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Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches
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 meth...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172541/ https://www.ncbi.nlm.nih.gov/pubmed/34079049 http://dx.doi.org/10.1038/s41746-021-00465-w |
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author | Bent, Brinnae Cho, Peter J. Henriquez, Maria Wittmann, April Thacker, Connie Feinglos, Mark Crowley, Matthew J. Dunn, Jessilyn P. |
author_facet | Bent, Brinnae Cho, Peter J. Henriquez, Maria Wittmann, April Thacker, Connie Feinglos, Mark Crowley, Matthew J. Dunn, Jessilyn P. |
author_sort | Bent, Brinnae |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8172541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81725412021-06-07 Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches Bent, Brinnae Cho, Peter J. Henriquez, Maria Wittmann, April Thacker, Connie Feinglos, Mark Crowley, Matthew J. Dunn, Jessilyn P. NPJ Digit Med Article 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. Nature Publishing Group UK 2021-06-02 /pmc/articles/PMC8172541/ /pubmed/34079049 http://dx.doi.org/10.1038/s41746-021-00465-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Bent, Brinnae Cho, Peter J. Henriquez, Maria Wittmann, April Thacker, Connie Feinglos, Mark Crowley, Matthew J. Dunn, Jessilyn P. Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches |
title | 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_short | Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches |
title_sort | engineering digital biomarkers of interstitial glucose from noninvasive smartwatches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172541/ https://www.ncbi.nlm.nih.gov/pubmed/34079049 http://dx.doi.org/10.1038/s41746-021-00465-w |
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