Effect of Cognitive Control and Dual-Task Training on Gait Stability and Fall Risk In Older Adults: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: With the proportion of the elderly population in Korea reaching 17.5% in 2022 and projected to increase to 20.6% by 2025, 30.1% by 2035, and 43% by 2050, the accelerated aging of the population is raising societal concerns about elderly care.Maintaining mobility is crucial for a healthy and independent old age.Design: Cross-sectional study Methods: This study investigates the effects of dual-task performance on gait variables and cognitive function in older adults.This cross-sectional study involved 60 older adults aged 65 and above, categorized into a dementia group (Korean version of Montreal Cognitive Assessment (MoCA-K) score ≤ 22) and a control group (MoCA-K score ≥ 23).Cognitive and gait functions were assessed using the GAITRite system (GAITRite system, CIR Systems Inc., USA), measuring gait variables (speed, stride length, etc.) before and after dual-task performance.The assessments were conducted under a single-blind condition, and data were analyzed using SPSS (ver.25.0, SPSS Inc., USA). Results:The dementia group scored lower on cognitive assessments compared to the control group (p＜0.05).In dual-task performance evaluations, the dementia group exhibited longer total task times and lower accuracy than the control group (p ＜0.001), while reaction times were longer but not statistically significant.GAITRite system analysis revealed that the dementia group had reduced gait speed and stride length compared to the control group (p＜0.05).However, the difference in gait time was not statistically significant.The study results indicate that older adults with dementia show significant differences in cognitive function and gait performance, with notable impacts under dual-task conditions.Conclusions: These findings underscore the effect of cognitive decline on gait and provide valuable insights for predicting gait and cognitive function deterioration in dementia, which can aid in developing fall prevention strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".