Navigating Immersion: Usability Study of a Virtual Reality Application Designed for Older Adults with Dementia and Their Caregivers
Bibliographic record
Abstract
A growing number of therapeutic virtual reality (VR) applications are targeted at older adults, but few are evidence-based and designed together with end-users, thus limiting the adoption of this potentially game-changing technology. There is also little research exploring VR applications developed for dyadic use between older adults and their caregivers, who are commonly a part of the safe and optimal experience. The objective of this study was to evaluate the initial usability of an application designed to make accessing immersive VR easier for older adults with dementia and their caregiver(s). This was a non-randomized, prospective mixed-methods think-aloud study that consisted of a two hour usability session with seven participant dyads (one person living with dementia and their caregiver). Descriptive statistical analyses were conducted on demographic quantitative data, and three researchers conducted thematic analysis on observation notes as well as the participants' qualitative responses. Themes identified included headset comfort and safety, usability and navigation, quality (spanning over sub-themes of video, audio, and connectivity), and content. Our main findings highlighted success in delivering the platform's main function as its immersive experience and ability to accommodate socialization and bonding between older adults and caregivers received positive reception from participants. Additionally, there was a need to improve the application's audio quality, as well as streamline its navigation and enlarge interface elements for easier use. These findings provide a guide for development of future VR interventions targeted toward older adults and their caregivers, of which there is currently a lack of in the commercial market and in evidence-based research, despite their demonstrated potential.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".