The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

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Mental disorders currently affect more than 500 million people in the world, among which, substance use disorders, such as alcohol, represent a major social problem, especially in developing countries. The use of mobile technologies in combination with behavioral and intervention sampling methodologies such as EMA and EMI allow a collection of information and intervention in real-time and in natural environments, which is of great benefit when dealing with patients suffering from mental disorders both in diagnosis and treatment. This work shows the stages of research, design, and development of experiments of a 4-year doctoral work, which aims to identify challenges at the intersection between mobile technologies and alcohol use disorder and propose different solutions to them.

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