Effect of Learning to Use a Mobility Aid on Gait and Cognitive Demands in People with Mild to Moderate Alzheimer’s Disease: Part II – 4-Wheeled Walker
Bibliographic record
Abstract
BACKGROUND: Cognitive deficits and gait problems are common and progressive in Alzheimer's disease (AD). Prescription of a 4-wheeled walker is a common intervention to improve stability and independence, yet can be associated with an increased falls risk. OBJECTIVES: 1) To examine changes in spatial-temporal gait parameters while using a 4-wheeled walker under different walking conditions, and 2) to determine the cognitive and gait task costs of walking with the aid in adults with AD and healthy older adults. METHODS: Twenty participants with AD (age 79.1±7.1 years) and 22 controls (age 68.5±10.7 years) walked using a 4-wheeled walker in a straight (6 m) and Figure of 8 path under three task conditions: single-task (no aid), dual-task (walking with aid), and multi-task (walking with aid while counting backwards by ones). RESULTS: Gait velocity was statistically slower in adults with AD than the controls across all conditions (all p values <0.025). Stride time variability was significantly different between groups for straight path single task (p = 0.045), straight path multi-task (p = 0.031), and Figure of 8 multi-task (0.036). Gait and cognitive task costs increased while multi-tasking, with performance decrement greater for people with AD. None of the people with AD self-prioritized gait over the cognitive task while walking in a straight path, yet 75% were able to shift prioritization to gait in the complex walking path. CONCLUSION: Learning to use a 4-wheeled walker is cognitively demanding and any additional tasks increases the demands, further adversely affecting gait. The increased cognitive demands result in a decrease in gait velocity that is greatest in adults with AD. Future research needs to investigate the effects of mobility aid training on gait performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".