Health Care Content and Engagement in Chronic Illness Instagram Posts: Content Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».