Behind the Headset: Predictive Accuracy of Patient-Reported Outcome Measures for Voice Symptoms in Call Centers

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dc.catalogadordfo
dc.contributor.authorCastillo-Allendes, Adrián
dc.contributor.authorCantor-Cutiva,Lady Catherine
dc.contributor.authorFuentes López, Eduardo
dc.contributor.authorHunter, Eric J.
dc.date.accessioned2024-03-14T20:40:01Z
dc.date.available2024-03-14T20:40:01Z
dc.date.issued2024
dc.description.abstractObjective. This study examines factors predicting self-reported voice symptoms in call center workers.Methods. Multivariate analysis and predictive modeling assess personal, work-re-lated, acoustic, and behavioral factors. Generalized Linear Models (GLMs) and Re-ceiver Operating Characteristic (ROC) curves are employed.Results. Age and sleep patterns impacted voice quality and effort, while workplace factors influenced symptom perception. Unhealthy vocal behaviors related to tense voice and increased effort, while hydration was protective. Voice acoustics showed diagnostic potential, supported by ROC data. These findings emphasize voice symp-tom complexity in call center professionals, necessitating comprehensive assessment.Limitations. This study recognizes its limitations, including a moderate-sized con-venience sample and reliance on PROM metrics. Future research should incorporate more objective measures in addition to self-reports and acoustic analysis.Value. This research provides novel insights into the interplay of personal, occu-pational, and voice-related factors in developing voice symptoms among call center workers. Predictive modeling enhances risk assessment and understanding of indi-vidual susceptibility to voice disorders.Conclusion. Results show associations between various factors and self-reported voice symptoms. Protective factors include sleeping more than six hours and consistent hydration, whereas risk factors include working conditions, such as location and behav-iors like smoking. Diagnostic models indicate good accuracy for some voice symptom PROMs, emphasizing the need for comprehensive models considering work factors, vocal behaviors, and acoustic parameters to understand voice issues complexity
dc.fuente.origenORCID
dc.identifier.doi10.46634/riics.240
dc.identifier.urihttps://doi.org/10.46634/riics.240
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/84443
dc.information.autorucDepartamento de Ciencias de la Salud; Fuentes Lopez Eduardo; 0000-0002-0141-0226; 1013849
dc.issue.numero1
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final72
dc.pagina.inicio44
dc.revistaRevista de Investigación e Innovación en Ciencias de la Salud
dc.rightsacceso abierto
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectVoice symptoms
dc.subjectCall center workers
dc.subjectPredictive factors
dc.subjectOccupational health
dc.subjectSelf-re-ported measures
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.titleBehind the Headset: Predictive Accuracy of Patient-Reported Outcome Measures for Voice Symptoms in Call Centers
dc.title.alternativeDetrás de los auriculares: precisión predictiva de las medidas de resultados informadas por el paciente para los síntomas de voz en call centers
dc.typeartículo
dc.volumen6
sipa.codpersvinculados1013849
sipa.trazabilidadORCID;2024-02-05
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