An Intervention on Anxiety Symptoms in Moderate Alzheimer’s Disease through Virtual Reality: A Feasibility Study and Lessons Learned
Bibliographic record
Abstract
(1) Background: Although cognitive impairment is considered the core deficit of dementia, anxiety disorders also have a negative influence on the social and daily life of the affected population. We have explored the exposure of relaxing scenarios in immersive Virtual Reality (iVR) as an intervention strategy for people with moderate Alzheimer's disease. (2) Methods: Three participants were recruited from a day center to participate in a five-week study, which included a Pre- and Post-evaluation with the Montreal Cognitive Assessment (MoCA), Neuropsychiatric Inventory-Questionnaire (NPI-Q), Clinical Dementia Rating Scale (CDR), Global Deterioration Scale (GDS), Hamilton Anxiety Rating Scale (HARS), State-Trait Anxiety Inventory (STAI), and the anxiety subdomain of the Neuropsychiatric Inventory (NPI). Participants' heart rate, oxygen saturation, arterial pressure, and respiratory rate were also monitored during intervention sessions. Three virtual scenarios from Nature Treks VR were used as the intervention over three weeks (a total of nine sessions). (3) Results: Post-intervention anxiety assessment showed a light reduction in psychological anxiety in the HARS questionnaire. A light reduction in heart rate was also observed during the exposure to iVR. (4) Discussion: The use of virtual scenarios was a satisfactory experience for all the participants. Preliminary data point to a relaxing effect of iVR scenarios and a potential reduction in psychological anxiety, but further research is required to confirm the efficacy of the intervention.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".