Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control
Open accessAbstract
BACKGROUND: The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise. MATERIALS AND METHODS: Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced. RESULTS: Ten adolescents (11.9-17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70-180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM <70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM >250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred. CONCLUSIONS: The use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges.
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Automated insulin delivery systems can potentially modify insulin dosing in response to activity monitors to reduce the risk or severity of exercise-induced hypoglycemia in people living with type 1 diabetes.
“Automated insulin delivery systems can potentially modify insulin dosing in response to activity monitors to reduce the risk (or severity) of exercise-induced hypoglycemia in people living with type 1 diabetes [7-10].”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [7]
The Discussion reports that the system reduced or suspended insulin delivery during or after exercise, supporting the potential to respond to exercise-related hypoglycemia. But the Results explicitly state that there was no activity measurement: the controller responded to continuous glucose monitor readings, not activity monitors. Our copy provides no page number for these passages. (LLM, conf=0.91)
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Early Stages of Automated Insulin Delivery
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Insulin Pumps and Artificial Pancreas
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Stochastic Model Predictive Control of Blood Glucose Levels using Probabilistic Meal Anticipation
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Early Stages of Automated Insulin Delivery
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- Authors
- Huyett, L. M., Ly, T. T., Forlenza, G. P., Reuschel-DiVirgilio, S., Messer, L. H., Wadwa, R. P., Gondhalekar, R., Doyle, F. J., Pinsker, J. E., Maahs, D. M., Buckingham, B. A., & Dassau, E.
- Affiliations
- University of California, Santa Barbara (US)
- Stanford Medicine (US)
- University of Colorado Anschutz (US)
- Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University, Stanford, California.
- Harvard University (US)
- Sansum Diabetes Research Institute (US)
- Corresponding author
- Dassau, Eyal
- ORCID
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2017-06
- Volume
- 19
- Issue
- 6
- Pages
- 331-339
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- PMC
- PMC5510043
- OpenAlex
- W2610632420
- Cited by
- 64
- References
- 34
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 9 · 2019: 13 · 2018: 14 · 2017: 5 · 2016: 1
- FWCI
- 8.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Virginia
- Dexcom
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (DP3DK104057, K12DK094712)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- type 1 diabetes, artificial pancreas, feedback control, adolescents, glycemic control, continuous glucose monitoring, sensor-augmented pump, hypoglycemia, outpatient settings
- MeSH terms
- Activities of Daily Living, Adolescent, Adolescent Behavior, Algorithms, Child, Child Behavior, Child Nutritional Physiological Phenomena, Combined Modality Therapy, Diabetes Mellitus, Type 1, Feasibility Studies, Female, Food Preferences, Humans, Hyperglycemia, Hypoglycemia, Male, Sports, United States, Cohort Studies, Exercise, Adolescent Nutritional Physiological Phenomena, Pancreas, Artificial
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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