4.7 Article

Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being

期刊

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 13, 期 3, 页码 1216-1235

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2020.3015018

关键词

Stress; Monitoring; Tools; Sensors; Biomedical monitoring; Stress measurement; Mood; Pervasive computing; mental well-being; machine learning; ubiquitous computing; physiological measures; diagnosis or assessment; health care

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Mental health problems are increasing globally, putting pressure on national healthcare systems. Mental disorders are often linked to stigma, financial burden, and lack of resources. Technology for mental well-being has attractive properties, allowing for advanced clinical monitoring. However, challenges such as data collection, privacy, and battery life need to be carefully addressed.
Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.

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