Self-Management Apps for People With Epilepsy: Systematic Analysis.

BACKGROUND: Patients with epilepsy (PWEs) are motivated to manage and cope with their disorder themselves (ie, self-management [SM] is encouraged). Mobile health (mHealth) apps have multiple features that have a huge potential to improve SM of individuals with chronic disorders such as epilepsy. OBJECTIVE: This study aimed to review all freely available apps related to the SM of PWEs and to determine the SM domains covered in these apps. METHODS: We performed a search of apps on Google Play and App Store using the keywords "epilepsy" or "seizures" from May to August 2018. Apps were included if they were free and in English language. We excluded apps with installation-related issues and not related to epilepsy self-management (eSM). RESULTS: A total of 22 eSM apps were identified in our search: 6 of these run only on iOS, 7 only on Android, and 9 run on both operating systems. Of the 11 domains of SM, seizure tracking and seizure response features were covered by most apps (n=22 and n=19, respectively), followed by treatment management (n=17) and medication adherence (n=15). Three apps (Epilepsy Journal, Epilepsy Tool Kit, and EpiDiary) were installed more than 10,000 times, with features focused specifically on a few domains (treatment management, medication adherence, health care communication, and seizure tracking). Two apps (Young Epilepsy and E-Epilepsy Inclusion) covered more than 6 SM domains but both had lower installation rates (5000+ and 100+, respectively). CONCLUSIONS: Both Android and iOS mHealth apps are available to improve SM in epilepsy, but the installation rate of most apps remains low. The SM features of these apps were different from one another, making it difficult to recommend a single app that completely fulfills the needs of PWEs. The common features of the apps evaluated included seizure tracking and seizure response. To improve the efficacy and availability of these apps, we propose the following: (1) involve the stakeholders, such as physicians, pharmacists, and PWEs, during the development of mHealth apps; (2) assess the efficacy and acceptance of the apps objectively by performing a usability analysis; and (3) promote the apps so that they benefit more PWEs.

A collaborative approach for research paper recommender system.

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.