Evidence map›Paper›PMID 41004680›Full record

ArticleJMIR formative research2025

Health Care Content and Engagement in Chronic Illness Instagram Posts: Content Analysis.

Lindsey D Daon, Fu-Shiuan Whitney Lee, Olga Saynina, C Jason Wang

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Lindsey D DaonDivision of General Pediatrics and Adolescent Medicine, Department of Pediatrics and Adolescent Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, United States, 1 5072936631.ORCID 0000-0001-7606-5052
Fu-Shiuan Whitney LeeDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0001-8939-9617
Olga SayninaDepartment of Health Policy, Stanford University, Stanford, CA, United States.ORCID 0000-0002-5623-5218
C Jason WangDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States.ORCID 0000-0002-6278-4889

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Instagram and other social media platforms provide a unique environment for people with chronic illnesses to share experiences, but posts with higher engagement may also shape behavior. The hashtag #ChronicIllness appears in over 5 million posts, reflecting the large digital community where users seek validation, connection, and support. Frameworks such as social cognitive theory, self-presentation theory, and illness identity theory suggest that highly engaging content can shift social norms and drive behavior change via observational learning. Despite the strong theoretical basis for this behavioral impact, little is known about what chronic illness-related content is the most engaging. Objective: The aim of this study is to identify the content of Instagram posts related to chronic illness and determine if health care content is associated with higher engagement. Methods: This study is a mixed methods content analysis of 279 publicly available Instagram posts tagged with #chronicillness, #chronicallyill, or #spoonie. Posts were selected via convenience sampling and included if they featured original, nonvideo content. Photos, hashtags, and captions were coded for themes including location, medical equipment, health care experience, and illness identity. Quantitative metrics, such as likes, comments, and overperforming scores (a normalized metric of engagement), were extracted using CrowdTangle. Multivariate analyses assessed if health care content (posts featuring health care experiences or photos in a medical setting or with medical equipment) was associated with a higher odds of overperforming. Results: Posts had a median of 25 (IQR 0-14,936) likes, 3 (IQR 0-525) comments, and 20 (IQR 1-31) hashtags. A total of 222 (80%) posts were created by women, and 110 (40%) were overperforming. Photo analysis (260 posts with 406 photos) showed 27 (10%) in health care settings, and 49 (19%) included medical equipment, with 10 (4%) featuring invasive devices (eg, intravenous lines and feeding tubes), which were strongly associated with higher engagement. Hashtag analysis revealed that 243 (87%) posts referenced a medical condition, most commonly chronic pain (n=101, 36%), fibromyalgia (n=56, 20%), and Ehlers-Danlos syndrome (n=38, 14%), while 57 (20%) included medical interventions. Captions reflected 4 main themes: medical experience, illness journey, connection, and nonillness experiences. In multivariate regression analysis, longer captions (odds ratio [OR] 2.44, 95% CI 1.05-5.67), health care content (OR 1.85, 95% CI 1.00-3.42), and invasive medical equipment (OR 6.19, 95% CI 1.16-32.99) were independently associated with overperforming. Conclusions: Posts featuring health care content and invasive medical equipment were associated with significantly more engagement, suggesting that medicalized portrayals of illness may be amplified on Instagram. This visibility may offer support but also risks reinforcing illness-centered identities and overmedicalization through the influence of observational learning and identity formation. Medical professionals must be aware of these trends and promote balanced, evidence-based content. Future research should explore how social media shapes health behaviors, identity, and utilization to mitigate potential harms while preserving support.

Indexed as

Social MediaChronic DiseaseFemaleHumansMalechronic illnesscontent analysisdigital healthillness identity theoryinfodemicsInstagramsocial cognitive theorysocial mediasocial presentation theoryweb-based communities

Identifiers

PMID41004680
PMCPMC12468905

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.