Simulator sickness in older adults during active and passive driving tasks.
Bibliographic record
Abstract
Advanced driving simulators are important for testing challenging driving scenarios under controllable, repeatable, and safe conditions in the context of driving rehabilitation, training, and assessment, which are particularly relevant for older drivers. Simulator sickness (e.g., nausea, fatigue, disorientation) is a common side-effect of driving simulators, not only affecting passive passengers but also the driver. The goal of the present study was to investigate how control over the vehicle may impact the severity of simulator sickness in a high-fidelity driving simulator, particularly in older adults. Thirty-four healthy participants (65+ years old; 13 women) were engaged in a driving simulator task under conditions that varied in the extent to which they had active control over the simulated vehicle (manual vs. fully automated). Various baseline measures (visual acuity, cognitive abilities, mood) were recorded, and simulator sickness was measured using the Simulator Sickness Questionnaire after each drive. No differences in simulator sickness severity were observed between the two driving control conditions (manual vs. fully automated), but women reported significantly more simulator sickness than men. In addition, a positive relationship between simulator sickness and cognitive abilities was found, indicating that better cognitive performance was associated with more simulator sickness. Additionally, in terms of sensory abilities, better visual acuity was linked to more severe simulator sickness. Our findings suggest that controllability of a vehicle may not have a large effect on the severity of simulator sickness in older adults, but that biological sex as well as cognitive and sensory abilities may be relevant factors worth considering.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".