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

(1) Background: Current uses of smartwatches wearable devices have been expanded not only in everyday routine life but also, they have a dynamic role in early detection of many behavioral patterns of users. The objective of this systematic literature review emphasizes in the role of AI wearable devices in early symptom detection of burnout in student population. (2) Methods: A systematic literature review was designed based on PRISMA guidelines. The general extracted aspect was to exploit all the current related research evidence about the effectiveness of wearable devices in student population. (3) Results: The reviewed studies document the importance of physiological monitoring, 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) being the most commonly used biomarkers that can be monitored and evaluated in 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.