Obstacle Negotiation, Gait Variability, and Risk of Falling: Results From the “Gait and Brain Study”
Bibliographic record
Abstract
BACKGROUND: Gait variability is an early fall predictor. However, it is unknown how gait variability of older adults at high risk of falls is affected by an obstacle negotiation task. We aimed to compare gait performance between older adults with significant history of falls (i.e. fallers) and nonfallers while approaching an obstacle crossing. METHODS: A total of 137 older adults without dementia were enrolled (72.7 ± 5.1 years of age; 60.5% women) from the "Gait and Brain Study." Fallers were defined as having at least one injurious fall or at least two noninjurious falls in the previous 12 months. Participants performed gait assessments under unobstructed and obstructed conditions. During the obstructed condition, participants walked and stepped over an ad hoc obstacle set at 15% of participants' height, transversally placed on a 6-meter electronic walkway. Gait speed and step-to-step variabilities were quantified from the last six steps prior to obstacle crossing. Analysis of variance models adjusted for age, sex, fear of falling, comorbidities, and unobstructed gait were used to compare gait performance of fallers and nonfallers during an obstacle approaching. RESULTS: In the study, 27 older adults were identified as fallers and 110 as nonfallers. Fallers had higher step time variability and step length variability when approaching an obstacle compared with nonfallers, although groups had comparable gait performance during unobstructed walking. CONCLUSION: Gait variability of older individuals at high risk of falling is more disturbed, compared with low-risk individuals, while approaching an obstacle crossing. High gait variability prior to crossing an obstacle may be a risk factor for falls.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".