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A ������Ƶ researcher will lend his data-analysis skills to a landmark study of Latina women funded by the National Institutes of Health.

Assistant Professor Congyu “Peter” Wu — who joined the Thomas J. Watson College of Engineering and Applied Science’s Department of Systems Science and Industrial Engineering faculty a year ago — was doing post-doctoral research at the University of Texas at Austin when he became part of the school’s

The transdisciplinary effort aims to help underserved communities in central Texas that face health disparities such as physical and emotional adversity as well as poor access to groceries, greenspace and medical care.

As part of UT Austin’s Grand Challenge Initiative, Wu said, “We pulled together a bunch of experts from different disciplines, from engineering, medicine, psychology, kinesiology and elsewhere to collect data from the community, analyze that data and then we return the insights back to the community for people to improve their health and behavior.”

For this latest study, titled the NIH granted $3.35 million to UT Austin, with Wu and ������Ƶ receiving $291,571.

The researchers will focus on the risk factors for metabolic syndrome, a cluster of conditions such as obesity, high blood pressure, high triglyceride levels and low HDL cholesterol levels that can lead to heart disease, stroke and Type 2 diabetes. Latina women in the Austin area are particularly at risk: 47% are obese, 36% have hypertension and 30% lack health insurance.

The study hopes to validate the use of low-cost mobile devices like smartphones, smartwatches and environmental sensors to capture sleep, physical activity, location and environmental hazards to identify and mitigate those risks.

Among the questions: How often are the subjects moving around or engaging in physical activity? What is their radius of travel — staying close to home or going farther out? Are they engaging with other people? What are their sleep quality and circadian rhythm like? Are they staying healthy mentally?

Wu’s role in the five-year study will be to pull together the “messy” data from different sources — including MRI imaging — to discern patterns that represent unhealthy choices or situations.

“If you view the different streams of health information overlaid on top of one another, you will be able to get a grander picture of the person’s behavior and lifestyle,” he said. “I will look at all these data points and do the analytics to predict the risks.”

Beyond this grant, Wu sees many other potential applications where mobile sensing and analytics would be important tools.

“This kind of measurement and data mining could be useful for a lot of other contexts, such as monitoring our healthcare practitioners and their mental workload as well as tracking safety for workers in manufacturing and transportation,” he said.