HRV as Biomarker of Burnout in HealthCare Workers

<b>ABSTRACT</b><b>Background: </b>Burnout is a critical issue among healthcare professionals, particularly in high-stress environments such as intensive care units (ICUs). While previous research has linked burnout to self-reported stress and psychological distress, objective physiological markers such as heart rate variability (HRV) may provide a more reliable assessment of occupational stress and burnout risk. Although our prior pilot study suggested an association between HRV and stress, it did not incorporate validated burnout assessments. This study aimed to bridge that gap by examining the relationship between HRV, self-reported stress, and standardized burnout scales. Additionally, it sought to identify key predictors of burnout and develop a predictive model for early risk detection. <b>Methods: </b>This cross-sectional observational study included 57 nurses and nursing assistants from ICUs and general hospital wards. Participants completed validated burnout assessments, including the Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo (CESQT), the Maslach Burnout Inventory (MBI), the Professional Quality of Life Scale (ProQOL), and the State-Trait Anxiety Inventory (STAI). HRV parameters were recorded using a biosignal acquisition system (Biosignals Plux) for 10 minutes at rest before the start of the work shift and analyzed with OpenSignals software. The extracted HRV metrics included root mean square of successive differences (rMSSD), low-frequency to high-frequency ratio (LF/HF), SD1/SD2 ratio, and Poincaré Area. Statistical analyses included descriptive statistics, correlation analysis, and group comparisons to examine differences in burnout across workplace conditions, shift types, and shift durations. A predictive model for burnout risk was developed using logistic regression with 10-fold cross-validation, integrating HRV parameters, psychological distress, and occupational factors. <b>Results: </b>HRV parameters were significantly associated with self-reported stress and burnout indicators, reinforcing their potential role as objective biomarkers of occupational stress. Night shift workers and those with extended work hours exhibited higher burnout levels and greater autonomic dysregulation. The predictive model demonstrated strong accuracy in identifying individuals at risk of burnout, with HRV and psychological stress emerging as key contributing factors. <b>Conclusion</b>: These findings highlight HRV as a promising tool for the objective assessment of burnout risk in healthcare professionals, including both nurses and nursing assistants. The predictive model developed in this study provides a valuable framework for early identification of high-risk individuals, enabling targeted interventions that may improve well-being and staff retention in healthcare settings. Future research should validate these findings in larger cohorts and assess the long-term applicability of HRV-based monitoring systems in occupational health programs.