Evidence map›Paper›PMID 37346663›Full record

ArticlePeerJ. Computer science2023

Content analysis of social media regarding obesity as a chronic disease.

Farah Abdul Razzak, Diane Saab, Firas Haddad, Jumana Antoun

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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  3. 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

4 authors.

Farah Abdul RazzakDepartment of Family Medicine, American University of Beirut, Beirut, Lebanon.
Diane SaabAmerican University of Beirut, Beirut, Lebanon.
Firas HaddadAmerican University of Beirut, Beirut, Lebanon.
Jumana AntounDepartment of Family Medicine, American University of Beirut, Beirut, Lebanon.ORCID 0000-0002-2159-427X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Social media is an effective online communication channel. Obesity has been classified as a chronic disease; yet, social media rarely portrays it as such. This study aims to explore the perception of obesity as a chronic disease through content analysis of social media content of obesity-related health organizations and weight loss commercial applications. Methods: Using a codebook adapted from the definition of chronic disease, content analysis was conducted to evaluate a set of posts sampled from 11 health-related organizations and 10 weight loss applications Facebook and Twitter accounts. Descriptive statistics were used to assess the extent obesity was portrayed as a chronic disease. Results: A total of 8,106 posts were extracted: 3,019 posts by organizations and 5,087 by weight loss commercial applications. Only 401 (4.5%) posts/tweets were related to obesity as a chronic disease and were posted by obesity-related health organizations. Only 69 (2.0%) posts from all the organizations' posts directly addressed the idea that obesity is a chronic disease. Almost none of the weight loss commercial apps' social media accounts tackled any aspect of obesity as a disease. Commercial applications' posts revolved mainly around recipes, exercise regimens, and behavioral advice, whereas organizations tackled more complications, treatment, and obesity bias. Conclusion: Using content analysis of social media content, obesity-related health organizations and weight loss applications did not emphasize obesity as a chronic disease on their social media platforms of Facebook and Twitter. Weight-loss commercial applications on social media should include more posts to modify the public's perception regarding obesity as a disease, contributing to health promotion. Further research should explore other social media platforms and posts with specific hashtags posted by the general population.

Indexed as

Chronic diseaseObesitySocial medial content analysis

Identifiers

PMID37346663
PMCPMC10280592

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.