Hypoglycemia risk with physical activity in type 1 diabetes: a data-driven approach

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dc.citation.articleNumber1142021en_US
dc.citation.journalTitleFrontiers in Digital Healthen_US
dc.citation.volumeNumber5en_US
dc.contributor.authorPrasanna, Sahanaen_US
dc.contributor.authorBarua, Souptiken_US
dc.contributor.authorSiller, Alejandro F.en_US
dc.contributor.authorJohnson, Jeremiah J.en_US
dc.contributor.authorSabharwal, Ashutoshen_US
dc.contributor.authorDeSalvo, Daniel J.en_US
dc.date.accessioned2023-07-21T16:13:49Zen_US
dc.date.available2023-07-21T16:13:49Zen_US
dc.date.issued2023en_US
dc.description.abstractPhysical activity (PA) provides numerous health benefits for individuals with type 1 diabetes (T1D). However, the threat of exercise-induced hypoglycemia may impede the desire for regular PA. Therefore, we aimed to study the association between three common types of PA (walking, running, and cycling) and hypoglycemia risk in 50 individuals with T1D. Real-world data, including PA duration and intensity, continuous glucose monitor (CGM) values, and insulin doses, were available from the Tidepool Big Data Donation Project. Participants' mean (SD) age was 38.0 (13.1) years with a mean (SD) diabetes duration of 21.4 (12.9) years and an average of 26.2 weeks of CGM data available. We developed a linear regression model for each of the three PA types to predict the average glucose deviation from 70 mg/dl for the 2 h after the start of PA. This is essentially a measure of hypoglycemia risk, for which we used the following predictors: PA duration (mins) and intensity (calories burned), 2-hour pre-exercise area under the glucose curve (adjusted AUC), the glucose value at the beginning of PA, and total bolus insulin (units) within 2 h before PA. Our models indicated that glucose value at the start of exercise and pre-exercise glucose adjusted AUC (p < 0.001 for all three activities) were the most significant predictors of hypoglycemia. In addition, the duration and intensity of PA and 2-hour bolus insulin were weakly associated with hypoglycemia for walking, running, and cycling. These findings may provide individuals with T1D with a data-driven approach to preparing for PA that minimizes hypoglycemia risk.en_US
dc.identifier.citationPrasanna, Sahana, Barua, Souptik, Siller, Alejandro F., et al.. "Hypoglycemia risk with physical activity in type 1 diabetes: a data-driven approach." <i>Frontiers in Digital Health,</i> 5, (2023) Frontiers Media S.A.: https://doi.org/10.3389/fdgth.2023.1142021.en_US
dc.identifier.digitalfdgth-05-1142021en_US
dc.identifier.doihttps://doi.org/10.3389/fdgth.2023.1142021en_US
dc.identifier.urihttps://hdl.handle.net/1911/114988en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.A.en_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleHypoglycemia risk with physical activity in type 1 diabetes: a data-driven approachen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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