Evidence map›Paper›PMID 40611052›Full record

ArticleBMC public health2025

Online takeaway food consumption and its association with health among young and middle-aged urban residents in mainland China: a cross-sectional study.

Ke Jin, Tianyuan Pan, Tao Zeng, Yuliang Wang, Jinyan Huang, Jie Wu, Jingjing Ren

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Ke JinDepartment of General Practice, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Tianyuan PanDepartment of General Practice, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Tao ZengBiomedical Big Data Centre, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Yuliang WangBiomedical Big Data Centre, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Jinyan HuangBiomedical Big Data Centre, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China. huangjinyan@zju.edu.cn.
Jie WuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Centre for Diagnosis and Treatment for Infectious Diseases, National Clinical Research Centre, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China. zjwujie@zju.edu.cn.
Jingjing RenDepartment of General Practice, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China. 3204092@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough ordering food online has become a leading lifestyle factor among the Chinese urban population, its health impact is understudied. This study aims to examine this aspect, focusing on online takeaway food impacts related to foodborne illness, nutrition, and suboptimal health (i.e., an intermediate status between being healthy and diagnosed with a disease) on consumers.

methodsA snowball sampling method was adopted, targeting urban consumers aged 18-59 across mainland China by posing an e-questionnaire survey. Information collected includes demographic data, foodborne illness occurrence, nutrition and suboptimal health impacts when ordering online takeaway foods. The questionnaire was adapted from the Diet Quality Questionnaire and Sub-Health Measurement Scale version 1.0. Descriptive statistical methods, Mann‒Whitney test, and Spearman's correlation test were employed to analyse the data.

resultsA total of 610 questionnaires were included. Consumers ordering online takeaway foods at least once per week (hereon termed as 'frequent consumers') often experienced fake reviews (50.71%), hygiene issues (54.03%) and foodborne illness (58.29% reporting lower gastrointestinal symptoms). Frequent consumers had lower diet quality, with higher Global Dietary Recommendation (GDR)-Limit scores (Mann-Whitney, p = 0.0011), lower GDR-Healthy (p = 0.0033) and overall GDR scores (p < 0.0001) than infrequent consumers. Additionally, the frequency of online takeaway food consumption was negatively associated with overall GDR scores (p = 0.0392). After adjusting for demographic characteristics, physical subhealth scores were lower among frequent consumers (p = 0.0157), with differences in GDR-Limit (p = 0.0087), GDR-Healthy (p = 0.0221) and overall GDR scores (p < 0.0001) remaining; after further adjusting for lifestyle factors, the differences in GDR-Healthy and overall GDR scores persisted. While the differences in the aforementioned scores were not significant within the medical subgroup after adjustment.

conclusionsThis study suggests that online takeaway food consumption in urban China is a significant lifestyle, posing a risk of foodborne illness and associating with poorer diet quality and physical health status to a certain extent. Monitoring of food safety and fake review in food delivery should be strengthened, along with public education on nutrition. Further clinical research should evaluate the association between online takeaway food consumption and health risks.

Indexed as

Fast FoodsHealth StatusInternetUrban PopulationAdolescentAdultChinaCross-Sectional StudiesFemaleFoodborne DiseasesHumansMaleMiddle AgedSurveys and QuestionnairesYoung AdultFoodborne illnessHygiene issueNutritional healthOnline takeaway foodSuboptimal health level

Identifiers

PMID40611052
PMCPMC12224518

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.