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Enregistrement W4409147045 · doi:10.2196/69243

Young Adults With Type 1 Diabetes and Their Perspectives on Diabetes-Related Social Media: Qualitative Study

2025· article· en· W4409147045 sur OpenAlexvenueno aff
TARA K. MAXWELL, LILLIAN A. BRANKA, N. Asher, Persis Commissariat, Lori M. Laffel

Notice bibliographique

RevueJMIR Diabetes · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueDiabetes Management and Research
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Diabetes and Digestive and Kidney Diseases
Mots-clésPreprintQualitative researchPsychologyType 2 diabetesSocial mediaDiabetes mellitusDevelopmental psychologyMedicineSociologyComputer scienceWorld Wide WebAnthropologyEndocrinology

Résumé

récupéré en direct d'OpenAlex

Background: Young adults with type 1 diabetes (T1D) often struggle with self-management and achieving target glycemic control, and thus, may benefit from additional support during this challenging developmental life stage. They are also some of the highest users of social media (SM), which may have some benefits to young people with T1D. Objective: Given the potential of SM support for people with diabetes, we sought to use qualitative methods to explore the perceptions of diabetes SM posts to influence self-care and emotional state of young adults with T1D. Methods: A series of Instagram (Meta) posts were selected by a multidisciplinary team of T1D experts. Young adults aged 18-25 years with T1D duration of 1 year or more were recruited from the clinic to participate in a 60-minute semistructured videoconferencing interview. First, they were queried about their SM use in general and specific to diabetes. Next, they reviewed 10 posts with the interviewer. For each post, perceptions and reactions were queried. Participants were asked about each post's impact on their emotional state and potential influence on diabetes self-care. Finally, they were asked to comment on what the posts emphasized and how they felt after viewing the posts. Interviews were transcribed and coded using thematic analysis. The participants' diabetes management information was extracted from the electronic health record. Results: There were 26 young adults who completed the study. Their mean (SD) age was 22.6 (SD 2.0) years, T1D duration 12.6 (SD 5.9) years, and glycated hemoglobin (HbA1c) 7.6 (SD 1.2%). In this sample, 65.3 were female and 84.6% White. All were using continuous glucose monitors (CGMs) and 80.7% used insulin pumps, 71.4% of which were hybrid closed loop. All participants used SM at least once daily, but most only sometimes or rarely used SM to access diabetes content and rarely or never posted diabetes content themselves. Major themes arising from the interviews centered on the potential for the young adult to connect emotionally through SM, which could be either positive or negative. Overall, for young adults with T1D, SM served to (1) highlight the existence of a community of people with T1D, (2) be a source of new diabetes information, (3) potentially influence diabetes self-management, (4) potentially influence emotional state, and (5) be appealing to the T1D community when the posts possessed certain characteristics (eg, medical accuracy, aesthetically appealing presentation of content). Conclusions: SM has the potential to help young adults with T1D feel a sense of community, seek and share objective and subjective thoughts and feelings about diabetes, motivate diabetes self-care, and positively affect emotional state. However, it may also have the potential to demotivate self-care and exacerbate negative emotional state with regards to diabetes.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,123
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,324
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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