Opportunities and challenges in using virtual reality to improve cognitive functioning of the elderly
Bibliographic record
Abstract
Over the past decade, researchers have utilized novel technologies to improve the lives of the elderly population. Virtual Reality (VR) is among the most promising platforms that could help the elderly stay cognitively active; However, the extent to which this population is willing to seek out and engage in VR activities remains unclear. In this document, we have discussed our two studies regarding VR and seniors. Our first study is a pilot project including three senior residents of Winnipeg. In this study, we assessed the impact of VR game name “DoVille” on the cognitive capacities of the elderly. We designed a two-week procedure with 20 minutes of VR sessions per day. While the comparison of pre-DoVille and post-DoVille test scores were statistically insignificant, we have gained valuable information about the feasibility and possible challenges of similar projects in the future. Among these issues, we have discussed the eligibility criteria, VR sessions’ setting and length of training sessions for the seniors. The second study is a survey project assessing the attitudes of the elderly toward VR during the COVID-19 pandemic. All senior residents of Manitoba between the ages of 65 and 90 were eligible to participate in the study. The survey was administered online and by phone, and 103 individuals responded to our questionnaire. Our results suggest that a large proportion of elderly individuals have become interested in VR technology as a result of the COVID-19 pandemic. We developed two models for VR use based on the responses. Our model of VR use for communication/interaction could account for approximately 50% of the variance in interest levels in VR, and our model of VR use for cognitive benefits accounted for 35% of the variance. These models included variables such as previous experience with technology, age and gender. In conclusion, these two studies provide us with a better understanding of the elderly’s interest in technology and how we could implement new VR interventions for them in the future.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".