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Objective To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). Research Design and Methods A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise. Results Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent Conclusions The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake. Register Clinicaltrials.gov: NCT03577158

Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a Compositional Data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations.

Resumen Objetivo Evaluar de forma exploratoria un sistema de pancreas artificial durante la realizacion de ejercicio aerobico (EAe) y anaerobico (EAn). Metodos Ensayo clinico piloto con 5 sujetos con diabetes tipo 1 (4 hombres) de 37 ± 10,9 anos, 21,2 ± 12,2 anos de evolucion de la diabetes tipo 1, usuarios de infusor de insulina y una HbA1c de 7,8 ± 0,5%. Cada uno de los pacientes realizo 3 estudios de EAe y 3 de EAn. El control de la glucemia se realizo mediante el algoritmo de pancreas artificial durante el ejercicio y las 4 h posteriores al mismo. Previo al inicio del ejercicio fisico se administraron 23 g de hidratos de carbono. Resultados La media de glucosa fue de 124,0 ± 25,1 mg/dL en los estudios de EAe y de 152,1 ± 34,1 mg/dL en los de EAn. Los porcentajes de tiempo en 70-180, > 180 y Conclusiones La respuesta glucemica al EAe y al EAn es diferente. El sistema de pancreas artificial evaluado parece controlar de forma eficaz y segura la glucemia durante el ejercicio y las 4 h posteriores al mismo, aunque es necesario el diseno de nuevas estrategias de control que minimicen la intervencion del paciente.

The promising potential of artificial intelligence methods and its convergence with the latest technologies such as medical devices, mobile computing, and sensor technologies allows for creation and delivery of better management services to deal with chronic diseases. Diabetes mellitus is one of the most lethal and prevalent chronic diseases and it is characterized by the malfunctioning of the glucose homeostasis process. The objective of this chapter is to provide a general overview of the key research efforts in the use of artificial intelligence techniques for decision support systems aimed to assist in the management of diabetes. On the one hand, this chapter introduces this chronic disease and its consequences, as well as delving into the more complex challenges of managing diabetes. On the other hand, we present a series of artificial intelligence applications for diabetes management support systems.

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms This work has been partially funded by the Spanish Government (grant no. DPI2016-78831-C2-2-R), National Counsel of Technological and Scientific Development, CNPq-Brazil (grant nos. 207688/2014-1 and 202050/2015-7), and Formación de Professorado Universitario (grant no. FPU0244 2015)

The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an unscented Kalman filter. After detection, the EHRA triggers the following three actions: First, a carbohydrate suggestion, second, a reduction in basal insulin and the insulin-on-board maximum limit, and finally, a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50 min at $60\%\text{VO}_{2\text{max}}$ on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70–180 mg/dl) of 94% and a percentage of time $ 70 mg/dl of 2%, 0%, and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and $-$ 25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.

[EN] The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government MINECO through Grants DPI-2016-78831-C2-1-R, DPI2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7, 207688/2014-1 and EU through FEDER funds.

Moderate physical activity improves overall health conditions in subjects with type 1 diabetes. However, insulin management during and after exercise is challenging due to the effects of exercise on glycemic control. Artificial pancreas (AP) systems aim to automatically control blood glucose levels, but exercise-induced hypoglycemia is a major challenge for these systems, especially in uni-hormonal configurations. The aim of this work was to evaluate the ability of several feed-forward (FF) actions to prevent exercise-induced hypoglycemia in a closed-loop setting.A closed-loop control algorithm combined with FF actions aimed at eliminating exercise-induced hypoglycemia was evaluated in silico using the UVa/Padova type 1 diabetes simulator. The simulator was modified with an exercise model fitted to clinical data. The FF actions were evaluated in two scenarios: (1) exercise sessions during postprandial period and (2) exercise sessions during fasting period.The mitigation methods proposed in this work were able to minimize the occurrence of hypoglycemic events related with exercise in both scenarios. The time spent in hypoglycemic range in the 2-h period after exercise decreased from 33.3% to 0.0% (P 0.01) and from 41.3% to 0.0% (P 0.01) in both scenarios tested. Besides that, the occurrence of hypoglycemic events after exercise sessions was also reduced.The combination of the FF actions presented in this article within an AP system showed to be an effective strategy to mitigate the risk of hypoglycemia in front of aerobic exercise.

[EN] Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found., This project was partially supported by the Spanish Government through grants DPI2013-46982-C2-1-R, DPI2013-46982-C2-2-R, DPI2016-78831-C2-1-R, and DPI2016-78831-C2-2-R, and by the National Council of Technological and Scientific Development, CNPq Brazil through grants 202050/2015-7 and 207688/2014-1.

To assess an artificial pancreas system during aerobic (AeE) and anaerobic exercise (AnE).A pilot clinical trial on five subjects with type 1 diabetes (4 males) aged 37±10.9 years, diabetes diagnosed 21.2±12.2 years before, insulin pump users, and with a mean HbAThe mean glucose level was 124.0±25.1mg/dL in the AeE studies and 152.1±34.1mg/dL in the AnE studies. Percent times in the different glucose ranges of 70-180,180 and70mg/dL were 89.8±18.6% and 75.9±27.6%; 7.7±18.4% and 23.2±28.0%; and 2.5±6.3% and 1.0±3.6% during the AeE and AnE sessions, respectively. Only six rescues with carbohydrates (15g) were required during the studies (4 in AeE and 2 in AnE). Total insulin dose during the five hours of the study was 3.1±1.0IU in the AeE studies and 3.5±1.3IU in the AnE studies.Blood glucose response to AeE and AnE exercise is different. The evaluated artificial pancreas system appeared to achieve effective and safe blood glucose control during exercise and up to four hours later. However, new control strategies that minimize patient intervention should be designed.

Since the 2000s, research teams worldwide have been working to develop closed-loop (CL) systems able to automatically control blood glucose (BG) levels in patients with type 1 diabetes. This emerging technology is known as artificial pancreas (AP), and its first commercial version just arrived in the market. The main objective of this paper is to present an extensive review of the clinical trials conducted since 2011, which tested various implementations of the AP for different durations under varying conditions. A comprehensive table that contains key information from the selected publications is provided, and the main challenges in AP development and the mitigation strategies used are discussed. The development timelines for different AP systems are also included, highlighting the main evolutions over the clinical trials for each system.

[EN] Background: Closed-loop (CL) systems aims to outperform usual treatments in blood glucose control and continuous glucose monitors (CGM) are a key component in such systems. Meals represents one of the main disturbances in blood glucose control, and postprandial period (PP) is a challenging situation for both CL system and CGM accuracy. Methods: We performed an extensive analysis of sensor¿s performance by numerical accuracy and precision during PP, as well as its influence in blood glucose control under CL therapy. Results: During PP the mean absolute relative difference (MARD) for both sensors presented lower accuracy in the hypoglycemic range (19.4 ± 12.8%) than in other ranges (12.2 ± 8.6% in euglycemic range and 9.3 ± 9.3% in hyperglycemic range). The overall MARD was 12.1 ± 8.2%. We have also observed lower MARD for rates of change between 0 and 2 mg/dl. In CL therapy, the 10 trials with the best sensor spent less time in hypoglycemia (PG < 70 mg/dl) than the 10 trials with the worst sensors (2 ± 7 minutes vs 32 ± 38 minutes, respectively). Conclusions: In terms of accuracy, our results resemble to previously reported. Furthermore, our results showed that sensors with the lowest MARD spent less time in hypoglycemic range, indicating that the performance of CL algorithm to control PP was related to sensor accuracy., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government through Grants DPI 2013-46982-C2-1-R, DPI 2016-78831-C2-1-R, DPI 2013-46982-C2-2-R, and DPI 2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7 and 207688/2014-1.

Redes de Sensores sem fio tem como objetivo monitorar algum fenomeno decorrente no ambiente, possuindo, assim, diversas aplicacoes. Este artigo apresenta a parte inicial do projeto que envolve uma RSSF projetada para monitoramento de temperatura de um determinado espaco atraves da modificacao do programa de teste apresentado em linguagem nesC proprio do software TinyOS. Devido a sua conversao de dados coletados analogicos para digitais, necessita-se de modificacao na implementacao do aplicativo Java para que seja codificada a temperatura em escala Celsius, facilitando a sua observacao. Diante disso, coletou-se em um determinado ambiente dados de temperatura na escala desejada.