Improving quality of life for institutionalized individuals with advanced dementia: A pilot study on efficacy of a semi-immersive virtual reality driving simulator for individuals with advanced dementia
Bibliographic record
Abstract
• A novel semi-immersive VR driving simulator (VRDS) for institutionalized older adults with advanced dementia. • The VRDS integrates realistic driving with a virtual environment to stimulate implicit memory and improve cognitive function. • Statistical analyses indicate improvements in game and mood scores over repeated sessions. • The system’s performance logging provides objective measures of implicit learning and motor adaptation in advanced dementia. • The logging system offers a practical alternative to standard cognitive assessments in advanced dementia. A popular new technology to be used to design serious games is virtual reality (VR). Besides gaming applications, the focus of VR experiments in medicine and neuroscience is to simulate a naturalistic environment to investigate brain function, cognitive training and/or improving one’s quality of life. This study aims to investigate the impact of a Semi-Immersive Virtual Reality Driving Simulator (VRDS) specifically tailored for institutionalized individuals with advanced dementia, on its potential to improve implicit cognitive performance and emotional well-being. The designed VRDS was installed in the physical car model available at the Alzheimer’s unit of the Riverview Health Centre (RHC). To create a semi-immersive environment, the laptop screen was projected onto the front window of a physical car model. The virtual environment features a country road includes traffic sounds as well as incoming cars. Ten residents of RHC with advanced dementia used VRDS over a four-month period 3–5 times/week. Data collected included the time spent in the game, crashes, braking responses to traffic lights and stop signs, and mood and behavior assessments using the Mood Assessment Questionnaire (MAQ). Quantitative and qualitative observational data were analyzed for any statistical differences. The daily MAQ filled by the participants’ health-worker aids, showed mood improvements during the VRDS usage. Their driving performance indicators demonstrated implicit memory improvement evidenced by a decrease in total crashes and an increase in the game score. The VRDS demonstrates potential as an effective intervention for improving cognitive and mood in individuals with advanced dementia. Future research should explore the sustainability of the positive outcomes after long-term usage of the technology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".