ArticleComputer methods and programs in biomedicine2017
Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability.
Article in Computer methods and programs in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 63 citations in OpenAlex.
- Explainable cluster-based learning for prediction of postprandial glycemic events and insulin dose optimization in type 1 diabetes.PLOS digital health · 2025Article
- Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.Journal of diabetes and metabolic disorders · 2025Review
- Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes.Frontiers in endocrinology · 2022Article
- Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability.Frontiers in endocrinology · 2022Article
- An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms.Journal of diabetes science and technology · 2022Article
- Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator.Journal of diabetes science and technology · 2021Article
- Developing Insulin Delivery Devices with Glucose Responsiveness.Trends in pharmacological sciences · 2021Review
- Closed-loop bioelectronic medicine for diabetes management.Bioelectronic medicine · 2020Review
- Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System.Journal of healthcare engineering · 2020Article
- An App-Delivered Self-Management Program for People With Low Back Pain: Protocol for the selfBACK Randomized Controlled Trial.JMIR research protocols · 2019Article
- The Bio-inspired Artificial Pancreas for Type 1 Diabetes Control in the Home: System Architecture and Preliminary Results.Journal of diabetes science and technology · 2019Article
- Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs.Journal of diabetes science and technology · 2019Article
- Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal.Sensors (Basel, Switzerland) · 2019Article
- Postprandial Glucose Control in Type 1 Diabetes: Importance of the Gastric Emptying Rate.Nutrients · 2019Review
- A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model.PloS one · 2019Article
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.Journal of medical Internet research · 2018Review
- Automatic Adaptation of Basal Insulin Using Sensor-Augmented Pump Therapy.Journal of diabetes science and technology · 2018Article
- The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day.Journal of diabetes science and technology · 2018Article
- Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the "On-Board" Concept.Journal of diabetes research · 2018Review
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
objectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.
methodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.
resultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.
conclusionUsing an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.
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