Developing a Dyadic Immersive Virtual Environment Technology Intervention for Persons Living With Dementia and Their Caregivers: Multiphasic User-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: Persons living with dementia and their caregivers experience frequent emotional health challenges. Across the illness spectrum, engaging in shared pleasant activities is an important feature of well-being for persons living with dementia-caregiver dyads. Under the umbrella of virtual reality, immersive virtual environment technology (IVET) offers artificial sensory experiences and shows promise in this population. IVET development benefits from a user-centered design approach, and as an emerging field, preliminary testing of safety, usability, and engagement for person living with dementia-caregiver dyads is required. OBJECTIVE: We aimed to develop a preliminary IVET intervention for psychosocial health among person living with dementia-caregiver dyads. In doing so, we highlight design considerations and user preferences to ensure the safety and usability of technology-based interventions in the context of dementia. METHODS: We engaged 10 clinicians, 8 caregivers, and 3 persons living with dementia in 5 rounds of focus groups to evaluate the safety and usability of preliminary intervention features. Following prototype development, we engaged caregivers and persons living with dementia (n=9 dyads) in beta testing workshops to observe real-time user interaction with the intervention and guide refinements. Rapid data analysis was used to extract themes relevant to intervention development. RESULTS: The following themes emerged from focus groups to inform prototype development: (1) designing flexibly to allow users to tailor the intervention experience to their own environmental context and circumstance, (2) designing with the dyad's clinical and relational needs in mind, and (3) accounting for illness and aging-related challenges in design. The following themes emerged from workshops to inform prototype refinements: (1) increasing user support through more feedback and (2) increasing variety of visual and auditory feedback. CONCLUSIONS: Using user feedback throughout the development process, we developed a prototype of an IVET intervention, Toolkit for Experiential Well-Being in Dementia (the Isle of TEND), tailored to the needs of persons living with dementia and their caregivers. Our prototype uses specific design features to promote safety, usability, and engagement in the context of dementia. Future feasibility testing of the intervention is warranted. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/52799.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".