Evidence map›Paper›PMID 36296912›Full record

ArticleNutrients2022

Food Outlet Access and the Healthiness of Food Available 'On-Demand' via Meal Delivery Apps in New Zealand.

Dru Norriss, Rose Crossin, Angela Curl, Susan Bidwell, Elinor Clark, Tessa Pocock, Ryan Gage, Christina McKerchar

Abstract read
In one paragraph

Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
–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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Combo Deals, Junk Meals: A Systematic Review Examining the Healthiness of Foods Promoted on Meal Delivery Apps.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
    Pooled it
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  3. Mapping the Healthiness of Community Food Environments and Their Association With the Socioeconomic Index for Areas (SEIFA) in the South Coast of NSW.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2026
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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

8 authors.

Dru NorrissDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.
Rose CrossinDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.ORCID 0000-0003-1814-1330
Angela CurlDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.ORCID 0000-0002-8325-190X
Susan BidwellDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.
Elinor ClarkDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.
Tessa PocockDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.
Ryan GageDepartment of Public Health, University of Otago Wellington, Wellington 6021, New Zealand.
Christina McKercharDepartment of Population Health, University of Otago Christchurch, Christchurch 8013, New Zealand.ORCID 0000-0003-4443-4241

Funding

Lotteries NZ 2022-185288University of Otago 2022 -UORG
6 · The paper itself

Abstract

Access to unhealthy commodities is a key factor determining consumption, and therefore influences the prevalence of non-communicable diseases. Recently, there has been an increase in the availability of food 'on-demand' via meal delivery apps (MDAs). However, the public health and equity impacts of this shift are not yet well understood. This study focused on three MDAs in New Zealand and aimed to answer (1) what is the health profile of the foods being offered on-demand, (2) how many food outlets are available and does this differ by physical access or neighbourhood demographics and (3) does the health profile of foods offered differ by physical access or neighbourhood demographics? A dataset was created by sampling a set of street addresses across a range of demographic variables, and recording the menu items and number of available outlets offered to each address. Machine learning was utilised to evaluate the healthiness of menu items, and we examined if healthiness and the number of available outlets varied by neighbourhood demographics. Over 75% of menu items offered by all MDAs were unhealthy and approximately 30% of all menu items across the three MDAs scored at the lowest level of healthiness. Statistically significant differences by demographics were identified in one of the three MDAs in this study, which suggested that the proportion of unhealthy foods offered was highest in areas with the greatest socioeconomic deprivation and those with a higher proportion of Māori population. Policy and regulatory approaches need to adapt to this novel mode of access to unhealthy foods, to mitigate public health consequences and the effects on population groups already more vulnerable to non-communicable diseases.

Indexed as

Noncommunicable DiseasesFast FoodsFood SupplyHumansMealsNew ZealandResidence Characteristicsaccessdemographicsequityfoodinternetnutritiononlinerapid delivery

Identifiers

PMID36296912
PMCPMC9607030

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.