A review of mobile apps for epilepsy self-management.

Mobile health app developers increasingly are interested in supporting the daily self-care of people with chronic conditions. The purpose of this study was to review mobile applications (apps) to promote epilepsy self-management. It investigates the following: 1) the available mobile apps for epilepsy, 2) how these apps support patient education and self-management (SM), and 3) their usefulness in supporting management of epilepsy. We conducted the review in Fall 2017 and assessed apps on the Apple App Store that related to the terms "epilepsy" and "seizure". Inclusion criteria included apps (adult and pediatric) that, as follows, were: 1) developed for patients or the community; 2) made available in English, and 3) less than $5.00. Exclusion criteria included apps that were designed for dissemination of publications, focused on healthcare providers, or were available in other languages. The search resulted in 149 apps, of which 20 met the selection criteria. A team reviewed each app in terms of three sets of criteria: 1) epilepsy-specific descriptions and SM categories employed by the apps and 2) Mobile App Rating Scale (MARS) subdomain scores for reviewing engagement, functionality, esthetics, and information; and 3) behavioral change techniques. Most apps were for adults and free. Common SM domains for the apps were treatment, seizure tracking, response, and safety. A number of epilepsy apps existed, but many offered similar functionalities and incorporated few SM domains. The findings underline the need for mobile apps to cover broader domains of SM and behavioral change techniques and to be evaluated for outcomes.

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

OBJECTIVE: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. METHODS: Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. RESULTS: The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. SIGNIFICANCE: The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.