WASHINGTON: Scientists have developed a wearable tool that can identify anxiety and depression in young children, paving the way for early detection and treatment of such disorders.
Anxiety and depression are surprisingly common among young children — as many as one in five kids suffer from one of them, starting as early as the preschool years.
However, it can be hard to detect these conditions, known as “internalising disorders,” because the symptoms are so inward-facing that parents, teachers and doctors often fail to notice them.
If left untreated, children with internalising disorders are at greater risk of substance abuse and suicide later in life.
“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,” said Ryan McGinnis, a biomedical engineer at the University of Vermont in the US.
Researchers develop a tool that could help screen children for internalising disorders to catch them early enough to be treated.
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.
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.
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.
For the study published in the journal Plos One, 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.
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
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