Does Practicing with a Virtual Reality Driving Simulator Improve Spatial Cognition in Older Adults? A Pilot Study
Bibliographic record
Abstract
Memory, cognition, executive functioning, and spatial cognition loss are prevalent in the normal aging process, but these impairments are observed more extensively in individuals with dementia, specifically Alzheimer's disease. To improve the impaired functions, serious games targeting the lost functions are commonly developed and used in training programs. In this study, we designed a virtual reality driving simulator (VRDS) as a serious game with different difficulty levels for improving the spatial cognition; we evaluated it on 11 participants with different levels of dementia for two weeks, every day except weekends (10 sessions of practice in total) and 30 min/day. We assessed the participants' spatial cognition before and after the intervention by an independent assessment (the VR replica of Morris Water test) and also by their performance playing the VRDS during the intervention. We also assessed the participants' mood by a standard depression scale as well as their plausible experience of simulation sickness. The results showed significant improvement in Morris water test. The participants' normalized correct trajectory (to find the target) was improved significantly by 44.4% at post-intervention with respect to baseline. Furthermore, on average, the participants progressed to higher (more challenging) levels of the game, and their spatial learning score increased throughout the sessions. Their mood also showed improvement with respect to baseline. Overall, the results hold promise for the designed VRDS as a mood-lifting and enhancing spatial skills serious game for older adults if it is played regularly. Trial Registry name: Investigating the Effect of Training with a Virtual Reality Driving Simulator URL: https://clinicaltrials.gov/ct2/show/NCT04074655 Clinical Trials.gov ID: NCT04074655.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".