Prediction of hypoglycemia during aerobic exercise in adults with type 1 diabetes
Open accessAbstract
BACKGROUND: Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise. METHODS: We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D. RESULTS: Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity. CONCLUSIONS: Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.
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Data collected by activity trackers can be used to automatically detect exercise sessions, predict exercise-induced hypoglycemia, and if necessary recommend the consumption of carbohydrates to avoid hypoglycemia.
“The data collected by activity trackers can be used to automatically detect exercise sessions [65], predict exercise-induced hypoglycemia [60], and if necessary, recommend the consumption of carbohydrates to avoid hypoglycemia.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [60]
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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors · doi:10.3390/s20143870 · Cited by 176 · see citing works
- Authors
- Reddy, R., Resalat, N., Wilson, L. M., Castle, J. R., El Youssef, J., & Jacobs, P. G.
- Affiliations
- Oregon Health & Science University (US)
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Corresponding author
- Jacobs, Peter G.
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2019-01-17
- Volume
- 13
- Issue
- 5
- Pages
- 919-927
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC6955453
- OpenAlex
- W2909057746
- Cited by
- 75
- References
- 38
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 13 · 2023: 14 · 2022: 8 · 2021: 13 · 2020: 7 · 2019: 1
- FWCI
- 5.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- national center for advancing translational sciences (UL1TR002369)
- national institute of diabetes and digestive and kidney diseases (1DP3DK101044)
- 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, aerobic exercise, hypoglycemia prediction, heart rate, decision tree, random forest, artificial pancreas, decision support system, sensor-augmented pump therapy
- MeSH terms
- Machine Learning, Adult, Blood Glucose, Diabetes Mellitus, Type 1, Female, Heart Rate, Humans, Hypoglycemia, Male, Exercise, Pancreas, Artificial
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