# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Stress Management
Their approach focuses on a range of physiological measurements--such as heart activity monitoring (with emphasis on HRV changes), electrodermal activity (EDA), and other complementary data--that are combined with machine learning techniques to distinguish between stress and relaxation states. The research methodology includes an analysis of different contexts in which the measurements are taken, including laboratory settings, workplaces, car commutes, and everyday unrestricted environments. The authors also thoroughly examine challenges in data collection--such as synchronizing multiple sensors, the limitations of non-invasive measurement methods, and energy conservation issues in real-world applications. Through active analysis and the integration of theoretical and practical dimensions, their study demonstrates the evolving potential of stress detection technologies and paves the way for future interdisciplinary applications in digital health. Additionally, the app includes guided meditation tools that help users lower anxiety through breathing and relaxation techniques. Users can set session duration and breathing cycles, while the app also provides feedback on cardiovascular health (e.g., measuring heart rate before and after sessions). The study's results showed a decrease in anxiety and immediate improvements in users' sense of well-being following the meditation sessions. Moreover, [PERSON] [22] explored stress detection using commercially available smartwatch sensors, examining whether anxiety can be detected without the need for specialized sensors like electrodermal activity (EDA). The study found that anxiety can be accurately detected using only built-in smartwatch sensors, such as heart rate monitors and skin temperature sensors. The findings revealed that stress detection can be achieved effectively even without EDA, provided that the right sensor data (e.g., heart rate, skin temperature) and appropriately configured analysis windows are used. The study of [PERSON] et al. [23] examined the utility of smartwatches in predicting relapses in patients with severe mental illness (SMI). Participants used smartwatches to monitor heart rate, activity, and sleep patterns, with results confirming that EDA and activity data were particularly valuable for tracking changes and enabling early warnings of relapse. Moreover, [PERSON] et al. [24] conducted a systematic review on wearable devices for anxiety monitoring. Their research examined the full process from data collection and preprocessing to machine learning models for detecting anxiety using sensor data (e.g., ECG, EDA, skin temperature). The study emphasized the need for accurate data collection and highlighted that combining wearable devices with ML techniques yields high-accuracy stress detection models while also calling for a deeper understanding of physiological-psychological correlations and algorithm refinement. Also, [PERSON] et al. [25] explored the use of both wearable and environmental sensors in mental health monitoring, emphasizing how real-time physiological tracking (e.g., cardiorespiratory frequency, skin temperature) can empower patients and enable early diagnosis and adaptive treatment. They stressed the transformative potential of wearables in everyday life for those affected by mental disorders. At the same time, [PERSON] et al. [26] evaluated the performance of smartwatches like Apple Watch Series 6 and Fitbit Sense in stress detection. Through experimental tasks using the Multi-Attribute Task Battery II (MATB-II), data collected from HR and HRV sensors were compared with subjective stress reports using the NASA-TLX index. The results showed that while participants reported significantly higher stress during high workload conditions, smartwatch data (HR and HRV) did not reflect these changes accurately. This suggests that smartwatch cardiac data may not reliably mirror perceived stress, though future enhancements in signal interpretation could improve accuracy. Additionally, [PERSON] et al.