Publication: Factor structure of the 10-item CES-D scale among patients with persistent COVID-19
| dc.contributor.author | Ramírez Vélez, Robinson | |
| dc.contributor.author | Olabarrieta Landa, Laiene | |
| dc.contributor.author | Rivera, Diego | |
| dc.contributor.author | Izquierdo Redín, Mikel | |
| dc.contributor.department | Ciencias de la Salud | es_ES |
| dc.contributor.department | Osasun Zientziak | eu |
| dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa | es |
| dc.date.accessioned | 2023-02-15T13:43:23Z | |
| dc.date.available | 2023-02-15T13:43:23Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-02-15T12:54:28Z | |
| dc.description.abstract | The presence of persistent coronavirus disease 2019 (COVID-19) might beassociated with significant levels of psychological distress that would meet thethreshold for clinical relevance. The Center for Epidemiologic Studies DepressionScale (CES-D) version 10 has been widely used in assessing psychological distressamong general and clinical populations from different cultural backgrounds. To ourknowledge, however, researchers have not yet validated these findings amongpatients with persistent COVID-19. A cross-sectional validation study wasconducted with 100 patients from the EXER-COVID project (69.8% women;mean (±standard deviation) ages: 47.4 ± 9.5 years). Confirmatory factor analyses(CFAs) were performed on the 10-item CES-D to test four model fits: (a)unidimensional model, (b) two-factor correlated model, (c) three-factor correlatedmodel, and (d) second-order factor model. The diagonal-weighted least-squares estimator was used, as it is commonly applied to latent variable modelswith ordered categorical variables. The reliability indices of the 10-item CES-D in patients with persistent COVID-19 were as follows: depressive affect factor(α=0.82Ord;ω=0.78u−cat), somatic retardation factor (α=0.78Ord;ω=0.56u−cat),and positive affect factor (α=0.56Ord;ω=0.55u−cat). The second‐order model fitshowed good Omega reliability (ω=0.87ho). Regarding CFAs, the unidimensional‐factor model shows poor goodness of fit, especially residuals analysis (root meansquare error of approximation [RMSEA] = 0.081 [95% confidence interval,CI = 0.040–0.119]; standardized root mean square residual [SRMR] = 0.101). The two‐factor correlated model, three‐factor correlated model, and second‐order factormodel showed adequate goodness of fit, and theχ2difference test (∆X2) did not show significant differences between the goodness of fit for these models(∆X= 4.11282;p= 0.127). Several indices showed a good fit with the three‐factor correlated model: goodness‐of‐fit index = 0.974, comparative fit index = 0.990,relative noncentrality index = 0.990, and incremental fit index = 0.990, which were all above 0.95, the traditional cut‐off establishing adequate fit. On the other hand RMSEA = 0.049 (95% CI = 0.000–0.095), where an RMSEA < 0.06–0.08 indicates anadequate fit. Item loadings on the factors were statistically significant (λ≥0.449j;p's < 0.001), indicating that the items loaded correctly on the corresponding factors and the relationship between factors (φ≥0.382;p's≤0.001. To our knowledge, thisis the first study to provide validity and reliability to 10‐item CES‐D in a persistentCOVID‐19 Spanish patient sample. The validation and reliability of this shortscreening tool allow us to increase the chance of obtaining complete data in aparticular patient profile with increased fatigue and brain fog that limit patients' capacity to complete questionnaires. | en |
| dc.description.sponsorship | The EXER‐COVID study was supported by “Proyectos de I+D+i” de los Programas Estatales de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i Orientadaa los Retos de la Sociedad, en el marco del Plan Estatal deInvestigación Científica y Técnica y de Innovación 2017‐2020(PID2020‐113098RB‐I00). Open access funding provided by Uni-versidad Pública de Navarra. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Algueta-Miguel, J. M., Beato-López, J. J., & López-Martín, A. J. (2022). Analog lock-in amplifier design using subsampling for accuracy enhancement in gmi sensor applications. Sensors, 23(1), 57. https://doi.org/10.3390/s23010057 | en |
| dc.identifier.doi | 10.1002/jmv.28236 | |
| dc.identifier.issn | 0146-6615 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/44727 | |
| dc.language.iso | eng | en |
| dc.publisher | Wiley | en |
| dc.relation.ispartof | Journal of Medical Virology 2022;95:e28236 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113098RB-I00/ES/ | en |
| dc.relation.publisherversion | https://doi.org/10.1002/jmv.28236 | |
| dc.rights | Creative Commons Attribution 4.0 International (CC BY 4.0) | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | COVID-19 | en |
| dc.subject | Depression | en |
| dc.subject | Mental illness | en |
| dc.subject | Statistical factor analyses | en |
| dc.title | Factor structure of the 10-item CES-D scale among patients with persistent COVID-19 | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
| dc.type.version | info:eu-repo/semantics/publishedVersion | en |
| dspace.entity.type | Publication | |
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