Wearables sensors could help detect depression in children – study

Scientists from the University of Vermont have found that wearable sensors could help detect anxiety and depression in young children.

As many as one in five children suffer from such conditions, known as ‘internalising disorders’, and are at greater risk of substance abuse and suicide in later life.

Ryan McGinnis, a biomedical engineer and Ellen McGinnis, a clinical psychologist at the University of Vermont undertook research that led them to develop a tool that could help screen children for internalising disorders. Their findings were published in the journal PLOS ONE.

The team used a “mood induction task,” a common research method designed to elicit specific behaviours and feelings such as anxiety. The researchers tested 63 children, some of whom were known to have internalising disorders.

Normally, trained researchers would watch a video of the task and score the child’s behaviour and speech during the task to diagnose internalising disorders. In this work, the team used a wearable motion sensor to monitor a child’s movement, and a machine learning algorithm to analyse their movement to distinguish between children with anxiety or depression and those without.

Children were led into a dimly lit room, while the facilitator gave scripted statements to build anticipation, such as “I have something to show you” and “Let’s be quiet so it doesn’t wake up.” At the back of the room was a covered terrarium, which the facilitator quickly uncovered, then pulled out a fake snake. The children were then reassured by the facilitator and allowed to play with the snake.

After processing the movement data, the algorithm identified differences in the way the two groups moved that could be used to separate them, identifying children with internalising disorders with 81% accuracy.

Ryan McGinnis said: “Because of the scale of the problem, this begs for a screening technology to identify kids early enough so they can be directed to the care they need.

“The way that kids with internalising disorders moved was different than those without.”

The algorithm determined that movement during the first phase of the task, before the snake was revealed, was the most indicative of potential psychopathology. Children with internalising disorders tended to turn away from the potential threat more than the control group. It also picked up on subtle variations in the way the children turned that helped distinguish between the two groups.

Ellen McGinnis explained this lined up with psychological theory. Children with internalising disorders would be expected to show more anticipatory anxiety, and the turning-away behaviour is the kind of thing that human observers would code as a negative reaction when scoring the video. The advantage is that the sensors and algorithm work much faster, with the algorithm using 20 seconds of data from the anticipation phase to make its decision.

Ellen McGinnis said: “Something that we usually do with weeks of training and months of coding can be done in a few minutes of processing with these instruments.

“Children with anxiety disorders need an increased level of psychological care and intervention. Our paper suggests that this instrumented mood induction task can help us identify those kids and get them to the services they need.”

The next step will be to refine the algorithm and develop additional tests to analyse voice data and other information that will allow the technology to distinguish between anxiety and depression.

Bishal Bhandari, epidemiologist at GlobalData, a data and analytics company, said: “Wearable technology is already well established in monitoring general health and used by athletes or those wanting to live active lifestyles. But as the technology becomes more advanced through machine learning, diagnosing complex diseases such as psychiatric disorders in children should be possible in the near future. There is a need for inexpensive as well as accurate diagnosing tools for childhood brain disorders and the advanced wearable technology could be the answer.

“Childhood brain disorders are less well understood and there is a possibility that a large number of cases are not diagnosed. The number of childhood disorders such as attention deficit hyperactivity disorder is increasing and the diagnosed cases in the US will increase from 21 million cases in 2014 to 28 million cases in 2024, according to GlobalData. As wearable technology becomes more mainstream and reliable in screening psychiatric illness, we expect the number of child patients to spike as more hidden cases will come to light.

“Early diagnosis and monitoring of psychiatric illness in children is notoriously difficult, as children struggle communicating or even may not be aware that they can talk about it. Wearable technology thus could play an important role in diagnosis, so that interventions can be given early when they have the highest chance for success.”

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