# Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training

## TL;DR

This study uses mixed reality to train nursing students and measures their stress responses through physiological indicators like heart rate and skin temperature.

## Contribution

The study introduces a mixed reality training system with dynamic patient simulations and achieves high stress classification accuracy using machine learning.

## Key findings

- The Stacking Classifier achieved 96.4% accuracy in classifying stress levels from physiological data.
- Physiological markers like heart rate and electrodermal activity showed clear stress patterns during simulations.
- Significant differences in stress responses were identified across simulation segments.

## Abstract

This study explores nursing students’ stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal activity, and skin temperature, the study assesses the level of stress when the students interact with digital patients whose vital signs and symptoms interact dynamically to respond to student inputs. The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. Among the models tested, the Stacking Classifier demonstrated the highest classification accuracy of 96.4%, outperforming both Random Forest (96.18%) and Gradient Boosting (95.35%). The results showed clear patterns of stress during the simulation segments. Statistical analysis also found significant differences in stress responses and identified key physiological markers linked to each stress level. This pioneering study demonstrates the effectiveness of MR as a training tool for healthcare professionals in high-pressured scenarios and lays the groundwork for further studies on stress management, adaptive training procedures, and real-time detection and intervention in MR-based nursing training.