Introduction
Nearly 1 in 5 patients discharged from the hospital are readmitted within 30 daysThe use of remote patient monitoring (RPM) devices such as smartphones and wearables is increasing, and prior work has demonstrated they are accurate for tracking daily activity patternsThe objective of our study was to evaluate whether hospital readmission prediction models could be improved by incorporating RPM data on activity patterns after hospital discharge and by using machine learning approaches. To evaluate whether there were differences in prediction based on the type of RPM device, we randomly assigned patients discharged from medicine services at two hospitals in Philadelphia to use either their smartphone or a wearable device to track daily activity patterns. Many patients already have a smartphone and these devices have been demonstrated to lead to longer-term utilization for RPM than wearables