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Behavior change approaches aim to assist patients in making lifestyle adjustments so that they are able to self-manage their condition effectively. Self-efficacy (SE) is central to behavior change, and different behavior change theories propose a range of targeted strategies and action plans to gradually induce behavior adjustments amongst patients so that they are able to achieve efficacy in self-management of their condition. Social cognitive theory (SCT) is a comprehensive behavior change theory that proposes self-efficacy construct as central to behavior change. In this thesis, we have taken a knowledge management approach to computerize specialized self-efficacy constructs stipulated by SCT to formulate a high-level SCT knowledge model. We have collected and computerized behavior change content targeting healthy living and physical activity. Semantic web technologies have been used to develop a SCT ontology and SWRL rules to infer personalized self-management plans based on a given patient profile. We present formative evaluation of the clinical correctness and relevance of the generated personalized action plans for a range of test patient profiles.