EpiLog: A User-Centered Approach to Biometric-Assisted Seizure Tracking and Pattern Discovery

Epilepsy management often depends on detailed self-tracking, yet many existing tools place a high burden on users while offering limited support for pattern recognition and shared care. This paper presents EpiLog, a user-centered mobile app concept for seizure tracking, trigger identification, and caregiver collaboration. The project followed a mixed-methods product research and design process that included a literature review, competitive analysis, a survey conducted in December 2024 (n = 21), semi-structured interviews in January 2025 (n = 3), affinity mapping, persona and journey-map development, and moderated remote evaluation of a clickable Figma prototype. Recruitment was supported by the Epilepsy Society. Findings showed that users wanted simple and trustworthy logging, easier data sharing with caregivers and clinicians, and more meaningful interpretation of seizure-related patterns rather than raw records alone. In response, EpiLog was designed around linked caregiver accounts, smartwatch-informed monitoring, voice-assisted logging, medication management, journaling, and a conceptual risk indicator based on known precipitating factors and user-specific trends. This paper contributes a healthcare design case study showing how seizure tracking can be reframed from recordkeeping toward actionable self-management support.