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The following research has been conducted using the open source tools that we describe in these pages. You can add to this page via a pull request here, or by getting in touch.
OSN Mood Tracking: Exploring the Use of Online Social Network Activity as an Indicator of Mood Changes.
J. Lee, C. Efstratiou, L. Bai.
Workshop on Mental Health Sensing and Intervention in conjunction with UBICOMP'16, 12-16 Sep 2016, Heidelberg, Germany.
Happier People Live More Active Lives: Using Smartphones to Link Happiness and Physical Activity.
N. Lathia, G. M. Sandstrom, C. Mascolo, P. J. Rentfrow.
In PLoS ONE. Accepted July 2016.
Putting Mood in Context: Using Smartphones to Examine How People Feel in Different Locations.
G. M. Sandstrom, N. Lathia, C. Mascolo, P. J. Rentfrow.
Journal of Research in Personality.
The Feasibility of a Context Sensing Smoking Cessation Smartphone Application (Q Sense): A Mixed Methods Study.
F. Naughton, S. Hopewell, N. Lathia, R. Schalbroeck, C. Brown, C. Mascolo, S. Sutton.
JMIR mHealth uHealth.
Behavior vs. Introspection: Refining prediction of clinical depression via smartphone sensing data.
A. Farhan, C. Yue, R. Morillo, S. Ware, J. Lu, J. Bi, J. Kamath, A. Russell, A. Bamis, and B. Wang.
Proceedings of IEEE Wireless Health Conference, October 2016.
My Phone and Me: Understanding People's Receptivity to Mobile Notifications.
A. Mehrotra, V. Pejovic, J. Vermeulen, R. Hendley and M. Musolesi.
In Proceedings of the 2016 ACM International Conference on Human Factors in Computing Systems (ACM CHI'16). San Jose, CA, USA. May 2016.
Trajectories of Depression: Unobtrusive Monitoring of Depressive States by means of Smartphone Mobility Traces Analysis.
L Canzian, M. Musolesi.
In ACM International Joint Conference on Pervasive and Ubiquitous Computing. Osaka, Japan. September, 2015.
Designing Content-driven Intelligent Notification Mechanisms for Mobile Applications.
A. Mehrotra, M. Musolesi, R. Hendley, V. Pejovic.
In ACM International Joint Conference on Pervasive and Ubiquitous Computing. Osaka, Japan. September, 2015.
Opportunities for Smartphones in Clinical Care: The Future of Mobile Mood Monitoring.
G. Sandstrom, N. Lathia, C. Mascolo, P. Rentfrow.
The Journal of Clinical Psychiatry. Accepted May 2015.
InterruptMe: Designing Intelligent Prompting Mechanisms for Pervasive Applications.
V. Pejovic, M. Musolesi.
In ACM International Joint Conference on Pervasive and Ubiquitous Computing. Seattle, WA, USA. September 2014.
SenSocial: A Middleware for Integrating Online Social Networks and Mobile Sensing Data Streams.
A. Mehrota, V. Pejovic, M. Musolesi.
In Proceedings of the 15th ACM/IFIP/USENIX International Middleware Conference. Bordeaux, France. December 2014.
Contextual Dissonance: Design Bias in Sensor-Based Experience Sampling Methods.
N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow.
In ACM International Joint Conference on Pervasive and Ubiquitous Computing. Zurich, Switzerland. September, 2013.
Open Source Smartphone Libraries for Computational Social Science.
N. Lathia, K. Rachuri, C. Mascolo, G. Roussos.
In 2nd ACM Workshop on Mobile Systems for Computational Social Science. Zurich, Switzerland. September 8, 2013.
Emotion Sense. Emotion Sense is an Android application developed by researchers at the University of Cambridge that lets you explore how your mood relates to the data that your smartphone can invisibly capture as you carry it throughout the day.
Easy M. Easy M is an application for researchers to conduct experience sampling studies that collect smartphone sensor data.
Q Sense. Q Sense is a smartphone app being developed by researchers at the University of Cambridge for people who want to quit smoking. It works by learning about your high-risk locations and situations before you quit, so that it can send situation-specific personalised support once you begin your quit attempt.
Mood Traces is an Android application for statistical analysis of mobility patterns developed at the University of Birmingham. The data collected will be analyzed to better understanding the correlation between mobility patterns, activity patterns, and emotional states of individuals.
My Phone and Me can help you to monitor your phone usage pattern and your interaction with notifications. The app presents an avatar which tells your addiction level at the current moment. Also, it shows the amount of time the phone has been used, the app which is used the most and the app which triggers most notifications. Moreover, the app offers you to visualise your phone activities such as hourly phone usage, hourly usage of an individual app, and your interaction with notifications.