Person:
González Pérez, Mariano

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First Name
Mariano
Last Name
González Pérez
URI
https://hdl.handle.net/20.500.14352/79887
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Óptica y Optometría
Department
Optometría y Visión
Area
Optica
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2014 - 201922020 - 20221
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Author: Susi García, María Del Rosario × Item Type: Publication ×

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Now showing 1 - 3 of 3
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    The Computer-Vision Symptom Scale (CVSS17): Development and Initial Validation
    (IOVS (Investigative Ophthalmology & Vision Science, 2014) González Pérez, Mariano; Susi García, María Del Rosario; Antona Peñalba, Beatriz; Barrio de Santos, Ana Rosa; González Díaz-Obregón, Enrique
    Purpose.: To develop a questionnaire (in Spanish) to measure computer-related visual and ocular symptoms (CRVOS). Methods.: A pilot questionnaire was created by consulting the literature, clinicians, and video display terminal (VDT) workers. The replies of 636 subjects completing the questionnaire were assessed using the Rasch model and conventional statistics to generate a new scale, designated the Computer-Vision Symptom Scale (CVSS17). Validity and reliability were determined by Rasch fit statistics, principal components analysis (PCA), person separation, differential item functioning (DIF), and item–person targeting. To assess construct validity, the CVSS17 was correlated with a Rasch-based visual discomfort scale (VDS) in 163 VDT workers, this group completed the CVSS17 twice in order to assess test-retest reliability (two-way single-measure intraclass correlation coefficient [ICC] and their 95% confidence intervals, and the coefficient of repeatability [COR]). Results.: The CVSS17 contains 17 items exploring 15 different symptoms. These items showed good reliability and internal consistency (mean square infit and outfit 0.88–1.17, eigenvalue for the first residual PCA component 1.37, person separation 2.85, and no DIF). Pearson's correlation with VDS scores was 0.60 (P < 0.001). Intraclass correlation coefficient for test–retest reliability was 0.849 (95% confidence interval [CI], 0.800–0.887), and COR was 8.14. Conclusions.: The Rasch-based linear-scale CVSS17 emerged as a useful tool to quantify CRVOS in computer workers.
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    From linear questionnaires to computer-adaptive tests: Content development and calibration of the Digital Eye Strain Computer Adaptive Test (DESCAT)
    (2022) Susi García, María Del Rosario; González Pérez, Mariano; Barrio De Santos, Ana Rosa; Antona Peñalba, Beatriz
    We published in 2015 a linear Rasch-based scale (Computer Vision Symptom Scale, aka CVSS17) for measuring the computer-related visual and ocular symptoms in workers using video-display terminals. Because Computer adaptive testing (CAT) is currently considered a more efficient and less time-consuming (for test-takers) method than traditional linear questionnaires, we decided to create a new CAT for assessing these symptoms in general population. Therefore, the aim of our study is to identify content for this new CAT and to calibrate the items included in it.
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    Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis
    (PLoS ONE, 2018) González Pérez, Mariano; Susi García, María Del Rosario; Barrio De Santos, Ana Rosa; Antona Peñalba, Beatriz
    Purpose: To quantify the levels of performance (symptom severity) of the computer-vision symptom scale (CVSS17), confirm its bifactorial structure as detected in an exploratory factor analysis, and validate its factors as subscales. Methods: By partial credit model (PCM), we estimated CVSS17 measures and the standard error for every possible raw score, and used these data to determine the number of different performance levels in the CVSS17. In addition, through discriminant analysis, we checked that the scale's two main factors could classify subjects according to these determined levels of performance. Finally, a separate Rasch analysis was performed for each CVSS17 factor to assess their measurement properties when used as isolated scales. Results: We identified 5.8 different levels of performance. Discriminant functions obtained from sample data indicated that the scale's main factors correctly classified 98.4% of the cases. The main factors: Internal symptom factor (ISF) and external symptom factor (ESF) showed good measurement properties and can be considered as subscales. Conclusion: CVSS17 scores defined five different levels of performance. In addition, two main factors (ESF and ISF) were identified and these confirmed by discriminant analysis. These subscales served to assess either the visual or the ocular symptoms attributable to computer use.