# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Smartwatches for Mental Health Monitoring
All of these studies support the idea that, when used appropriately, smartwatches and wearable devices can provide reliable data for the detection and monitoring of conditions such as anxiety, depression, and insomnia. The integration of this data with machine learning models can improve prediction and allow for early diagnosis and intervention [8; 10; 50]. [PERSON] et al. [51] focused on psychological stress during radiotherapy (RT) and how this can be captured using wearables. Patients wore the Galaxy Watch Active 2 during and before sessions to track heart rate and anxiety scores. The results showed increased HR and perceived stress during treatment, especially in initial sessions, indicating that smartwatches can monitor patient mental states and support stress-reduction strategies during therapy. Moreover, [PERSON] et al. [10] used cardiopulmonary and GSR data collected via  
Figure 4: Overview of wearable device applications in healthcare monitoring [5]. smartwatches to detect stress in real time with the BoostNet algorithm. The model achieved 94% accuracy, demonstrating smartwatches' capability to provide reliable indicators of mental health in high-stress conditions. [PERSON] et al. [52] conducted a review of wearable tech for detecting stress, depression, and anxiety. HRV and EDA were key parameters, but the highest accuracy for anxiety detection was achieved when combined with EEG. EEG-based depression detection remains a challenge due to commercial unavailability. In addition, [PERSON] et al. [53] used Fitbit HRV data to predict stress responses in medical students and general population participants during a Trier Social Stress Test (TSST). While patterns varied, HR and HRV significantly changed under stress, supporting HRV as a stress indicator. Moreover, [PERSON] and [PERSON] [54] reviewed biochemical markers and sensor technology for wearable-based mental health monitoring. Despite technological progress, they emphasized the need for improved accuracy and reliability in detecting stress-related markers. According to the study of [PERSON] et al. [55], a well-being monitoring system using physiological and psychological data from smartwatches and questionnaires was developed. Among the six ML algorithms that had been tested, Random Forest achieved the best performance (88% accuracy) in predicting user well-being. Also, [PERSON] et al. [56] evaluated wearable sensors for the real-time detection of anxiety and panic. Using ML algorithms on HR, EDA, and skin temperature data, the study emphasized wearables' potential in personalized stress intervention, though accuracy improvements are needed. Finally, the important study of [PERSON] et al. [9] focused on flexible wearable electronics for continuous health monitoring, particularly for early diagnosis and disease prevention. The study assessed advances in flexible sensors for tracking body temperature, heart rate, and blood pressure. It included analysis of detection mechanisms (e.g., piezoelectric, thermoelectric) and material design. The findings highlighted their potential for continuous home-based medical applications. At the same time, [PERSON] et al. [11] developed a wearable device for monitoring mental well-being using both social and physiological signals. The study aimed to understand the relationship between physical and mental states by collecting data on behavior, physiology, and audio. The results showed that the device could offer valuable insights into mental health monitoring, particularly for autism and anxiety contexts. Indeed, participants in digital mental health programs have reported that smartwatch-based mood and stress tracking feels less intrusive than traditional methods--offering greater privacy and convenience--which in turn improves their engagement with therapy.