Depression is the leading health issue on college campuses in the U.S. Today, college students are dealing with depression at some of the highest rates in decades. Unfortunately, university counseling centers (UCCs), which are the primary access points for students to receive mental health services, are facing significant challenges in meeting the increasing demands. Specifically, clinicians at UCCs still rely on patients' inaccurate and biased self-reported symptoms for depression assessment. In addition, UCCs provide mental health services only during working hours in clinical settings. The lack of service access when needed could leave patients floundering helplessly and lead to lifelong consequences. Furthermore, with tight budgets, clinicians at UCCs have not grown and some UCCs even downsized. As a consequence, more students did not receive timely treatment. This project focuses on designing and developing iSee, a smart device based behavior monitoring and analytics platform. iSee harnesses smartphones/wristbands to extend the reach of mental health care far beyond clinical settings and to deliver timely therapies when needed. Furthermore, the continuously tracked depression symptoms allow UCCs to be more accurately informed with the severity of each patient and thus reduces unnecessary visits so that clinician time can be better utilized. If successful, iSee has the potential to enhance mental health services in thousands of colleges and universities, benefiting millions of college students. Although focusing on depression of college students, the technology can be extended to other mental health conditions such as anxiety, bipolar disorder, dementia, and schizophrenia; adapted to patients beyond college students; and deployed at other settings such as public hospitals and private clinics.
iSee consists of a smartphone/wristband sensing system running on the patient side to continuously and passively track patient's daily behaviors using onboard sensors; a behavior analytics engine using machine learning and causality analysis algorithms running on the cloud side to translate behavior sensor data into meaningful analysis results for identifying the patient's depression severity and revealing behavioral causes that lead to the mitigation or the deterioration of the patient's status; and a dashboard running on the clinician side to visualize behavior information as well as analysis results to help clinicians make clinical decisions and conduct treatment. The system would allow clinicians to access an objective, quantitative, and longitudinal record of patients' daily behavior to support evidence-based clinical assessment. This project involves a multi-disciplinary and cross-organizational team of researchers from Michigan State University (lead institution) and Northwestern University (Chicago, IL). The primary industry partner is Microsoft Research (Redmond, WA), which is a large business company in U.S. Michigan State University Counseling Center (East Lansing, MI), which will be the test bed for the integration and evaluation of the iSee smart service system. Finally, the broader context partners include the MSU Office of the Vice President for Student Affairs and Services and MSU Technologies (East Lansing, MI).
This award is partially supported by funds from the Directorate for Computer and Information Science and Engineering (CISE), Division of Information and Intelligent Systems (IIS).
