Evidence map›Paper›PMID 41229469›Full record

ArticleFrontiers in public health2025

Research on the expected utility of the nudge-based intervention strategy for healthy eating behavior in online catering.

Xiaoting Dai, Linhai Wu

Abstract read
In one paragraph

Article in Frontiers in 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

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2 · The registry

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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

2 authors.

Xiaoting DaiJiangsu Provincial Laboratory of Food Safety and National Strategic Governance, Jiangnan University, Wuxi, China.
Linhai WuJiangsu Provincial Laboratory of Food Safety and National Strategic Governance, Jiangnan University, Wuxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: While the rise of the new consumption format of "Internet + Food" has changed the global food environment, it has also caused people to worry about the health risks brought about by the unreasonable dietary structure of online catering foods. Under the online catering consumption model based on electronic transactions, compared with traditional tough intervention policies, the nudging strategy can make full use of the advantages of information technology and effectively promote consumers' choice of healthy diet to interfere with the dietary behavior of online catering consumers. Methods: The objective of this study is to design a choice environment on online platforms through nudge strategies, so as to reduce the cognitive resistance and inertia of consumers in decision-making. By means of guidance rather than coercion, it enables consumers to make healthy choices more easily and proactively, thereby promoting the occurrence of healthy eating behaviors invisibly. Based on the subdividing of respondents based on the characteristics of consumer groups, we explored the different expected intervention effects of providing decision information, changing decision structure, and providing decision-making assistance-type nudging strategies on consumers with different characteristics. This paper divides the nudging strategies into three categories: providing decision information type, changing decision structure type, and providing decision assistance type. The subjects of this paper must be consumers who have their own experience of eating online food and beverages. A total of 44,050 valid samples were collected. Results: The results found that nudging strategies have a positive impact on healthy eating behaviors; specifically, women, lower education, lower income consumers, and chronic patients are more susceptible to the healthy diet-nudging strategies. Discussion: Compared with providing decision-making information type and changing decision-making structure type by constructing multinomial logistic regression models, the intervention effect of providing decision-making auxiliary nudging strategies is slightly insufficient. Changing the structural nudge strategy of decision-making is more influential among high-age, low-education and low-income consumer groups.

Indexed as

Diet, HealthyFeeding BehaviorHealth PromotionInternetAdolescentAdultChoice BehaviorConsumer BehaviorDecision MakingFemaleFood PreferencesHealth BehaviorHumansMaleMiddle AgedSurveys and Questionnairesdietary interventioneating behaviorhealthy eatingnudging strategyonline food

Identifiers

PMID41229469
PMCPMC12602542

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LicenceCC BY
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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.