Evidence map›Paper›PMID 40686824›Full record

ArticleFrontiers in digital health2025

Exploring #HealthLiteracy on LinkedIn: a first look at health hashtag engagement in a professional social media context.

Siva Sai Chandragiri, Bikash Baral, Fabian Peter Hammerle, Olena Litvinova, Elisa Opriessnig, Maima Matin, Himel Mondal, Michel-Edwar Mickael, Maria Kletecka-Pulker, Atanas G Atanasov and 1 more

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. 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

11 authors.

Siva Sai ChandragiriDepartment of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
Bikash BaralOrganismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
Fabian Peter HammerleLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Olena LitvinovaLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Elisa OpriessnigMedical University of Graz, Graz, Austria.
Maima MatinInstitute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland.
Himel MondalDepartment of Physiology, All India Institute of Medical Sciences, Deoghar, India.
Michel-Edwar MickaelInstitute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland.
Maria Kletecka-PulkerLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Atanas G AtanasovLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Thomas Wochele-ThomaLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

LinkedIn, despite its large and professionally credentialed user base, remains an underexplored platform for digital health communication, unlike X (formerly Twitter), which has been widely studied for health-related hashtag trends. Health literacy, a key determinant of public health, is increasingly promoted through social media hashtags such as #HealthLiteracy. However, to date, no studies have systematically examined how this hashtag is used on LinkedIn. This study aimed to analyze the use of #HealthLiteracy on LinkedIn, identify thematic patterns in post content, and evaluate user engagement trends, with comparisons to prior X-based research. A one-week retrospective dataset of #HealthLiteracy posts was collected using the SingleFile browser extension. The content was cleaned and analyzed using RStudio with standard text mining packages. Word frequencies, co-occurring hashtags, and engagement metrics (likes, comments, reposts) were extracted, and a chi-square goodness-of-fit test was performed to assess engagement distribution. A total of 370 posts with 3,174 engagements were analyzed. The most frequent co-occurring hashtags included #agedcare, #residentialagedcare, and #healthquality, indicating a focus on institutional eldercare. The most common words, care, aged, solutions, and transition reinforced this thematic alignment. Engagement was primarily passive, with reactions far outnumbering comments and reposts. This study establishes a proof-of-concept for LinkedIn-based hashtag analysis in health research. LinkedIn demonstrates strong potential for targeted dissemination of health literacy content to professionals and policymakers, although engagement strategies may need to be tailored to the platform's predominantly passive interaction culture.

Indexed as

digital engagementhashtag analysishealth literacyLinkedInpublic health communicationsocial media

Identifiers

PMID40686824
PMCPMC12271182

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.