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KINGSTON, R.I. — March 4, 2024 — Pedometers measure steps, but what if there were similar automatic devices that measured eating behavior? Evidence from nutrition research shows that the Speed, timing, and duration have long been shown to be strongly associated with obesity and other health problems. Although feeding behavior can be accurately measured in a controlled laboratory setting, a blind spot exists when researchers attempt to study how participants actually eat “in nature.”
A new National Institutes of Health-funded project by three scientists from the University of Rhode Island and the University of Texas at Austin aims to use AI-enabled wearable technology to uncover real-world eating behavior. is. The four-year, $2.4 million grant from the National Institute of Diabetes and Digestive and Kidney Diseases was awarded to URI nutrition professor Kathleen Melanson, psychology professor Theodore Walls, and University of Texas at Austin electrical and computer engineering professor Edison.・Supporting Thomas’ research.
They plan to develop a system that detects detailed information about meal movements and, in some cases, every bite and chew. Researchers plan to combine more than 60 years of expertise in nutrition, behavioral statistics, and engineering to develop a new interdisciplinary project that will provide researchers with more complete data on the nutritional habits and behaviors of study participants. .
“Eating behavior data collected in the lab is the most accurate, but because people don’t live in the lab, we don’t know what they do in their daily lives in the real world,” Melanson says. he says. “We want to compare the results of the new system to existing systems in the lab to make sure the data is being collected properly. It’s about being able to test interventions for remediation.”
The study uses two devices: a typical smartwatch and an unobtrusive, custom-made sensor that is placed on the participant’s jawline. A smartwatch captures participants’ arm and wrist movements as they perform typical eating gestures, measuring speed and frequency. This is combined with data captured by a small, button-sized sensor that records the movements of the jaw as it chews, recording the speed and intensity of the movements.
“This study, through several consecutive experiments from a laboratory environment to a ‘natural environment,’ transformed the internal validity of inferences in a laboratory-controlled environment to the external validity of inferences in the real world. We’re gradually moving towards that,” Walls said. Its research produces statistical tools for understanding real-time, intensive, longitudinal health behavior data.
Researchers will study participants across four progressive stages. After being fitted with the discreet system, they will consume a prescribed diet measured according to standardized laboratory procedures and close supervision of researchers. The next stage will involve cafeteria-style meals, still with close assistance from researchers. The test then moves to a restaurant setting, giving participants more control over their meals. These stages more closely reflect real-world eating situations.
“We’re trying to answer: Can you tell when someone is eating something?” That may sound like a very simple question, but if you’re not in a very controlled environment. , that turned out to be very difficult to do,” Thomas said. “When we speak, our jaw bones do move, but they don’t move in the rhythmic motion that we do when we chew food. These are the things we’re trying to exploit with sensors and AI algorithms. Kind of a hint. We’ll connect the two devices and see if we can devise a more powerful system for detecting this kind of eating behavior.”
Finally, study participants return to their normal lives, but wear sensors to monitor their eating habits. Researchers will measure data such as eating speed, chewing speed, oral processing speed, how long food stays in the mouth before swallowing, and how quickly food disappears from the plate.
“This type of mastication and oral processing behavior, such as eating too quickly, eating in large quantities, not taking breaks between bites, and not chewing thoroughly enough, can cause people to consume too many calories before satiety signals occur.” Melanson says. “Thus, assessing these behaviors can help develop systems that can be used for interventions to help people adapt their intake behaviors to maximize satiety and aid energy intake.”
Walls added that this study will allow researchers to add other sensors, perhaps ones that monitor facial skin stretch. “We want to make sure we get that advancement out of the lab in a way that actually works in the overall behavioral monitoring approach. These are people who want to get started. This phase is about measurement, but then we’ll test actual programs to help people manage their eating behavior. ”
The participants in this study will be those who are most likely to benefit from the research: those at risk of obesity-related harm. The researchers plan to recruit participants from Latinx communities in both Rhode Island and Texas, allowing them to explore their unique cultural eating habits. Members of these communities, on average, have higher rates of obesity and related health problems.
Anyone interested in participating in the study can contact the researchers at dibs@uri.edu.
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