# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 1 Introduction
(**C**) Computer-based math tasks used for this study [3]. From a clinical standpoint, smartwatches have not fully reached the diagnostic reliability of medical-grade devices but offer promising complementary utility. Some devices, such as the Apple Watch, have received FDA clearance for atrial fibrillation detection, yet many others operate under general wellness classifications. Moreover, privacy concerns regarding continuous biometric data collection remain a key ethical consideration. The aim of their research was to compare different stress prediction models using both objective indicators (predefined stress-inducing stimuli) and subjective participant self-reports. To achieve this, they supervised a controlled experiment with 32 healthy volunteers, each performing a series of stress-inducing tasks--social, cognitive, and physical--while wearing a commercially available smartwatch equipped with appropriate sensors. Throughout the tasks, the watch continuously recorded physiological signals (e.g., heart rate), and participants were periodically asked to report their perceived stress levels. This methodology allowed the researchers to develop two types of AI models: one using the objective stress events as ground truth, assuming participants were under stress during tasks, and another using participants' subjective reports to label stress episodes. The results are particularly encouraging regarding the effectiveness of smartwatches in stress detection and prediction. The best-performing algorithm--a Support Vector Machine (SVM) classifier trained on objective stress-inducing events--achieved an Area Under the ROC Curve (AUROC) of approximately 0.79, indicating a strong ability to distinguish between stress and calm states. Even when models were based on subjective self-reports of stress, performance remained solid, with AUROC values around 0.72, which is slightly lower due to the variability introduced by individual perceptions of stress. A noteworthy finding is that tailoring models to individual differences--by calibrating personal thresholds for what constitutes \"stress\"--further improved prediction accuracy. With this personalized approach, performance reached AUROC levels (0.75) comparable to the objective models. [PERSON] et al. emphasize that the accuracy achieved using a commercial smartwatch is comparable to results from more controlled, laboratory-based studies, demonstrating the potential of consumer-grade wearables in serious health applications [3]. However, they also recognize the lower reliability of models based on subjective reports, underlining the need for further research into how to best collect reliable \"ground truth\" stress data in real-world settings. In summary, these recent scientific studies highlight the complementary dimensions of the role smartwatches play in health monitoring, painting a comprehensive picture of both their potential and their challenges. The review by [PERSON] et al. outlines the broad functionality of these devices, providing the context of benefits and limitations [1]. The work of [PERSON] et al. confirms that technological value only translates into real benefit when users accept and use the devices--certain aspects are enhanced when smartwatches are viewed as both useful and user-friendly [2]. Finally, the study by [PERSON] et al. demonstrates that under suitable conditions, smartwatches can provide reliable measurements for critical health indicators such as stress, achieving results comparable to specialized medical equipment [3]. A common thread across all studies is that smartwatches have the potential to significantly contribute to health promotion: They offer continuous data that supports early diagnosis, encourage users to adopt healthier behaviors, and allow for more personalized wellness monitoring.