Involving physical activity in insulin recommender systems with the use of wearables

Abstract

Type Diabetes Mellitus 1 (TDM1 patients are able to determine the amount of insulin to be injected in a dose according to the most recent food ingest and other factors such as physical activity, or menstruation. Recently, other elements such as stress, have been determined as key factors too, which influence this decision. Dealing with all these factors is a complex task, and patients suffering this illness are very active in looking for tools that can help them in these daily decisions. In that regard, insulin recommender systems are decision support system (DSS) designed with the aim of providing the appropriate insulin dose to a given patient in a given moment. Moreover, the deployment of such kind of DSS in mobile devices is offering the opportunity to use new sensors that may provide additional information to improve the recommendations. For example, some researchers are exploring mobile cameras to process the food ingested in order to automatically count the carbohydrates to be considered. Other sensors, like smartwatches or wrist bands offer the opportunity to track patients’ physical activity or even their stress level in order to feed the next insulin recommendation decision with this information. Our work concerns the development of an adaptive recommender system that exploits the information from wearables, in order to improve the recommendation provided to TDM1 patients