December 12, 2017

WPI app aims to stop overeating

Courtesy/WPI
A demonstration of the SlipBuddy app. When the app finds a potential trigger for overeating, it suggests actions like taking a walk or turning off the TV.

An app being developed by researchers at Worcester Polytechnic Institute and the University of Connecticut tracks eating patterns and provides reminders aimed at helping to stop overeating.

The app, called SlipBuddy, was shown in a pilot study to stop people from "slipping up," WPI said, something, if effective more broadly, could make major strides in a country where 38 percent of adults are obese and 71 percent are overweight, according to the Centers for Disease Control and Prevention.

A study of 16 overweight adults, who were not necessarily trying to lose weight, showed nine lost an average of five pounds, WPI said. Another three participants' weight did not change, and four gained an average of two pounds.

The study was conducted by two WPI researchers, Bengisu Tulu, associate professor in WPI's Foisie Business School, and Carolina Ruiz, associate professor of computer science at WPI; and by Sherry Pagoto, professor of allied health sciences at the University of Connecticut and director of UConn's Center for mHealth and Social Media.

SlipBuddy was built for phones running on an Android operating system, but will also be available for iOS, or Apple, devices. It is expected to hit the market as early as 2019, with a larger user study due to take place later this year.

The app will enter an already-crowded market. Nearly 29,000 weight-related apps are already available, WPI said, citing a report from the International Journal of Obesity.

SlipBuddy, unlike most, is designed to change the user's behavior, the researchers said. The app tracks eating and stress, with researchers working to find triggers causing people to overeat, like late-night eating or watching TV.

"Most weight-loss apps are all about tracking something — tracking your calories, tracking your blood glucose, tracking your steps," Tulu said in a statement. "This goes beyond that. We're using machine learning to make this about intervention."

The app requires some work by users, however. Users are asked to check in three times a day to note stress levels, fatigue and hours of sleep, and whenever they feel they've eaten too much. Over time, the app collects information and works to find patterns to predict when the user may overeat. The information is stored anonymously on a WPI server, the school said.

When the app finds a trigger taking place, it suggests actions like taking a walk or turning off the TV.

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