title: A Diabetes Self-Management Prototype in an AAL-Environment to Detect Remarkable Health States.
abstract: Every year life span is increasing and simultaneously the proportion of people with one or more chronic diseases. This paper presents an implementation of a prototype with a decision tree to detect dangerous health conditions for Diabetes Type 1 and Diabetes Type 2. With the information we collect from Personal Health Devices and data from the Active-Assisted-Living environment, we are in the position to customize thresholds and to get individual results. With the help of a modified Glucose-Insulin Model (based on the minimal model of Stolwijk & Hardy) we predicted the future glucose concentration of the patient. We validated our model with an intention-to-treat pilot study including 8 subjects and obtained a significantly better (p < 2.2-16) result than the original model.
authors.affiliation: Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent Systems.
authors.forename: Denise
authors.initials: D
authors.lastname: Schindelboeck
authors.affiliation: University of Applied Science Technikum Wien.
authors.forename: Friedrich
authors.initials: F
authors.lastname: Praus
authors.affiliation: Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent Systems.
authors.forename: Walter
authors.initials: W
authors.lastname: Gall
mesh_terms: D015190:Blood Glucose Self-Monitoring; D003663:Decision Trees; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis* / Q000628:therapy; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000628:therapy; D000072936:Fitness Trackers; D006801:Humans; D010865:Pilot Projects; D000073278:Self-Management