Interdisciplinary development of manual and automated product usability assessments for older adults with dementia: lessons learned
Bibliographic record
Abstract
The changes in cognitive abilities that accompany dementia can make it difficult to use everyday products that are required to complete activities of daily living. Products that are inherently more usable for people with dementia could facilitate independent activity completion, thus reducing the need for caregiver assistance. The objectives of this research were to: (1) gain an understanding of how water tap design impacted tap usability and (2) create an automated computerized tool that could assess tap usability. 27 older adults, who ranged from cognitively intact to advanced dementia, completed 1309 trials on five tap designs. Data were manually analyzed to investigate tap usability as well as used to develop an automated usability analysis tool. Researchers collaborated to modify existing techniques and to create novel ones to accomplish both goals. This paper presents lessons learned through the course of this research, which could be applicable in the development of other usability studies, automated vision-based assessments and the development of assistive technologies for cognitively impaired older adults. Collaborative interdisciplinary teamwork, which included older adult with dementia participants, was key to enabling innovative advances that achieved the projects' research goals. Implications for Rehabilitation Products that are implicitly familiar and usable by older adults could foster independent activity completion, potentially reducing reliance on a caregiver. The computer-based automated tool can significantly reduce the time and effort required to perform product usability analysis, making this type of analysis more feasible. Interdisciplinary collaboration can result in a more holistic understanding of assistive technology research challenges and enable innovative solutions.
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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 | 0.040 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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 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".