Evidence map›Paper›PMID 37836570›Full record

ArticleNutrients2023

Using Crowdsourced Food Image Data for Assessing Restaurant Nutrition Environment: A Validation Study.

Weixuan Lyu, Nina Seok, Xiang Chen, Ran Xu

Abstract read
In one paragraph

Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

4 authors.

Weixuan LyuDepartment of Geography, University of Connecticut, Storrs, CT 06269, USA.
Nina SeokDepartment of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA.
Xiang ChenDepartment of Geography, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0002-5045-9253
Ran XuDepartment of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0002-5832-9226

Funding

National Institute of Food and Agriculture CONS01031
6 · The paper itself

Abstract

Crowdsourced online food images, when combined with food image recognition technologies, have the potential to offer a cost-effective and scalable solution for the assessment of the restaurant nutrition environment. While previous research has explored this approach and validated the accuracy of food image recognition technologies, much remains unknown about the validity of crowdsourced food images as the primary data source for large-scale assessments. In this paper, we collect data from multiple sources and comprehensively examine the validity of using crowdsourced food images for assessing the restaurant nutrition environment in the Greater Hartford region. Our results indicate that while crowdsourced food images are useful in terms of the initial assessment of restaurant nutrition quality and the identification of popular food items, they are subject to selection bias on multiple levels and do not fully represent the restaurant nutrition quality or customers' dietary behaviors. If employed, the food image data must be supplemented with alternative data sources, such as field surveys, store audits, and commercial data, to offer a more representative assessment of the restaurant nutrition environment.

Indexed as

CrowdsourcingFoodNutritional StatusNutrition AssessmentRestaurantscrowdsourcingFAFHfood environmentfood image dataHartfordimage recognitionnutrition assessmentrestaurantvalidation

Identifiers

PMID37836570
PMCPMC10574450

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.