Measuring the susceptibility to visually induced motion sickness and its relationship with vertigo, dizziness, migraine, syncope and personality traits
Bibliographic record
Abstract
The widespread use of visual technologies such as Virtual Reality increases the risk of visually induced motion sickness (VIMS). Previously, the 6-item short version of the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ short form) has been validated for predicting individual variation in VIMS. The aim of the current study was to investigate how the susceptibility to VIMS is correlated with other relevant factors in the general population. A total of 440 participants (201 M, 239F), mean age 33.6 (SD 14.8) years, completed an anonymous online survey of various questionnaires including the VIMSSQ, Motion Sickness Susceptibility Questionnaire (MSSQ), Vertigo in City questionnaire (VIC), Migraine (scale), Social & Work Impact of Dizziness (SWID), Syncope (faintness), and Personality ('Big Five' TIPI). The VIMSSQ correlated positively with the MSSQ (r = 0.50), VIC (r = 0.45), Migraine (r = 0.44), SWID (r = 0.28), and Syncope (r = 0.15). The most efficient Multiple Linear Regression model for the VIMSSQ included the predictors MSSQ, Migraine, VIC, and Age and explained 40% of the variance. Factor analysis of strongest correlates with VIMSSQ revealed a single factor loading with VIMSSQ, MSSQ, VIC, Migraine, SWID, and Syncope, suggesting a common latent variable of sensitivity. The set of predictors for the VIMSSQ in the general population has similarity with those often observed in patients with vestibular disorders. Based on these correlational results, we suggest the existence of continuum of underlying risk factors for sensitivity, from healthy population to patients with extreme visual vertigo and perhaps Persistent Postural-Perceptual Dizziness.
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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 | 0.003 |
| 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 | 0.000 | 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".