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A Digital Intervention for Capturing Real-Time Health Data for Epilepsy Seizure Forecasting: Protocol for the
Lauren Thompson1, Emily Nielsen1, Emily E V Quilter1
1School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom.
Background:
Epilepsy is a chronic neurological disorder marked by recurrent and apparently unpredictable seizures and associated with premature death, injury, and diminished quality of life. The unpredictability of seizures is a major concern for people with epilepsy. Thus, developing tools for seizure prediction is a research priority. The Artificial Intelligence to Optimise Seizure Prediction to Empower People With Epilepsy (ATMOSPHERE) project focuses on the development and evaluation of seizure forecasting technology involving mobile technology and machine learning to provide personalized seizure forecasting (risk of seizure in the near future). The project is informed by complex intervention frameworks, which recommend phases of development, feasibility study, clinical evaluation, and implementation.
Objective:
Objective 1 aims to conduct a feasibility study to test and refine the trial methods for a future clinical trial. Objective 2 aims to test and refine the data collection technology, considering usability and technical performance. Objective 3 aims to collect longitudinal data on seizures and their precipitants to refine seizure forecasting.
Methods:
This study is a single-arm, mixed methods feasibility study, testing a prototype of the data collection technology, with phase 2 testing a minimum viable product. In total, 60 participants will be recruited via specialist National Health Service epilepsy clinics. Inclusion criteria are adults with epilepsy, experiencing seizures twice per month, able to consent, and engage with technology. Clinicians will screen and gain consent to contact, with researchers obtaining full consent. Participants will be invited to complete the following study procedures: (1) onboarding, (2) use the data collection technology (phase 1 or 2) in their lived context for up to 6 months, (3) complete patient-reported outcome measures and capture clinical-reported outcome measures at baseline and 3 months, and (4) complete a qualitative interview exploring their views of the data collection technology. A study flow diagram will report recruitment rates (outcome 1), diversity of the recruited sample (outcome 2), barriers and facilitators to recruitment (outcome 3), retention rates (outcome 4), and barriers and facilitators to retention (outcome 5). To assess the data collection technology, quantitative technology use data and qualitative interview data will be analyzed to assess usability (outcome 6) and technical performance (outcome 7) of the data collection technology. These outcomes will inform iterative minimum viable product development and testing cycles with stakeholders.
Results:
Recruitment is planned to begin in quarter 1 of 2026, with data collection expected to be completed by quarter 2 of 2027. Data analysis will take place during quarter 3, and the results will be published in quarter 4.
Conclusions:
This project aims to improve clinical outcomes for people with epilepsy through seizure forecasting technology. To evaluate clinical outcomes, robust trial methodology is critical. This feasibility study will optimize methods for a future full-scale clinical trial as well as refine the seizure forecasting intervention.
International Registered Report Identifier (Irrid):
PRR1-10.2196/85993.
