Computer program that uses data from wearable technology to detect depression

The Ycogni model detects the risk of depression by analyzing an individual’s physical activity, sleep patterns and circadian rhythms using data from wearable devices that measure their steps, heart rate, energy expenditure and sleep data. Credit: Nanyang Technological University

A team of scientists from Nanyang Technological University, Singapore (NTU Singapore) have developed a predictive computer program that could be used to detect people at increased risk of 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.

Depression affects 264 million people worldwide and goes undiagnosed or untreated in half of cases, according to the World Health Organization. In Singapore, the COVID-19 pandemic has raised concerns about mental well-being. A new study from the Singapore Institute of Mental Health has highlighted a likely increase in mental health problems, including pandemic-related depression.

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 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 participants was 33, with the sample closely reflecting Singapore’s ethnic population. Participants were instructed to wear trackers at all times and only remove them when taking a shower or when the device needs to be charged.

Professor Josip Car, director of the Center for Population Health Sciences at NTU’s Lee Kong Chian School of Medicine (LKCMedicine), who co-led the study, said: “Our study has successfully shown that we can exploit data from wearable device sensors to help detect the risk of developing depression in individuals. By exploiting our machine learning program, along with the growing popularity of wearable devices, it could one day be used for timely and discreet 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 lay the groundwork for using wearable technology to help individuals, researchers, mental health practitioners and policy makers. But on a more generic and futuristic application, we believe that such signals could be incorporated into Smart Buildings or even Smart Buildings initiatives. Cities: Imagine a hospital or military unit that could use these signals to identify people at risk.”

The results of the study have been published in the peer-reviewed scientific journal JMIR mHealth and uHealth in November.

Vital signs linked to depressive symptoms

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.

The study also associated less regular sleep patterns, such as variable wake-up times and bedtimes, with a higher tendency to have depressive symptoms.

The scientists explained that although the rhythms of the week are mainly determined by the work routine, the ability to follow this routine better differentiates depressed individuals from healthy individuals, where healthy people have demonstrated greater regularity. in waking and falling asleep times.

Professor Car added: “We look forward to extending our research to include other vital signs in detecting the risk of depression, such as skin temperature. Adjusting our program could help facilitate a detection early, discrete, continuous and profitable. of depression in the general population.”

Prof Assoc Christopoulos added: “Our team will also be working on extending it to other types of psychological states, such as mental fatigue, which seems to be an alarming problem these days. ‘a feedback system that could help therapists better assess the psychological state of their patients, for example improved sleep quality.

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 cell phone, as well as their addiction to social media.

Changes in sleep and biological rhythms from late pregnancy to postpartum linked to depression and anxiety

More information:
Yuri Rykov et al, Digital biomarkers for depression screening with wearable devices: a cross-sectional study with machine learning modeling, JMIR mHealth and uHealth (2021). DOI: 10.2196/24872

Provided by Nanyang Technological University

Quote: Computer program that uses data from wearable technology to detect depression (2022, January 24) Retrieved August 7, 2022 from

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Gordon K. Morehouse