Scientists and engineers in Europe are embarking on a quest to see if they can change the way young people at risk for becoming obese eat. Key to this will be developing unobtrusive technology that monitors how quickly or slowly a person is eating and guides them toward a healthier pace.
“It’s a behavioral issue,” explains Anastasios Delopoulos, the project leader and a professor of electrical and computer engineering at the Aristotle University of Thessaloniki, in Greece. When a person begins to eat they typically begin at a high rate and slow down until they feel full. “It’s similar to the voltage of a capacitor as more and more electrons accumulate in it,” he says.
However, obese people or people at risk for the condition have difficulty feeling full, and so they tend to eat a constant, high rate. Some people at risk for eating disorders, such as anorexia, have a similar problem. But for them, the rate is unusually low. “It’s two sides of the same coin,” says Delopoulos.
Scientists at the Karolinska Institute in Sweden have already measured these rates in patients using a device called a mandometer, which was developed by AB Mando Group. The mandometer is essentially a scale that sits beneath a patient’s plate and records how quickly it lightens as the patient eats. Scientists have found that by making patients mimic a normal eating curve, they can train them to have a more normal sense of satiety — thereby treating the obesity or the eating disorder.
The challenge for the new, three-year European Union-funded project, called Splendid, is to bring that monitoring and treatment out of the clinic and into the real world. “Now we want to move toward prevention,” says Delopoulos. “We want to target some students who are not obese and identify who [among them] are at risk of becoming obese.”
For that they’ll need to develop less-obtrusive monitoring and behavioral modification technology, and the software to run it. On the hardware side of things, the Splendid researchers are working on developing wearable tech that would be able to understand and monitor chewing. The first option is to use a well-placed microphone. The idea is that the sensor would capture chewing noises and be able to interpret the rate of chewing and some information about the texture of the food. They won’t be able to tell Coca-Cola from Pepsi, jokes Delopoulos, but they should be able to tell chewy things from crispy ones or liquids.