Timely information and education can enhance the ability of consumers to make informed choices about their health, lifestyle and modifiable disease risk factors. Due to its unstructured and varied format, and lack of targeted delivery methods, information and knowledge does not often reach consumers, when they need it most. The aim of this project is to explore ways to provide personalized, relevant information for consumers to better manage a chronic disease such as diabetes. Methods: Based on surveys carried out by Colagiuri et al. (2004) and analysing information gathered from various reliable knowledge sources, we have identified information required by consumers at different diabetic stages to support better decisions. Information needs of diabetic patient include information about short term complications such as hypoglycemia, hyperglycaemia and ‘sick day’ management; managing co-morbidities; general self-care issues; information and education to promote lifestyle change; understanding of diabetes with its long-term nature; how to navigate, access the health system and identify geographically near health services. Currently, most of this information is available in different online formats but not personalized to the needs of the consumers. Information required to assist in making informed decision can only be obtained by aggregating and disaggregating information from different sources. This leads to the creation of a consumer-centric information flow model that interacts with the different types of information sourced from the various information sources and major stakeholders in the health domain. Once the information flow model is designed, information needs to be personalized for the consumer to enable informed decision. By mapping each consumer to an existing situation where decisions relating to diabetes needs to be taken and identifying the information required to make this decision, a decision context can be created for each consumer. To derive contextual information a context model is proposed that captures individual specific information such as an individual’s life stage, disease stage, demographics, geographic location, access to care and learning styles. The identified context model and the decision context is then used to filter out the relevant information for the consumers. We are planning to create a mobile based information system, as smartphones have the necessary infrastructure to act as the medium to capture, process and disseminate information based on context. We will use a scenario based analysis to identify the different business cases that can empower and motivate consumers to act on the provided information. Conclusions: This research focuses on delivering context-sensitive information via the ubiquitous smart phone for chronic diseases. While diabetes is used as the exemplar disease, the solution is not limited to one specific chronic disease. The context model together with the decision context is generic and can be generalized to other chronic disease management cases.