Background And Objective: The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered.
Methods: Several physiological models describing the glycemic response to physical activity are proposed by incorporating model terms proportional to the physical activity intensity and duration describing endogenous glucose production (EGP), glucose utilization, and glucose transfer from the plasma to tissues. Leveraging clinical data of T1D during physical activity, each model fit is assessed.
Results: The proposed model with terms accommodating EGP, glucose transfer, and insulin-independent glucose utilization allow for an improved simulation of physical activity glycemic responses with the greatest reduction in model error (mean absolute percentage error: 16.11 ± 4.82 vs. 19.49 ± 5.87, p = 0.002).
Conclusions: The development of a physiologically plausible model with model terms representing each major contributor to glucose metabolism during physical activity can outperform traditional models with physical activity described through glucose utilization alone. This model accurately describes the relation of plasma insulin and physical activity intensity on glucose production and glucose utilization to generate the appropriately increasing, decreasing or stable glucose response for each physical activity condition. The proposed model will enable the in silico evaluation of automated insulin dosing algorithms designed to mitigate the effects of physical activity with the appropriate relationship between the reduction in basal insulin and the corresponding glycemic excursion.
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http://dx.doi.org/10.1016/j.cmpb.2022.107153 | DOI Listing |
Biomed Phys Eng Express
January 2025
F. Joseph Halcomb III, MD, Department of Biomedical Engineering, University of Kentucky, 143 Graham Ave., Lexington, Kentucky, 40506, UNITED STATES.
Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls.
View Article and Find Full Text PDFAm J Physiol Heart Circ Physiol
January 2025
Sport Medicine Unit, Careggi University Hospital, Via delle Oblate 4, 50134 Florence, Italy.
The study was designed to investigate the pattern of intraventricular Hemo-Dynamic Forces (HDF) and myocardial performance during exercise in Elite Cyclists (EC). Transthoracic stress echocardiography was performed on nineteen EC and thirteen age-matched sedentary controls (SC) at three incremental exercise intensities based on Heart Rate Reserve (HRR). Left Ventricular (LV) HDF were computed from echocardiography long-axis data sets using a novel technique based on endocardial boundary tracking, both in apex-base and latero-septal directions.
View Article and Find Full Text PDFJ Physiol
January 2025
Department of Psychiatry, University of Southern Denmark, Odense C, Denmark.
Background: Adolescents who engage in physical activity experience positive mental health outcomes. However, the increasing prevalence of physical inactivity combined with high screen time use among adolescents is a growing concern. Parents play an important role in shaping adolescents' physical activity and screen time levels through active participation and involvement.
View Article and Find Full Text PDFClin Transplant
January 2025
Department of Pediatric Nephrology and Transplantation, New Children's Hospital, Helsinki University Hospital, Helsinki, Finland and University of Helsinki, Helsinki, Finland.
Background: Physical performance capacity (PPC) of pediatric heart transplant (HT) recipients is reportedly low to normal, and longitudinal follow-up of these patients is recommended. However, no recommendation for a follow-up method is available. In this study, the correlation between the 6-min walk test (6MWT), various clinical parameters, and a physical performance test set was evaluated to develop a simple follow-up tool for PPC.
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