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Integrating human activity into food environments can better predict cardiometabolic diseases in the United States

The prevalence of cardiometabolic diseases in the United States is presumably linked to an obesogenic retail food environment that promotes unhealthy dietary habits. Past studies, however, have reported inconsistent findings about the relationship between the two. One underexplored area is how human...

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Autores principales: Xu, Ran, Huang, Xiao, Zhang, Kai, Lyu, Weixuan, Ghosh, Debarchana, Li, Zhenlong, Chen, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643374/
https://www.ncbi.nlm.nih.gov/pubmed/37957191
http://dx.doi.org/10.1038/s41467-023-42667-8
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author Xu, Ran
Huang, Xiao
Zhang, Kai
Lyu, Weixuan
Ghosh, Debarchana
Li, Zhenlong
Chen, Xiang
author_facet Xu, Ran
Huang, Xiao
Zhang, Kai
Lyu, Weixuan
Ghosh, Debarchana
Li, Zhenlong
Chen, Xiang
author_sort Xu, Ran
collection PubMed
description The prevalence of cardiometabolic diseases in the United States is presumably linked to an obesogenic retail food environment that promotes unhealthy dietary habits. Past studies, however, have reported inconsistent findings about the relationship between the two. One underexplored area is how humans interact with food environments and how to integrate human activity into scalable measures. In this paper, we develop the retail food activity index (RFAI) at the census tract level by utilizing Global Positioning System tracking data covering over 94 million aggregated visit records to approximately 359,000 food retailers across the United States over two years. Here we show that the RFAI has significant associations with the prevalence of multiple cardiometabolic diseases. Our study indicates that the RFAI is a promising index with the potential for guiding the development of policies and health interventions aimed at curtailing the burden of cardiometabolic diseases, especially in communities characterized by obesogenic dietary behaviors.
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spelling pubmed-106433742023-11-13 Integrating human activity into food environments can better predict cardiometabolic diseases in the United States Xu, Ran Huang, Xiao Zhang, Kai Lyu, Weixuan Ghosh, Debarchana Li, Zhenlong Chen, Xiang Nat Commun Article The prevalence of cardiometabolic diseases in the United States is presumably linked to an obesogenic retail food environment that promotes unhealthy dietary habits. Past studies, however, have reported inconsistent findings about the relationship between the two. One underexplored area is how humans interact with food environments and how to integrate human activity into scalable measures. In this paper, we develop the retail food activity index (RFAI) at the census tract level by utilizing Global Positioning System tracking data covering over 94 million aggregated visit records to approximately 359,000 food retailers across the United States over two years. Here we show that the RFAI has significant associations with the prevalence of multiple cardiometabolic diseases. Our study indicates that the RFAI is a promising index with the potential for guiding the development of policies and health interventions aimed at curtailing the burden of cardiometabolic diseases, especially in communities characterized by obesogenic dietary behaviors. Nature Publishing Group UK 2023-11-13 /pmc/articles/PMC10643374/ /pubmed/37957191 http://dx.doi.org/10.1038/s41467-023-42667-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Xu, Ran
Huang, Xiao
Zhang, Kai
Lyu, Weixuan
Ghosh, Debarchana
Li, Zhenlong
Chen, Xiang
Integrating human activity into food environments can better predict cardiometabolic diseases in the United States
title Integrating human activity into food environments can better predict cardiometabolic diseases in the United States
title_full Integrating human activity into food environments can better predict cardiometabolic diseases in the United States
title_fullStr Integrating human activity into food environments can better predict cardiometabolic diseases in the United States
title_full_unstemmed Integrating human activity into food environments can better predict cardiometabolic diseases in the United States
title_short Integrating human activity into food environments can better predict cardiometabolic diseases in the United States
title_sort integrating human activity into food environments can better predict cardiometabolic diseases in the united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10643374/
https://www.ncbi.nlm.nih.gov/pubmed/37957191
http://dx.doi.org/10.1038/s41467-023-42667-8
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