ArticleJournal of diabetes science and technology2022
Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluation of an Automated Priming Bolus for Improving Prandial Glucose Control in Full Closed Loop Delivery.Diabetes technology & therapeutics · 2025Trial
- Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes.Diabetes care · 2023Trial
- On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.PLOS digital health · 2026Article
- Identifying and Intervening on Glucose Patterns in Multivariate Data Using Block-Based Recurrence Quantification Analysis.Journal of diabetes science and technology · 2025Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionHyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control.
methodsA model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module.
resultsPostprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%).
conclusionsThe BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.
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