PERsonal ContExtual Precision healTh

The exponential growth of physiological, behavioral and environmental data generated through consumer mobile health (mHealth) devices and Internet of Things (IoT) technology provide unprecedented sources of personalized and contextual health information. If linked to clinical health data from the Electronic Health Record (EHR), these data can provide dynamic and individualized views of patient health states and trajectories that can greatly inform clinical care and health-related research. The investigators propose to advance precision health through the development and evaluation of a mobile application and data platform that collects, harmonizes and integrates mHealth and environmental data from patients' daily lives with their clinical histories and electronic health record data. The investigators propose a participatory design approach to implement and evaluate a precision health platform through the study and modeling of hypertension (HTN) and depression in patient communities of UC Davis (UCD) and UC San Francisco (UCSF). These chronic diseases have high prevalence across geography, socioeconomic status, and race/ethnicity, and have significant economic, societal and personal costs. They are considerably challenging to manage due to difficulties in acquiring high-quality and consistent data from patients outside of their clinical care appointments that is so needed for a full view of the patient's disease state. Despite a broad array of self-monitoring devices and consumer applications, mHealth data are not getting into the clinical care process, and patients do not regularly monitor their own health states, particularly during periods of medication change, when frequent assessments are especially important. The investigators propose to conduct a 6-month single arm feasibility study of 200 ambulatory men and women (100 each at UCSF and UCD) with either hypertension or depression to implement an open, web-accessible, standards driven and patient-centric data