Evidence map›Paper›PMID 38621591›Full record

ArticleAppetite2024

Creation and validation of a NOVA scored picture set to evaluate ultra-processed foods.

Zach Hutelin, Monica Ahrens, Mary Elizabeth Baugh, Mary E Oster, Alexandra L Hanlon, Alexandra G DiFeliceantonio

Abstract readValidation Study
In one paragraph

Article in Appetite, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. Trial
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

6 authors.

Zach HutelinGraduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, United States; Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States. Electronic address: hutelin@vt.edu.
Monica AhrensCenter for Biostatistics and Health Data Science, Department of Statistics, Virginia Tech, Roanoke, VA, United States.
Mary Elizabeth BaughFralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.
Mary E OsterFralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.
Alexandra L HanlonCenter for Biostatistics and Health Data Science, Department of Statistics, Virginia Tech, Roanoke, VA, United States.
Alexandra G DiFeliceantonioFralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States; Department of Human Nutrition, Foods, and Exercise, Virginia Tech, Blacksburg, VA, United States.

Funding

Ultra-processed food reward: neural and metabolic factorsR01DK132389 · NIDDK · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI Alexandra Gold DiFeliceantonio · 2023 to 2026
$1.8M
NIDDK NIH HHS R01 DK132389
6 · The paper itself

Abstract

There has been a rapid shift in the modern food environment towards increased processing in foods consumed in the United States (US) and globally. The NOVA system (not an acronym) for classifying food on degree of processing currently has the most empirical support. Consumption of foods in the NOVA 4 category, ultra-processed foods (UPF), is a risk factor for a host of poor health outcomes including heart disease, stroke, and cancer. Despite these poor health outcomes, UPF make up 58% of calories consumed in the US. Methodologies for assessing the reinforcing and rewarding properties of these foods are necessary tools. The Becker-DeGroot-Marschak auction paradigm (BDM) is a well validated tool for measuring value and is amenable to neuromonitoring environments. To allow for the testing of hypotheses based on level of food processing, we present a picture set of 14 UPF and 14 minimally-processed foods (MPF) matched on visual properties, food characteristics (fat, carbohydrate, cost, etc.), and rated perceptual properties. Further, we report our scoring of these foods using the NOVA classification system and provide additional data from credentialed nutrition professionals and on inter-rater reliability using NOVA, a critique of the system. Finally, we provide all pictures, data, and code used to create this picture set as a tool for researchers.

Indexed as

Food HandlingFood, ProcessedNutritive ValueReproducibility of ResultsUnited States

Identifiers

PMID38621591
PMCPMC11092385

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.