Helping universities fight depression with mobile technology

Depression is the leading mental health issue on college campuses in the United States In 2015, a survey of more than 90,000 students at 108 U.S. colleges and universities found that in the previous year, more than a third of them had felt so depressed at some point that it was difficult to function. More than two-thirds had felt hopeless in the previous school year.

Students today are suffering from depression at an alarming rate and are increasingly seeking help from on-campus mental health services. Depression is also an underlying cause of other common problems on college campuses, including alcohol and drug abuse, eating disorders, self-harm, suicide, and dropping out of school.

But university counseling centers, the main sources of mental health care for students, are struggling to meet this growing demand. First, it can take a long time for clinicians to get a complete picture of what students are going through: depressed students’ accounts of their symptoms are often inaccurate and incomplete.

Additionally, budget constraints and limited office hours mean that the number of clinicians on campus has not increased, and in some cases has decreased, despite growing demand. There are simply not enough academic clinicians available to serve every student – ​​and few, if any, at critical times like nights and weekends. The number of students on council waiting lists doubled from 2010 to 2012. This can leave students waiting for long periods without help. In the worst case, it can have lifelong or even fatal consequences.

The use of mobile technology for the diagnosis and treatment of mental illnesses is becoming a hot research topic these days due to the ubiquity of mobile devices and their behavior tracking capabilities. Building on the work of others, we have found a way to improve counseling services through mobile technology and big data analytics. It can help students and clinicians by offering a new depression assessment tool that can shed more light on a difficult-to-study condition.

Measuring well-being

The main screen of the iSee application displays activity trends.
Mi Zhang and Jingbo MengCC BY-ND

We are developing a system to deal with this campus mental health crisis, called iSee. When ready to roll out, students participating in the program will need to take along a smartphone and a smartwatch. The data collected by these devices is transmitted to a computer system and analyzed by it. This allows the relatively few counselors to better follow a larger number of students and extend the service to a larger number of students in need.

The smartphone and the smartwatch have several integrated sensors:

  • a GPS sensor, to track geographic locations,
  • a luminosity sensor, to measure the ambient luminosity,
  • an accelerometer, to capture physical movements, and
  • a touchscreen, to monitor the frequency and duration of user interactions with their phone, such as browsing social networks.

These sensors capture many of students’ daily activities that can help indicate mental well-being, including walking or other physical exercise, sleep patterns, social interactions, and how often they go to class or local businesses – or if they stay at home or in a dorm all day.

iSee can compensate for the inaccuracies and shortcomings inherent in patients’ self-reporting of their depression. He may even observe symptoms that the students themselves do not notice or think to mention to a counselor. And because the data is continually collected, it can identify moments of vulnerability and resilience and provide a picture of a student’s progress over time. This can help not only monitor but also treat depression.

Improve the advisory service

Counselors can keep an eye on their patients, without even having to make direct contact.
Mi Zhang and Jingbo MengCC BY-ND

Our work is based on the algorithms we have designed to analyze data from mobile devices in order to detect depression. In a 2015 study, we showed that the severity of a person’s depression can be predicted by tracking their GPS locations and how often they use their phone: people with more severe symptoms of depression tended to moving from place to place and staying home more than people with fewer symptoms of depression – or none at all. The movements of more severely depressed people also tended to be less regular and were more likely to use their phones frequently and for longer durations. For iSee, we will integrate data from other sensors, translating the raw measurements into models of student behavior.

Next, iSee will look for patterns of behavior that may be linked to mental health issues, such as staying home all the time or having irregular sleep. By sharing this information with the patient and counselor, iSee will help to better describe and more accurately describe the severity of an individual’s symptoms.

A mental health counselor can see when a student’s behavior suggests a change in their depression.
Mi Zhang and Jingbo MengCC BY-ND

Treat depression

Additionally, iSee can detect in real time whether a student’s behavior matches certain symptoms of depression, such as being socially isolated for three days. If this happens, the app can automatically provide instant therapies to help, without relying on the patient, or even the clinician, to initiate them. For example, if iSee notices that a socially isolated person is home alone on a sunny Saturday afternoon, it might suggest calling friends or going for a walk.

This is exactly the kind of suggestion an advisor would make. Unfortunately, counselors can usually only make these suggestions retrospectively during therapy sessions. The smartphone can provide this assistance when help is needed.

By finding ways the many sensors in smartphones and smartwatches can shed light on people’s daily lives and habits, and analyzing that data in ways that highlight potential mental health issues, we can help students stay healthier and reduce the workload of overworked professionals at the same time.

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