%0 Journal Article
%@ 1929-0748
%I JMIR Publications
%V 13
%N 
%P e60129
%T A Digital Intervention for Capturing the Real-Time Health Data Needed for Epilepsy Seizure Forecasting: Protocol for a Formative Co-Design and Usability Study (The ATMOSPHERE Study)
%A Quilter,Emily E V
%A Downes,Samuel
%A Deighan,Mairi Therese
%A Stuart,Liz
%A Charles,Rosie
%A Tittensor,Phil
%A Junges,Leandro
%A Kissack,Peter
%A Qureshi,Yasser
%A Kamaraj,Aravind Kumar
%A Brigden,Amberly
%+ School of Engineering Mathematics and Technology, University of Bristol, 1 Cathedral Square, Bristol, BS1 5DD, United Kingdom, 44 01174282343, amberly.brigden@bristol.ac.uk
%K epilepsy
%K seizure forecasting
%K data science
%K artificial intelligence
%K machine learning
%K wearable technology
%K mobile phone
%D 2024
%7 19.9.2024
%9 Protocol
%J JMIR Res Protoc
%G English
%X 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 apparent 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 aimed to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualized 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 modeling. The first aim was to collaboratively design the prototype (work completed). The second aim is to conduct an “in-the-wild” study to assess usability and refine the prototype (planned research). Methods: This study uses a person-based approach to design and test the usability of a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and health care 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, functional prototype for 4 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 3 rounds of deployment and data collection, aiming to recruit 5 participants per round, with prototype refinement between rounds. Results: The phase-1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, functional prototype based on identified requirements, including the tracking of evidence-based and personalized seizure precipitants. The upcoming phase-2 usability study is expected to provide insights into the prototype’s real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of phase 2 is the last quarter of 2024. Conclusions: The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centered, noninvasive 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 personalized 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. International Registered Report Identifier (IRRID): DERR1-10.2196/60129 
%R 10.2196/60129
%U https://www.researchprotocols.org/2024/1/e60129
%U https://doi.org/10.2196/60129