# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 5 Conclusions
While current limitations--such as sensor accuracy, data privacy concerns, motion artifacts, battery constraints, and inconsistent usability--pose real challenges to clinical adoption, ongoing advancements in wearable sensing, algorithm design, and user-centered development offer promising avenues for improvement. Beyond technical progress, user acceptance remains essential: Comfortable and intuitive devices are more likely to foster long-term engagement and perceived benefit. Studies already demonstrate meaningful outcomes, from the early detection of psychiatric relapses to healthier lifestyles in older adults. To fully harness the value of these tools, future work must prioritize ergonomic design, robust validation, regulatory alignment, and ethical safeguards. With these foundations in place, smartwatch-based interventions can evolve into credible assets in personalized care and scalable mental health support. Despite the progress made, several limitations hinder the widespread adoption of smartwatch-based mental health applications. The key challenges include data heterogeneity, lack of standardized validation protocols, and concerns around privacy and data security. To address these, future research should aim to establish shared datasets and evaluation standards, improve interoperability across devices, and ensure the transparent reporting of algorithm performance. Additionally, strong ethical frameworks, user trust, and practical usability must be prioritized to translate research into real-world impact. Conceptualization, N.-A.K. ; methodology, N.-A.K. and A.S.; validation N.-A.K. and A.S.; formal analysis, N.-A.K. and A.S.; investigation, N.-A.K. and A.S.; resources, N.-A.K. and A.S.; data curation, N.-A.K. and A.S.; writing--original draft preparation, N.-A.K. and A.S.; writing--review and editing, C.-N.A. [PERSON]. N.-A.K. and A.S.; supervision, C.-N.A. [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Stress detection using smartwatches with machine learning: A survey. In Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2-4 July 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 306-310. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Smartphone and smartwatch-based biometrics using activities of daily living. _IEEE Access_**2019**, \(7\), 133190-133202. [CrossRef]
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