BiAffect in the News
Dr. Alex Leow & Dr. Peter Nelson create an app to identify users' moods
BiAffect, a phone app that can detect when a user is having a depressive or manic episode, was developed by UIC faculty members Dr. Alex Leow, associated professor of psychiatry, bioengineering, and computer science and Dr. Peter Nelson, professor of computer science and Dean of UIC College of Engineering. This app was built in collaboration with the University of Michigan, Arbormoon Software and Sage Bionetworks.
The app works by collecting metadata such as typing speed, use of spell check, frequency, and lengths of text to determine a user's mood. By monitoring this metadata - but not the actual text - researchers are able to detect the onset of a depressive or manic episode. This innovation can be useful for the 5.7 million American adults who suffer from bipolar disorder, and the 3 million American teenagers who suffer from depression.
BiAffect won the Mood Challenge for Research Kit, a contest administered by the New Venture Fund program by the Robert Wood Johnson Foundation. The goal of the contest was to encourage innovators to create ways to better study mood disorders using the open-source platform: Apple's Research Kit. BiAffect won the award in May 2017, and the $200,000 grand prize, which was used to continue development on the App.
App is available in App Store here: BiAffect ResearchKit Mood Study
See more articles about BiAffect here:
Rolling Stone: Could Smartphone Apps Curb Teen Depression?
New York Times: Detecting Depression: Phone Apps Could Monitor Teen Angst.
Associated Press: Detecting Depression: Phone Apps Could Monitor Teen Angst.
UIC Today: App Developed at UIC to Track Mood, Predict Biopolar Disorder Episodes.
UIC Today: App that Tracks Bipolar Manic, Depressive Episodes Wins Award.