ArticleDiabetes technology & therapeutics2018
Multivariable Artificial Pancreas for Various Exercise Types and Intensities.
Article in Diabetes technology & therapeutics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05145374 (Multivariable Artificial Pancreas to Detect and Mitigate the Effects of Unannounced Physical Activities and Acute Psychological Stress), which is not on this map. Cited by 22 papers.
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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
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.
Multivariable Artificial Pancreas to Detect and Mitigate the Effects of Unannounced Physical Activities and Acute Psychological Stress
Who cites it
22 citing papers in PubMed.
- Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial.The Lancet. Digital health · 2023Trial
- 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
- Dosing Algorithms for Insulin Pumps.Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes.Diabetes technology & therapeutics · 2024Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes.Journal of diabetes science and technology · 2024Article
- Recent advances in the precision control strategy of artificial pancreas.Medical & biological engineering & computing · 2024Review
- Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.Journal of diabetes science and technology · 2023Article
- Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes.American journal of physiology. Endocrinology and metabolism · 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
- Adaptive Personalized Prior-Knowledge-Informed Model Predictive Control for Type 1 Diabetes.Control engineering practice · 2023Article
- Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems.Bioengineering (Basel, Switzerland) · 2022Article
- Acute changes in glucose induced by continuous or intermittent exercise in children and adolescents with type 1 diabetes.Archives of endocrinology and metabolism · 2022Article
- Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.JMIR diabetes · 2022Review
- Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable 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
- Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions.Computers in biology and medicine · 2021Article
- Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives.Journal of diabetes science and technology · 2019Review
- In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System.Journal of diabetes science and technology · 2019Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
backgroundExercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden.
methodsThe MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals. To analyze performance, 18 60-h closed-loop experiments with 96 exercise sessions with three different protocols were completed. Each day, the subjects completed one resistance and one treadmill exercise (moderate continuous training [MCT] or high-intensity interval training [HIIT]). The primary outcome is time spent in each glycemic range during the exercise + recovery period. Secondary measures include average GC and average change in GC during each exercise modality.
resultsThe GC during exercise + recovery periods were within the euglycemic range (70-180 mg/dL) for 69.9% of the time and within a safe glycemic range for exercise (70-250 mg/dL) for 93.0% of the time. The exercise sessions are defined to begin 30 min before the start of exercise and end 2 h after start of exercise. The GC were within the severe hypoglycemia (<55 mg/dL), moderate hypoglycemia (55-70 mg/dL), moderate hyperglycemia (180-250 mg/dL), and severe hyperglycemia (>250 mg/dL) for 0.9%, 1.3%, 23.1%, and 4.8% of the time, respectively. The average GC decline during exercise differed with exercise type (P = 0.0097) with a significant difference between the MCT and resistance (P = 0.0075). To prevent large GC decreases leading to hypoglycemia, MAAP recommended carbohydrates in 59% of MCT, 50% of HIIT, and 39% of resistance sessions.
conclusionsA consistent GC decline occurred in exercise and recovery periods, which differed with exercise type. The average GC at the start of exercise was above target (185.5 ± 56.6 mg/dL for MCT, 166.9 ± 61.9 mg/dL for resistance training, and 171.7 ± 41.4 mg/dL HIIT), making a small decrease desirable. Hypoglycemic events occurred in 14.6% of exercise sessions and represented only 2.22% of the exercise and recovery period.
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