Learning from Human Behavior to Improve Preventative Health Information Systems
Health services are often oriented to be ubiquitous for persons comfortable with mobile device and web technologies usage. In this environment, the main question is: can computer systems learn from human behavior to improve preventative health information systems? Current healthcare information systems focus on ensuring adherence to treatment through Just-In-Time Adaptive Interventions (JITAI). JITAI is used for preventative health and lifestyle behavior change interventions. The main problem for JITAI is lack of proper models of user’s behavior allowing effective interventions. Our novel approach adds contextual variables to the behavior learning system embedded in the message contents of lifestyle modifications interventions. The main result obtained from the proposal was a 33,8% perceived effective SMS intervention using behavior modeling with reduced specialized feedback. Chronic diseases like diabetes type 2 (DT2) are preventative when lifestyle behavior can be modified towards healthier habits. The research results open a new range of applications in context-aware computing and improve the effectiveness of JITAI for preventative chronic diseases.
KeywordsLifestyle intervention Human behavior modelling Preventative health systems
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