Exploring virtual reality gaming‐related cybersickness in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background In the last decade, virtual reality has become a popular tool for rehabilitation. It is quite useful in spatial rehabilitation for Alzheimer’s disease (AD) as it allows safe navigation in a virtual environment which looks realistic. However, a drawback of virtual reality is cybersickness. The symptoms and severities of cybersickness can vary among users. Possible cybersickness symptoms include headache, nausea, disorientation. Cybersickness can be distracting for participants and can affect their performance on the virtual reality task. Hence, in our rehabilitation game called Barn Ruins Navigation (BRN), we used software design mechanisms that would reduce cybersickness, including (1) keeping the field of view to 60 degrees, (2) lowering speed of rotation and translation, (3) using a joystick which causes less cybersickness, and (4) using a laptop screen instead of a head‐mounted display. There have been contradicting results on the effects cybersickness has on people with AD. We wanted to test if cybersickness felt by the BRN game differed between people with AD and cognitively healthy adults. Method Cybersickness was measured using the Simulator Sickness Questionnaire (SSQ) after playing BRN. Thirty adults were recruited for the BRN validation study between the ages of 20‐88. This includes ten younger adults (five males, 26±3.39 years), ten cognitively healthy older adults (three males, 70.7±5.31 years), and ten people living with mild to moderate AD (seven males, 77.8±5.94 years). All participants played the game once. Result Regression analysis on the data showed that males had significantly lower SSQ scores by about 17.47 points compared to females (p = 0.0106), while adjusting for participant groups. The AD group had the highest SSQ scores in comparison to cognitively healthy older adults and younger adults while adjusting for sex. Only the younger adults had a statistically lower SSQ score than the AD group by 18 points (p = 0.023). Conclusion We found males were less prone to cybersickness than females, matching the cybersickness literature. Our study found people with AD were more prone to cybersickness, however not to a statistically higher degree than cognitively healthy older adults. We would need a larger sample size to draw solid conclusions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".