Students’ Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review

(1) Background: The current uses of smartwatch wearable devices have expanded, not only being a part of everyday routine life but also playing a dynamic role in the early detection of many behavioral patterns of users. Furthermore, in the modern era, there is an increasing trend of mental disturbances even in early adolescence, a phenomenon that continues into academic life. Taking into account the current situation, the objective of this systematic literature review emphasizes the role of AI wearable devices in the early symptom detection of burnout in the student population. (2) Methods: A systematic literature review was designed based on the PRISMA guidelines. The general extracted aspect was to exploit all the current related research evidence about the effectiveness of wearable devices in the student population. (3) Results: The reviewed studies document the importance of physiological monitoring and AI-driven predictive models, with the collaboration of self-reported scales in assessing mental well-being. It is reported that stress is the most frequently studied burnout-related symptom. Meanwhile, heart rate (HR) and heart rate variability (HRV) are the most commonly used biomarkers that can be used to monitor and evaluate early burnout detection. (4) Conclusions: Despite the promising potential of these technologies, several challenges and limitations must be addressed to enhance their effectiveness and reliability.