PARTNERING WITH ADOLESCENTS AND YOUNG ADULTS WITH CHILDHOOD-ONSET LUPUS TO IMPROVE ENGAGEMENT IN CARE
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".