Relationships between Cognitive Functioning and Powered Mobility Device Use: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Powered mobility devices (PMD) promote independence, social participation, and quality of life for individuals with mobility limitations. However, some individuals would benefit from PMD, but may be precluded access. This is particularly true for those with cognitive impairments who may be perceived as unsafe and unable to use a PMD. This study explored the relationships between cognitive functioning and PMD use. The objectives were to identify cognitive functions necessary to use a PMD and describe available PMD training approaches. METHODS: A scoping review was undertaken. RESULTS: Seventeen studies were included. Four examined the predictive or correlational relationships between cognitive functioning and PMD use outcomes with intellectual functions, visual and visuospatial perception, attention, abstraction, judgement, organization and planning, problem solving, and memory identified as having a relation with PMD use outcome in at least one study. Thirteen others studied the influence of PMD provision or training on users' PMD capacity and cognitive outcomes and reported significative improvements of PMD capacities after PMD training. Six studies found improved cognitive scores after PMD training. CONCLUSIONS: Cognitive functioning is required to use a PMD. Individuals with heterogeneous cognitive impairment can improve their PMD capacities. Results contribute to advancing knowledge for PMD provision.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".