P1‐212: Determining the needs of informal caregivers for smart home user interfaces: A first look
Bibliographic record
Abstract
Intelligent home technologies that can guide older adults with dementia (OAWDs) through activities of daily living (ADLs) hold potential to facilitate aging in place, reduce caregiver burden, and improve quality of life. Clinical trials evaluating Mihailidis et al.'s COACH system, which prompts OAWDs through hand-washing, have demonstrated system efficacy in long-term care settings. To adapt and expand COACH to support multiple ADLs in home environments, a richer understanding of how informal caregivers wish to interact with the system is needed. Six informal dementia caregivers were recruited to participate in two participatory design sessions. The first group session involved a discussion of predicted needs, concerns, challenges, and interaction requirements related to introducing and managing COACH in the home. The second session involved reviewing, critiquing, and suggesting improvements to a preliminary user interface (UI) design. Group sessions were transcribed verbatim and collaboratively analyzed, and design considerations were summarized. A second UI design iteration was then built into a paper prototype. Individual usability test sessions were conducted in two selected participants' homes, where they were asked to perform five tasks using the paper prototype. Video-recordings and field notes were reviewed and discussed, and additional design recommendations were generated for functional prototype development. Participants wished to be able to set COACH up for the first time in the home; select when and with which activities it would assist with; and personalize all audio and visual prompts. Participants also desired alerts for assistance and the ability to review reports on the OAWD's activity completion. Participants suggested multiple interfaces would be needed to interact with COACH, including installed home intercoms, portable mobile (e.g. tablet PC), and wearable devices. The greatest concerns raised by participants were related to safety, customizability, flexibility, and a potential shift of burden from caregiving to technology management.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".