Evidence map›Paper›PMID 41767876›Full record

ArticleDigital health

#healthypregnancy: A content analysis of TikTok posts about nutrition and exercise during pregnancy.

Cooker Perkins Storm, Lauren M Amaro, Loan P Kim

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Cooker Perkins StormNatural Science Division, Pepperdine University, Malibu, CA, USA.
Lauren M AmaroCommunication Division, Pepperdine University, Malibu, CA, USA.ORCID https://orcid.org/0000-0003-4283-7362
Loan P KimNatural Science Division, Pepperdine University, Malibu, CA, USA.ORCID https://orcid.org/0000-0002-7719-1162

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The present study explored the characteristics and accuracy of TikTok messaging regarding nutrition and exercise during pregnancy. Methods: Using quantitative content analysis, researchers coded TikTok top posts ( Results: Nutrition posts created by credentialed individuals were 69.2% congruent with expert recommendations and largely did not provide specific calorie (97.5%) or weight gain recommendations (92.4%). Content created by an exercise specialist focused on strength training (67.1%) over aerobic training. When all exercise types and intensities were grouped together, 40.6% fully adhered to and 59.4% deviated from published expert recommendations. Content on strength training was significantly less likely to adhere to the guidelines compared to content on aerobic exercise. Those who indicated their own professional training were not more likely to publish content that was congruent with guidelines than those without known expertise. Conclusions: Experts and recommending groups in the United States need to address the gap between the apparent interest in strength training during pregnancy and the relative lack of specific and usable guidelines on this type of exercise.

Indexed as

nutritionphysical activitypregnancySocial mediaTikTok

Identifiers

PMID41767876
PMCPMC12949303

What OpenQuestion holds

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LicenceCC BY-NC
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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.