Evidence map›Paper›PMID 33126236›Full record

ArticlePloS one2020

Nutrition labels' strengths & weaknesses and strategies for improving their use in Iran: A qualitative study.

Shirin Seyedhamzeh, Saharnaz Nedjat, Elham Shakibazadeh, Azam Doustmohammadian, Hedayat Hosseini, Ahmadreza Dorosty Motlagh

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Relationships Between Misinformation Variables and Nutritional Health Strategies: A Scoping Review.International journal of environmental research and public health · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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.

Shirin SeyedhamzehDepartment of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.
Saharnaz NedjatDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Elham ShakibazadehDepartment of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Azam DoustmohammadianGastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran.
Hedayat HosseiniDepartment of Food Sciences & Technology, National Nutrition & Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ahmadreza Dorosty MotlaghDepartment of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.ORCID 0000-0002-6937-7776

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to explain the strengths and weaknesses of the Traffic light label (TLL) and nutrition facts label (NFL) and the strategies for improving their use in Iran, based on the perspectives of different stakeholders, including mothers, food quality control experts (FQC), nutritionists and food industry experts.

methodsWe conducted 10 Focus Group Discussions (FGDs) with 63 mothers, 10 semi-structured interviews with FQCs, 1 FGD with 6 nutritionists and 1 FGD with 8 food industry experts. To clarify some of the questions that arose from the interviews, the researcher interviewed three policy makers who had sufficient information about the TLL. The discussions and interviews were transcribed verbatim and MAXQDA10 software was used for coding.

resultsThe most important findings of this study based on different stakeholders' perspectives were as follows: mothers believed that nutrition labels reduced the consumption of high-calorie products, although they found the TLL to be easier to understand than the NFL because of its red color. However, their weaknesses were their incompatibility with culture and the lack of trust in the information provided by manufacturers. FQCs pointed out the possibility of changing formulations and the appropriateness of the traffic light for patients, but like mothers, they believed that the labels did not suit the governing culture. Further weaknesses were, misleading the consumer, problems in the colorings reported by different laboratories, and different approaches adopted by regulatory experts. The simplicity of understanding TLL for the general public has been suggested by some nutritionists. Nevertheless, the multiplicity of colors of the TLL was the most important weakness presented by nutritionists and food industry experts and the failure to implement nutrition labels was another issue raised by experts. To improve the use of nutrition labels, notification via media especially television, community education and culture building were suggested by all stakeholders.

conclusionsThe findings of this study underscore the importance of implementing the policy of nutrition labeling in Iran. Mothers and nutritionists believed TLL to be more appropriate for the public to understand, however, FQCs and food industry experts believed that NFL was more suitable in guiding consumers toward healthy food choices. Education and information dissemination via media on interpretive TLL may affect consumer behavior toward food purchases.

Indexed as

AdultChildConsumer BehaviorFemaleFocus GroupsFood LabelingFood PreferencesHealth BehaviorHealth PromotionHumansIranMothersNutritive ValueQualitative Research

Identifiers

PMID33126236
PMCPMC7598474

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.