Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training
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 th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3222 |
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| author | Kamelia Sepanloo Daniel Shevelev Young-Jun Son Shravan Aras Janine E. Hinton |
| author_facet | Kamelia Sepanloo Daniel Shevelev Young-Jun Son Shravan Aras Janine E. Hinton |
| author_sort | Kamelia Sepanloo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1dddc56285fb4298b6a379fe2e57c4db |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1dddc56285fb4298b6a379fe2e57c4db2025-08-20T02:34:02ZengMDPI AGSensors1424-82202025-05-012510322210.3390/s25103222Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality TrainingKamelia Sepanloo0Daniel Shevelev1Young-Jun Son2Shravan Aras3Janine E. Hinton4Edwardson School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USASchool of Information Science, University of Arizona, Tucson, AZ 85721, USAEdwardson School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USACenter for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, AZ 85721, USACollege of Nursing, University of Arizona, Tucson, AZ 85721, USAThis 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.https://www.mdpi.com/1424-8220/25/10/3222physiological measures analysiswearable sensorsmixed realitynursing |
| spellingShingle | Kamelia Sepanloo Daniel Shevelev Young-Jun Son Shravan Aras Janine E. Hinton Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training Sensors physiological measures analysis wearable sensors mixed reality nursing |
| title | Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training |
| title_full | Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training |
| title_fullStr | Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training |
| title_full_unstemmed | Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training |
| title_short | Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training |
| title_sort | assessing physiological stress responses in student nurses using mixed reality training |
| topic | physiological measures analysis wearable sensors mixed reality nursing |
| url | https://www.mdpi.com/1424-8220/25/10/3222 |
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