Evidence map›Paper›PMID 31731796›Full record

ArticleNutrients2019

Segmenting Young Adult University Student's Eating Behaviour: A Theory-Informed Approach.

Anna Kitunen, Sharyn Rundle-Thiele, Julia Carins

Abstract read
In one paragraph

Article in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

3 authors.

Anna KitunenSocial Marketing @ Griffith, Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia.
Sharyn Rundle-ThieleSocial Marketing @ Griffith, Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia.
Julia CarinsSocial Marketing @ Griffith, Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this paper is to extend behavioural theory and segmentation application. Specifically, this paper draws on three segmentation bases and behavioural theory that extends focus beyond individual psychological predispositions to form segments within the healthy eating context for young adult university students (20-35 years) in Queensland, Australia. Participants were invited to take part in an online survey via email and through face to face intercept to ensure a diverse cross section was obtained. Structural equation modelling revealed that the Motivation, Opportunity, and Ability (MOA) framework can be utilised to explain healthful eating behaviour and two-step cluster analysis uncovered two distinct segments with education, motivation to eat healthily and Turconi's eating behaviour scores being the most important variables within the wider multivariate segment formation. This paper contributes to literature in the following ways. First, it confirms the importance of behavioural bases in segment formation and supports inclusion of other bases, namely demographics and psychographics. Next, it provides evidence of the value of including behavioural theory, which extends focus beyond what individuals think to understand how the environment may support them. Finally, this paper demonstrates that the MOA framework together with eating behaviour and demographic factors (education) can produce theoretically informed segments.

Indexed as

Psychological TheoryDiet, HealthyFeeding BehaviorFemaleHealth BehaviorHumansMaleMotivationQueenslandStudentsSurveys and QuestionnairesUniversitiesYoung Adultbehaviour changeeating behavioursegmentationsocial marketingyoung adults

Identifiers

PMID31731796
PMCPMC6893594

What OpenQuestion holds

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LicenceCC BY
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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.