Evidence map›Paper›PMID 40577369›Full record

ArticlePloS one2025

Analysing fast food consumption behaviour patterns: The case of Jordan.

Noura Abu Asab, Randah Barakat

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Noura Abu AsabDepartment of Business Economics, School of Business, The University of Jordan, Amman, Jordan.ORCID 0000-0002-8114-617X
Randah BarakatDepartment of Counselling and Mental Health, School of Educational Sciences, The World Islamic Sciences and Education University, Amman, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to examine the factors influencing fast food consumption among a Jordanian population by utilizing the extended theory of planned behavior as its theoretical framework. The research employed theory-related scales, income, BMI, ability to consume (intention), indirect intention, and a retrospective measure of fast-food consumption within a cross-sectional sample of 408 university students aged 18-23 years. Structural equation modeling was used to analyze the determinants of consumption. The findings indicated that the self-reported intention measure failed to mediate the attitude-behavior relationship. However, the use of projective intention (indirect) measures revealed that personal factors such as attitude, subjective norms, and self-identification significantly explained over 50% of the variation in the intention to consume fast food, suggesting that participants often underestimated their intention to consume fast food. Furthermore, the study identified available personal funds, perceived and actual behavioral control, and BMI as significant external predictors of fast-food consumption. The originality of this study lies in its contribution to understanding the growing preference for fast food in the Middle East, particularly in Jordan. It introduces a theoretical model that employs projection-based assessments to implicitly gauge intentions and explores the relationship between income, BMI, and consumption behavior. The practical implications of these findings underscore the importance of key psychosocial elements in developing and implementing preventive strategies aimed at promoting healthy eating behaviors among university students.

Indexed as

Fast FoodsFeeding BehaviorAdolescentAdultBody Mass IndexCross-Sectional StudiesFemaleHumansIntentionJordanMaleStudentsUniversitiesYoung Adult

Identifiers

PMID40577369
PMCPMC12204562

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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.