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Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities
Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently in...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877334/ https://www.ncbi.nlm.nih.gov/pubmed/36714600 http://dx.doi.org/10.3389/fendo.2022.1096325 |
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author | Zaharieva, Dessi P. Senanayake, Ransalu Brown, Conner Watkins, Brendan Loving, Glenn Prahalad, Priya Ferstad, Johannes O. Guestrin, Carlos Fox, Emily B. Maahs, David M. Scheinker, David |
author_facet | Zaharieva, Dessi P. Senanayake, Ransalu Brown, Conner Watkins, Brendan Loving, Glenn Prahalad, Priya Ferstad, Johannes O. Guestrin, Carlos Fox, Emily B. Maahs, David M. Scheinker, David |
author_sort | Zaharieva, Dessi P. |
collection | PubMed |
description | Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data. |
format | Online Article Text |
id | pubmed-9877334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98773342023-01-27 Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities Zaharieva, Dessi P. Senanayake, Ransalu Brown, Conner Watkins, Brendan Loving, Glenn Prahalad, Priya Ferstad, Johannes O. Guestrin, Carlos Fox, Emily B. Maahs, David M. Scheinker, David Front Endocrinol (Lausanne) Endocrinology Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC9877334/ /pubmed/36714600 http://dx.doi.org/10.3389/fendo.2022.1096325 Text en Copyright © 2023 Zaharieva, Senanayake, Brown, Watkins, Loving, Prahalad, Ferstad, Guestrin, Fox, Maahs and Scheinker https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Zaharieva, Dessi P. Senanayake, Ransalu Brown, Conner Watkins, Brendan Loving, Glenn Prahalad, Priya Ferstad, Johannes O. Guestrin, Carlos Fox, Emily B. Maahs, David M. Scheinker, David Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities |
title | Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities |
title_full | Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities |
title_fullStr | Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities |
title_full_unstemmed | Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities |
title_short | Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities |
title_sort | adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: keys to implementation and opportunities |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877334/ https://www.ncbi.nlm.nih.gov/pubmed/36714600 http://dx.doi.org/10.3389/fendo.2022.1096325 |
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