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NTU Researchers Develop Wearables to Help Detect Depression

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A team of scientists from Nanyang Technological University, Singapore (NTU Singapore) has developed a predictive computer programme that could be used to detect individuals who are at increased risk of depression. In trials using data from groups of depressed and healthy participants, the programme achieved an accuracy of 80% in detecting those individuals with a high risk of depression and those with no risk.

Powered by machine learning, the programme, named the Ycogni model, screens for the risk of depression by analysing 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.

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 devices for 14 consecutive days and completed two health surveys, which screened for depressive symptoms, at the start and end of the study.

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 programme, as well as the increasing popularity of wearable devices, it could one day be used for timely and unobtrusive depression screening.

– Professor Josip Car, Director, Centre for Population Health Sciences at NTU’s Lee Kong Chian School of Medicine

This is a study that 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, the researchers 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.

Besides being able to accurately determine if individuals had a higher risk of contracting depression, the researchers successfully associated certain patterns in the participants’ behaviours to depressive symptoms, which include feelings of helplessness and hopelessness, loss of interest in daily activities, and changes in appetite or weight.

From analysing their findings, the scientists found that those who had more varied heart rates between 2 am to 4 am, and between 4 am to 6 am, 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.

Over the next year, the team hopes to explore the impact of smartphone usage on depressive symptoms and the 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.

As reported by OpenGov Asia, NTU Singapore has produced more advanced COVID-19 tools, hitting another milestone in the country’s efforts to combat COVID-19. A group of university scientists recently developed a laser-powered device that can trap and move viruses using light. Since it can precisely ‘move’ a single virus to target a specific section of a cell, the device, which can manipulate light to act as ‘tweezers,’ could contribute to the development of new approaches to disease diagnosis and virus studies.

The technology would also benefit vaccine development as it allows scientists to identify damaged or incomplete viruses from a group of thousands of other specimens in under one minute, compared to present techniques that are time-consuming and inaccurate, according to the scientists.

Associate Professor from NTU’s Lee Kong Chian School of Medicine, a medical geneticist who co-led the research, said: “The conventional method of analysing viruses today is to study a population of thousands or millions of viruses. We only know their average behaviour as an entire population. With our laser-based technology, single viruses could be studied individually.”

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