Print Email Facebook Twitter Social relations and diabetes Title Social relations and diabetes: Creating a support system for people with diabetes Author Kesteloo, Mitchell (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Bruijnes, M. (mentor) Brinkman, W.P. (mentor) Neerincx, M.A. (graduation committee) Kraal, J.J. (graduation committee) Degree granting institution Delft University of Technology Date 2021-03-03 Abstract People with diabetes often show symptoms heavily associated with depression, but these symptoms are often caused by the burden of daily diabetes management. The negative feelings caused by this burden are defined as "diabetes burnout''. Some of these negative feelings are caused by social issues. People with diabetes often resort to online sources to find out how to deal with these social issues since health care providers do not focus on this side of diabetes. Furthermore, the social stigma surrounding treatment for mental problems stops people with diabetes from going to a psychologist. In this project, a conversational agent is designed, implemented and evaluated to investigate whether it is capable of reducing social diabetes distress. The agent was designed to give personalized tips based on a social issue the person with diabetes shares. A longitudinal experiment was done over three sessions to evaluate the agent. The results show that the agent is able to reduce the diabetes distress more than a plain textual delivery of tips. The successful application shows the value of conversational agents and provide a basis to deploy such conversational agents in the e-mental health domain. The design we created can be used in future work, where a further personalized approach and a tool measuring the personalization should be investigated in order to better understand why the conversational agent is able to reduce the diabetes distress. Subject diabetes distressconversational agentchatbotpersonalizationshared decision makingchatbot designdiabetes burnout To reference this document use: http://resolver.tudelft.nl/uuid:0721f147-dd7f-42e5-b810-711462355f2d Part of collection Student theses Document type master thesis Rights © 2021 Mitchell Kesteloo Files PDF Thesis_4_.pdf 915.46 KB Close viewer /islandora/object/uuid:0721f147-dd7f-42e5-b810-711462355f2d/datastream/OBJ/view