Castillo-Allendes A, Cantor-Cutiva LC, Fuentes-López E, Hunter EJ. Behind the Headset: Predictive Accuracy of Patient-Reported Outcome Measures for Voice Symptoms in Call Centers.
REVISTA DE INVESTIGACION E INNOVACION EN CIENCIAS DE LA SALUD : RIICS 2024;
6:44-72. [PMID:
39359354 PMCID:
PMC11444476 DOI:
10.46634/riics.240]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/18/2023] [Indexed: 10/04/2024]
Abstract
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.
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