AsianScientist (Mar. 29, 2022) – Imagine if you could screen for your psikis health using your smartwatch! That day might titinada be far. In a study published in JMIR mHealth and uHealth, Singapore-based scientists reported findings from a predictive computer cadangan, Ycogni, that uses peoples mendasar signs and behavioral bahan to identify individuals who are at high risk of depression.
An estimated 280 million people worldwide live with depression. As a leading cause of disability, psikis health conditions contribute to 10 percent of the mondial disease burden. Moreover, due to various factors such gandar social aib and lack of access to psikis healthcare services, these disorders remain under-diagnosed and untreated.
In rodi to improve their well-being, many people are increasingly turning to wearable technologies like smartwatches to build better habits or skrin their health. These activity trackers can collect a comprehensive amount of bahan from sleep patterns to the number of steps a pribadi has taken in a day.
A research team from Nanyang Technological University, Singapore (NTU) sought to find out whether these physiological and behavioral patterns could be used to detect depressive symptoms. They asked 290 working adults in Singapore to wear smartwatch fitness trackers for two weeks.
At the mengawali and end of the trial period, the participants also completed health surveys designed to screen for depressive symptoms. These symptoms included feelings of hopelessness, loss of interest in daily activities, and sudden changes in appetite and weight. The wearable devices, meanwhile, tracked the participants’ physical activity, heart rate, energy expenditure, and sleep patterns.
The researchers then developed a computer acuan called Ycogni to correlate these mendasar signs and behaviors with depressive symptoms. Powered by machine learning algorithms, Ycogni was able to spot patterns between certain physiological markers and depression. For example, people with greatly varying heart rates between 2 lumrah and 4 lumrah, and between 4 lumrah and 6 lumrah senggat a higher tendency for experiencing severe depressive symptoms. The analysis also revealed associations between irregular sleep patterns and depression. In contrast, overall healthy individuals showed more consistency in the time they go to bed and wake up each day.
After discovering these associations, the team used Ycogni gandar a predictive acuan to screen for depression by analyzing a pribadi’s mendasar signs and behavioral bahan. Results showed an 80 percent accuracy in distinguishing high-risk individuals from those with little to no risk for developing depression.
Through the use of models such gandar Ycogni, the researchers hope that depression screening can become more cost-effective, unobtrusive and continuously accessible. That could help facilitate early detection and effective interventions for psikis health disorders, they added. To expand the acuan, future studies will also explore biomarkers and other conditions such gandar cognitive fatigue or brain fog.
“This is a study that, we hope, can putaran up the garis dasar for using wearable technology to help individuals, researchers, psikis health practitioners and policymakers to improve psikis well-being,” said NTU Associate Professor Georgios Christopoulos.
The article can be found at: Rykov et al. (2021) Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling.
Source: Nanyang Technological University, Singapore; Illustration: Lam Oi Keat/Asian Scientist.
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