Passive restraint reduces visually induced motion sickness in older adults.
Bibliographic record
Abstract
Virtual environments such as those used in video games and driving/flight simulators are used for entertainment and training, but are often associated with visually induced motion sickness (VIMS). In this study, we asked whether passive restraint of the head and torso could reduce VIMS in younger and older adults. Twenty-one younger (18-35 years) and 16 older (65 + years) healthy adults engaged in a simulated driving task using a console video game while seated. On different days, participants completed 2 conditions: (a) in the unrestrained condition, participants were seated in a chair without a backrest and were free to move and (b) in the restrained condition, participants' head and torso were passively restrained to the backrest and headrest of the seat using tense elastic strips. Before and after exposure to the driving game, we measured standing postural sway with eyes closed. VIMS severity was quantified using the Fast Motion Sickness Scale and the Simulator Sickness Questionnaire. Results showed that older (but not younger) participants who became sick in the unrestrained condition reported significantly less VIMS when they were passively restrained. The present findings suggest that passive restraint may be useful to reduce, but not fully prevent, VIMS, particularly in older adults. (PsycINFO Database Record
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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 | 0.001 |
| 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".