VRx@Home: A virtual reality at‐home intervention for persons living with dementia and their family caregivers
Bibliographic record
Abstract
BACKGROUND: Virtual Reality (VR) therapies are increasingly being evaluated for Persons Living with Dementia (PLwD), with most research revealing benefits of VR for general well-being. VR-therapies, however, have been assessed primarily in long-term care or community program settings and are typically administered by formal caregivers or researchers. Comparatively little research has explored the application of VR-therapy in an at-home setting and for the purpose of enhancing PLwD and family caregiver interactions. The goal of this study was to evaluate whether communication between PLwD and their caregivers could be facilitated through a VR at-home intervention (VRx@Home), and to compare VR technology to traditional Tablet-based technology. METHOD: The study began with at-home training and a baseline data collection session. Next, families first completed either 2 weeks of VR (with paired tablet for the caregiver) or 2 weeks of the Tablet-only condition. During weekly remote sessions with a researcher, families watched 20-min sets of videos across four themes (animals, travel, sports, entertainment), followed by completing a semi-structured interview. They were then asked to try sessions on their own. The intervention concluded with a final interview. A comprehensive set of measures relevant to communication, technology preferences, and general feedback were collected. RESULT: 25 PLwD and 25 caregivers participated in the intervention, with 20 families completing the entire intervention and 5 families completing a partial intervention. From those who completed the full intervention, 18 reported a preference for VR (7 PLwD, 11 caregivers), 17 for Tablet-only (10 PLwD, 7 caregivers), and 5 for both devices equally (3 PLwD, 2 caregivers). Entertainment and animals were the favourite themes reported across all participants and all sessions. Regarding communication outcomes, our preliminary results from surveys completed by caregivers revealed that both devices improved communication (more frequent, engaging, natural, longer) compared to baseline, but VR did so to a greater extent. CONCLUSION: The results suggest that VR-therapy can be implemented in an at-home setting and has the potential to enhance communication between PLwD and caregivers. The research also highlights the variability in preferences and the importance of providing options to allow for long-term VR adoption in at-home settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".