Evidence map›Paper›PMID 34832012›Full record

ArticleInternational journal of environmental research and public health2021

Analysis of Chinese Consumers' Nutrition Facts Table Use Behavior Based on Knowledge-Attitude-Practice Model.

Zeying Huang, Haijun Li, Jiazhang Huang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  5. A comparative analysis of the effects of knowledge-attitude-practice nursing on mental state, sleep quality, and self-efficacy in cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
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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

3 authors.

Zeying HuangInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing 100081, China.ORCID 0000-0001-8668-6506
Haijun LiSchool of Information &Intelligence Engineering, University of Sanya, Sanya 572022, China.
Jiazhang HuangInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The nutrition facts table is a nutrition labeling tool designed to help consumers understand the nutritional content of food and make informed food choices. However, the utilization rate of the nutrition facts table has been low in China since its introduction in 2013. This study employed structural equation modeling to investigate consumers' knowledge, attitudes, and practices related to the nutrition facts table by using an online survey of 930 valid samples from 31 provinces in China based on the knowledge, attitude, practice (KAP) model. The analysis revealed that most respondents had a positive attitude, but not a good level of knowledge or practice in their use of the labeling. Respondents' knowledge about the nutrition facts table directly affected their attitude, through which it could then affect their practice, whereas knowledge played an indirect role in nutrition labeling use. The results confirmed that the KAP model is suitable for explaining consumers' use of nutrition labels in China, and that the first step to promoting labeling usage is to enrich consumers' knowledge on the nutrition facts table.

Indexed as

Consumer BehaviorHealth Knowledge, Attitudes, PracticeChinaFood LabelingHealth BehaviorHumansSurveys and QuestionnairesChinese residentsknowledge-attitude-practice modelnutrition facts tablenutrition labelingstructural equation model

Identifiers

PMID34832012
PMCPMC8625815

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.