VRCT: Randomized Controlled Trial Evaluating the Impact of Virtual Reality Therapy on BPSD and QoL of Acute Care In‐Patients With Dementia
Bibliographic record
Abstract
Abstract Background Virtual Reality (VR) is increasingly considered a valuable therapy tool for managing behavioural and psychological symptoms (BPSDs) and quality of life (QoL) in dementia (Parsons, 2013). However, rigorous studies are still needed to evaluate its impact in acute care settings (Appel, 2021). This study evaluated the impact of VR‐therapy on managing BPSDs, falls, and length of stay (LoS) and QoL for inpatients with dementia admitted to an acute care hospital. Method An open longitudinal interventional randomized controlled trial was conducted between April 2019 and March 2020 (ClinicalTrials.gov, ID:NCT03941119). A total of 69 participants (age ≥65, diagnosis of dementia, did not meet exclusion criteria) (Figure 1) who were randomly assigned either followed standard of care (Control Arm, n = 35 or received VR‐therapy every 1‐3 days (Intervention Arm, n = 34) (Figure 2). VR‐therapy entailed watching 360‐degree‐VR‐films on a HMD for up to 20 minutes (Figures 3 and 4). Instances of daily BPSDs documented in EMR nursing notes were categorized based on the Neuropsychiatric Inventory (NPI). QoL measures included the Quality of Life in Late‐Stage Dementia scale (QUALID) and semi‐structured interviews conducted at scheduled visits. Structured observations (including the standardized “ObsRVR” tool) and interviews were used to measure treatment feasibility (Figure 5). Result VR‐therapy had a statistically significant effect (p = .014) in reducing aggressivity (i.e., physical aggression and loud vociferation). A sentiment analysis of patient responses to the semi‐structured interviews on QoL revealed a statistically significant impact of VR therapy (p = .013). No statistically significant impact of VR therapy was found for other BPSDs (e.g., apathy), falls, or LoS or QoL as measured by the QUALID. VR‐therapy was overall an acceptable and enjoyable experience for participants and no adverse events occurred as a result of VR‐therapy. Conclusion Immersive VR‐therapy appears to have an effect on aggressive behaviours and QoL in acute care patients with dementia. Although the RCT was stopped before reaching the intended sample size due to COVID‐19 restrictions, trends in the results are promising. We suggest conducting future trials with larger samples and, in some cases, more sensitive data collection instruments.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".