Why the study?
College students often experience high stress levels linked to negative academic, physical, and mental health outcomes, motivating continuous real-time monitoring to predict burnout.
Can an Information System using wearable devices and machine learning accurately monitor stress levels in medical students?
Can an Information System using wearable devices and machine learning accurately monitor stress levels in medical students?
A neural network model using wearable-derived heart rate and heart rate variability indices showed good fit for monitoring stress in medical students.
May aid early burnout detection among students; leaves open prospective validation before any clinical adoption.
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit.
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Silva et al. (2020) studied this question.
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