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. Recognizing this, there is significant interest to leverage popular wearable devices to monitor an individual’s mental health by measuring markers such as activity levels, sleep and heart rate.
A team of researchers at Washington University in St. Louis and at the University of Illinois Chicago used data from wearable devices to predict outcomes of treatment for depression on individuals who took part in a randomized clinical trial. They developed a novel machine learning model that analyzes data from two sets of patients — those randomly selected to receive treatment and those who did not receive treatment — instead of developing a separate model for each group. This unified multitask model is a step toward personalized medicine, in which physicians design a treatment plan specific to each patient’s needs and predict outcome based on an individual’s data.
Results of the research were published in the Proceedings of the ACM on Interactive, Model, Wearable and Ubiquitous Technologies and will be presented at the UbiComp 2022 conference in September.
Chenyang Lu, PhD, the Fullgraf Professor at the McKelvey School of Engineering, led a team including Ruixuan Dai, who worked in Lu’s lab as a doctoral student and is now a software engineer at Google; Thomas Kannampallil, PhD, associate professor of anesthesiology and associate chief research information officer at the School of Medicine and associate professor of computer science and engineering at McKelvey Engineering; and Jun Ma, MD, PhD, professor of medicine at the University of Illinois Chicago (UIC); and colleagues to develop the model using data from a randomized clinical trial conducted by UIC with about 100 adults with depression and obesity.
“Integrated behavioral therapy can be expensive and time consuming,” Lu said. “If we can make personalized predictions for individuals on whether it is likely a patient would be responsive to a particular treatment, then patients may continue with treatment only if the model predicts their conditions are likely to improve with treatment but less likely without treatment. Such personalized predictions of treatment response will facilitate more targeted and cost-effective therapy.”
In the trial, patients were given Fitbit wristbands and psychological testing. About two-thirds of the patients received behavioral therapy, and the remaining patients did not. Patients in both groups were statistically similar at baseline, which gave the researchers a level playing field from which to discern whether treatment would lead to improved outcomes based on individual data.