The increasing prevalence of mobile devices among patients of all demographic groups has the potential to transform the ways we diagnose, monitor, treat, and study chronic disease. Sensors in our phones leave a stream of digital traces. Personal sensing refers to collecting and analysing data from sensors embedded in the context of daily life with the aim of identifying human behaviours and traits. The emerging paradigm of Internet of Things (IoT) and Artificial Intelligence (AI) offers unprecedented opportunities for m-Health by providing connected and intelligent healthcare solutions. IoT enables the gathering of several data about human health that can be further utilized by AI to extract useful information/trends (http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7937777).
The modern smartphone is an ideally suited technology for healthcare because it embeds a plethora of programmable sensors including gyroscope, ambient light sensor, camera, proximity sensor, microphone, digital compass, touch-sensitive screen, accelerometer, and Global Positioning System (GPS), which can be used to gather various behavioural and physiological information. Furthermore, most smartphone vendors offer a set of open software development kits (SDKs), which allows developers to develop novel m-Health applications (apps), essentially transforming smartphones into medical kits.
Ab.Acus took advantage of the experience gained while participating in the European m-Resist project (http://www.mresist.eu/) aimed at developing a therapeutic program that draws on the support of mobile devices to actively involve patients with treatment-resistant schizophrenia, to make them capable of self-managing their own illness, as well as to support their careers.
Unlike most other health conditions, the treatment of mental illness relies on subjective measurement and literature shows growing evidence in support of the feasibility of using mobile tools in severe mental illness and the clinical potential of these new tools in psychiatry. The criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behaviour in situ.
This promises to lead to more precise assessment of human behaviour and ultimately individual mental health. More refined modelling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare.
Starting from m-Resist experience, where Ab.Acus took care of data acquisition from wearable sensors (heart rate) and smartphone (positioning), Ab.Acus has developed a general purpose application that acquires quantitative data directly from the sensors and registers from user’s smartphone. Through data processing algorithms we extract features to analyse and describe patient life and behaviours’ changes.
The application is set up and managed through a dashboard available through the web.
Acquired data are saved on a cloud and periodically processed to extract descriptive features that are made available to the clinician for visualization through the same dashboard.