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Mobile Health App for Bipolar Patients Wins RWJF Mood Challenge

The mobile health app, BiAffect, helps healthcare providers and their patients identify manic and depressive episodes in people with bipolar disorder by analyzing how that person uses a smartphone.

Source: ThinkStock

By Eric Wicklund

- A mobile health app that can spot manic and depressive episodes by analyzing how someone uses a smartphone has won top honors in the Mood Challenge for ResearchKit.

It’s one of a growing number of digital health and telehealth resources – including communication apps, social media platforms and even facial and voice recognition tools - designed to measure mood disorders and give healthcare providers better insight into diagnosing and treating them.

Dubbed a “fitness tracker for the brain” by its creators, the BiAffect app beat out more than 70 other mHealth apps in the year-long challenge. The winning team receives $200,000 and will prepare the app for launch in Apple’s App Store.

BiAffect was created by researchers from the University of Illinois at Chicago, the University of Michigan and Sage Bionetworks. Designed for people diagnosed with bipolar disorder, it tracks keystroke behaviors and looks for variations common to those experiencing manic episodes, such as typing short, quick sentences or repeatedly ignoring auto-correct suggestions.

“During a manic episode, people with bipolar disorder exhibit some common behaviors, such as talking really, really fast, with diminished self-control and flight of ideas,” Alex Leow, an associate professor of psychiatry at the UIC College of Medicine, a professor of bioengineering and computer science and the winning team’s leader, said in a press release issued by UIC. “It is thus natural that they also exhibit similar abnormalities in non-verbal communications that are typed on their phones.”

“People in the midst of a manic episode commonly have reduced impulse control, so it is not surprising that our pilot data supported that they tend to blow through the spell-check alerts,” she added.

Leow and Peter Nelson, a professor of computer science and Dean of the UIC College of Engineering, say the app will give healthcare providers a tool to study not only mood disorders like bipolar disorder, which affects some 5.7 million Americans, but also cognitive disorders like Parkinson’s and Alzheimer’s disease.

“Unobtrusively monitoring health from an iPhone combines low-cost scalability with far-reaching impact to potentially improve the lives of millions of people,” said Nelson, whose adult son was diagnosed with bipolar disorder in college.

“I began working on this idea many years ago as a way to help my son, and to see it come to fruition with this kind of recognition, and to know that the app will be out there to help people get a better understanding of this disorder is thrilling,” he added.

The Mood Challenge, a New Venture Fund program funded by the Robert Wood Johnson Foundation, makes use of Apple’s ResearchKit platform, which has spawned dozens of mHealth programs and projects targeting a wide variety of population health concerns.

“This program will help advance mood research in the U.S.,” Paul Tarini, senior program officer for the RWJF, said in a separate press release. “The participating studies are showing how platforms like ResearchKit offer the potential for new breakthroughs in mental health research, and we’re excited to see BiAffect’s novel work in bipolar disorder research.”

BiAffect was one of five teams recommended by a panel of judges to participate as semi-finalists in the Virtual Accelerator. Each team received $20,000 and mentorship and attended a 2-day boot camp, where BiAffect and Aware Study were named finalists. Each received an additional $100,000 to develop their ResearchKit designs into prototypes and pilot with users.

The runner-up in the challenge, Aware Study, focuses on the estimated 5 million to 7 million adults in the U.S. who are living with PTSD and millions more who might have symptoms but are undiagnosed, including veterans, police, fire and rescue workers and others in high-risk situations. The app is designed to passively collect data from users over a 28-day period, using weekly surveys and two daily tasks.

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