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

Objective. This study examines factors predicting self-reported voice symptoms in call center workers. Methods. Multivariate analysis and predictive modeling assess personal, work-related, acoustic, and behavioral factors. Generalized Linear Models (GLMs) and Receiver Operating Characteristic (ROC)...

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Published in:Revista de Investigación e Innovación en Ciencias de la Salud (RIICS) Vol. 6; no. 1; pp. 44 - 73
Main Authors: Castillo-Allendes, Adrián, Catherine Cantor-Cutiva, Lady, Fuentes-López, Eduardo, Hunter, Eric J.
Format: research tables/charts Journal Article
Published: Fundacion Universitaria Maria Cano 2024
Online Access:View this record in EBSCOhost
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      dt: 2024
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      pub: Fundacion Universitaria Maria Cano
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        atl: Behind the Headset: Predictive Accuracy of Patient-Reported Outcome Measures for Voice Symptoms in Call Centers.
      aug:
        au:
          Castillo-Allendes, Adrián
          Catherine Cantor-Cutiva, Lady
          Fuentes-López, Eduardo
          Hunter, Eric J.
        affil: Department of Communicative Sciences and Disorders; Michigan State University; East Lansing; Michigan; United States
      sug:
        subj:
          Patient-Reported Outcomes
          Voice Disorders Risk Factors
          Occupational Diseases Risk Factors
          Risk Assessment
          Telephone Information Services Labor Supply
          Occupational Health
          Human
          Self Report
          Multivariate Analysis
          Prediction Models
          Acoustics
          ROC Curve
          Age Factors
          Sleep
          Voice Quality
          Work Environment
          Health Behavior
          Hydration Status
          Smoking Complications
          Chile
          Nonprobability Sample
          Clinical Assessment Tools
          Questionnaires
          Summated Rating Scaling
          Visual Analog Scaling
          Exploratory Research
          Odds Ratio
          Descriptive Statistics
          Confidence Intervals
          Data Analysis Software
          Adult
          Middle Age
          Male
          Female
          Univariate Statistics
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Male
          Female
      ab:
        Objective. This study examines factors predicting self-reported voice symptoms in call center workers. Methods. Multivariate analysis and predictive modeling assess personal, work-related, acoustic, and behavioral factors. Generalized Linear Models (GLMs) and Receiver 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 symptom complexity in call center professionals, necessitating comprehensive assessment. Limitations. This study recognizes its limitations, including a moderate-sized convenience 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, occupational, and voice-related factors in developing voice symptoms among call center workers. Predictive modeling enhances risk assessment and understanding of individual 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 behaviors 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.
        Objetivo. Este estudio examina los factores que predicen los síntomas de voz en los trabajadores de call centers. Métodos. Se utilizan análisis multivariados y modelos predictivos para evaluar factores personales, laborales, acústicos y de comportamiento. Se emplean Modelos Lineales Generalizados (GLM) y curvas ROC. Resultados. La edad y los patrones de sueño afectaron la calidad vocal y el esfuerzo, mientras que los factores laborales influyeron en la percepción de síntomas. Los comportamientos vocales no saludables se relacionaron con voz tensa y mayor esfuerzo, mientras que la hidratación fue protectora. Los parámetros acústicos de voz mostraron potencial diagnóstico respaldado por datos de ROC. Los hallazgos subrayan complejidad de síntomas vocales en profesionales de centros de llamadas, requiriendo una evaluación integral. Limitaciones. Este estudio reconoce sus limitaciones, que incluyen una muestra de conveniencia de tamaño moderado y la dependencia de medidas PROMs. Futuras investigaciones deberían incorporar medidas objetivas, además de los autorreportes y análisis acústico. Importancia. Esta investigación aporta nuevos conocimientos sobre factores personales, laborales y síntomas de voz en trabajadores de call centers. El modelado predictivo mejora la evaluación de riesgos y la comprensión de la susceptibilidad individual a trastornos de la voz. Conclusión. Los resultados muestran asociaciones entre diversos factores y los síntomas vocales reportados. Los factores de protección incluyen dormir más de seis horas y una hidratación constante; los factores de riesgo incluyen las condiciones de trabajo, como la ubicación y comportamientos como fumar. Los modelos de diagnóstico indican una buena precisión para algunas PROMs de síntomas de la voz, lo que subraya la necesidad de modelos integrales que tengan en cuenta los factores laborales, los comportamientos vocales y los parámetros acústicos para comprender la complejidad de los problemas de la voz.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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