Evidence map›Paper›PMID 40238743›Full record

ArticlePloS one2025

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

Mariano González-Pérez, Carlos Pérez-Garmendia, Kathleen Hoang, Rosario Susi, Beatriz Antona, Ana-Rosa Barrio, Mark Rosenfield

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Mariano González-PérezOptometry and Vision Department, Faculty of Optics and Optometry, Complutense University of Madrid, Madrid, Spain.ORCID 0000-0001-5967-5000
Carlos Pérez-GarmendiaDXC Technology Company, Madrid, Spain.
Kathleen HoangDowntown Eyecare, New York, United States of America.
Rosario SusiDepartment of Statistics and Data Science, Faculty of Statistical Studies, Complutense University of Madrid, Madrid, Spain.
Beatriz AntonaOptometry and Vision Department, Faculty of Optics and Optometry, Complutense University of Madrid, Madrid, Spain.
Ana-Rosa BarrioOptometry and Vision Department, Faculty of Optics and Optometry, Complutense University of Madrid, Madrid, Spain.
Mark RosenfieldSUNY College of Optometry, New York, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBecause 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.

methodsAfter 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).

resultsA 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.

conclusionThe 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.

Indexed as

LanguageSurveys and QuestionnairesAdolescentAdultAgedCross-Cultural ComparisonFemaleHumansMaleMiddle AgedPsychometricsReproducibility of ResultsTranslationsYoung Adult

Identifiers

PMID40238743
PMCPMC12002468

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.