Evidence map›Paper›PMID 41168878›Full record

ArticleThe international journal of behavioral nutrition and physical activity2025

Profiling users and non-users of meal delivery services in Belgium using latent class analysis.

Laura Helena Oostenbach, Matthew Keeble, Thomas Vanoutrive, Maartje P Poelman, Carlijn B M Kamphuis, Wendy Van Lippevelde, Lukar Thornton

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In one paragraph

Article in The international journal of behavioral nutrition and physical activity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

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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

7 authors.

Laura Helena OostenbachDepartment of Marketing, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, Antwerp, 2000, Belgium. laura.oostenbach@uantwerpen.be.ORCID http://orcid.org/0000-0002-1514-6390
Matthew KeebleDepartment of Marketing, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, Antwerp, 2000, Belgium.ORCID http://orcid.org/0000-0002-1512-7421
Thomas VanoutriveResearch Group for Urban Development, Faculty of Design Sciences & Urban Studies Institute, University of Antwerp, Mutsaardstraat 31, Antwerp, 2000, Belgium.ORCID http://orcid.org/0000-0001-7422-3475
Maartje P PoelmanDepartment of Social Sciences, Chair Group Consumption & Healthy Lifestyles, Wageningen University & Research, Hollandseweg 1, Wageningen, 6706 KN, The Netherlands.ORCID http://orcid.org/0000-0003-4346-4481
Carlijn B M KamphuisDepartment of Interdisciplinary Social Science, Utrecht University, Padualaan 14, Utrecht, 3584 CH, The Netherlands.
Wendy Van LippeveldeDepartment of Marketing, Innovation, and Organisation, Faculty of Economics and Business administration, Ghent University, Tweekerkenstraat 2, Ghent, 9000, Belgium.ORCID http://orcid.org/0000-0002-8535-0215
Lukar ThorntonDepartment of Marketing, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, Antwerp, 2000, Belgium.ORCID http://orcid.org/0000-0001-8759-8671

Funding

Fonds Wetenschappelijk Onderzoek G080823N
6 · The paper itself

Abstract

backgroundReady-to-eat meal delivery services have become increasingly popular in recent years, with potential detrimental health implications as the majority of food promoted and sold is energy-dense and nutrient-poor. However, limited research has examined who uses these services and why. This study explores the profiles of different users and non-users of meal delivery services and further describes the socio-demographic and health characteristics of these profiles.

methodsData were from 1086 adults who completed the online 2024 Meal Delivery Survey conducted in the Flanders and Brussels regions of Belgium. Users of meal delivery services reported on usage indicators (e.g., valuing fast delivery, ordering to avoid traffic) and non-users reported on reasons for non-use (e.g., lacking trust in meal hygiene, finding it bad for the environment). Latent class analysis categorised users based on patterns of indicator endorsement. Amongst non-users, reasons for non-use were examined descriptively for both former users and never users. Socio-demographic and health characteristics were compared across profiles.

resultsOver 66% of the sample (n = 720) were users and 34% (n = 366) non-users. Latent class analysis identified two user profiles. Efficiency-focused users (48% of user sample) solely prioritised fast and affordable service. Variety- and convenience-driven users (52% of user sample) endorsed a wider range of indicators, including the ability to try different cuisines, avoid supermarkets, and have more leisure time. The latter group included more frequent users. Amongst non-users, both former users (52% of non-user sample) and never users (48% of non-user sample) most commonly reported preferences for in-store food shopping and home cooking as reasons why they did not order meals for delivery. Profiles differed in socio-demographic and health characteristics. For example, variety- and convenience-driven users reported poorer health than efficiency-focused users. Non-users (former and never) were older than users, with never users having the oldest age profile.

conclusionsThis study provides a better understanding of determinants of use and non-use of meal delivery services, informing public health action aimed at improving food behaviours and reducing the burden of diet-related diseases. Results can support the development of targeted interventions addressing main drivers of meal delivery service use.

Indexed as

Consumer BehaviorFood ServicesMealsAdolescentAdultAgedBelgiumFast FoodsFemaleHumansLatent Class AnalysisMaleMiddle AgedSurveys and QuestionnairesYoung AdultBelgiumConsumer behaviourLatent class analysisMeal delivery servicesOnline food environment

Identifiers

PMID41168878
PMCPMC12577361

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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.