Prediction Using a Randomized Evaluation of Data Collection Integrated Through Connected Technologies for Diabetes Management

Patients with suboptimal glycemic control could be better managed if these higher risk patients could be identified and effective interventions were then targeted towards them. However, most practice settings perform infrequent laboratory testing every 3 to 6 months, if not at longer intervals. Current models to predict change in glycemic control perform poorly and do not take into account the behaviors that occur between these intervals. In this study, we will compare different methods to use data on daily health behaviors collected by wearable devices to enhance risk prediction models. Adults with pre-diabetes will complete a series of surveys and baseline assessments and then will be randomly assigned to use a waist-worn or wrist-worn wearable device for 6 months. Measures of HbA1c and LDL will be obtained at baseline and at 6 months.