MoodTraces is an Android application for statistical analysis of mobility patterns. It periodically samples phone's sensors - including location, activity, and application usage - and collect the answers to questionnaires concerning the user emotional state. The data collected will be analyzed to better understanding the correlation between mobility patterns, activity patterns, and emotional states of individuals.
MoodTraces is a mobile phone application that periodically samples location, activity, application usage, and asnwers to questionnaires. The information collected in this way will be sent on a central server and will be analysed. We expect that the results of this analysis will identify patterns among the mobility traces, the mobile phone usage, and the emotional state of individuals, which can be used to develop technologies that can help the end user in his day-to-day life.
This application will collect data from the phone's sensors at periodic intervals, including::
1) Current location;
2) The activity that the user is performing among the following types of activities: in vehicle, on bicycle, on foot, running, walking, still, unknown;
3) The list of the applications that are running on the phone;
4) The number of characters and of words of the inbound and outbound SMS messages;
5) The duration of the incoming and outgoing calls.
The application will NOT collect the number or the identity of the callee/caller or the recipient/sender of the messages. The content of messages and calls is not collected either.
This application will also collect the answers that the participant will provide to daily questionnaires. Each questionnaire consists of nine yes-no questions and one ot two 5-choice questions. The yes-no questions concern the occurrence of specific depressive symptoms in the current day (e.g., whether the participant had trouble falling or staying asleep, or sleeping too much, etc.); these are the questions of the widely-used “PHQ-8” depression test. The answers to these questions will be used to assess the presence of a depressed mood and how this varies in time. The multi-choice questions are personality questions (e.g., to what extent the participant see himself as reserved, trusting, lazy, etc.); these are the questions of the standard personality test “Big Five Inventory”. The answers to these questions across multiple days are used to classify user's personality into one of the five broad factors of personality traits. Each questionnaire takes less than 1 minute to complete. Finally, the partipant has also the possibility to report its current mood (e.g., to what extent he/she feels happy in that moment) whenever he/she wants. These reports will be used for visualization purposes.
If you have a phone running Android OS, simply download the app from the Google App Store and install it. In order to start your participation you need to agree with our terms that will be clearly presented to you. In case you have any questions, do not hesitate to contact us. After you agree with the terms, fill out a registration form that asks, among other things, to select a password, which is needed in case you wish to access or delete the data collected from your phone. Once you installed the application, agreed with the terms and filled out the form, the application will run on your phone. The application will run quietly in the background and will not interfere with your daily mobile phone usage.
Please, note that in order to participate to this experiment you must be older than 18 years old and you must be the sole prominent user of the mobile device in which the application will be installed.
Unfortunately, due to a wide variety of phones we cannot guarantee that the application will run flawlessly on your specific phone, but we welcome any feedback and will try to fix any application errors as soon as possible.
If for any reason you want to withdraw from the experiment you can easily do so by uninstalling the application. Simply follow the instructions for uninstalling an Android application, and uninstall MoodTraces. No further data sensing will be performed.
Until 1 September 2015 you have also the possibility to remove from the database all or any portion of your data. If you want to use this option, please email us with "withdrawal" in the subject line or talk to us in person during one of the application demo sessions.
Data that are not removed from the database before 1 September 2015 will be preserved and accessible for ten years.
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 months span, 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.
Moreover, we hope that you will find the application useful, as it will allow you to visualize your mobility traces, and the activities you performed and the emotional states you experienced in different locations and at different times.
Finally, we will send you a summary of the results of the study.
We try to minimize the impact of the application on the user’s life and phone. The application will be energy-efficient and will run in the background without hindering or effecting the user’s day-to-day tasks, unless the user deliberately brings it to the front in order to view the front-end of the application, which allows the user to visualize, through an interactive map, part of the collected data.
The application will perform occasional uploads and downloads, however the application will be developed in such a way that it will always prefer using Wi-Fi to transfer the information. Cellular data connection, which may cause the user to incur mobile data plan charges, will only be used if there has not been an upload for a long interval of time. However, the amount of data transferred will be minimal: less than 1MB per day. In case that a user finds the application too burdensome, he/she can simply uninstall it.
Once the research is complete we will notify the user about it, such that the user can uninstall the application from the device and, consequently, stop the data collection process.
The mobile phone application data collection and on-the-phone storage is designed in accordance to the state-of-the-art best practices in application development. This data will be inaccessible by any other (malicious) application installed on the same phone. To assure that the data is unreadable to any third party in case that a phone is lost/stolen, the application will encrypt all the data that will be stored on the phone's memory card.
Personal information such as contacts (telephone numbers, email addresses, etc.) and the contents of the conversations and of the SMS messages will not be collected by the application.
The collected information will be transferred to the server using secure transmission. The server itself is at a secure location in the Computer Science building of the University of Birmingham. Login to the server is only available to researchers involved in the MoodTraces experiment and members of Networked Systems and Data Science Lab, for research purposes only.
This application is primarily used as a research tool, and we do not plan to release it on Google Play or support it as an open source project. However, you might be interested in open source libraries for smartphone sensing, sensor data management and notification triggering. Such libraries have been developed by Neal Lathia and Kiran Rachuri, University of Cambridge and are available here.
We thank Neal Lathia and Kiran Rachuri, University of Cambridge, for the development of useful open source libraries that are extensively used in the MoodTraces application. Specifically, we exploit the ES Sensor Manager library to collect data from some smartphone sensors, and the ES Sensor Data Manager library to manage the collected data and upload them to a server.
We thank Veljko Pejovic, University of Birmingham, for the development of the Android application InterruptMe,
that served as a starting point for the development of the MoodTraces application.
For more information about the InterruptMe application, we refer the reader to:
V. Pejovic and M. Musolesi, "InterruptMe: Designing Intelligent Prompting Mechanisms for Pervasive Applications", to appear in UbiComp'14, Seattle, WA, USA, September 2014.