Kevin Volpp, MD, PhD
Daniel Polsky, PhD
Dylan Small, PhD
Center for Health Incentives and Behavioral Economics, University of Pennsylvania
Leonard Davis Institute of Health Economics, University of Pennsylvania
Nearly 90 million adults in the United States have prediabetes, with an elevated risk of developing diabetes in the future.
Current models to predict changes in glycemic control perform poorly and do not consider daily health behaviors such as exercise. Automated monitoring of daily health behaviors could inform enhanced risk prediction models that detect patients likely to experience a decline in glycemic control. And if such patients could be identified effectively, providers would be better equipped to intervene and improve outcomes sooner.
We conducted a randomized trial with adults with prediabetes to explore if data from wearables could help inform risk prediction models. Participants were randomly assigned to use a waist-worn or wrist-worn wearable to track activity patterns over six months. We tested three models to predict continuous changes in glycemic control during this time. Way to Health was used to administer the study.
In all three models, prediction improved when machine learning was used vs. traditional regression, when baseline information with wearable data was used vs. baseline information alone, and when wrist-worn wearables were used vs. waist-worn wearables.
Our findings indicate that predictive models can accurately identify changes in glycemic control among prediabetic adults and, therefore, could be leveraged to better allocate resources and target interventions to prevent the progression of diabetes.