Endocrine Abstracts

Background: Intercurrent illnesses may precipitate life-threatening adrenal crises in children with adrenal insufficiency (AI). Incidence of these may be reduced by effective home management in response to early symptoms of illness; however, such symptoms are not well characterized and often individual. We showed recently that symptom-tracking is popular in families living with childhood AI. In this study we present parent/carer perspectives from the Manchester Adrenal Clinic to guide development of a web-based AI symptom-tracker app.

Methods: We convened a 90-minute online focus group workshop with 12 parents/carers of children aged 3–13 years with AI due to Congenital Adrenal Hyperplasia (CAH). After semi-structured walkthroughs of a low-fidelity prototype, an open question and answer (Q&A) session captured facilitators’ notes and audio transcripts for thematic analysis.

Results: Thematic analysis revealed five key areas of participant enthusiasm: 1. Structured, yet flexible, symptom taxonomy: Pre-defined visual menus with icon-based 1–5 severity ratings, plus an optional free-text box for unique observations. 2. Integrated dose logging: Real-time entry of hydrocortisone doses, missed doses, and emergency injections with automated alerts for late or missed doses. 3. Multi-user role access: Graded permissions allowing school staff and secondary caregivers to submit symptoms. 4. Mood and emotional-health tracking: A simple well-being slider alongside free-text notes to capture AI-related stress and mood changes. 5. Automated trend reports & data sharing: One-click generation of clinician-friendly summaries (email or print), helping families recall symptom and dosing patterns between infrequent clinic visits and streamline communication with care teams.

Conclusion: Parent/carer feedback strongly supported the need for an AI symptom-tracker app combining structured symptom logging, real-time dose management, role-based access, mood monitoring, and automated report sharing. These features may enhance home management, facilitate timely interventions, and improve communication with clinical teams to improve the care of childhood AI. Next steps involve iterative prototyping and a pilot usability study to evaluate clinical impact.