ArticleSensors (Basel, Switzerland)2018
Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 83 citations in OpenAlex.
- On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.PLOS digital health · 2026Article
- Performance of continuous glucose monitoring-based meal detection algorithms in young healthy adults.Scientific reports · 2026Article
- A personalized and automated real-time meal detection algorithm based on continuous glucose monitoring and heart rate data for individuals with post-bariatric hypoglycemia.Frontiers in digital health · 2026Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.Scientific reports · 2024Article
- DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions.Scientific data · 2023Article
- Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence.NPJ digital medicine · 2023Article
- Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review.Frontiers in nutrition · 2023Article
- Review
- Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.Nutrients · 2022Article
- Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.JMIR diabetes · 2022Review
- A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems.Sensors (Basel, Switzerland) · 2022Article
- A New Meal Absorption Model for Artificial Pancreas Systems.Journal of diabetes science and technology · 2022Article
- Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives.Journal of diabetes science and technology · 2021Review
- A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems.Sensors (Basel, Switzerland) · 2021Article
- Where Do We Stand with Closed-Loop Systems and Their Challenges?Diabetes technology & therapeutics · 2020Review
- Closed-loop bioelectronic medicine for diabetes management.Bioelectronic medicine · 2020Review
- Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement.Journal of diabetes science and technology · 2019Article
- MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of type 1 diabetes mellitus: overview of its safety and efficacy.Expert review of medical devices · 2019Review
- A Clinical Guide to Advanced Diabetes Devices and Closed-Loop Systems Using the CARES Paradigm.Diabetes technology & therapeutics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the same meal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 ± 2%, 93 ± 5%, and 47 ± 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes.
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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 Socratic graph.