A team of scientists from Nanyang Technological University, Singapore (NTU Singapore) has developed a predictive computer program that could be used to detect people who are at increased risk for depression. In trials using data from depressed and healthy groups of participants, the program achieved 80% accuracy in detecting those at high risk for depression and those at no risk.
Powered by machine learning, the program, named the Ycogni model, screens for depression risk by analyzing an individual’s physical activity, sleep patterns and circadian rhythms derived from data from wearable devices that measure their steps. , heart rate, energy expenditure, and sleep data.
Activity trackers are estimated to be worn by almost a billion people, up from 722 million in 2019. To develop the Ycogni model, scientists conducted a study involving 290 active 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 leverage sensor data from wearable devices to help detect the risk of developing depression in individuals. By leveraging our machine learning program, along with the growing popularity of wearable devices, it could one day be used for timely and unobtrusive screening for depression.
– Professor Josip Car, director of the Center for Population Health Sciences at the Lee Kong Chian School of Medicine at NTU
This is a study that can lay the groundwork for using wearable technology to help individuals, researchers, mental health practitioners, and policy makers improve mental well-being. But on a more generic and futuristic application, the researchers believe that such signals could be integrated into Smart Buildings or even Smart Cities initiatives: imagine a hospital or a military unit that could use these signals to identify people at risk.
In addition to being able to accurately determine whether individuals had a higher risk of developing depression, the researchers were able to associate certain behavioral patterns of the participants with depressive symptoms, including feelings of helplessness and hopelessness, loss of interest in daily activities and changes in appetite. or weight.
Analyzing their results, the scientists found that those who had more varied heart rhythms between 2 a.m. and 4 a.m. and between 4 a.m. and 6 a.m. tended to be prone to more severe depressive symptoms. This observation confirms the results of previous studies, which had stated that changes in heart rate during sleep could be a valid physiological marker of depression.
Over the next year, the team hopes to explore the impact of smartphone use on depressive symptoms and the risk of developing depression by enriching their model with data on smartphone use. This includes how long and how often they use their mobile phone, as well as their addiction to social media.
As OpenGov Asia reported, NTU Singapore has produced more advanced COVID-19 tools, taking another step 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 a ‘tweezer’, could help develop new approaches for diagnosis diseases and viral studies.
The technology would also benefit vaccine development because it allows scientists to identify damaged or incomplete viruses from a pool of thousands of other specimens in less than a minute, compared to current techniques which take time and effort. are imprecise, according to the scientists.
NTU Medical School Associate Professor Lee Kong Chian, a medical geneticist who co-led the research, said: “The conventional way of analyzing viruses today is to study a population of thousands or millions of viruses. . We only know their average behavior as a whole population. With our laser-based technology, unique viruses could be studied individually. »