According to recent data from the National Survey of Children's Health, among children aged 3-17 years, 7.1 percent had current anxiety problems, 7.4 percent had a current behavioral/conduct problem, and 3.2 percent had current depression. The prevalence of each disorder was higher with increased age.

Nearly 80 percent of those with depression received treatment in the previous year, compared with 59.3 percent of those with anxiety problems, and 53.5 percent of those with behavioral/conduct problems. Among children aged 2 to 8 years, boys were more likely than girls to have a mental, behavioral, or developmental disorder.

Despite the statistics, anxiety and depression in young children are difficult to detect and when untreated often lead to suicide and drug abuse in later years.

As many as 1 in 5 children suffer from anxiety or depression, starting in preschool years. Early diagnosis and appropriate services for children and their families can make a difference in the lives of children with mental disorders. However, when children suffer in silence because they never exhibit the disruptive behaviors that would lead to a referral for diagnostic assessment, how can parents be sure their child is anxious or depressed?

Ryan McGinnis, a biomedical engineer at the University of Vermont teamed up with Ellen McGinnis, a clinical psychologist at the University of Vermont, and colleagues in the Department of Psychiatry at the University of Michigan, Maria Muzik, Katherine Rosenblum and Kate Fitzgerald, to develop a tool that could help screen children who were internalizing disorders to catch them for early treatment.

The team used a "mood induction task," a common research method designed to elicit specific behaviors and feelings such as anxiety, to test 63 children, some of whom were known to have internalizing disorders. Researchers led the children 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 facilitator reassured the children who were then allowed to play with the snake.

The research team used a wearable motion sensor to monitor a child's movement and a machine learning algorithm to analyze 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 identify children who had internalizing disorders with 81 percent accuracy, which was better than the standard parent questionnaire.

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 internalizing disorders tended to turn away from the potential threat more than the control group. Researchers were also able to detect subtle variations in the way the children turned that helped distinguish between the two groups.

According to McGinnis, children with internalizing disorders would be expected to show more anticipatory anxiety, and the turning-away behavior 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. The algorithm needs just 20 seconds of data from the anticipation phase to make its decision.

This study suggests that the instrumented mood induction task can help identify emotionally vulnerable children and get them the help they need to avert full-blown anxiety and mood disorders that increase risk for substance abuse or suicide later in life.