NTU scientists develop computer program that can detect those at higher risk of depression

The predictive program analyses a person's physical activity, sleep patterns and circadian rhythms through data from wearable devices, such as Fitbit watches.

Professor Josip Car, the director of the Centre for Population Health Sciences at the Lee Kong Chian School of Medicine, who led the study, said on Monday that to fine-tune and improve the machine learning algorithm, the team is planning larger studies of more than1,000 participants monitored over a course of two years.Depression affects 264 million people globally, and is undiagnosed and untreated in half of all cases, according to the World Health Organisation's website.

The team also found that certain patterns in a person's behaviour can be associated with depressive symptoms, such as feelings of helplessness and hopelessness, loss of interest in daily activities, and changes in appetite or weight. Although weekday rhythms are mainly determined by work routines, a person's ability to follow these routines differs between depressed and healthy individuals. Those who are healthy are more regular in when they wake up and go to sleep.

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