Self-Reporting Technologies for Supporting Epilepsy Treatment

Epilepsy diagnosis and treatment relies heavily on patient self-reporting for informing clinical decision-making. These self-reports are traditionally collected from handwritten patient journals and tend to be either incomplete or inaccurate. Recent mobile and wearable health tracking developments stand to dramatically impact clinical practice through supporting patient and caregiver data collection activities. However, the specific types and characteristics of the data that clinicians need for patient care are not well known. In this study, we conducted interviews, a literature review, an expert panel, and online surveys to assess the availability and quality of patient-reported data that is useful but reported as being unavailable, difficult for patients to collect, or unreliable during epilepsy diagnosis and treatment, respectively. The results highlight important yet underexplored data collection and design opportunities for supporting the diagnosis, treatment, and self-management of epilepsy and expose notable gaps between clinical data needs and current patient practices

From hospital to home. The application of e-health solutions for monitoring and management of people with epilepsy

Background. In the last 10 years, there has been an explosion in the development of mobile and wearable technologies. Recent events such as Covid 19 emergency, showed the world how clinicians need to focus more on the application of these technologies to monitor and manage their patients. Despite this, the use of innovative technologies is not now a common practice in epilepsy. This thesis aims to demonstrate how people with epilepsy (PWE) are ready to use these mobile and wearable technologies and how data collected from these solutions can have a direct impact on PWE’s life. Methods. A systematic literature search was performed to provide an accurate overview of new non- invasive EEGs and their applications in epilepsy health care and an online survey was performed to fill the literature gap on this topic. To accurately study the PWE’s experience using wearable sensors, and the value of physiological and non-physiological data collected from wearable sensors, we used EEG data collected from the hospital (RADAR-CNS), and we collected original data from an at-home study (EEG@HOME). The data can be divided into two main categories: qualitative data (online survey, semi-structured interviews), and quantitative data analysis (questionnaires, EEG, and additional non-invasive physiological variables). Results. The systematic review showed us how non-invasive portable EEGs could provide valuable data for clinical purposes in epilepsy and become useful tools in different settings (i.e., rural areas, Hospitals, and homes). These are well accepted and tolerated by PWE and health care providers, especially for the easy application, cost, and comfort. The information obtained on the acceptability of repeated long-term non-invasive measures at home (EEG@HOME) showed that the use of the portable EEG cap was in general well tolerated over the 6 months but, the use of a smartwatch and the e-seizure diary was usually preferred. The level of compliance was good in most of the individuals and any barriers or issues which affected their experience or quality of the data were highlighted (i.e., life events, issues with equipment, and hairstyle of patients). Semi-structured interviews showed that participants found the combination of the three solutions very well-integrated and easy to use. The support received and the possibility to be trained and monitored remotely were well accepted and no privacy issues were reported by any of the participants. Most of the participants also suggested how they will be happy to have a mobile solution in the future to help to monitor their condition. The graph theory measures extracted from short and/or repeated EEG segments recorded from hospitals (RADAR-CNS) allowed us to explore the temporal evolution of brain activity prior to a seizure. Finally, physiological data and non-physiological data (EEG@HOME) were combined to understand and develop a model for each participant which explained a higher or lower risk of seizure over time. We also evaluated the value of repeated unsupervised resting state EEG recorded at home for seizure detection. Conclusion. The use of new technologies is well accepted by PWE in different settings. This thesis gives a detailed overview of two main points. First: PWE can be monitored in the hospital or at home using new wearable sensors or smartphone apps, and they are ready to use them after a short training and minimal supervision. Second: repeated data collection could provide a new way of a monitor, managing, and diagnosing people with epilepsy. Future studies should focus on balancing the acceptability of the solutions and the quality of the data collected. We also suggest that more studies focusing on seizure forecasting and detection using data collected from long-term monitoring need to be conducted. Digital health is the future of clinical practice and will increase PWE safety, independency, treatment, and monitoring