This study protocol outlines the development of a machine-learning model to predict short-term increases in day-to-day home blood pressure variability using IoT-based environmental and activity data.
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.
Nakao et al. (2026) studied this question.