Digital Interventions to Reduce Distress Among Frontline Health Care Providers: Analysis of Self-Perceived Stress.

Abstract

Due to the constraints of the COVID-19 pandemic, healthcare workers have reported behaving in ways that are contrary to their values, which may result in distress and injury. This work is the first of its kind to evaluate the presence of stress in the COVID-19 VR Healthcare Simulation for Distress dataset. The dataset collected passive physiological signals and active mental health questionnaires. This paper focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with the Perceived Stress Scale (PSS)-10 questionnaire. The analysis involved data-driven techniques for a robust evaluation of stress among participants. Low-complexity pre-processing and feature extraction techniques were applied and support vector machine and decision tree models were created to predict the PSS-10 scores of users. Imbalanced data classification techniques were used to further enhance our understanding of the results. Decision tree with oversampling through Synthetic Minority Oversampling Technique achieved an accuracy, precision, recall, and F1 of 93.50%, 93.41%, 93.31%, and 93.35%, respectively. Our findings offer novel results and clinically valuable insights for stress detection and potential for translation to edge computing applications to enhance privacy, longitudinal monitoring, and simplify device requirements.