Beyond Mobile Apps: A Survey of Technologies for Mental Well-being (original) (raw)
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A review about Technology in mental health sensing and assessment
ITM Web of Conferences
Information and communication technologies (ICT) such as smart devices, the Internet of Things and wireless sensor networks are gradually being introduced into the health system for early diagnosis and management of certain diseases. The state of the art of the use of these technologies in mental health identified 37 articles published in indexed high impact journals in the period 2003-2021. The snowball sampling method was used to select these papers. From this literature review, it appears that several of these technologies are used to support the early detection of mental disorders. Various systems based on wearable sensor networks, the Internet of Things and pervasive and ubiquitous computing have been designed and implemented in this sense. However, most of the applications are designed for academic purposes. 29% of the papers deal with the use of mobile technology in the detection of mental illness, while 67% have studied other technologies such as wearable sensor networks. 4%...
Current practices in mental health sensing
XRDS: Crossroads, The ACM Magazine for Students, 2021
The ubiquity of smartphones and wearables makes it an attractive option to passively study human behavior. We explore the current practices of using passive sensing devices to assess mental health and wellbeing, including the limitations and future directions.
Mobile psychiatry: Personalised Ambient Monitoring for the mentally ill
2011
Mental health has long been a neglected problem in global healthcare. The social and economic impacts of conditions affecting the mind are still underestimated. However, in recent years it is becoming more apparent that mental disorders are a growing global concern that is not to be trivialised. Considering the rising burden of psychiatric illnesses, there is a necessity of developing novel services and researching effective means of providing interventions to sufferers. Such novel services could include technology-based solutions already used in other healthcare applications but are yet to make their way into standard psychiatric practice. This thesis presents a study on how pervasive technology can be utilised to devise an “early warning” system for patients with bipolar disorder. The system, containing wearable and environmental sensors, would collect behavioural data and use it to inform the user about subtle changes that might indicate an upcoming episode. To test the feasibili...
Journal of medical Internet research, 2018
Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition. The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data. Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32). On average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relativel...
A Survey on Wearable Sensors for Mental Health Monitoring
Sensors
Mental illness, whether it is medically diagnosed or undiagnosed, affects a large proportion of the population. It is one of the causes of extensive disability, and f not properly treated, it can lead to severe emotional, behavioral, and physical health problems. In most mental health research studies, the focus is on treatment, but fewer resources are focused on technical solutions to mental health issues. The present paper carried out a systematic review of available literature using PRISMA guidelines to address various monitoring solutions in mental health through the use of wearable sensors. Wearable sensors can offer several advantages over traditional methods of mental health assessment, including convenience, cost-effectiveness, and the ability to capture data in real-world settings. Their ability to collect data related to anxiety and stress levels, as well as panic attacks, is discussed. The available sensors on the market are described, as well as their success in providin...
Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring
Frontiers in Digital Health, 2021
Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavi...
Towards personalised ambient monitoring of mental health via mobile technologies
Technology and health care : official journal of the European Society for Engineering and Medicine, 2010
Managing bipolar disorder is an important health issue that can strongly affect the patient's quality of life during occurrences of depressive or manic episodes and is therefore a growing burden to healthcare systems. A widely practised method of monitoring the course of the disorder is by mood and general mental health questionnaires, which are nowadays often implemented on mobile electronic devices.Detecting changes to daily routine and behaviour is of crucial importance as they can be symptomatic of an ongoing episode, or in the case of an external cause, may trigger such an episode.Current mobile phones and geospatial technology provide a means of monitoring aspects of daily routine and lifestyle which may be valuable in facilitating self-management of the condition.This manuscript introduces a methodology for analysing data obtained from a simple wearable system based on a mid-range mobile phone, along with trial results from a control group of three participants with no hi...
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
JMIR mHealth and uHealth, 2016
Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and provid...
Journal of medical Internet research, 2017
There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, c...