ArticleDiabetes technology & therapeutics2013
Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.
Article in Diabetes technology & therapeutics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.Medical & biological engineering & computing · 2022Review
- Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration.Sensors (Basel, Switzerland) · 2019Article
- [Glucose metabolism modeling of diabetes patients with different intensities of aerobic exercise: an in silico study].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Article
- Different Types of Physical Activity and Metabolic Control in People With Type 1 Diabetes Mellitus.Frontiers in physiology · 2019Article
- Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the "On-Board" Concept.Journal of diabetes research · 2018Review
- How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study.Journal of diabetes science and technology · 2016Article
- Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review.Sensors (Basel, Switzerland) · 2016Review
- Routine daily physical activity and glucose variations are strongly coupled in adults with T1DM.Physiological reports · 2015Article
- Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2015Article
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Authors and funding
9 authors at 2 institutions in 2 countries.
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Abstract
backgroundIn type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. SUBJECTS AND
methodsIn the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA.
resultsCorrelation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min.
conclusionsQuantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.
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