Power4FitFoot focuses on an ecosystem to support data-driven personalized and persuasive monitoring & coaching for cardiovascular patients with diabetic foot. The result will be an early warning system (EWS) on risk detection of deterioration (healthcare providers), with feedback and coaching (patients and informal caregivers). This project shows that the combination of big data technologies and real-time sensor data analytics is the key ingredient to tailor and personalize self-management programs. Power4FitFoot follows a participatory multidisciplinary development approach: researchers (medicine, computer and behavioral sciences) work together with patients, caregivers and industrial companies to develop personalized self-management products and services to prevent Diabetic Foot Ulcers (DFU) or amputations. We outline 4 complementary studies to accomplish our goals. Study 1 focuses on the identification of the major risk factors using existing retrospective data sets. Outcomes will be transformed into dependent variables we aim to predict. Study 2 entails the co-creation of a smart monitoring ecosystem (platform & body sensors) to enable personalized feedback via data analyses. Data sets from various sources (medical, lifestyle, geo-spatial) and with high variety will be collected real-time to further develop the prognostic models. Study 3 focuses on real-time coaching dealing with high risk contexts using the insights of the big data sets from both previous studies. Prognostic models will facilitate a dynamic and context aware heuristics for a self-management support system. Study 4 is an overarching study, to guarantee the infrastructure for data-driven monitoring & coaching and to develop a sustainable data management plan.