Evidence map›Paper›PMID 38297021›Full record

ArticleScientific reports2024

National prevalence and trends in food labeling awareness, comprehension, usage, and COVID-19 pandemic-related factors in South Korea, 2014-2022.

Yujin Choi, Hyeon Jin Kim, Jaeyu Park, Seung Won Lee, Masoud Rahmati, Ai Koyanagi, Lee Smith, Min Seo Kim, Guillermo F López Sánchez, Elena Dragioti and 5 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Yujin Choi *Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea.
Hyeon Jin KimCenter for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea. hyeonjin7418@gmail.com.
Jaeyu Park *Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea.
Seung Won LeeDepartment of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, South Korea.
Masoud RahmatiDepartment of Physical Education and Sport Sciences, Faculty of Literature and Human Sciences, Lorestan University, Khoramabad, Iran.
Ai KoyanagiResearch and Development Unit, Parc Sanitari Sant Joan de Deu, Barcelona, Spain.
Lee SmithCentre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK.
Min Seo KimCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Guillermo F López SánchezDivision of Preventive Medicine and Public Health, Department of Public Health Sciences, School of Medicine, University of Murcia, Murcia, Spain.
Elena DragiotiPain and Rehabilitation Centre, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Jinseok LeeDepartment of Biomedical Engineering, Kyung Hee University, Yongin, South Korea.
Sang Youl RheeCenter for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea.
Sunyoung KimDepartment of Family Medicine, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
Hyunjung LimDepartment of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, South Korea. hjlim@khu.ac.kr.
Dong Keon YonCenter for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea. yonkkang@gmail.com.

Funding

Korea Health Industry Development Institute HE23C002800Ministry of Food and Drug Safety 21153MFDS601
6 · The paper itself

Abstract

Although food labeling on food packages is crucial for promoting a healthy diet, limited research has been conducted on how the COVID-19 pandemic (hereinafter "the pandemic") has affected food labeling awareness. Therefore, this study aims to analyze the changes in trends in food labeling awareness, comprehension, and usage in South Korea during the pandemic. We utilized a nationwide, large-scale, and long-term dataset provided by the Korea Community Health Survey (KCHS) from 2014 to 2022 (total = 1,756,847 participants). This allowed the researchers to assess the long-term trends in the prevalence of food labeling awareness, comprehension, and usage. Furthermore, we investigated the factors associated with awareness specifically related to the pandemic. In total, 1,756,847 adults (54.19% women) participated in this study. The upward slope in overall food labeling awareness became less pronounced and even exhibited a downward slope during the pandemic (βdiff - 1.759; 95% CI - 1.874 to - 1.644). The upward slope in food labeling comprehension and usage became more pronounced during the pandemic (comprehension: βdiff 0.535; 95% CI 0.436-0.634; usage: βdiff 0.693; 95% CI 0.601-0.785). The vulnerability factors associated with lower food labeling awareness during the pandemic included older age, male, obesity, residing in rural areas, lower household income, lower educational level, smoking, and increased alcohol consumption. This study analyzed the 9-year trend in the prevalence of food labeling awareness, comprehension, and usage based on nationally representative data of adults in South Korea from 2014 to 2022. Our findings suggest that personalized nutrition strategies are needed to recognize vulnerable groups with risk factors and improve food labeling awareness among Korean adults during the pandemic.

Indexed as

COVID-19PandemicsAdultComprehensionFemaleFood LabelingHumansMaleNutrition SurveysPrevalenceRepublic of Korea

Identifiers

PMID38297021
PMCPMC10831073

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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.