Evidence map›Paper›PMID 36145108›Full record

ArticleNutrients2022

Do Individuals Use Nutrition Labels on Food Packages to Make Healthy Choices? Testing the Dual-Process Model in Two Laboratory-Based Experiments.

Xiaoyan Li, Qi Wang, Chun-Qing Zhang

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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

3 authors.

Xiaoyan LiDepartment of Psychology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0000-0001-8912-8382
Qi WangDepartment of Psychology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0000-0002-3534-081X
Chun-Qing ZhangDepartment of Psychology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0000-0002-0683-4570

Funding

Fundamental Research Funds for the Central Universities 22wkqb08
6 · The paper itself

Abstract

Nutrition labels on food packages are designed to assist consumers in making healthy decisions. Based on the model of a dual-process system, the current study examined how people might be affected by nutrition labels and consuming contexts when making choices about healthy foods. Using four types of nutrition labels (i.e., the NuVal label, 5-Color nutrition label, traffic light label, and daily value label), participants were instructed to choose the healthier foods with or without time constraints in two experiments. In Experiment 1, participants were presented with pairs of foods accompanied by the same type of nutrition labels to measure the efficiency of their health evaluation. In Experiment 2, two types of labels with inconsistent nutritional information were presented to participants simultaneously to measure their preference regarding the nutrition labels. Findings of the current study support the notion that the traffic light label is advantageous in terms of both the efficiency of, and preferences regarding, nutrition judgment, especially with time constraints. When there was only one type of nutrition label, participants made decisions fastest and most accurately when observing the NuVal label, regardless of time constraints. Overall, the reliable interactions between the time constraints and patterns of nutrition labels have theoretical implications for the appeal-based heuristics and rational-based processing when making health-related food decisions.

Indexed as

Consumer BehaviorFood PreferencesChoice BehaviorFood LabelingHealth BehaviorHumansNutritive Valuedecision-makingdual-process systemnutritional informationnutrition labeltime constraints

Identifiers

PMID36145108
PMCPMC9505394

What OpenQuestion holds

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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.