JMIR Publications

ABSTRACT

Background:

Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies.

Objective:

The ATMOSPHERE study aims to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualised seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modelling. The aims were/are to (1) collaboratively design the prototype (work completed) and (2) conduct an 'in-the-wild' study to assess usability and to refine the prototype (planned research).

Methods:

This study employs a person-based approach to design and usability test a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and healthcare professionals. Sessions explored users’ requirements for the prototype, followed by iterative design of low fidelity, static prototypes. Phase 2 (planned research) will be an 'in-the-wild' usability study involving the deployment of a mid-fidelity, interactive prototype for four weeks, with the collection of mixed-methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in three waves of deployment and data collection, aiming to recruit five participants per wave, with prototype refinement between waves.

Results:

The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centred, non-invasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalised machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life through increased predictability and seizure management.

Conclusions:

The ATMOSPHERE study represents a significant step forward in epilepsy management, focusing on the development of a user-centred, non-invasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalised machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving patients' clinical outcomes, including quality of life through increased predictability and control over their seizures.