Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review

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Dades bibliogràfiques
Publicat a:	Algorithms vol. 18, no. 7 (2025), p. 419-444
Autor principal:	Nikoletta-Anna, Kapogianni
Altres autors:	Sideraki Angeliki, Anagnostopoulos Christos-Nikolaos
Publicat:	MDPI AG
Matèries:	Physiology Accuracy Mental health User behavior Biometrics Heart rate Artificial neural networks Signal processing User experience Personal health Privacy Machine learning Data collection Smartwatches Perceptions Stress Support vector machines Skin temperature Sensors Medical research Wearable computers Design Algorithms Technology Acceptance Model Systematic review
Accés en línia:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripció
Resum:	This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. Smartwatches, equipped with sensors for physiological signals such as heart rate, heart rate variability, electrodermal activity, and skin temperature, have demonstrated promise in detecting and predicting stress and mood fluctuations in both clinical and everyday contexts. This review emphasizes the need for interdisciplinary collaboration to advance technological precision, ethical data handling, and user experience design. Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). Furthermore, it identifies performance trends and challenges across lab-based vs. real-world deployments, emphasizing the trade-off between generalizability and personalization in model design.
ISSN:	1999-4893
DOI:	10.3390/a18070419
Font:	Engineering Database