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

## TL;DR

This survey reviews technological solutions for mental well-being, highlighting their potential to improve mental health monitoring and intervention through sensing devices, machine learning, and portable interfaces, despite existing challenges.

## Contribution

It provides a comprehensive overview of traditional and modern mental health technologies, discussing challenges and future opportunities in deploying effective mental well-being tools.

## Key findings

- Technologies enable real-time mental health monitoring.
- Challenges include data privacy and battery life.
- Machine learning enhances clinical decision support.

## Abstract

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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Source: https://tomesphere.com/paper/1905.00288