The Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ): Estimating Individual Susceptibility to Motion Sickness-Like Symptoms When Using Visual Devices
Why this work is in the frame
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Bibliographic record
Abstract
Objective Two studies were conducted to develop and validate a questionnaire to estimate individual susceptibility to visually induced motion sickness (VIMS). Background VIMS is a common side-effect when watching dynamic visual content from various sources, such as virtual reality, movie theaters, or smartphones. A reliable questionnaire predicting individual susceptibility to VIMS is currently missing. The aim was to fill this gap by introducing the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ). Methods A survey and an experimental study were conducted. Survey: The VIMSSQ investigated the frequency of nausea, headache, dizziness, fatigue, and eyestrain when using different visual devices. Data were collected from a survey of 322 participants for the VIMSSQ and other related phenomena such as migraine. Experimental study: 23 participants were exposed to a VIMS-inducing visual stimulus. Participants filled out the VIMSSQ together with other questionnaires and rated their level of VIMS using the Simulator Sickness Questionnaire (SSQ). Results Survey: The most prominent symptom when using visual devices was eyestrain, and females reported more VIMS than males. A one-factor solution with good scale reliability was found for the VIMSSQ. Experimental study: Regression analyses suggested that the VIMSSQ can be useful in predicting VIMS ( R 2 = .34) as measured by the SSQ, particularly when combined with questions pertaining to the tendency to avoid visual displays and experience syncope ( R 2 = .59). Conclusion We generated normative data for the VIMSSQ and demonstrated its validity. Application The VIMSSQ can become a valuable tool to estimate one’s susceptibility to VIMS based on self-reports.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it