Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data
OBJECTIVE: To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatm...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Diabetes Association
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154647/ https://www.ncbi.nlm.nih.gov/pubmed/36805169 http://dx.doi.org/10.2337/dc22-2290 |
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author | Lehmann, Vera Föll, Simon Maritsch, Martin van Weenen, Eva Kraus, Mathias Lagger, Sophie Odermatt, Katja Albrecht, Caroline Fleisch, Elgar Zueger, Thomas Wortmann, Felix Stettler, Christoph |
author_facet | Lehmann, Vera Föll, Simon Maritsch, Martin van Weenen, Eva Kraus, Mathias Lagger, Sophie Odermatt, Katja Albrecht, Caroline Fleisch, Elgar Zueger, Thomas Wortmann, Felix Stettler, Christoph |
author_sort | Lehmann, Vera |
collection | PubMed |
description | OBJECTIVE: To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. RESULTS: Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA(1c) 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. CONCLUSIONS: Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning. |
format | Online Article Text |
id | pubmed-10154647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Diabetes Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-101546472023-05-04 Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data Lehmann, Vera Föll, Simon Maritsch, Martin van Weenen, Eva Kraus, Mathias Lagger, Sophie Odermatt, Katja Albrecht, Caroline Fleisch, Elgar Zueger, Thomas Wortmann, Felix Stettler, Christoph Diabetes Care Brief Report OBJECTIVE: To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. RESULTS: Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA(1c) 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. CONCLUSIONS: Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning. American Diabetes Association 2023-05 2023-02-17 /pmc/articles/PMC10154647/ /pubmed/36805169 http://dx.doi.org/10.2337/dc22-2290 Text en © 2023 by the American Diabetes Association https://www.diabetesjournals.org/journals/pages/licenseReaders may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license. |
spellingShingle | Brief Report Lehmann, Vera Föll, Simon Maritsch, Martin van Weenen, Eva Kraus, Mathias Lagger, Sophie Odermatt, Katja Albrecht, Caroline Fleisch, Elgar Zueger, Thomas Wortmann, Felix Stettler, Christoph Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data |
title | Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data |
title_full | Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data |
title_fullStr | Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data |
title_full_unstemmed | Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data |
title_short | Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data |
title_sort | noninvasive hypoglycemia detection in people with diabetes using smartwatch data |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154647/ https://www.ncbi.nlm.nih.gov/pubmed/36805169 http://dx.doi.org/10.2337/dc22-2290 |
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