A Semantic Web based Knowledge Management Framework to model Behavior Change Constructs for generation of personalized Action Plans

Abstract

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.