Remote Clinical Trial Operations: Patient Education for Medical and Wearable Device Use

Abstract

Consumer wearable devices with the capability to remotely collect longitudinal physiological data used for machine learning and artificial intelligence are set to revolutionize healthcare, including enabling remote clinical trials. Yet, there is no regulatory framework in place to standardize their utilization. In traditional clinical studies, user-related error is minimized, as a designated clinician performs all physiological measurements on each subject. However, in remote clinical settings, this standardization is lost, as each participant becomes responsible to collect their own physiological data. Patient education materials for remote studies must be designed intentionally to minimize user-related factors such as misuse and nonuse of device, as these mistakes introduce heterogeneity into and devalue longitudinal physiological data sets. This thesis project addressed the current state of remote clinical trial operations and provides a framework for human-subjects researchers to establish their own standardized remote clinical trial operations. Specifically, it focuses on the creation of intentional patient education materials with respect to fundamental principles of human cognition to reduce user-related error in wearable device operation.