Trajectories of Depression: Investigating the Correlation between Human Mobility Patterns and Mental Health Problems by means of Smartphones

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Depression does not only affect the personal life of individuals and their families and social circles but it has also a strongly negative economic impact as shown in several reports. According to a recent study, workers in the United Kingdom suffer high levels of depression than those anywhere else in Europe. The survey found that 1 in 10 employees had taken time off at some point in their working lives because of depression problems. Novel strategies for tackling the problem of depression and preventing suicides are needed. We believe that new emerging technologies, in particular mobile ones, together with the possibility of mining large amount of data in real-time can help to tackle this problem in new and more effective ways. Existing interview-based studies have shown that depression is significantly associated with a marked decline of physical activity. The goal of this project is to investigate how mobile phones can be used to collect and analyse mobility patterns of individuals in order to understand how mental health problems affect their daily routines and behaviour and how potential changes can be automatically detected. In particular, mobility patterns and levels of activity can be quantitatively measured by means of mobile phones, exploiting the GPS receiver and the accelerometers embedded in the devices. The data can be extremely helpful to understand the behaviour of a depressed person, and in particular, to detect potential changes in his or her behaviour, which might be linked to a worsening depressive state. By monitoring this information in real-time, health officers and charity workers might intervene by means of digital behaviour intervention delivered through mobile phones or by means of traditional methods such as by inviting the person for a meeting or by calling him or her by phone.In order to support these novel applications, it is necessary to build mathematical tools for analysing the mobility traces in real-time for the detection of gradual or sudden changes related to the emotional states of the individual. More specifically, we plan to devise analytical techniques for studying the relationships between human mobility patterns and emotional states. We plan to use existing datasets of human mobility and to collect data by means of a smartphone application distributed to people affected by depression. This can be considered as a sort of pilot study for a wider deployment of these technologies and it will provide a sound theoretical basis for further studies in this area. Finally, a key aspect of the proposed research work is the implementation of mechanisms for preserving the privacy of the individuals involved in the study.