Explanations in Digital Health: The Case of Supporting People Lifestyles

Systems that aim at supporting users on behavior change are expected to implement strategies that can both motivate and gain the users’ trust, like the use of human understandable justifications for system’s decisions. While the literature has dedicated great effort on providing accurate system’s decisions, less focus has been given on addressing the problem of explaining to the user the reasons for a decision. This work presents a SPARQL-based reasoner enabling explainability on systems thought for supporting users in following healthy lifestyles. Our results demonstrate that users that received such information were able to reduce unhealthy behaviors over time.