Personal Health Records (PHR) containing physiological data collected by multiple sensors are being increasingly used for wellness monitoring or disease management. These abundant complementary raw data could be nevertheless disregarded given the challenges to understand and process it. We propose a knowledge-based integration model of PHR data from sensors and personal observations, intended to facilitate decision support in scenarios of cardiovascular disease monitoring. The model relates knowledge at three data integration layers: elements identification, relations assessment, and refinement. Details on specific elements of each layer are provided, along with a discussion of use and implementation guidelines.