Virtual Reality Experience Intervention May Reduce Responsive Behaviors in Nursing Home Residents with Dementia: A Case Series
Bibliographic record
Abstract
BACKGROUND: People with advanced dementia often exhibit responsive behaviors such as apathy, depression, agitation, aggression, and psychosis. Non-pharmacological approaches (e.g., listening to music, watching television, doing arts and crafts) are now considered as a first-line strategy to manage responsive behaviors in clinical practice due to the potential risks associated with the antipsychotic medications. To date, no evaluations of immersive non-head mounted virtual reality (VR) experience as a non-pharmacologic approach for people with advanced dementia living in nursing homes have been reported. OBJECTIVE: To evaluate the feasibility (acceptance and safety) of VR experience. METHODS: A single site case series (nonrandomized and unblinded) with a convenience sample (N = 24; age = 85.8±8.6 years; Cognitive Performance Scale score = 3.4±0.6) measuring depression and agitation before and after the intervention. The intervention was a 30-min long research coordinator- facilitated VR experience for two weeks (10 sessions). RESULTS: The intervention was feasible (attrition rate = 0% ; adverse events = 0). A reduction in depression and in agitation was observed after the intervention. However, we suggest extreme caution in interpreting this result considering the study design and small sample size. CONCLUSION: This study provides the basis for conducting a randomized controlled trial to evaluate the effect of VR experience on responsive behaviors in nursing homes. Since our intervention uses a smart remote-controlled projector without a headset, infectious exposure can be avoided following the COVID-19 pandemic-induced physical distancing policy in care homes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".