# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Stress Management
Using the \"Stress-Predict Dataset\" [6] with data from 35 participants wearing the Empatica E4 smartwatch, the study found that the LSTM model achieved a stress detection accuracy of 91.78%, demonstrating the potential of this technology for real-time stress monitoring and early diagnosis. [PERSON] et al. [36] explored the use of commercially available smartwatches (e.g., Samsung Watch, Apple Watch) for detecting stress in daily life by monitoring biometric data such as heart rate and combining it with user-reported contextual data. The study involved two stages: In a lab, 26 participants took part in controlled experiments to record physiological stress responses, while in real-world settings, 18 participants were monitored. The method achieved an F1 score of 0.84 in lab conditions and 0.71 in real-life conditions, indicating promising results for real-world stress detection using consumer wearables. In addition, [PERSON] et al. [4] investigated the use of electrodermal activity (EDA) from wearable sensors (e.g., smartwatches) for stress detection. The study evaluated different machine learning algorithms using four public datasets [4] containing EDA data collected in real-world contexts. Five ML models were tested to classify stress versus non-stress conditions, with the Support Vector Machine (SVM) achieving the highest accuracy at 92.9%. Figure 3: Comparison of machine learning algorithms employed for stress detection based on smartwatch biosignals (e.g., HRV, sweat cortisol), highlighting model accuracy and data integration methods [36]. [PERSON] and [PERSON] [5] conducted a review of various stress detection methods using smartwatches and machine learning algorithms. Their methodology analyzed physiological signals such as HRV, GSR, skin temperature, and sleep changes. Algorithms like SVM, KNN, and Random Forest were used for classification. The findings indicated that combining physiological signals with SVM provided the most accurate stress detection. Also, [PERSON] et al. [37] examined the use of smartwatch data and social media analysis for mental health diagnostics. Their aim was to develop a methodology to detect users' emotional states based on social media activity and identify abnormal patterns through smartwatch-collected data. Heart rate, stress levels, and other biometrics were analyzed alongside sentiment analysis to support mental health professionals in decision-making. The collective evidence demonstrates that smartwatch technology offers significant promise for stress detection, health monitoring, and well-being, yet it also reveals critical limitations and trade-offs. On one hand, these wearables can continuously capture key physiological indicators--from heart rate variability to electrodermal activity--enabling the early detection of stress-related anomalies and encouraging users toward healthier habits. This suggests that tailoring stress detection to each user's physiology can mitigate variability and improve reliability. However, translating these successes to real-world environments remains an ongoing challenge. Studies that moved from the lab to daily life report a drop in model accuracy (e.g., F1 scores declining from 0.84 in controlled tests to 0.71 in free-living conditions), highlighting how factors like context and motion introduce noise that algorithms must learn to handle. Likewise, what constitutes \"ground truth\" stress is much harder to pin down outside the lab--self-reported stress levels can be subjective and inconsistent, underscoring the need for better methods of labeling and validating stress in natural settings. Controlled trials echo these positive trends; a randomized trial reported that using a smartwatch led to better stress management, higher physical activity, and increased self-esteem compared to relying on mobile phone apps alone.