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Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?
Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and...
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/PMC10354549/ https://www.ncbi.nlm.nih.gov/pubmed/37475772 http://dx.doi.org/10.3389/fpubh.2023.1178491 |
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author | Velmovitsky, Pedro Elkind Lotto, Matheus Alencar, Paulo Leatherdale, Scott T. Cowan, Donald Morita, Plinio Pelegrini |
author_facet | Velmovitsky, Pedro Elkind Lotto, Matheus Alencar, Paulo Leatherdale, Scott T. Cowan, Donald Morita, Plinio Pelegrini |
author_sort | Velmovitsky, Pedro Elkind |
collection | PubMed |
description | Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device. |
format | Online Article Text |
id | pubmed-10354549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103545492023-07-20 Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? Velmovitsky, Pedro Elkind Lotto, Matheus Alencar, Paulo Leatherdale, Scott T. Cowan, Donald Morita, Plinio Pelegrini Front Public Health Public Health Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device. Frontiers Media S.A. 2023-07-05 /pmc/articles/PMC10354549/ /pubmed/37475772 http://dx.doi.org/10.3389/fpubh.2023.1178491 Text en Copyright © 2023 Velmovitsky, Lotto, Alencar, Leatherdale, Cowan and Morita. 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 | Public Health Velmovitsky, Pedro Elkind Lotto, Matheus Alencar, Paulo Leatherdale, Scott T. Cowan, Donald Morita, Plinio Pelegrini Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? |
title | Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? |
title_full | Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? |
title_fullStr | Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? |
title_full_unstemmed | Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? |
title_short | Can heart rate variability data from the Apple Watch electrocardiogram quantify stress? |
title_sort | can heart rate variability data from the apple watch electrocardiogram quantify stress? |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354549/ https://www.ncbi.nlm.nih.gov/pubmed/37475772 http://dx.doi.org/10.3389/fpubh.2023.1178491 |
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