Predicting changes in glycemic control among adults with prediabetes from activity patterns collected by wearable devices

Abstract

Abstract The use of wearables is increasing and data from these devices could improve the prediction of changes in glycemic control. We conducted a randomized trial with adults with prediabetes who were given either a waist-worn or wrist-worn wearable to track activity patterns. We collected baseline information on demographics, medical history, and laboratory testing. We tested three models that predicted changes in hemoglobin A1c that were continuous, improved glycemic control by 5% or worsened glycemic control by 5%. Consistently in all three models, prediction improved when (a) machine learning was used vs. traditional regression, with ensemble methods performing the best; (b) baseline information with wearable data was used vs. baseline information alone; and (c) wrist-worn wearables were used vs. waist-worn wearables. These findings indicate that models can accurately identify changes in glycemic control among prediabetic adults, and this could be used to better allocate resources and target interventions to prevent progression to diabetes.