Rhythms guide our lives. Our biological clocks tell us when we need to sleep, eat and wake. But our use of technology can interrupt and obstruct these rhythms, making it difficult for our bodies to get what they need to stay healthy and balanced. MoodRhythm helps individuals with bipolar disorder to live a more naturally rhythmic day, ensuring that they can maintain balanced and healthy lives.
It is based on a clinically validated therapy, Interpersonal Social Rhythm Therapy, developed by Dr. Ellen Frank at University of Pittsburgh. The key goals of MoodRhythm are to use patients’ smartphones to both actively and passively track daily rhythms and to provide affective feedback that can help patients to maintain a regular daily rhythm, while feeding this clinically valuable information back to their physicians.
Mark Matthews, Saeed Abdullah, Elizabeth Murnane, Steven Voida, Tanzeem Choudhury, Geri Gay, Ellen Frank. Development and evaluation of a smartphone-based measure of social rhythms for bipolar disorder. Special Issue of Assessment on measuring, modeling, and implementing dynamic processes. Assessment (ASM) (In Press).
Saeed Abdullah, Mark Matthews, Ellen Frank, Gavin Doherty, Geri Gay, Tanzeem Choudhury. Automatic detection of social rhythms in Bipolar Disorder. Journal of the American Medical Informatics Association (JAMIA).
Mark Matthews, Stephen Voida, Saeed Abdullah, Gavin Doherty, Tanzeem Choudhury, Sangha Im, and Geri Gay. In Situ Design for Mental Illness: Considering the Pathology of Bipolar Disorder in mHealth Design. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI, 2015).
The MoodRhythm project has won the $100K Heritage Open mHealth Challenge.