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Unlocking stress and forecasting its consequences with digital technology
Chronic stress is a major underlying origin of the top leading causes of death, globally. Yet, the mechanistic explanation of the association between stress and disease is poorly understood. This stems from the inability to adequately measure stress in its naturally occurring state and the extreme h...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668457/ https://www.ncbi.nlm.nih.gov/pubmed/31372508 http://dx.doi.org/10.1038/s41746-019-0151-8 |
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author | Goodday, Sarah M. Friend, Stephen |
author_facet | Goodday, Sarah M. Friend, Stephen |
author_sort | Goodday, Sarah M. |
collection | PubMed |
description | Chronic stress is a major underlying origin of the top leading causes of death, globally. Yet, the mechanistic explanation of the association between stress and disease is poorly understood. This stems from the inability to adequately measure stress in its naturally occurring state and the extreme heterogeneity by inter and intraindividual characteristics. The growth and availability of digital technologies involving wearable devices and mobile phone apps afford the opportunity to dramatically improve measurement of the biological stress response in real time. In parallel, the advancement and capabilities of artificial intelligence (AI) and machine learning could discern heterogeneous, multidimensional information from individual signs of stress, and possibly inform how these signs forecast the downstream consequences of stress in the form of end-organ damage. The marriage of these tools could dramatically enhance the field of stress research contributing to impactful and empowering interventions for individuals bridging knowledge to practice, and intervention to real-world use. Here we discuss this potential, anticipated challenges, and emerging opportunities. |
format | Online Article Text |
id | pubmed-6668457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66684572019-08-01 Unlocking stress and forecasting its consequences with digital technology Goodday, Sarah M. Friend, Stephen NPJ Digit Med Comment Chronic stress is a major underlying origin of the top leading causes of death, globally. Yet, the mechanistic explanation of the association between stress and disease is poorly understood. This stems from the inability to adequately measure stress in its naturally occurring state and the extreme heterogeneity by inter and intraindividual characteristics. The growth and availability of digital technologies involving wearable devices and mobile phone apps afford the opportunity to dramatically improve measurement of the biological stress response in real time. In parallel, the advancement and capabilities of artificial intelligence (AI) and machine learning could discern heterogeneous, multidimensional information from individual signs of stress, and possibly inform how these signs forecast the downstream consequences of stress in the form of end-organ damage. The marriage of these tools could dramatically enhance the field of stress research contributing to impactful and empowering interventions for individuals bridging knowledge to practice, and intervention to real-world use. Here we discuss this potential, anticipated challenges, and emerging opportunities. Nature Publishing Group UK 2019-07-31 /pmc/articles/PMC6668457/ /pubmed/31372508 http://dx.doi.org/10.1038/s41746-019-0151-8 Text en © The Author(s) 2019 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/. |
spellingShingle | Comment Goodday, Sarah M. Friend, Stephen Unlocking stress and forecasting its consequences with digital technology |
title | Unlocking stress and forecasting its consequences with digital technology |
title_full | Unlocking stress and forecasting its consequences with digital technology |
title_fullStr | Unlocking stress and forecasting its consequences with digital technology |
title_full_unstemmed | Unlocking stress and forecasting its consequences with digital technology |
title_short | Unlocking stress and forecasting its consequences with digital technology |
title_sort | unlocking stress and forecasting its consequences with digital technology |
topic | Comment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668457/ https://www.ncbi.nlm.nih.gov/pubmed/31372508 http://dx.doi.org/10.1038/s41746-019-0151-8 |
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