Evidence map›Paper›PMID 38212556›Full record

ArticleClinical rheumatology2024

Content analysis of patient support groups related to myositis on Facebook.

Aiman Perween Afsar, Shounak Ghosh, Renil Sinu Titus, Karen Cheng, Arundati A Kanawala, Peter Kerkhof, Jessica Day, Latika Gupta

Open access · hybridAbstract read
In one paragraph

Article in Clinical rheumatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.7field-weighted citation impact, top 7% of its field
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

6 citing papers in PubMed, 7 citations in OpenAlex.

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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

8 authors at 7 institutions in 4 countries.

Aiman Perween Afsar *Maulana Azad Medical College, New Delhi, India.
Shounak Ghosh *Department of Rheumatology, Calcutta Medical Research Institute, Kolkata, 700027, India.ORCID https://orcid.org/0000-0002-3546-4695
Renil Sinu TitusSeth G.S. Medical College and K.E.M. Hospital, Acharya Donde Marg, Parel, Mumbai, India.
Karen ChengPatient with Myositis, Basel, Switzerland and EU Patient Advocacy Lead, Patient-Led Research Team, Myositis Support & Understanding (MSU), Lincoln, DE, USA.
Arundati A KanawalaSmt. Kashibai Navale Medical College, Pune, India.
Peter KerkhofDepartment of Communication Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Jessica DayDepartment of Rheumatology, Royal Melbourne Hospital, Parkville, VIC, 3050, Australia.
Latika GuptaDept. of Rheumatology, Royal Wolverhampton Trust, Wolverhampton, WV10 0QP, UK. drlatikagupta@gmail.com.ORCID http://orcid.org/0000-0003-2753-2990
King Edward Memorial Hospital and Seth G.S. Medical College · INMaulana Azad Medical College · INMedical College and Hospital, Kolkata · INNIHR Oxford Musculoskeletal Biomedical Research Centre · GBSmt. Kashibai Navale Medical College and General hospital · INThe Royal Melbourne Hospital · AUVrije Universiteit Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIdiopathic inflammatory myopathies (IIM) are heterogeneous and complex, and routine consultation can be overwhelming for patients, or sometimes so rushed that patients feel unable to discuss their needs and wishes adequately. As a result, online patient support groups (PSGs) on social media platforms like Facebook may help provide them with information they are seeking, and the support of the patient community who are living with this condition. Our goal is to explore the current landscape of PSGs in IIM to discuss the future of such groups and their role in supporting patient-driven self-management of complex connective tissue diseases.

methodsWe investigated factors that influence engagement in publicly accessible support groups on Facebook for patients with myositis. We analysed posts from myositis-related Facebook groups and pages between July 10, 2022, and October 2022. Data were extracted from each post, including presentation format (text, picture, video or mixed media) and content type (news, personal feelings or information). To gauge the post's impact, we measured engagement metrics, such as likes, comments, shares and reactions.

resultsNearly three-quarters of the groups were private. Among the open ones, most posts seem to comprise pictures with text. Notably, engagement levels were higher for multimedia posts, with the exception of comments in groups, where engagement was comparatively lower. In terms of content, the majority of posts fell under the 'personal' category, followed by 'information' and 'news' posts, with information posts in groups receiving the most interactions. Moreover, groups exhibited higher total engagement compared to pages when considering all posts cumulatively.

conclusionsOur observations indicate that patients with myositis seek information on the condition online, and the multimedia nature of content presentation significantly influences engagement. These digital forums serve as valuable platforms for fostering connections among diverse individuals, providing a perceived safe space for sharing their personal experiences and varied perspectives, and potentially mitigating social isolation. Key Points • Patient support groups on myositis are a key source of support and information for patients. • Public posts with multimedia content garner the most engagement. • The majority of posts are personal in nature, with a smaller proportion of content providing news or information.

Indexed as

Social MediaHealth ServicesHumansSelf-Help GroupsFacebookMuscleMyositisOnline support groupRare diseasesSocial Media

Identifiers

PMID38212556
PMCPMC10834555
OpenAlexW4390766263

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.