(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.

The artificial pancreas (AP) system or closed-loop blood glucose (BG) regulation is a technological advancement that aims to relieve diabetic subjects from their current decision-making burden while tightening their BG levels. However, large disturbances such as meals and exercise still pose great challenges to a fully closed-loop system. In this paper, the problem of BG regulation with unannounced physical activity for type 1 diabetic subjects is addressed. We use a coordinated control strategy with insulin infusion and extra carbohydrates (CHO) for hypoglycemia avoidance. The insulin algorithm is based on a proportional-derivative controller with insulin feedback and the so-called safety auxiliary feedback element (SAFE) layer, and the algorithm for CHO is based on a predictive, quantified proportional-derivative controller. The UVa/Padua simulator glucose-insulin model is modified to include the effects of physical activity and is used to test the new AP. We consider scenarios where the subject does not announce physical activity and with challenging meals. Then, we analyze the performance and robustness of the combined insulin and CHO recommender system and compare them to the insulin-only controller. The simulation results show that the new AP system is able to mitigate daily hypoglycemia episodes (0.9 versus 0.2, p < 0.01) and increase the time in range during day (91.5% versus 92.4%, p < 0.01) without increasing the time above 180 mg/dl (6.3% versus 6.4%, p > 0.05).

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.

OBJECTIVE To evaluate the safety and performance of a new multivariable closed-loop glucose controller with automatic carbohydrate recommendation (MCL) during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS A randomized, three-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-min 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 HbA_1c between 6.0-8.5% were eligible. We investigated glucose control during and 3 hours after exercise. RESULTS Ten subjects (40.8±7.0 years-old; an HbA_1c of 7.3±0.8%) participated. The use of the MCL in both closed-loop arms decreased the time <70 mg/dl of sensor glucose (0.0%,[0.0-16.8] and 0.0%,[0.0-19.2] vs. 16.2%,[0.0-26.0], (%,[Percentile 10-90]) CLNA and CLA vs. OL respectively, p=0.047,p=0.063) and the number of hypoglycemic events when compared to OL (CLNA 4 and CLA 3 vs. OL 8; p=0.218,p=0.250). The use of MCL system increased the proportion of time within 70-180 mg/dl (87.8%,[51.1-100] and 91.9%,[58.7-100] vs. 81.1%,[65.4-87.0], (%,[Percentile 10-90]) CLNA and CLA vs. OL respectively, p=0.227, p=0.039). This was achieved with the administration of similar doses of insulin and less amount of carbohydrates. CONCLUSIONS 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 of less amount of carbohydrate intake.

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies.