VRx@Home protocol: A virtual reality at-home intervention for persons living with dementia and their care partners
Bibliographic record
Abstract
INTRODUCTION: Virtual reality (VR) technology is increasingly used by researchers and healthcare professionals as a therapeutic intervention to improve the quality of life of persons living with dementia (PLwD). However, most VR interventions to date have mainly been explored in long-term or community care settings, with fewer being explored at home. Setting is important, given that the majority of PLwD live at home and are cared for by their family care partners. One of the challenges affecting PLwD and care partner relationships is barriers in communication, which can lead to social isolation and poor quality of life for both parties. Thus, the goal of the proposed project is to explore whether an immersive, multisensory VR intervention can facilitate communication between PLwD and their care partners and, in turn, enhance personal relationships and improve well-being. METHODS AND ANALYSIS: Thirty dyads comprised of PLwD and their family/friend care partners will participate in this at-home intervention. Their interactions will be recorded as they experience a series of 360° videos together (eg, concert, travel) either using a VR headset (PLwD) with a paired tablet (care partner) or using only a tablet together. The two conditions will allow us to compare immersive VR technology to more common non-immersive tablet-based technology. The study will begin with at-home training and baseline data collection. The intervention will then take place over a 4-week period, with the two conditions (VR vs tablet-only) experienced 2 weeks each. A comprehensive set of measures will be employed to assess the quality and quantity of dyadic interactions, such as verbal/non-verbal language (eg, informativity, gestures) and self-reported measures of well-being and quality of life. ETHICS AND DISSEMINATION: Ethical approval for the study was granted by the University Health Network (#21-5701). Findings will be shared with all stakeholders through peer-reviewed publications and presentations. CLINICAL REGISTRATION: This study has been registered on clinicaltrials.gov (NCT06568211).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".