Mild Cognitive Impairment Affects Obstacle Negotiation in Older Adults: Results from “Gait and Brain Study”
Bibliographic record
Abstract
BACKGROUND: Older adults with Mild Cognitive Impairment (MCI) are at higher risk of falls and injuries, but the underlying mechanism is poorly understood. Inappropriate anticipatory postural adjustments to overcome balance perturbations are affected by cognitive decline. However, it is unknown whether anticipatory gait control to avoid an obstacle is affected in MCI. OBJECTIVE: Using the dual-task paradigm, we aim to assess whether gait control is affected during obstacle negotiation challenges in older adults with MCI. METHODS: Seventy-nine participants (mean age = 72.0 ± 2.7 years; women = 30.3%) from the "Gait and Brain Study" were included in this study (controls = 27; MCI = 52). In order to assess the anticipatory control behaviour for obstacle negotiation, a 6-m electronic walkway embedded with sensors recorded foot prints to measure gait speed and step length variability, during early (3 steps before the late phase) and late (3 steps before the obstacle) pre-crossing phases of an ad hoc obstacle, set at 15% of participant's height. Participants walked under single- and dual-task gait (counting backwards by 1's from 100 while walking) conditions. Three-way mixed repeated-measures analysis of variance models examined differences in gait performance between groups when transitioning between pre-crossing phases towards an obstacle during single- and dual-task conditions. Analyses were adjusted for age, sex, years of education, lower limb function, fear of falling, medical status, depressive symptoms, baseline gait speed and executive function. RESULTS: A significant three-way interaction among groups, pre-crossing phases and task showed that participants with MCI attenuated the gait deceleration (p = 0.02) and performed fewer step length adjustments (p = 0.03) when approaching the obstacle compared with controls while dual-tasking. These interactions were attenuated when executive function performance was added as a covariate in the adjusted statistical model. CONCLUSION: Older adults with MCI attenuate the anticipatory gait adjustments needed to avoid an obstacle when dual-tasking. Deficits in higher-order cognitive processing may limit obstacle negotiation capabilities in MCI populations, being a potential falls risk factor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".