Researchers at Nanyang Technological University (NTU) in Singapore, have developed a predictive computer program that uses data from wearable technology to detect individuals who are at increased risk of depression.
In trials using data from groups of depressed and healthy participants, the program achieved an accuracy of 80 per cent in detecting those individuals with a high risk of depression and those with no risk.
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Powered by machine learning, the program, named the Ycogni model, screens for the risk of depression by analyzing an individual’s physical activity, sleep patterns, and circadian rhythms derived from data from wearable devices that measure his or her steps, heart rate, energy expenditure, and sleep data.
Depression affects 264 million people globally1, and is undiagnosed and untreated in half of all cases, according to the World Health Organization. In Singapore, the COVID-19 pandemic has led to increased concerns over mental well-being. A new study by Singapore’s Institute of Mental Health pointed to a likely increase in mental health issues, including depression related to the pandemic.
Activity trackers are estimated to be worn by nearly a billion people, up from 722 million in 2019.
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, reports NTU.
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.
Professor Josip Car, Director, Centre for Population Health Sciences at NTU’s Lee Kong Chian School of Medicine (LKCMedicine), who co-led the study, said: “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.”
Associate Professor Georgios Christopoulos, from NTU’s Nanyang Business School, who co-led the study, said: “This is a study that, we hope, can set up the basis for using wearable technology to help individuals, researchers mental health practitioners and policy makers 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.”
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The results of the study were published in the peer-reviewed academic journal JMIR mHealth and uHealth in November.
Over the next year, the team hopes to explore the impact of smartphone usage on depressive symptoms and risk of developing depression by enriching their model with data on smartphone usage. This includes how long and frequent individuals use their mobile phones, as well as their reliance on social media.
