Emerging Technologies

Can smartphones detect depression?

Sohrob Saeb
Research Fellow, Northwestern University

Today’s smartphones are equipped with powerful sensing capabilities. Using these sensors, your smartphone potentially has a record of how active you are, how much you sleep and where you go. If we look at the data those sensors gather, we can get a pretty good idea of what someone’s typical behavior is like.

When a person is depressed, their behavior often changes. You may lose interest in activities, experience changes in your sleep cycles or withdraw from social interactions. And your phone, typically close at hand, could be used to detect these behavior changes.

In a study recently published in the Journal of Medical Internet Research, we investigated whether a person’s movements and activities as recorded by their smartphone signaled behavioral changes associated with depression. And we found that they are, in fact, closely correlated.

How did we use smartphones to detect depression?

We recruited 28 participants, 14 with depressive symptoms and 14 without. We started the experiment by quantifying their depressive symptoms by using a test called patient health questionnaire (PHQ-9). The PHQ-9 consists of nine questions asking about the presence of several symptoms of depression such as loss of interest, hopelessness, changes in sleep, tiredness and having trouble in concentration. It’s a very common test. In fact, you might have taken it at your last doctor’s appointment.

Then we collected data on GPS location and phone usage recorded by the built-in sensors on each participant’s phone for two weeks. We also developed a sensor to calculate how long and how often participants used their phones. It tracked all phone activity except for calls.

Then, we developed algorithms that estimated certain behavioral markers that we thought might be related to depression. These markers included the patterns of movement though geographical space, the total distance a person moved during the two-week period, the number of locations visited, the speed at which the individual moved between locations, and the amount of time he or she spent in different locations.

Finally, we analyzed the relationship between these markers and the severity of depressive symptoms.

Which behaviors can identify depression?

We found that a number of behavioral markers strongly correlated with PHQ-9 depression scores. These included markers that captured patterns of movement, mobility, the time spent in different locations, and phone usage duration.

Participants who were more depressed had more irregular movement patterns. This means that, for example, they left home for work at a different time each day, while less depressed individuals went to work around the same time every day.

In addition, the more depressed participants were less mobile and spent most of their time in fewer locations. We also found a correlation between phone usage and depression scores. The more depressed participants used their mobile phones more often and for longer periods of time, but not for making phone calls. This activity may have included texting, playing games, reading or other activities.

Pushing mHealth beyond treatment to diagnosis

This study used a relatively small sample, but still an interesting piece of evidence on how mobile phones could detect symptoms of depression.

Another study from Dartmouth College used mobile phone sensors to look into several aspects of students’ lives, and also found a number of them, including sleep, sociability, and physical activity, to be correlated with depression. We still need to see what happens with a larger group of people to see what daily-life behaviors are related to depression in the general population.

As mobile phones have become more ubiquitous, they have become important tools for health care. This is called mobile health, or mHealth for short.

mHealth interventions are effective, and are part of national health care systems in many European countries and Australia.

mHealth is sometimes used to assist with diagnosis. For example, in mobile telemedicine, patients can provide information, such as a picture of a skin injury, to their doctors using their mobile devices.

In mental health care, mHealth has been used to monitor mental health patients by sending them daily questionnaires about their mood and daily activities either through SMS or specialized smartphone apps.

However, without human support from therapists or coaches, patients tend not to use these tools. In addition, patients repeatedly need to input data about their mood and behaviors, mostly a few times per day, which is a major factor in their non-adherence to the treatment procedure.

For people at risk of depression, our research means that their health can be passively monitored without any burden on their side. They don’t need to input data about their mood, daily activities or sleep quality, and care providers can check in if they see a behavior that needs more personal support.

In addition, mobile phone data could also help clinicians understand how depressive symptoms and depression change over time. This could help us develop better treatments or strategies to help people with depression. Depression is fairly common – about 6.9% of US adults have at least one major depressive episode each year – so this could really make a difference.

More than two-thirds of all depressed patients want psychological support, but more than 70% of them face barriers such as high costs, transportation, stigma concerns and lack of motivation that make it hard to access traditional psychotherapy.

mHealth can help overcome these barriers by eliminating the need to have regular, usually costly, visits with the therapists and the need to transport, and provide care to those in need in place.

 

This article is published in collaboration with The Conversation. Publication does not imply endorsement of views by the World Economic Forum.

 To keep up with the Agenda subscribe to our weekly newsletter.

Author: Sohrob Saeb is a Research Fellow at Northwestern University, United States. 

 Image: Men are silhouetted against a video screen as they pose with Samsung Galaxy S3, Nokia Lumia 820 and iPhone 4 smartphones (R-L) in this photo illustration taken in the central Bosnian town of Zenica, May 17, 2013. REUTERS/Dado Ruvic. 

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

Sign up for free

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Stay up to date:

Future of Global Health and Healthcare

Related topics:
Emerging TechnologiesIndustries in Depth
Share:
The Big Picture
Explore and monitor how Media, Entertainment and Sport is affecting economies, industries and global issues
World Economic Forum logo

Forum Stories newsletter

Bringing you weekly curated insights and analysis on the global issues that matter.

Subscribe today

Here’s why it’s important to build long-term cryptographic resilience

Michele Mosca and Donna Dodson

December 20, 2024

How digital platforms and AI are empowering individual investors

About us

Engage with us

  • Sign in
  • Partner with us
  • Become a member
  • Sign up for our press releases
  • Subscribe to our newsletters
  • Contact us

Quick links

Language editions

Privacy Policy & Terms of Service

Sitemap

© 2024 World Economic Forum