Evidence map›Paper›PMID 37208720›Full record

Trial reportThe international journal of behavioral nutrition and physical activity2023

The impact of altering restaurant and menu option position on food selected from an experimental food delivery platform: a randomised controlled trial.

Filippo Bianchi, Madison Luick, Lauren Bandy, Jonathan Bone, Stefan Kelly, James Farrington, Jovita Leung, Abigail Mottershow, Filip Murar, Susan A Jebb and 2 more

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in The international journal of behavioral nutrition and physical activity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
4.9field-weighted citation impact, top 4% of its field
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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  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
  2. Pooled it
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Observational
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

12 authors at 3 institutions in 1 country.

Filippo Bianchi *Behavioural Insights Team, London, UK. filippo.bianchi@nesta.org.uk.
Madison Luick *Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. madison.luick@phc.ox.ac.uk.ORCID 0000-0002-4120-6457
Lauren BandyNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Jonathan BoneNesta, London, UK.
Stefan KellyBehavioural Insights Team, London, UK.
James FarringtonBehavioural Insights Team, London, UK.
Jovita LeungBehavioural Insights Team, London, UK.
Abigail MottershowBehavioural Insights Team, London, UK.
Filip MurarBehavioural Insights Team, London, UK.
Susan A JebbNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Hugo Harper *Behavioural Insights Team, London, UK.
Rachel Pechey *Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Nesta · GBThe Behavioural Insights Team · GBUniversity of Oxford · GB

Funding

Department of HealthWellcome TrustWellcome Trust 222566/Z/21/Z
6 · The paper itself

Abstract

backgroundOverconsumption is one of the most serious public health challenges in the UK and has been linked to increased consumption of food ordered through delivery platforms. This study tested whether repositioning foods and/or restaurant options in a simulated food delivery platform could help to reduce the energy content of users' shopping basket.

methodsUK adult food delivery platform users (N = 9,003) selected a meal in a simulated platform. Participants were randomly allocated to a control condition (choices listed randomly) or to one of four intervention groups, (1) food options listed in ascending order of energy content, (2) restaurant options listed in ascending order of average energy content per main meal, (3) interventions 1 and 2 combined (4) interventions 1 and 2 combined, but food and restaurant options repositioned based on a kcal/price index to display options lower in energy but higher in price at the top. Gamma regressions assessed the impact of interventions on total energy content of baskets at checkout.

resultsThe energy content of participants' baskets in the control condition was 1382 kcals. All interventions significantly reduced energy content of baskets: Compared to control, repositioning both foods and restaurants purely based on energy content of options resulted in the greatest effect (-209kcal; 95%CIs: -248,-168), followed by repositioning restaurants (-161kcal; 95%CIs: -201,-121), repositioning restaurants and foods based on a kcal/price index (-117kcals; 95%CI: -158,-74) and repositioning foods based on energy content (-88kcals; 95%CI: -130,-45). All interventions reduced the basket price compared to the control, except for the intervention repositioning restaurants and foods based on a kcal/price index, which increased the basket price.

conclusionsThis proof-of-concept study suggests repositioning lower-energy options more prominently may encourage lower energy food choices in online delivery platforms and can be implemented in a sustainable business model.

Indexed as

Energy IntakeRestaurantsAdultFood LabelingFood PreferencesHumansMealsEnergy contentFood delivery platformRCTRepositioning

Identifiers

PMID37208720
PMCPMC10197857
OpenAlexW4377137922

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.