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YouTube User Traffic to Paired Epilepsy Education Videos in English and Spanish: Comparative Study

YouTube User Traffic to Paired Epilepsy Education Videos in English and Spanish: Comparative Study

Epilepsy, a common chronic condition, highlights the challenges faced by patients needing access to clinical specialists. Children and youth with epilepsy and their families need accurate and trustworthy information to manage their condition effectively [2]. Social media platforms, particularly You Tube, have become significant sources of information for many people.

Luna Kimahri Varela, Stephanie Horton, Ahmed Abdelmoity, Jean-Baptiste Le Pichon, Mark A Hoffman

JMIR Form Res 2025;9:e56720

Determinants of Dropping Out of Remote Patient-Reported Outcome–Based Follow-Up Among Patients With Epilepsy: Prospective Cohort Study

Determinants of Dropping Out of Remote Patient-Reported Outcome–Based Follow-Up Among Patients With Epilepsy: Prospective Cohort Study

Thus, this study aimed to investigate potential determinants for dropout in remote PRO-based follow-up among patients with epilepsy. We examined the association between dropout in PRO-based follow-up and determinants from the following three domains: health-related self-management, general and mental health status, and patient satisfaction.

Sofie Bech Vestergaard, Mette Roost, David Høyrup Christiansen, Liv Marit Valen Schougaard

JMIR Form Res 2025;9:e58258

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis

This allows for more proactive and effective intervention for patients, making it an effective tool for the evaluation and diagnosis of epilepsy [5]. EEG is currently the gold standard for diagnosing neonatal epilepsy [6]. However, the interpretation of EEG primarily relies on the clinician’s previous experience, which can impact the interpretation of some critical signals. Therefore, monitoring epilepsy, especially in real time, remains a challenging task [7].

Zhuan Zou, Bin Chen, Dongqiong Xiao, Fajuan Tang, Xihong Li

J Med Internet Res 2024;26:e55986

Collaborative Care to Improve Quality of Life for Anxiety and Depression in Posttraumatic Epilepsy (CoCarePTE): Protocol for a Randomized Hybrid Effectiveness-Implementation Trial

Collaborative Care to Improve Quality of Life for Anxiety and Depression in Posttraumatic Epilepsy (CoCarePTE): Protocol for a Randomized Hybrid Effectiveness-Implementation Trial

Anxiety and depression in epilepsy are highly prevalent, and they are stronger independent predictors of poor quality of life than seizure frequency [1]. They are particularly relevant to people with posttraumatic epilepsy (PTE), including veterans.

Heidi M Munger Clary, Beverly M Snively, Christian Cagle, Richard Kennerly, James N Kimball, Halley B Alexander, Gretchen A Brenes, Justin B Moore, Robin A Hurley

JMIR Res Protoc 2024;13:e59329

Data Visualization Preferences in Remote Measurement Technology for Individuals Living With Depression, Epilepsy, and Multiple Sclerosis: Qualitative Study

Data Visualization Preferences in Remote Measurement Technology for Individuals Living With Depression, Epilepsy, and Multiple Sclerosis: Qualitative Study

While user experience research has been conducted for pre-existing apps tailored toward the management of our 3 chosen relapsing conditions: multiple sclerosis (MS) [9], epilepsy [18], and depression [10,23], and has identified themes to consider in device selection and RMT design for individuals living with these conditions [19], none of these previous studies have focused specifically on data visualization.

Sara Simblett, Erin Dawe-Lane, Gina Gilpin, Daniel Morris, Katie White, Sinan Erturk, Julie Devonshire, Simon Lees, Spyridon Zormpas, Ashley Polhemus, Gergely Temesi, Nicholas Cummins, Matthew Hotopf, Til Wykes, RADAR-CNS Consortium

J Med Internet Res 2024;26:e43954

Semiology Extraction and Machine Learning–Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis

Semiology Extraction and Machine Learning–Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis

We designated “generalized epilepsy” as label A and “focal epilepsy” as label B. TP(A) represents true positives, FP(A) represents false positives, and FN(A) represents false negatives for label A, and similarly for label B.

Yilin Xia, Mengqiao He, Sijia Basang, Leihao Sha, Zijie Huang, Ling Jin, Yifei Duan, Yusha Tang, Hua Li, Wanlin Lai, Lei Chen

JMIR Med Inform 2024;12:e57727

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 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)

The disability-adjusted life-years rates in the United Kingdom, estimated at 92,400 per 100,000 population [5], highlight the extensive health loss attributed to epilepsy. Sudden unexpected death in epilepsy remains a critical concern, with an incidence rate of approximately 1 in 1000 people with epilepsy annually, emphasizing the need for ongoing research and innovative management strategies [6,7].

Emily E V Quilter, Samuel Downes, Mairi Therese Deighan, Liz Stuart, Rosie Charles, Phil Tittensor, Leandro Junges, Peter Kissack, Yasser Qureshi, Aravind Kumar Kamaraj, Amberly Brigden

JMIR Res Protoc 2024;13:e60129

The Effectiveness of Medical Adherence Mobile Health Solutions for Individuals With Epilepsy: Protocol for a Systematic Review

The Effectiveness of Medical Adherence Mobile Health Solutions for Individuals With Epilepsy: Protocol for a Systematic Review

Epilepsy is considered a chronic neurological condition that has to be consistently managed and treated to optimize patient outcomes [1]. With the advancement of digital health technologies, various mobile health (m Health) solutions have been developed to improve the adherence of patients with epilepsy to treatment [2].

Pantea Keikhosrokiani, Manria Polus, Sharon Guardado Medina, Minna Isomursu

JMIR Res Protoc 2024;13:e55123

Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review

Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review

Based on the most globally prevalent and costly neurological disorders [11], studies investigating the use of NLP in Alzheimer disease (exclusive of Alzheimer disease–related disorders), Parkinson disease, stroke and transient ischemic attack, epilepsy, multiple sclerosis (MS), and migraine were included.

Ilana Lefkovitz, Samantha Walsh, Leah J Blank, Nathalie Jetté, Benjamin R Kummer

JMIR Neurotech 2024;3:e51822

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

Regarding the remote monitoring of patients with epilepsy, there is a need to develop an efficient smartphone app that processes long-term EEG recordings for seizure detection. Therefore, the goal of this paper was to develop and evaluate the feasibility of a mobile app for the remote monitoring of patients with epilepsy.

Natarajan Sriraam, S Raghu, Erik D Gommer, Danny M W Hilkman, Yasin Temel, Shyam Vasudeva Rao, Alangar Satyaranjandas Hegde, Pieter L Kubben

JMIR Neurotech 2023;2:e50660