Evidence map›Paper›PMID 41004639›Full record

ArticleOnline journal of public health informatics2025

Accuracy of Nutrition-Related Awareness Messages on Twitter (Rebranded as X) by the Nutrition Awareness Providers in the Kingdom of Saudi Arabia: Validity Content Analysis.

Duaa Alammari, Iman A Bindayel, Noura Althukair, Noura AlRomi, Khalid Aldubayan, Najla Khateeb, Ahmed Alabdrabalnabi, Banan Banamah

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Article in Online journal of public health informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Duaa AlammariDepartment of Health Systems Management, College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah ibn Abdulaziz Rd, Riyadh, 13213, Saudi Arabia, 966 500844881.ORCID 0000-0002-3559-3841
Iman A BindayelDepartment of Community Health and Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.ORCID 0000-0001-5452-115X
Noura AlthukairDepartment of Community Health and Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.ORCID 0009-0009-6655-3728
Noura AlRomiDepartment of Community Health and Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.ORCID 0009-0007-6960-3140
Khalid AldubayanDepartment of Community Health and Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.ORCID 0000-0002-0924-7400
Najla KhateebCollege of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Al Ahsa, Saudi Arabia.ORCID 0000-0001-8621-9967
Ahmed AlabdrabalnabiDepartment of Public Health, College of Health Sciences, Saudi Electronic University, Dammam, Saudi Arabia.ORCID 0000-0001-7326-7847
Banan BanamahORCID 0009-0001-7178-3351

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the increasing use of social media, platforms like Twitter (rebranded as X in 2023) have become popular channels for disseminating health information. In Saudi Arabia, Twitter is widely used, making it an effective tool for health awareness. However, the accuracy of nutrition-related content on social media is often questioned. Objective: The study aims to evaluate the accuracy and evidence-based quality of nutrition-related tweets posted by reputable Saudi health and nutrition awareness providers. Methods: A mixed methods content analysis was conducted on tweets published by 7 Saudi health organizations, examining content in Arabic and English over 12 months. Nutrition-related tweets were analyzed for accuracy, popularity, and evidence inclusion by a panel of experts in clinical nutrition, food science, and technology. Results: A total of 531 nutrition-related tweets were included in the study. Findings indicate that 445 (84%) of the tweets were accurate, of which only 17 (4%) included cited evidence. Yet, only 13 (2%) were inaccurate. The highest number of tweets are from Saudi Food and Drug Administration (SFDA) 96 (18%), Gulf Health Council (GHC) 91 (17%), Saudi Society for Clinical Nutrition (SSCN) 89 (16%), Kayl Association for Combating Obesity 83 (16%) and National Nutrition Committee (NNC) 80 (15%) and the lowest is Ministry of Health (MOH) 31 (5%). Significant relationships were observed between tweet accuracy and the source organization (P=.009, 95% CI 0.008-0.01), content type (P=.03, 95% CI 0.03-0.03), and tweet timing (P=.04, 95% CI 0.04-0.04). Governmental sources had higher popularity and were more frequently accurate compared to nongovernmental sources. Conclusions: Reputable Twitter accounts in Saudi Arabia generally provide accurate nutrition-related content, though evidence citation is minimal. Users are encouraged to rely on reputable accounts for health information, and further research is suggested to explore the quality of evidence in such posts.

Indexed as

Asianawarenessclinical nutritioncontent analysisdietaryfood sciencehealth informationmediamixed methodsnutritionnutritionalnutrition awarenessSaudi Arabiansocial mediatwitter (X)

Identifiers

PMID41004639
PMCPMC12468160

What OpenQuestion holds

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LicenceCC BY
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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.