ArticleDiabetes technology & therapeutics2013
Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement.
Article in Diabetes technology & therapeutics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
61 citing papers in PubMed.
- Safety and Feasibility Evaluation of Step Count Informed Meal Boluses in Type 1 Diabetes: A Pilot Study.Journal of diabetes science and technology · 2022Trial
- Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting.Diabetes technology & therapeutics · 2018Trial
- Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas.Journal of diabetes science and technology · 2016Trial
- Randomized trial of a dual-hormone artificial pancreas with dosing adjustment during exercise compared with no adjustment and sensor-augmented pump therapy.Diabetes, obesity & metabolism · 2016Trial
- Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.Diabetes care · 2016Trial
- Preliminary evaluation of a new semi-closed-loop insulin therapy system over the prandial period in adult patients with type 1 diabetes: the WP6.0 Diabeloop study.Journal of diabetes science and technology · 2014Trial
- Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes.Diabetes technology & therapeutics · 2014Trial
- An integrated multivariable artificial pancreas control system.Journal of diabetes science and technology · 2014Trial
- On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.PLOS digital health · 2026Article
- Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.Journal of diabetes science and technology · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025Review
- Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.Diabetes technology & therapeutics · 2025Review
- Cyber-Physical-Human Systems in Precision Medicine: Advances in Artificial Pancreas for Treatment of Diabetes.Annual reviews in control · 2025Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.Journal of diabetes science and technology · 2023Article
- In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes.Frontiers in endocrinology · 2023Article
- Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes.Current cardiology reports · 2022Review
- Non-von Neumann multi-input spike signal processing enabled by an artificial synaptic multiplexer.Science advances · 2022Article
- Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.JMIR diabetes · 2022Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAccurate closed-loop control is essential for developing artificial pancreas (AP) systems that adjust insulin infusion rates from insulin pumps. Glucose concentration information from continuous glucose monitoring (CGM) systems is the most important information for the control system. Additional physiological measurements can provide valuable information that can enhance the accuracy of the control system. Proportional-integral-derivative control and model predictive control have been popular in AP development. Their implementations to date rely on meal announcements (e.g., bolus insulin dose based on insulin:carbohydrate ratios) by the user. Adaptive control techniques provide a powerful alternative that do not necessitate any meal or activity announcements. MATERIALS AND
methodsAdaptive control systems based on the generalized predictive control framework are developed by extending the recursive modeling techniques. Physiological signals such as energy expenditure and galvanic skin response are used along with glucose measurements to generate a multiple-input-single-output model for predicting future glucose concentrations used by the controller. Insulin-on-board (IOB) is also estimated and used in control decisions. The controllers were tested with clinical studies that include seven cases with three different patients with type 1 diabetes for 32 or 60 h without any meal or activity announcements.
resultsThe adaptive control system kept glucose concentration in the normal preprandial and postprandial range (70-180 mg/dL) without any meal or activity announcements during the test period. After IOB estimation was added to the control system, mild hypoglycemic episodes were observed only in one of the four experiments. This was reflected in a plasma glucose value of 56 mg/dL (YSI 2300 STAT; Yellow Springs Instrument, Yellow Springs, OH) and a CGM value of 63 mg/dL).
conclusionsRegulation of blood glucose concentration with an AP using adaptive control techniques was successful in clinical studies, even without any meal and physical activity announcement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.