# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Smartwatches for Mental Health Monitoring  
The systematic review by [PERSON] et al. [20] offers a broad overview of smartwatch-based interventions aimed at improving health and well-being. Following rigorous PRISMA standards, their study analyzes the characteristics of research that incorporates such interventions, focusing on both study design and outcomes. The review shows that most interventions address specific clinical conditions--such as heart disease, diabetes, depression, and stress--and that the use of smartwatches as tools for self-management and vital sign monitoring improves both objective and subjective outcomes for patients. Moreover, the review highlights challenges related to technology, such as the need for frequent charging, the reliability of data collection, and the integration of smartwatches with other devices, all of which affect both the acceptance and practical application of these tools in clinical environments [20]. The study by [PERSON] et al. [38] also presents an innovative approach by integrating a smartwatch into a web-based intervention for depression. Their research aims to reduce the burden of self-reporting by automating the recording of parameters such as sleep and physical activity, thereby providing high-accuracy real-time data. The methodology includes a structured testing protocol that evaluates a patient's acceptance of the technology and its impact on therapeutic progress, underscoring the value of non-invasive self-monitoring systems in digital mental health interventions. Finally, the systematic review by [PERSON] et al. [39] explores the potential of wearable devices for monitoring and managing mental health disorders such as depression and anxiety. Drawing on a wide range of studies, the authors highlight the characteristics of available technologies, methodological approaches, and major challenges involved in applying wearable devices to mental healthcare. This analysis notes that although consumer devices are increasingly accessible and embedded in daily life, issues remain concerning the reliability of data collection, scalability, and the accurate measurement of psychological parameters [39]. A consistent picture emerges regarding how wearable devices can contribute to the monitoring and management of stress and mental health. While [PERSON] et al. [21] focus on the technical aspects of non-invasive physiological signal detection, the review by [PERSON] and [PERSON] [13] maps the applications of smartwatches in health, with emphasis on practical challenges and opportunities for improvement. [PERSON] et al. [PERSON] et al. (2018) expand this perspective by integrating wearable devices into therapeutic interventions for depression, and the systematic review by [PERSON] et al. [PERSON] et al. (2018) provides a comprehensive evaluation of technological solutions for monitoring mental disorders. This holistic approach highlights the necessity for continuous interdisciplinary collaboration to fully leverage the potential of wearables in mental health, thereby enhancing future therapeutic interventions and digital health applications. According to [PERSON] (2018), the Foqus app was developed as an innovative solution for managing mental conditions such as Attention Deficit Hyperactivity Disorder (ADHD) using a smartwatch. The main goal of the app is to help individuals improve their focus and reduce anxiety by offering tools based on scientific management strategies. Foqus includes three key features: the Pomodoro technique to enhance focus, guided meditation to reduce stress, and positive messaging to boost mood. The study also highlighted the importance of window size in data analysis. Longer windows (e.g., 120 s) provided higher accuracy in recognizing emotional states than shorter ones (e.g., 15 s). Specifically, by using Linear Discriminant Analysis (LDA) and combining data from skin temperature, BVP, and ECG sensors, the study achieved an anxiety recognition accuracy of 87.4%. Similarly, [PERSON] et al. [PERSON] et al.