Type 1 diabetes mellitus patients on intensive insulin therapy use advanced carbohydrate counting to proper dose prandial insulin. Therefore, the patient’s ability to accurately estimate the meal’s carbohydrate content is paramount. However, despite its significance, several studies show that the patient’s ability to estimates the meal’s carbohydrate content is far from ideal and identify the need for continuous education on carbohydrate counting. In this context, the authors have proposed in previous works an analytic method to determine the maximum error to the carbohydrate counting regarding each patient’s insulin-to-carb ratio and the insulin sensitivity factor. This maximum can be of great significance to design patient-specific educational programs and to define learning outcomes according to the specific characteristics of each patient. This work presents a methodology and conditions to assess the previously proposed method, using the FDA-approved University of Virginia(UVA)/Padova Type 1 Diabetes Simulator.

Wireless sensor networks are being used as the enabling technology that helps to support the development of new applications and services targeting the domain of healthcare, in particular, regarding data collection for continuous health monitoring of patients or to help physicians in their diagnosis and further treatment assessment. Therefore, due to the critical nature of both medical data and medical applications, such networks have to satisfy demanding quality of service requirements. (...)

Biomedical wireless sensor networks enable the development of real time patient monitoring systems, either to monitor chronically ill persons in their homes or to monitor patients in step-down hospital units. However, due to the critical nature of medical data, these networks have to meet demanding quality of service requirements, ensuring high levels of confidence to their users. These goals depend on several factors, such as the characteristics of the network deployment area or the network topology. In such context, this chapter surveys the main applications of biomedical wireless sensor networks, taking into account the key quality of service requirements of each one of them. Finally, it presents an analytic method, and its experimental validation, to help engineers managing the radio power of the network nodes in order to improve the communications and the quality of service provided by the network while minimising the energy consumption and, thus, maximising the network lifetime.

The use of wireless sensor networks in healthcare has the potential to enhance the services provided to citizens. In particular they play an important role in the development of state-of-the-art patient monitoring applications. Nevertheless due to the critical nature of the data conveyed by such patient monitoring applications they have to fulfil high standards of quality of service in order to obtain the confidence of all players in the healthcare industry. In such context vis-à-vis the quality of service being provided by the wireless sensor network this work presents an adaptive and context-aware method to detect and classify performance degradation events. The proposed method has the ability to catch the most significant and damaging variations on the metrics being used to quantify the quality of service provided by the network without overreacting to small and innocuous variations on the metric’s value.

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.

Accurate self-monitoring of blood glucose is the key to an effective and safe intensive insulin therapy. Indeed, most insulin dosing decisions are made based on the blood glucose values obtained from home glucose meters, in particular for those using diet planning and carbohydrate counting. Patients on that therapeutic regimen depend not only on their ability to accurately estimate the carbohydrate content of each meal but also on the accuracy of the glucose meter being used. Therefore, in order to avoid postprandial hypoglycemia and hyperglycemia, it is of great importance to realize how important is the accuracy of blood glucose meters according to the particular characteristics of each patient. In this regard, we propose an analytic method to find the limits of the blood glucose meters accuracy according to the insulin-to-carb ratio, the insulin sensitivity factor and the ability of each patient to estimate the carbohydrate content of each meal.

Evidence suggests that accurate carbohydrate counting along with self-monitoring of blood glucose is the key to a successful diabetes management, in particular for patients on intensive insulin regimens. However, despite its benefits, accurate carbohydrate counting is a complex, difficult, time-consuming, and error-prone task for most patients. Several studies show that most patients frequently estimate the carbohydrate content of meals within an error of about 10-15 g of the real value. In addition, fearing hypoglycemic events, patients frequently underestimate the carbohydrate content of meals and, consequently, they have high levels of HbA1C. Therefore, is important to avoid the consequences of incorrect carbohydrate counting in order to improve the patient’s glycemic control. To that end, this work presents an adaptive mealtime bolus calculator that uses the patient’s glycemic data to dynamically adjust the mealtime bolus and counterbalance the negative effects of inaccurate carbohydrate counting.

Smartphone overuse can lead to a series of physical, mental and social disturbances. This problem is more prevalent among young adults as compared to other demographic groups. Additionally, university students are already undergoing high cognitive loads and stress conditions; therefore, they are more susceptible to smartphone addiction and its derived problems. In this paper, we present a novel approach where a conversational mobile agent uses persuasive messages exploring the reflective mind to raise users’ awareness of their usage and consequently induce reduction behaviors. We conducted a four-week study with 16 university students undergoing stressful conditions—a global lockdown during their semester—and evaluated the impact of the agent on smartphone usage reduction and the perceived usefulness of such an approach. Results show the efficacy of self-tracking in the behavior change process: 81% of the users reduced their usage time, and all of them mentioned that having a conversational agent alerting them about their usage was useful. Before this experiment, only 68% of them considered such an approach could be useful. In conclusion, users deemed it essential to have an engaging conversational agent on their smartphones, in terms of helping them become more aware of usage times.

Biomedical wireless sensor networks enable the development of real time patient monitoring systems, either to monitor chronically ill persons in their homes or to monitor patients in step-down hospital units. However, due to the critical nature of medical data, these networks have to meet demanding quality of service requirements, ensuring high levels of confidence to their users. These goals depend on several factors, such as the characteristics of the network deployment area or the network topology. In such context, this chapter surveys the main applications of biomedical wireless sensor networks, taking into account the key quality of service requirements of each one of them. Finally, it presents an analytic method, and its experimental validation, to help engineers managing the radio power of the network nodes in order to improve the communications and the quality of service provided by the network while minimising the energy consumption and, thus, maximising the network lifetime.

Biomedical wireless sensor networks enable the development of real-time patient monitoring systems, either to monitor chronically ill persons in their homes or to monitor patients in step-down hospital units. However, due to the critical nature of medical data, these networks have to meet demanding quality of service requirements, ensuring high levels of confidence to their users. These goals depend on several factors, such as the characteristics of the network deployment area or the network topology. In such context, this work proposes a method to find the best network physical topology in order to maximise the quality of service provided by the network. The proposed method makes use of “virtual sensor nodes” to estimate the impact of adding real sensor nodes to the network in a specific location. Thus, assessing different locations, it is possible to find the best location to place the new sensor node while maximising the quality of service provided by the network. In particular, this work studies the feasibility of using “virtual sensor nodes” to assess the impact of adding a new sensor node to a biomedical wireless sensor network and presents some results showing the viability of the proposed method.