Rodney Buckley has lost 100 pounds in less than a year, not by using a GLP-1 drug but with the help of a digital twin. Last March, the 55-year-old retired firefighter turned village mayor of Third Lake, Illinois, was 376 pounds. He had tried different diets over the years and would typically lose some weight but eventually gain it back. When his wife’s employer started offering a program from startup Twin Health, he thought he would give it a try.
With demand for Ozempic and other GLP-1 drugs soaring, employers are grappling with their high costs. At around $1,000 to $1,500 a month per person, these medications represent a rapidly increasing health care expense. It’s why some employers are turning to non-medication alternatives to help people go off, reduce, or avoid GLP-1s entirely.
Mountain View, California–based Twin Health’s approach uses a combination of wearables, AI, and on-demand health coaching to help manage diabetes, prediabetes, and obesity. The company sends users a kit with a continuous glucose monitor, blood pressure cuff, smart scale, and fitness tracker. Together, the devices collect data points on blood sugar, weight, stress, blood pressure, sleep, and activity and feed them into a single app. Using a predictive AI model, the app analyzes all this information to generate a virtual replica of the user’s metabolism—the digital twin.
A clinical trial found that Twin can help people with type 2 diabetes control their blood sugar with fewer medications and lose weight. The program has led to medication savings for asset manager Blackstone, which has been using Twin Health for several years, while helping employees there lose weight.
“We are making an impact on people, and we are sustaining the health outcomes,” says Jahangir Mohammed, cofounder and CEO of Twin Health. He says he was inspired to start the company in 2018 because type 2 diabetes runs in his family.
The company has enrolled tens of thousands of people across nearly 200 employers. Twin gets paid only when users achieve certain clinical outcomes, such as lower blood sugar, weight loss, or the reduction of metabolic medications.
Users log what they’re eating throughout the day by scanning food labels, taking pictures of their meals, or recording meals via voice. The app uses AI to analyze nutritional content and identifies foods as “green,” “yellow,” or “red”—green being the healthiest options and red being foods to avoid. Those colors can change as a person’s metabolic health improves. A food that was once red might eventually turn yellow or green.