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Digital envirotyping: quantifying environmental determinants of health and behavior

Digital phenotyping efforts have used wearable devices to connect a rich array of physiologic data to health outcomes or behaviors of interest. The environmental context surrounding these phenomena has received less attention, yet is critically needed to understand their antecedents and deliver cont...

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Detalles Bibliográficos
Autores principales: Engelhard, Matthew M., Oliver, Jason A., McClernon, F. Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067787/
https://www.ncbi.nlm.nih.gov/pubmed/32195371
http://dx.doi.org/10.1038/s41746-020-0245-3
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author Engelhard, Matthew M.
Oliver, Jason A.
McClernon, F. Joseph
author_facet Engelhard, Matthew M.
Oliver, Jason A.
McClernon, F. Joseph
author_sort Engelhard, Matthew M.
collection PubMed
description Digital phenotyping efforts have used wearable devices to connect a rich array of physiologic data to health outcomes or behaviors of interest. The environmental context surrounding these phenomena has received less attention, yet is critically needed to understand their antecedents and deliver context-appropriate interventions. The coupling of improved smart eyewear with deep learning represents a technological turning point, one that calls for more comprehensive, ambitious study of environments and health.
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spelling pubmed-70677872020-03-19 Digital envirotyping: quantifying environmental determinants of health and behavior Engelhard, Matthew M. Oliver, Jason A. McClernon, F. Joseph NPJ Digit Med Comment Digital phenotyping efforts have used wearable devices to connect a rich array of physiologic data to health outcomes or behaviors of interest. The environmental context surrounding these phenomena has received less attention, yet is critically needed to understand their antecedents and deliver context-appropriate interventions. The coupling of improved smart eyewear with deep learning represents a technological turning point, one that calls for more comprehensive, ambitious study of environments and health. Nature Publishing Group UK 2020-03-12 /pmc/articles/PMC7067787/ /pubmed/32195371 http://dx.doi.org/10.1038/s41746-020-0245-3 Text en © The Author(s) 2020 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
Engelhard, Matthew M.
Oliver, Jason A.
McClernon, F. Joseph
Digital envirotyping: quantifying environmental determinants of health and behavior
title Digital envirotyping: quantifying environmental determinants of health and behavior
title_full Digital envirotyping: quantifying environmental determinants of health and behavior
title_fullStr Digital envirotyping: quantifying environmental determinants of health and behavior
title_full_unstemmed Digital envirotyping: quantifying environmental determinants of health and behavior
title_short Digital envirotyping: quantifying environmental determinants of health and behavior
title_sort digital envirotyping: quantifying environmental determinants of health and behavior
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067787/
https://www.ncbi.nlm.nih.gov/pubmed/32195371
http://dx.doi.org/10.1038/s41746-020-0245-3
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