ArticleJournal of diabetes science and technology2019
Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs.
Article in Journal of diabetes science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes.American journal of physiology. Endocrinology and metabolism · 2021Trial
- 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
- Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review.Annals of medicine and surgery (2012) · 2024Review
- Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems.Journal of diabetes science and technology · 2023Article
- Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2023Article
- Heart rate processing algorithms and exercise duration on reliability and validity decisions in biceps-worn Polar Verity Sense and OH1 wearables.Scientific reports · 2023Article
- Strengths and Challenges of Closed-Loop Insulin Delivery During Exercise in People With Type 1 Diabetes: Potential Future Directions.Journal of diabetes science and technology · 2023Article
- Review
- Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.JMIR diabetes · 2022Review
- Latest Advancements in Artificial Intelligence-Enabled Technologies in Treating Type 1 Diabetes.Journal of diabetes science and technology · 2021Article
- Continuous Glucose and Heart Rate Monitoring in Young People with Type 1 Diabetes: An Exploratory Study about Perspectives in Nocturnal Hypoglycemia Detection.Metabolites · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPeople with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals' different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions.
methodsA new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient's insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control.
resultsIncorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%.
conclusionAdapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic 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 OpenQuestion graph.