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. 2025 Apr 16;20(4):e0316936.
doi: 10.1371/journal.pone.0316936. eCollection 2025.

English version of the Computer Vision Symptom Scale (CVSS17): Translation and Rasch analysis-based cultural adaptation

Mariano González-Pérez  1 , Carlos Pérez-Garmendia  2 , Kathleen Hoang  3 , Rosario Susi  4 , Beatriz Antona  1 , Ana-Rosa Barrio  1 , Mark Rosenfield  5
Affiliations

English version of the Computer Vision Symptom Scale (CVSS17): Translation and Rasch analysis-based cultural adaptation

Mariano González-Pérez et al. PLoS One. .

Abstract

Background: Because the CVSS17 was originally developed in Spanish, the objective of this study was to adapt it linguistically and culturally into English while evaluating its psychometric properties.

Methods: After translating and adapting the CVSS17 to English, 441 participants (aged 18 to 65 years) from a general population, recruited from an on-line panel, completed the English version (CVSS17ENG). To determine the measurement properties of CVSS17ENG, we used the partial credit model. To assess convergent validity, coefficients of correlation between CVSS17ENG and the Ocular Comfort Index or Visual Discomfort Scale were calculated. A subset of 218 subjects was tested for test-retest reliability. In addition, differences between CVSS17ENG and CVSS17 were tested through Differential Item Functioning (a Rasch statistic used to check item bias).

Results: A total of 441 responses to CVSS17ENG (average age, 38.57; age range, 19-65; females, 50.24%) showed good fit to the Rasch model, good precision (person separation index = 2.73), and suboptimal targeting (-1.43). Residual principal component analysis suggested multidimensionality, but this was ruled out by a disattenuated correlation coefficient value of 0.82, and no DIF according to sex or age was found. Pearson correlation for CVSS17ENG-VDS was 0.54 (p < 0.01) and Spearman correlation for CVSS17ENG-Ocular Comfort Index was 0.66 (p < 0.001). For test- retest reliability, the limits of agreement were 9.39 and -8.61. Rasch analysis results were similar for CVSS17 and CVSS17ENG and there was no evidence of item bias based on gender or age.

Conclusion: The English version of CVSS17 demonstrates comparable performance to the original, indicating its suitability for clinicians and researchers to reliably assess Digital Eye Strain among English-speaking users of screen-based electronic devices.

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Conflict of interest statement

The authors have read the journal’s policy and have the following competing interests: Carlos Pérez-Garmendia is an employee of DXC Technology Company, Madrid. Kathleen Hoang is an employee of Downtown Eyecare, New York. Mariano González-Pérez was an employee and received consultancy fees from ALAIN AFFLELOU Óptico during the project. There are no patents, products in development, or marketed products associated with this research to declare. This declaration does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Flow diagram showing the study details.
Fig 2
Fig 2. Scatter plot of correlation between CVSS17ENG and VDS.
Values are expressed in logits.
Fig 3
Fig 3. Rasch item-person map, displaying the self-reported symptoms level of the patients in our study (left side) along with the corresponding item difficulty (right side).
Fig 4
Fig 4. Scatter plot of the association between CVSS17ENG and Ocular Comfort Index (OCI).
OCI and CVSS17ENG scores are expressed in logits.
Fig 5
Fig 5. Bland-Altman plot for CVSS17ENG.
where the dotted line represents the mean difference between scores obtained from completing the questionnaire on two occasions. The solid lines represent the lower and upper 95% limits of agreement. The scores are expressed in raw score units.
See this image and copyright information in PMC

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