Heart Rate Variability as a Biomarker of Burnout in Healthcare Workers: A Predictive Model Integrating Psychosocial and Occupational Factors

BackgroundBurnout is a significant concern among healthcare professionals, particularly in high-stress environments such as intensive care units (ICUs). While prior research has linked burnout to self-reported stress and psychological distress, objective physiological markers like heart rate variability (HRV) may offer a more reliable assessment of occupational stress and burnout risk. Our previous pilot study suggested an association between HRV and stress; however, it did not incorporate standardized burnout assessments. This study aims to bridge that gap by examining the relationship between HRV, self-reported stress, and validated burnout scales. Additionally, it seeks to identify key predictors of burnout and develop a predictive model for early risk detection. MethodsThis cross-sectional observational study included 57 nurses and nursing assistants working in ICUs and general hospital wards. Participants completed validated burnout assessments, including the Cuestionario para la Evaluacion del Sindrome de Quemarse por el Trabajo (CESQT; Spanish Burnout Inventory), 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 Biosignals Plux system for 10 minutes at rest before the start of the work shift and analyzed with the OpenSignals software. Extracted HRV metrics included the root mean square of successive differences (rMSSD), low-frequency to high-frequency ratio (LF/HF), Standard Deviation 1 and 2 Ratio (SD1/SD2 ratio), and Poincare area. Statistical analyses involved descriptive statistics, correlation analysis, and group comparisons to examine differences in burnout across workplace conditions, shift types, and shift durations. A logistic regression model with 10-fold cross-validation was developed to predict burnout risk, integrating HRV parameters, psychological distress, and occupational factors. ResultsHRV 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. The model integrating HRV parameters, psychological distress, and occupational factors (Model 2) achieved an AUC-ROC of 0.832 (95% CI: 0.735- 0.929) and an accuracy of 79.1%, outperforming the model based solely on demographic and psychometric data (Model 1, AUC-ROC = 0.791, 95% CI: 0.685-0.897, accuracy = 76.3%). HRV and psychological stress emerged as key contributing factors. ConclusionThese findings highlight HRV as a promising tool for the objective assessment of burnout risk in healthcare professionals. The predictive model developed provides a framework for early identification of high-risk individuals, enabling targeted interventions to 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.