Eleni I. Georga

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.

The management of chronic diseases requires the continuous monitoring and control of an extensive set of medical and lifestyle parameters affecting the health status of patients. The purpose of this chapter is to present an overview of the state of the art in wearable medical systems and mobile self-management support interventions in the daily care of Chronic Obstructive Pulmonary Disease (COPD) and Type 1 and Type 2 diabetes. In both cases, research and commercial approaches to the integration of specialized sensors in a wearable smart module are presented and their ability to provide real-time estimations for crucial parameters is emphasized. Moreover, special emphasis is placed on mobile self-monitoring applications which are progressively enhanced with decision support, pattern recognition and predictive capabilities which can be used by the patient. The way in which mobile health technology can improve health outcomes is discussed and future research directions are described.

The management of chronic diseases requires the continuous monitoring and control of an extensive set of medical and lifestyle parameters affecting the health status of patients. The purpose of this chapter is to present an overview of the state of the art in wearable medical systems and mobile self-management support interventions in the daily care of Chronic Obstructive Pulmonary Disease (COPD) and Type 1 and Type 2 diabetes. In both cases, research and commercial approaches to the integration of specialized sensors in a wearable smart module are presented and their ability to provide real-time estimations for crucial parameters is emphasized. Moreover, special emphasis is placed on mobile self-monitoring applications which are progressively enhanced with decision support, pattern recognition and predictive capabilities which can be used by the patient. The way in which mobile health technology can improve health outcomes is discussed and future research directions are described.

In this study, an individualized predictive model of the subcutaneous glucose concentration in type 1 diabetes is presented, which relies on the Random Forests regression technique. A multivariate dataset is utilized concerning the s.c. glucose profile, the plasma insulin concentration, the intestinal absorption of meal-derived glucose and the daily energy expenditure. In an attempt to capture daily rhythms in glucose metabolism, we also introduce a time feature in the predictive analysis. The dataset comes from the continuous multi-day recordings of 27 type 1 patients in free-living conditions. Evaluating the performance of the proposed method by 10-fold cross validation, an average RMSE of 6.60, 8.15, 9.25 and 10.83 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively, was attained.