Wearables can predict near-term blood sugar control in prediabetes patients

Instead of relying on traditional approaches that can only predict whether patients’ blood sugar control will progress from prediabetes to diabetes in the next five to 10 years, a team of researchers found that combining real-time data from wearable monitors and machine learning approaches could create accurate and near-term blood sugar control prediction with just six months of data. The research, led by the Perelman School of Medicine, opens the door to potentially preventing diabetes among many in this population through more immediate interventions. These findings are published in NPJ Digital Medicine.

A person in a doctor's office checks their fitness tracker on their wrist.

“While one in three adults in the United States have prediabetes, we lack a way to identify in real-time if a patient is progressing toward or moving away from developing diabetes,” says lead author Mitesh Patel, an associate professor of medicine and vice president for Clinical Transformation at Ascension. “Health systems and insurers may be able to use this type of information to better recommend changes in behavior or medications to prevent diabetes in the same way that risk prediction scores are already being used to prevent heart disease.”

Prediabetes is a condition in which a patient’s blood sugar is elevated, but not to the levels seen in diabetes. These patients run the risk of progressing to that disease, so physicians typically make decisions on patients’ care based on models developed to predict blood sugar control—technically called “glycemic” control—with point-in-time baseline data, such as tests or information gleaned from an appointment. Data on short-term prediction remain limited, and most predictions focus on the next five to 10 years.

That leaves a lot to be desired when it comes to prevention. So researchers at Penn Medicine set out to see whether a model could be created that would make predictions more immediate, using combinations of wearable devices and prediction formulas with or without machine learning techniques applied.

This story is by Frank Otto. Read more at Penn Medicine News.