Evidence map›Paper›PMID 41050621›Full record

ArticleDigital health

Pregnancy, power, and perception: An AI analysis of Instagram sentiment on prenatal weightlifting.

Grace Basralian, Claire Wolford, Kaitlyn Voity, Sharon Galperin, Antonia F Oladipo

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

5 authors.

Grace BasralianHackensack Meridian School of Medicine, Nutley, NJ, USA.ORCID https://orcid.org/0009-0009-4339-1711
Claire WolfordHackensack Meridian School of Medicine, Nutley, NJ, USA.ORCID https://orcid.org/0000-0001-9255-6441
Kaitlyn VoityHackensack Meridian School of Medicine, Nutley, NJ, USA.ORCID https://orcid.org/0009-0008-0731-480X
Sharon GalperinHackensack University Medical Center, Department of Obstetrics & Gynecology, Hackensack, NJ, USA.
Antonia F OladipoHackensack Meridian School of Medicine, Nutley, NJ, USA.ORCID https://orcid.org/0000-0002-5285-0600

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to assess the prevalence of weight training content across various demographics as well as public attitudes toward weight training using social media as a proxy for organic sentiment. Methods: A cross-sectional analysis was conducted using the hashtags #pregnancystrengthtraining (#PST) and #pregnancyweightlifting (#PWL). A new Instagram account was created, and two independent reviewers analyzed the first 100 posts under each hashtag. One hundred sixty-nine posts met inclusion criteria and were analyzed. Each post was evaluated for the demographics of the content creator (skin color, influencer type, professional background, and geographic region) and the sentiment of associated comments. Findings: Sentiment analysis was performed using two natural language processors (NLP): ChatGPT-3.5 and MonkeyLearn. For both #PSL and #PWL, both NLPs were able to identify majority positive sentiment under analyzed posts. Posts from Europe and lighter skin toned influencers dominated top results, but posts were relatively well-distributed across the follower size and profession of the influencer. In general, analysis of #PWL yielded a lower percentage of positive-sentiment comments when compared to #PST. This trend held regardless of skin tone, occupation of influencer, follower count, or global region, though small sample sizes within each category limited statistical significance for these subgroups. Discussion: Sentiment toward pregnancy-related weight training content on Instagram is largely positive across both hashtags. Given the demographic consistency shown in our results (despite lower sample sizes in some skin tones), social media attitudes may reflect larger societal beliefs regarding strength training during pregnancy as a positive endeavor. Because top posts did not have a significant correlation to follower count, providing hashtag-based health guidance may be an achievable way for the healthcare community to interact with pregnant women regarding healthy lifestyle behaviors.

Indexed as

artificial intelligenceexercisehealth communicationsphysical activityPregnancyreproductive healthsocial mediastrength trainingweight training

Identifiers

PMID41050621
PMCPMC12489205

What OpenQuestion holds

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LicenceCC BY-NC
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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.