The Long-term Effects of Immersive Virtual Reality Reminiscence in People With Dementia: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Novel nonpharmacological therapies are being developed to prevent cognitive decline and reduce behavioral and psychological symptoms in patients with dementia. Virtual reality (VR) reminiscence was reported to improve anxiety, apathy, and cognitive function immediately after intervention in individuals at residential aged care facilities. However, its effect on elderly patients with dementia and how long this effect could last remain unknown. OBJECTIVE: The aim of this paper is to investigate the effect of immersive VR reminiscence in people with dementia both immediately after and 3-6 months after intervention. METHODS: A pilot study was conducted in 2 dementia care units. VR reminiscence therapy sessions were conducted twice per week for a 3-month period. Cognitive function, global status, depressive symptoms, and caregiver burden were assessed before and immediately after VR intervention in 20 participants. Subsequently, 7 participants were reassessed 3-6 months after the VR intervention. Wilcoxon sign-rank test was used for statistical comparisons of the changes. RESULTS: There were no significant changes in cognitive function, global status, and caregiver burden immediately after the VR intervention, but there was a significant reduction in depressive symptoms (P=.008). Moreover, compared with the cognitive function immediately after VR, it kept declining 3-6 months after. CONCLUSIONS: Immersive VR reminiscence can improve mood and preserve cognitive function in elderly patients with dementia during the period of the intervention. Studies using a control group and comparing the use of VR with traditional forms of reminiscence should be conducted in the future to confirm and expand on these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".