German Translation and Validation of the Visually Induced Motion Sickness Susceptibility Questionnaire Short (VIMSSQ-short)
Bibliographic record
Abstract
Motion sickness is a condition that is characterized by symptoms like dizziness, nausea, or vomiting, especially during transportation or immersive visual experiences such as gaming and virtual reality (VR). Visually Induced Motion Sickness (VIMS) is of particular concern due to its increasing relevance with the rise of immersive technologies. The 6-item version of the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ-short), a modified version of the established Motion Sickness Susceptibility Questionnaire, was developed to quickly assess individual susceptibility to VIMS. This study focuses on its translation into German and the validation of this German-language version of the VIMSSQ-short. The translation process included independent translations by experts and a back-translation to identify and resolve discrepancies. An online survey collected normative data from 200 participants, revealing a mean score of 5.85 (SD = 3.31) for the translated VIMSSQ-short. The results indicated significant gender differences, with females exhibiting higher susceptibility scores than males. Additionally, a significant negative correlation between age and susceptibility was observed. An experimental study involving 70 participants further confirmed these findings in terms of mean scores, gender, and age. Additionally, the findings demonstrate that higher VIMSSQ scores predict symptom severity during VR exposure ( r s = 0.58 with Simulator Sickness Questionnaire total score). Overall, the translated VIMSSQ-short shows promise as a reliable tool for assessing VIMS susceptibility in German-speaking populations, contributing to the understanding of motion sickness in immersive environments. The identification of susceptible individuals is relevant both for practical applications (e.g. in the training of emergency forces) and in experimental settings for the randomization or screening of participants.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".