Illustration: Jordon Cheung

At the Global Obesity Prevention Center at Johns Hopkins University in Baltimore, Maryland, one might imagine researchers hard at work on the next discovery to finally get us all to eat less and exercise more. After all, that is precisely how obesity has been treated for decades within society and the medical profession.

As it turns out, though, obesity, like many other chronic conditions, is the outcome of a confluence of interconnected factors that extend far beyond diet and exercise to include income, environment, social network, and more. And what differentiates how we study obesity today from how we did in the past is that those factors are now measurable.

“Big Data is going to play a major role,” says Bruce Y. Lee, M.D., director of the center.

In light of this fact, Lee and his colleagues are studying obesity as a complex system. He’s not alone in his thinking. Obesity was designated a chronic disease by the American Medical Association in 2013 and half of all adults in the United States suffer from some form of chronic illness. According to the CDC, one in four suffer from at least two. Doctors and technologists are realizing that the key to prevention and treatment lies in more precisely studying the habits and forces that give rise to these diseases.

To that end, the ability to collect massive quantities of data—known as Big Data—in this digital age and the newfound capability to analyze it have led to a revolution in healthcare. Emerging technologies, driven by data, are providing physicians and patients with new approaches and insights to address the widespread pandemic of chronic disease, starting with effectively treating the generations afflicted by them today and ultimately preventing them in the populations of tomorrow.

“We are entering a point in time where everything around us is instrumented and connected and people are wearing Fitbits and Apple watches and other fitness devices,” says Kathy McGroddy Goetz, vice president of Partnerships & Solutions at IBM Watson Health. As a result, she adds, “There’s an opportunity to leverage a much larger volume of data.”

According to IBM, the average person is projected to generate more than 1 million gigabytes of health-related data in his or her lifetime, whether through electronic health records or wearable fitness devices and mobile apps. And with the explosion of the Internet of Things, everyday devices from our bathroom scales to our pharmacists’ blood-pressure meter to some hospital beds can be Internet-enabled, churning out still more information on us.

But all of this information is most powerful when it is either combined with other data or deeply analyzed, which are two new fields of discovery. “A big part of Big Data is merging existing data streams,” agrees Lee. “We’re trying to get information from all those different systems and merge them to look for connections and relationships.”

The Global Obesity Prevention Center is in the midst of an unprecedented study of childhood obesity that’s combining streams of information from 37 counties in Pennsylvania, including health records, epigenetics data (on chemical factors in the environment that can turn genes on or off), and food sources. This type of groundbreaking research could lead to a future in which urban planners are able to predict geographical regions at risk for childhood obesity and take actions to prevent that occurrence, such as placement of green spaces, bike trails, and farmers’ markets.

As data begins to reveal the big picture of human health, it is also driving a transformation in personalized treatment and interventions. It's spurring new tools capable of engaging people at the level of their own unique environments, habits, and circumstances. “Big Data allows us to look at historical data as well as use predictive technologies to understand what could happen,” says McGroddy Goetz. “From there, you can use it to come up with solutions that are individualized.”

The most transformative thing to happen to Big Data—and a potentially game-changing tool in moving toward more individualized care—is the development of advanced cognitive analytics and artificial intelligence, of which IBM Watson is a leading example.

Watson was originally built as a natural language-processing, artificial-intelligence machine that could outplay humans on the quiz show Jeopardy, which it famously did in 2011. Today, Watson is put to work in fields as varied as banking, sports, and healthcare through a variety of services that can be accessed via the IBM Cloud by any provider or company, anywhere in the world.

“One of the really cool things about Watson is that we’ve been able to train it. For example, it’s ‘read’ all of PubMed, which includes more than 24 million biomedical citations, such as research papers and clinical studies,” explains McGroddy Goetz. “There’s no way that a single doctor or a healthcare professional would be able to get their head around all that information.” In fact, they wouldn’t be able to do it in a lifetime: Watson is capable of processing up to 60 million pages of text per second, and that includes information from mobile fitness apps.

Watson is currently at work making sense of the troves of information collected from Apple’s mobile health platforms, ResearchKit and HealthKit, amassing global data from iPhones and tablets. While ResearchKit offers doctors and medical researchers data to enrich and uncover medical discoveries, HealthKit is helping developers design engaging and interactive wellness apps.

Johnson & Johnson, a leading medical devices and pharmaceutical company, is using Watson and HealthKit to develop a coaching app, which is intended to guide patients through pre-surgery routines, courses of medication, and physical therapy to make the process of knee or hip replacement less intimidating and to help improve surgery results. “This tool may mean you have the potential to go into surgery in better shape, and up your chances to have a better outcome from the procedure,” says McGroddy Goetz.

CVS Health is working with IBM Watson to develop personalized, data-driven services they can offer to medical experts at CVS MinuteClinics and pharmacies, among other places. The thinking is that Big Data and cognitive analytics may offer health professionals an early-warning system to spot a person at risk for a health decline—before that decline happens—and recommend actions that person can take to stay healthy.

For addressing chronic illness, which people manage and deal with day-in and day-out, Watson may represent a huge leap forward. "It is really powerful to give folks with chronic conditions more of an ability to take control of their own health and wellness," says McGroddy Goetz. "And leveraging technology, Big Data and cognitive analytics are key to putting power in their own hands.”