The COOK assistive technology for cognition for older adults with cognitive deficits: a usability study
Bibliographic record
Abstract
Objectives The significantly accelerated use of technology by older adults during the COVID-19 pandemic provides an ideal opportunity to link the use of technology to home safety and aging in place. Our team developed COOK (Cognitive Orthosis for coOKing), an assistive technology for cognition. The study aimed to identify (1) usability issues of COOK for older adults living with and/or without cognitive impairments and (2) the modifications needed to improve its usability and facilitate its deployment.Methods: We conducted a mixed user-centred co-design study including (1) laboratory task scenarios and (2) real-world setting use. We also administered usability and user experience questionnaires. Qualitative deductive thematic analyses and descriptive statistical analyses were used.Results Evaluation of the perceived usability of COOK indicated generally positive prospects. A first evaluation (Study 1) allowed us to simplify several safety-related functionalities, which emerged as key elements to consider. Additionally, a version named COOK – My Safety emerged and was also evaluated (Study 2).Discussion This article confirms the potential of COOK to assist this clientele, with some reported usability problems. Modifications to rectify them were proposed, particularly to prevent safety hazards. The assistive features may require further prototyping.Conclusion The next step will be to develop a suitable version to test long-term use in a real-world setting.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".