Carbohydrate counting is an important meal-planning tool for patients on intensive insulin regimens. Preprandial insulin bolus is adjusted taking into account the carbohydrate content of each meal and the insulin-to-carb ratio of each patient throughout the day. Evidence suggests that accurate carbohydrate counting may have positive effects not only on reducing glycosylated hemoglobin concentration but also on decreasing the incidence of hypoglycemic episodes. Nevertheless, despite its benefits, the efficacy of carbohydrate counting depends on the ability of each patient, or its caregiver, to accurately estimate the carbohydrate content of each meal. Therefore, it is of great importance to understand how accurate should carbohydrate counting be, and the impact of inaccurate carbohydrate counting on the glycemic control of each patient. Within this work, we propose an analytic method that uses the insulin-to-carb ratio and the insulin sensitivity factor, along with the glycemic targets of each patient to calculate the limits of accurate carbohydrate counting, in order to achieve better glycemic control and to reduce hypoglycemic episodes.

Biomedical wireless sensor networks are a key technology to support the development of new applications and services targeting patient monitoring, in particular, regarding data collection for medical diagnosis and continuous health assessment. However, due to the critical nature of medical applications, such networks have to satisfy demanding quality of service requirements, while guaranteeing high levels of confidence and reliability. Such goals are influenced by several factors, where the network topology, the limited throughput, and the characteristics and dynamics of the surrounding environment are of major importance. Harsh environments, as hospital facilities, can compromise the radio frequency communications and, consequently, the network’s ability to provide the quality of service required by medical applications. Furthermore, the impact of such environments on the network’s performance is hard to manage due to its random and unpredictable nature. Consequently, network planning and management, in general or step-down hospital units, is a very hard task. In such context, this work presents a quality of service based management tool to help healthcare professionals supervising the network’s performance and to assist them managing the admission of new sensor nodes (i.e., patients to be monitored) to the biomedical wireless sensor network. The proposed solution proves to be a valuable tool both, to detect and classify potential harmful variations in the quality of service provided by the network, avoiding its degradation to levels where the biomedical signs would be useless; and to manage the admission of new patients to the network.

No abstract