JMIR Publications

ABSTRACT

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 (PWE). Thus, developing tools for seizure prediction is a research priority. The ATMOSPHERE project co-designed, developed and usability-tested mobile seizure forecasting technology (smartwatch and companion smartphone app) to collect real-time data on seizures and precipitants. This is the ‘data collection technology’. Subsequently, machine learning algorithms are employed for seizure forecasting. Research and development is informed by complex digital 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 of the evaluation phase (full-scale clinical trial within the National Health Service (NHS)). 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 the seizure forecasting based on machine learning.

Methods:

Phase 1 of this study is a mixed-methods feasibility study, testing a prototype of the data collection technology with Phase 2 testing a minimum viable product (MVP). Recruitment is expected to take place in Quarter 4 2025 recruiting 60 adults with epilepsy from specialist NHS Epilepsy clinics. Clinicians will screen and gain consent to contact potential participants, with researchers obtaining full consent. Participants will be invited to complete the following study procedures (1) Complete 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 (4) Complete a qualitative interview exploring their views of the data collection technology (with a sub-sample of 9-12 participants that use the MVP).

Results:

Feasibility outcomes: To assess and optimise trial procedures, a study flow diagram will report recruitment rates (outcome 1), the 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). Stakeholder consultation will inform strategies to improve recruitment and retention, as required. To assess and optimise the data collection technology, quantitative technology usage data and qualitative interview data will be analysed to assess usability (outcome 6) and technical performance (outcome 7). Findings will be used to refine the intervention.

Conclusions:

Ultimately, the aim of the project is to improve clinical outcomes for PWE through seizure forecasting technology. To evaluate clinical outcomes, robust trial methodology is critical. This feasibility study will optimise methods for a future full-scale clinical trial, as well as refine the seizure forecasting intervention.