My name is Mirco Musolesi - I am an academic working at the School of Computer Science at the University of Birmingham, UK. My group is actively involved in projects on mobile sensing and (big) data analytics.
I would like to introduce our MoodTraces app developed at the University of Birmingham, UK:
The application has been developed as part of the project "Trajectories of Depression"(http://www.cs.bham.ac.uk/research/projects/tod/). The goal of the project is to investigate the use of mobile technologies for mental health problems prevention.
The data collected by the app will be used to perform a statistical analysis of the correlation between mood and mobility patterns.
We really welcome your feedback!
More details below (there is also a prize!).
Mirco (on behalf of Luca Canzian, the main developer of the app).
*** EPSRC-funded Trajectories of Depression Project***
*** MoodTraces Application***
Trajectories of Depression(http://www.cs.bham.ac.uk/research/projects/tod/) is an EPSRC-funded project with the goal of investigating the use of mobile technologies for mental health problems prevention.
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.
As a part of this project, we have developed the Android application MoodTraces(http://www.cs.bham.ac.uk/research/projects/tod/MoodTraces.html). MoodTraces is a mobile phone application that periodically samples location, activity, and application usage, and emotional state of a person in order to understand the correlation between mobility behaviour and his/her mood.
Among all the participants that will download the application in their phone and will complete the daily questionnaire at least 50 times in a two month period, we will select (through a lottery) one winner of a Nexus 5 mobile phone and five winners that will receive a £ 10 Amazon voucher each.
You can download the application at this URL directly from Google Play:
We would like to thank you in advance for your downloading the application and for contributing to this research project!
More detailed information about the application can be found below.
Each questionnaire consists of eight yes-no questions, possibly followed by a 5-choice question. The first eight questions are about the occurrence of specific depressive symptoms in the last day; the answers provided by the user are used to compute the PHQ-8 score to assess the presence of a depressed mood and how this varies in time. The last question is a personality question; the answers provided by the user across multiple days are used to compute the Big Five score to classify the user personality into one of the five broad factors of personality traits. Each questionnaire takes less than 1 minute to complete.
The collected information is sent via a secure transmission protocol to a secure server. The application tries to always use Wi-Fi to transfer the information. Cellular data connection will only be used if there has not been an upload for a long interval of time. In any case, the amount of data transferred is minimal: less than 1MB per day. The information is stored in a database whose access will be granted to the MoodTraces research team only, and solely for the purpose of studying the correlation among the mobility traces, the mobile phone usage, and the emotional state of individuals. All the information will be anonymised and any published results will not refer to any single individual and will not reveal participants' identities.
The study has the full approval of the Ethics Review Board of the University of Birmingham.
Luca Canzian (primary contact): http://www.cs.bham.ac.uk/~canzianl/
Mirco Musolesi (PI of the project): http://www.cs.bham.ac.uk/~musolesm/
School of Computer Science, University of Birmingham
Edgbaston B15 2TT Birmingham, United Kingdom