Wearables and biosensors allow for the passive and non-invasive collection of complimentary data for clinical trials. In specific, wearables and biosensors are useful in the area of cardiovascular disease and heart failure as they can serve as proxies for measures of physical activity and quality of life. In a recent heart failure clinical trial, wearable device data capturing information regarding physical activity and activity intensity was captured for approximately 220 patients across a total of 28 days. This data was then analyzed utilizing a consensus clustering approach in which the patients were assigned to four separate clusters. Activity intensity and activity duration data were described by cluster, in addition to baseline medical history data. Differences were identified in the clinical phenotypes of different clusters of patients. The analytical work here represents an approach that can be utilized when looking to analyze wearable and biosensor data to detect whether potential heterogeneous clinical clusters of patients exist.