Early detection of physical activity for people with type 1 diabetes mellitus
Open accessAbstract
BACKGROUND: Early detection of exercise in individuals with type 1 diabetes mellitus (T1DM) may allow changes in therapy to prevent hypoglycemia. Currently there is limited experience with automated methods that detect the onset and end of exercise in this population. We sought to develop a novel method to quickly and reliably detect the onset and end of exercise in these individuals before significant changes in blood glucose (BG) occur. METHODS: Sixteen adults with T1DM were studied as outpatients using a diary, accelerometer, heart rate monitor, and continuous glucose monitor for 2 days. These data were used to develop a principal component analysis based exercise detection method. Subjects also performed 60 and 30 minute exercise sessions at 30% and 50% predicted heart rate reserve (HRR), respectively. The detection method was applied to the exercise sessions to determine how quickly the detection of start and end of exercise occurred relative to change in BG. RESULTS: Mild 30% HRR and moderate 50% HRR exercise onset was identified in 6 ± 3 and 5 ± 2 (mean ± SD) minutes, while completion was detected in 3 ± 8 and 6 ± 5 minutes, respectively. BG change from start of exercise to detection time was 1 ± 6 and -1 ± 3 mg/dL, and, from the end of exercise to detection time was 6 ± 4 and -17 ± 13 mg/dL, respectively, for the 2 exercise sessions. False positive and negative ratios were 4 ± 2% and 21 ± 22%. CONCLUSIONS: The novel method for exercise detection identified the onset and end of exercise in approximately 5 minutes, with an average BG change of only -6 mg/dL.
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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 [65]
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- Authors
- Dasanayake, I. S., Bevier, W. C., Castorino, K., Pinsker, J. E., Seborg, D. E., Doyle, F. J., & Dassau, E.
- Affiliations
- University of California, Santa Barbara (US)
- Sansum Diabetes Research Institute (US)
- Corresponding author
- Dassau, Eyal
- ORCID
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2015-06-30
- Volume
- 9
- Issue
- 6
- Pages
- 1236-1245
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC4667311
- OpenAlex
- W2244371260
- Cited by
- 40
- References
- 30
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 5 · 2022: 1 · 2021: 3 · 2020: 3 · 2019: 9 · 2018: 2 · 2017: 3 · 2016: 5 · 2015: 2
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Dexcom
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK085628, DP3DK094331)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- type 1 diabetes, exercise detection, hypoglycemia prevention, blood glucose, heart rate monitors, accelerometer, continuous glucose monitoring, principal component analysis, heart rate reserve
- MeSH terms
- Adolescent, Adult, Aged, Algorithms, Automation, Blood Glucose, Diabetes Mellitus, Type 1, Exercise Test, Female, Heart Rate, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin Infusion Systems, Male, Middle Aged, Motor Activity, Predictive Value of Tests, Signal Processing, Computer-Assisted, Time Factors, Blood Glucose Self-Monitoring, Reproducibility of Results, Biomarkers, Exercise, Treatment Outcome, Pancreas, Artificial, Principal Component Analysis, Early Diagnosis, Young Adult, Actigraphy
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