The Heritage Open mHealth Challenge announced its $100,000 prize winner at this week’s Health Datapalooza IV conference, awarding the top spot to an app that helps patients with bipolar disorder monitor their daily moods and activities to aid their treatment. The app, called MoodRhythm, lets patients input data about their habits and social interactions, integrating the information into clinical decision support while taking advantage of the Open mHealth architecture that allows communication and sharing between mobile apps and other systems.
“Rhythms guide our lives,” said Dr. Tanzeem Choudhury, team leader and Professor of Information Science at Cornell University. “Our biological clocks tell us when we need to sleep, eat and wake. When these rhythms are interrupted or obstructed, it can be difficult for our bodies to get what they need to stay healthy and balanced. The combination of automatic sensing and self-tracking aims to provide long-term low-maintenance support for people with bipolar disorder. The clinicians and patients who have used MoodRhythm to date have found it to be an enormously valuable tool for monitoring social rhythms and mood and for seeing the relationship between the two.”
The competition, launched in January, was cosponsored by the Heritage Provider Network, Open mHealth, and UCLA. Teams were required to submit a demo of their mobile app, and include team members with clinical experience and a user acting as a patient. Four finalists were also selected, ranging from software designed to improve mobility for obese and arthritic patients, treat other psychological disorders, and manage chronic diseases such as COPD and asthma.
“The Challenge was a great opportunity to encourage the development of shared platforms and the integration of different tools,” said Dr. Brian Quinn, one of the Challenge judges. “These are critical steps if we’re going to realize the potential of mobile health technologies to improve health. Among several promising applications, Mood Rhythm stood out because of its elegant approach to collecting data in a way that can truly improve [the] ability of patients and their doctors to make better decisions about treating bipolar disorder.”