# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Stress Management
[27] analyzed the contribution of wearables and IoT in mental well-being. Using activity and sleep data from the DISCover project, they built a logistic regression model that correlated changes in PHQ-9 scores with step count patterns, noting that adding emotional parameters could improve mental health detection accuracy (F1 score = 0.66). Also, [PERSON] et al. and [PERSON] [28] studied the integration of wearables in Virtual Reality (VR) environments. Using smart wristbands to provide biofeedback based on heart rate, they found that physiological integration improved the VR user's experience and engagement, suggesting potential for emotional regulation and stress management. [PERSON] et al. [8] developed a real-time stress detection system using smartwatches that monitored physiological signals such as galvanic skin response (GSR), heart rate variability (HRV), and body temperature (BT). Their experimental protocol confirmed that smartwatch-derived data can reliably support the diagnosis and tracking of psychological stress. Also, [PERSON] et al. [10] introduced Resp-BoostNet, an enhanced neural network model that integrates respiration rate prediction to improve smartwatch-based stress detection accuracy. Using heart rate and GSR data, the model achieved a 94% accuracy rate, outperforming previous techniques. Also, [PERSON] and [PERSON] [29] examined the use of wearables in supporting mental health and well-being by tracking biological data such as sleep quality, physical activity, and alcohol consumption using ECG and PPG sensors. Their results showed that smartwatches are effective tools for the continuous monitoring of mental states, highlighting the value of real-time data for personalized care and stress management. [PERSON] et al. [30] conducted a randomized controlled trial examining the psychological benefits of wearable technology. The study found that smartwatches significantly improved physical activity and stress management, boosting self-esteem and quality of life more than mobile apps alone. Moreover, [PERSON] et al. [31] explored HRV-based stress detection during Trier Social Stress and Stroop tests using ECG sensors. Their algorithms achieved 96.31% accuracy, suggesting that smartwatches are viable for detecting acute stress in daily life. Also, [PERSON] et al. [32] compared the HR accuracy of commercial wearables (Apple Watch and Fitbit) to medical ECGs. The results showed acceptable deviations (1.3 bpm for Apple Watch and 9.3 bpm for Fitbit), confirming their utility for non-clinical HR tracking, though limitations remain for medical use. [PERSON] et al. [33] investigated HRV monitoring via smartwatches for anxiety management. Their findings support the use of real-time biofeedback apps (e.g., breathing or meditation guidance) tailored to HRV patterns, though sensor accuracy remains an area for future enhancement. At the same time, [PERSON] and [PERSON] [34] proposed a continuous stress detection framework using smartwatch data and ensemble classifiers. Using the WESAD dataset [34] and methods like Discrete Wavelet Transform (DWT), their model classified emotional states (stress, joy, and calm) with promising results, particularly for healthcare workers under chronic stress. According to a study by [PERSON] et al. [6], their aim was to investigate the utility of sensors in smartphones and smartwatches for biometric recognition and identification using a variety of Activities of Daily Living (ADLs). The research explored several motion sensors, such as accelerometers and gyroscopes, to detect movement patterns used in user identification and device management. The results demonstrated that data from 18 different ADLs were successfully used for user recognition and authentication, paving the way for continuous and automatic biometric identification using mobile devices. [PERSON] et al. [35] focused on the use of Long Short-Term Memory (LSTM) models to detect stress through physiological signals collected from smartwatch sensors, such as heart rate, electrodermal activity (EDA), and skin temperature.