Prediction Using Connected Technologies for Diabetes

Overview

In this study, adults with pre-diabetes will be prospectively enrolled for data collection to design prediction models that integrate electronic health record data and patient-generated activity data. Patients will be randomized to receive either a waist-worn or wrist-worn wearable device for 6 months to capture patient-generated activity data.

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.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes