Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

Abstract Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninva...

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Autores principales: Brinnae Bent, Peter J. Cho, Maria Henriquez, April Wittmann, Connie Thacker, Mark Feinglos, Matthew J. Crowley, Jessilyn P. Dunn
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Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/8fe35e023f394264a7a7065a208754e2
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spelling oai:doaj.org-article:8fe35e023f394264a7a7065a208754e22021-12-02T18:24:55ZEngineering digital biomarkers of interstitial glucose from noninvasive smartwatches10.1038/s41746-021-00465-w2398-6352https://doaj.org/article/8fe35e023f394264a7a7065a208754e22021-06-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00465-whttps://doaj.org/toc/2398-6352Abstract Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.Brinnae BentPeter J. ChoMaria HenriquezApril WittmannConnie ThackerMark FeinglosMatthew J. CrowleyJessilyn P. DunnNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
institution DOAJ
collection DOAJ
language EN
topic Computer applications to medicine. Medical informatics
R858-859.7
spellingShingle Computer applications to medicine. Medical informatics
R858-859.7
Brinnae Bent
Peter J. Cho
Maria Henriquez
April Wittmann
Connie Thacker
Mark Feinglos
Matthew J. Crowley
Jessilyn P. Dunn
Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
description Abstract Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.
format article
author Brinnae Bent
Peter J. Cho
Maria Henriquez
April Wittmann
Connie Thacker
Mark Feinglos
Matthew J. Crowley
Jessilyn P. Dunn
author_facet Brinnae Bent
Peter J. Cho
Maria Henriquez
April Wittmann
Connie Thacker
Mark Feinglos
Matthew J. Crowley
Jessilyn P. Dunn
author_sort Brinnae Bent
title Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
title_short Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
title_full Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
title_fullStr Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
title_full_unstemmed Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
title_sort engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/8fe35e023f394264a7a7065a208754e2
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