Scientists in Singapore developed a machine learning program that could be used with a wearable device to detect individuals who are at increased risk of depression by analyzing physical activity, sleep patterns, and circadian rhythms.
The research found that those who had more varied heart rates between 2 to 4 a.m., and between 4 to 6 a.m., tended to be prone to more severe depressive symptoms. This observation confirms findings from previous studies, which had stated that changes in heart rate during sleep might be a valid physiological marker of depression.
The scientists explained that although weekday rhythms are mainly determined by work routine, the ability to follow this routine better differentiates between depressed and healthy individuals, where healthy people demonstrated a greater regularity in the timings when they woke up and went to sleep.
“This is a study that, we hope, can set up the basis for using wearable technology to help individuals, researchers mental health practitioners and policymakers to improve mental well-being. But on a more generic and futuristic application, we believe that such signals could be integrated with smart buildings or even smart cities initiatives: imagine a hospital or a military unit that could use these signals to identify people-at-risk,” says Georgios Christopoulos, an associate professor of business at Nanyang Technological University.
The study also associated less regular sleeping patterns, such as varying waking times and bedtimes, to a higher tendency to have depressive symptoms.
“Our study successfully showed that we could harness sensor data from wearables to aid in detecting the risk of developing depression in individuals. By tapping on our machine learning program, as well as the increasing popularity of wearable devices, it could one day be used for timely and unobtrusive depression screening,” says coauthor Josip Car, director of the Centre for Population Health Sciences at Nanyang Technological’s Lee Kong Chian School of Medicine, in a statement.
The program, named the Ycogni model, screens for the risk of depression by analyzing data from wearable devices that measure steps, heart rate, energy expenditure, and sleep data.
To develop the Ycogni model, the scientists conducted a study involving 290 working adults in Singapore. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed two health surveys, which screened for depressive symptoms, at the start and end of the study.
The average age of the participants was 33 years old, with the sample closely mirroring the ethnic population of Singapore. Participants were instructed to wear trackers all the time and to remove them only when taking a shower or when the device needs charging.
Besides being able to accurately determine if individuals had a higher risk of contracting depression, the researchers successfully associated certain patterns in the participants’ behaviors to depressive symptoms, which include feelings of helplessness and hopelessness, loss of interest in daily activities, and changes in appetite or weight.