Enabling Power Wheelchair Mobility with Long-term Care Home Residents with Cognitive Impairments
Bibliographic record
Abstract
For older adults, functional independent mobility is essential to well-being. Many care home residents have physical and cognitive impairments and use wheelchairs. Residents with difficulty self-propelling manual wheelchairs may benefit from power mobility; however, those with cognitive impairments may be precluded because of the potential for injury. My research goals were to apply novel power wheelchair technology to enable safe, independent mobility. Technology was developed to examine the value and implications of power mobility for residents with restricted mobility and mild or moderate cognitive impairments. The first study tested a prototype anti-collision wheelchair with a contact sensor skirt. Six single subject studies were completed. Distances travelled in manual and anti-collision wheelchairs were compared. Observational and interview data were collected. Focus groups (37 staff) and interviews (18 staff, six other residents, one spouse) were performed. Three of six residents were able or had potential to operate the prototype. One resident chose to use it beyond the study, and his mobility and well-being improved. Case analyses showed factors limiting prototype acceptance. Residents were unsatisfied with the appearance and slow speed, and found the interface frustrating to operate because of inadequate feedback. Social isolation and reduced autonomy restricted independence achievable with technology. Socialization and affective benefits of mobility were demonstrated in one case where prototype use was continually assisted. Residents and staff supported the anti-collision concept. On observation, the prototype compensated for absent or delayed responses of residents to obstacles below sensors and decreased injury risk. However, full sensor coverage of the environment was needed. The second study addressed acceptance and interface usability issues. A simulated collision-avoidance wheelchair with a multimodal feedback interface was evaluated. The interface provided audio, visual and haptic feedback to guide navigation away from obstacles. Through observations, interviews and questionnaires, five residents evaluated the device. High device acceptance and usability were found. The device was easy to use and assisted with performance of indoor mobility goals. Further research is necessary before power wheelchairs with new features are available for users; however, these results could play a fundamental role in shaping technology development and mobility interventions for this neglected population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".