Health Care Content and Engagement in Chronic Illness Instagram Posts: Content Analysis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".