The Association between Age-Related Cognitive Changes and Obstacle Avoidance: Focusing on Memory-Guided Limb Movements
Bibliographic record
Abstract
Abstract An association between cognitive impairment and tripping over obstacles during locomotion in older adults has been suggested. However, owing to its memory-guided movement, whether this is more pronounced in the trailing limb is poorly known. We examined the age-related changes in stepping-over, focusing on trailing limb movements, and their association with cognitive performance. Age-related change in obstacle avoidance was examined by comparing the foot kinematics of 105 older and 103 younger adults when stepping over an obstacle. The difference in clearance between the leading limb and trailing limb (Δ clearance) was calculated to determine the degree of decrement in the clearance of the trailing limb. A cognitive test battery was used to evaluate cognitive function among older adults for assessing their association with Δ clearance. Older adults showed a significantly lower clearance of the trailing limb than younger adults, resulting in a greater Δ clearance. The significant correlations between greater Δ clearance and scores of Montreal Cognitive Assessment and delayed recall of the Wechsler Memory Scale-Revised Logical Memory. Our results suggest that memory functions may contribute to the control of trailing limb movements, which can secure a safety margin to avoid stumbling on an obstacle, during obstacle avoidance locomotion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".