PARTNERING WITH ADOLESCENTS AND YOUNG ADULTS WITH CHILDHOOD-ONSET LUPUS TO IMPROVE ENGAGEMENT IN CARE
Notice bibliographique
Résumé
O071 / #598 Topic: AS18 - Pediatric SLE ABSTRACT CONCURRENT SESSION 12: PEDIATRIC SLE – ADVANCES IN DISEASE OUTCOMES AND MENTAL HEALTH 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Adolescents and young adults (AYA) with lupus, particularly those in the Bronx, New York, face significant barriers to care, contributing to inconsistent medical care and poor outcomes. Mobile health (mHealth) apps have the potential to improve engagement in care and can be tools to provide informational and psychosocial support and improve access and communication with the clinical team. This project aimed to adapt an existing mHealth app for AYA with lupus to improve engagement in care with input from youth through a structured participatory process. Methods The adaptation process followed the ADAPT-ITT framework. An initial literature search was conducted to determine potential targets and methods to improve engagement in care for youth with lupus. This identified psychological barriers (mainly depression) and disease education as potential targets and mobile health apps as a potential method effective in this age group. Initial key-informant meetings with a lupus educator, 2 patients, a patient advocate, a project manager for a mHealth app company, and research staff established broad app functionalities and major educational topics. Semistructured focus groups, termed “engagement studios,” were conducted with AYA with lupus to explore preferences for content, delivery methods, and engagement strategies for the app’s 2 primary functions: (1) educational support and (2) communication with a rheumatology nurse. Feedback from these sessions informed iterative refinements. A beta version of the app was subsequently theater-tested for usability and user experience. Results Over the course of a year of key-informant meetings a proposal for the educational and the nurse communication components of an existing mHealth app (Valera Health) was developed. The educational component was organized around 6 major educational topics (understanding lupus, symptom management, medications, mental health, general health & wellness, and social/financial support). Articles were reviewed from the Lupus Foundation of America’s National Resource Center on Lupus (funded by the Centers for Disease Control and Prevention) and sourced for material that covered the major topics and was thought to be appropriate and appealing to adolescents and young adults. To address the heterogeneity of disease the key-informant group determined that there should be “tracks” that users could choose from to customize educational material around particular needs. The proposal for the nurse communication function was organized around supporting users with visit and medication adherence and potential root barriers to care. Sixteen participants were recruited for the Engagement Studios, targeting AYA with a history of inconsistent lupus care. Through the Engagement Studios the educational component was refined to include 3 tracks (Lupus & Brain, Lupus & Kidneys, Lupus & Skin) with multimedia content to be sent to users and available through the app’s imbedded library. Reproductive health was added as an educational topic (originally proposed by key-informants as its own track). It was determined that over a 6-month trial, users would be messaged twice a week, with an educational piece and then for nurse outreach. Nurse outreach would address visit/medication adherence and assist with 3 key barriers (social needs, mental health, and disease knowledge/health literacy) through planned messages and include ad-hoc bi-directional messaging. AYA participating in theater-testing confirmed general usability and valued personalization, concise delivery formats, and the availability of information from a trusted source. Conclusions Partnering with and eliciting feedback from AYA with lupus in the adaptation process ensured that the resulting mHealth tool addressed their unique needs and preferences. Future work will evaluate the app’s effectiveness in improving engagement in care and clinical outcomes.
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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».