Development and Internal Validation of Machine-Learning Models for Short-Term Prediction of Day-to-Day Home Blood Pressure Variability Using IoT-Based Environmental and Activity Data　― Protocol for the AURA-BPV Study ―

Background: Day-to-day home blood pressure variability (BPV) is associated with cardiovascular risk and influenced by environmental conditions. However, it is unclear whether short-term increases in day-to-day BPV can be predicted from personal sensor data. In this study, our aim is to develop and validate a machine-learning prediction model for short-term increases in day-to-day BPV using personal sensor data on behavioral and environmental exposure.