Depression and anxiety are two of the most common mental health illnesses in the United States, although more than half of those affected are neither identified nor treated
Depression and anxiety are among the most common mental health disorders in the United States, but more than half of people struggling with the conditions are not diagnosed and treated.
A team of researchers from the McKelvey School of Engineering and the School of Medicine at Washington University in St. Louis are using Fitbit data and deep learning to detect depression and anxiety.
Over the past several years, managing one's mental health has become more of a priority with an increased emphasis on self-care. Depression alone affects more than 300 million people worldwide annually.
Using Fitbits and a novel machine learning model, a multi-institutional team led by Washington University in St. Louis' Chenyang Lu is ushering in the next step in personalization for treatment of depression.