Evidence map›Paper›PMID 37957191›Full record

ArticleNature communications2023

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

Ran Xu, Xiao Huang, Kai Zhang, Weixuan Lyu, Debarchana Ghosh, Zhenlong Li, Xiang Chen

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Trial
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ran XuDepartment of Allied Health Sciences, University of Connecticut, Storrs, CT, 06269, USA.
Xiao HuangDepartment of Environmental Sciences, Emory University, Atlanta, GA, 30322, USA.ORCID http://orcid.org/0000-0002-4323-382X
Kai ZhangDepartment of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY, 12144, USA.
Weixuan LyuDepartment of Geography, University of Connecticut, Storrs, CT, 06269, USA.
Debarchana GhoshInstitute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, 06269, USA.
Zhenlong LiGeoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA.ORCID http://orcid.org/0000-0002-8938-5466
Xiang ChenInstitute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, 06269, USA. xiang.chen@uconn.edu.ORCID http://orcid.org/0000-0002-5045-9253

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Cardiovascular DiseasesObesityEnvironmentFoodHuman ActivitiesHumansResidence CharacteristicsUnited States

Identifiers

PMID37957191
PMCPMC10643374

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.