BACKGROUND AND OBJECTIVE: Catamenial seizure exacerbation (CSE) is challenging to track given unreliable patient reports. This highlights the need for improved recognition of CSE. In this study, we discuss the feasibility of using the Seizure Cycle app for that purpose. DESIGN/METHODS: Eligible participants logged menstrual cycles and seizure data in the app for 6 months. CSE was defined based on criteria by Herzog et al [1], as two-fold increase in average daily seizure frequency (ADSF) during menstrual (C1) and ovulatory (C2) phases during ovulatory cycles, and the entire luteal phase during anovulatory cycles (C3). Feasibility was assessed by the proportion of participants who completed 4-month and 6-month documentation. RESULTS: Among 8 participants, 5 (62.5 %) shared >
4-month data and 4 (50 %) shared 6-month data. Among the 6 participants who shared at least one month of data, CSE type C3 was identified based on number of seizures in one participant who had variable cycle length and was presumed to have anovulatory cycles. This was not confirmed with calculation of ADSF. CONCLUSIONS: Seizure Cycle app can serve as a feasible tool to improve diagnosis of this underrecognized condition. Despite the small sample size, CSE was potentially identified in one participant, although use of ADSF did not confirm this classification. Clearer definition of the C3 pattern may be useful. Future work will prioritize app automation to streamline data collection, facilitating larger and more robust datasets. These advancements will ultimately support the systematic assessment of therapeutic interventions to improve the diagnosis and treatment of CSE.