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

Guardado en:
Udgivet i:Algorithms vol. 18, no. 7 (2025), p. 419-444
Hovedforfatter: Nikoletta-Anna, Kapogianni
Andre forfattere: Sideraki Angeliki, Anagnostopoulos Christos-Nikolaos
Udgivet:
MDPI AG
Fag:
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
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Resumen: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
Fuente:Engineering Database