identified in 20% of the participants and at least one component of frailty was present in 75%. Linear regression models were generated to explore the associations between frailty and gait variability. In the univariate regression model, frailty was associated with higher variability for all the gait parameters of interest. After adjustments, stride time variability under fast gait condition was the most prominent parameter consistently associated with frailty. This association remained significant in two additional frailty indexes that omit gait velocity criterion. CONCLUSION: Frailty is associated with low performance in several quantitative gait parameters beyond velocity of which the most prominent is high stride time variability. This finding may help to understand the high risk of falls and mobility decline in people with frailty.
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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) | 0.000 | 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) | 0.001 | 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".