Literature DB >> 31785481

Patients' experience of wearing multimodal sensor devices intended to detect epileptic seizures: A qualitative analysis.

Sara Katherine Simblett1, Andrea Biondi2, Elisa Bruno2, Dominic Ballard2, Amanda Stoneman3, Simon Lees4, Mark P Richardson5, Til Wykes6.   

Abstract

BACKGROUND: The health management of patients with epilepsy could be improved by wearing devices that reliably detect when epileptic seizures happen. For the devices to be widely adopted, they must be acceptable and easy to use for patients, and their views are very important. Previous studies have collected feedback from patients on hypothetical devices, but very few have examined experience of wearing actual devices.
PURPOSE: This study assessed the first-hand experiences of people with epilepsy using wearable devices, continuously over a period of time. The aim was to understand how acceptable and easy they were to use, and whether it is reasonable to expect that people will use them.
MATERIALS AND METHODS: Adults with a diagnosis of epilepsy admitted routinely to a hospital epilepsy monitoring unit were asked to wear one, or more, wearable biosensor devices, tested for seizure detection. The devices are designed to continuously monitor and record signals from the body (biosignals). Participants completed semistructured interviews about their experiences of wearing the device(s). A systematic thematic analysis extracted themes from the interviews, focusing on acceptability and usability. Feedback was organized into (1) participants' experiences of the devices, any support they required and reasons for stopping wearing them; (2) their thoughts about using this technology outside a hospital setting.
RESULTS: Twenty-one people with epilepsy wore one, or more, wearable devices for an average of 112.81 (SD = 71.83) hours. Participants found the devices convenient, and had no problem wearing them in hospital or sharing the data collected from them with the researchers and medical professionals. However, the presence of wires, bulky size, discomfort, and need for support, moderated experience. Participants' thoughts about wearing them in everyday life were strongly influenced by how visible and perceived accuracy. Willingness to use a smartphone app to complete questionnaires depended on the frequency, number of questions, and support.
CONCLUSIONS: Overall, this work provides evidence about the feasibility and acceptability of using wearable devices to monitor seizure activity in people with epilepsy. Key barriers and facilitators to use while in hospital and hypothetical use in everyday life were identified and will be helpful for guiding future implementation.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Acceptability; Epilepsy; Feasibility; Qualitative analysis; Seizure detection; Wearables

Mesh:

Year:  2019        PMID: 31785481     DOI: 10.1016/j.yebeh.2019.106717

Source DB:  PubMed          Journal:  Epilepsy Behav        ISSN: 1525-5050            Impact factor:   2.937


  10 in total

1.  Key Drivers and Facilitators of the Choice to Use mHealth Technology in People With Neurological Conditions: Observational Study.

Authors:  Sara Simblett; Mark Pennington; Matthew Quaife; Evangelia Theochari; Patrick Burke; Giampaolo Brichetto; Julie Devonshire; Simon Lees; Ann Little; Angie Pullen; Amanda Stoneman; Sarah Thorpe; Janice Weyer; Ashley Polhemus; Jan Novak; Erin Dawe-Lane; Daniel Morris; Magano Mutepua; Clarissa Odoi; Emma Wilson; Til Wykes
Journal:  JMIR Form Res       Date:  2022-05-23

2.  Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients.

Authors:  Sebastian Böttcher; Elisa Bruno; Nino Epitashvili; Matthias Dümpelmann; Nicolas Zabler; Martin Glasstetter; Valentina Ticcinelli; Sarah Thorpe; Simon Lees; Kristof Van Laerhoven; Mark P Richardson; Andreas Schulze-Bonhage
Journal:  Sensors (Basel)       Date:  2022-04-26       Impact factor: 3.847

3.  Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

Authors:  Andrea Biondi; Petroula Laiou; Elisa Bruno; Pedro F Viana; Martijn Schreuder; William Hart; Ewan Nurse; Deb K Pal; Mark P Richardson
Journal:  JMIR Res Protoc       Date:  2021-03-19

4.  A Patient Perspective on Seizure Detection and Forecasting.

Authors:  Aria Moss; Evan Moss; Robert Moss; Lisa Moss; Sharon Chiang; Peter Crino
Journal:  Front Neurol       Date:  2022-02-11       Impact factor: 4.003

5.  Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears.

Authors:  Lauren Swinnen; Christos Chatzichristos; Katrien Jansen; Lieven Lagae; Chantal Depondt; Laura Seynaeve; Evelien Vancaester; Annelies Van Dycke; Jaiver Macea; Kaat Vandecasteele; Victoria Broux; Maarten De Vos; Wim Van Paesschen
Journal:  Epilepsia       Date:  2021-09-07       Impact factor: 6.740

6.  Parental preferences for seizure detection devices: A discrete choice experiment.

Authors:  Anouk van Westrhenen; Ben F M Wijnen; Roland D Thijs
Journal:  Epilepsia       Date:  2022-03-04       Impact factor: 6.740

Review 7.  Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.

Authors:  Benjamin H Brinkmann; Philippa J Karoly; Ewan S Nurse; Sonya B Dumanis; Mona Nasseri; Pedro F Viana; Andreas Schulze-Bonhage; Dean R Freestone; Greg Worrell; Mark P Richardson; Mark J Cook
Journal:  Front Neurol       Date:  2021-07-13       Impact factor: 4.003

8.  Automated video-based detection of nocturnal motor seizures in children.

Authors:  Anouk van Westrhenen; George Petkov; Stiliyan N Kalitzin; Richard H C Lazeron; Roland D Thijs
Journal:  Epilepsia       Date:  2020-05-07       Impact factor: 5.864

Review 9.  Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review.

Authors:  Andrea Biondi; Viviana Santoro; Pedro F Viana; Petroula Laiou; Deb K Pal; Elisa Bruno; Mark P Richardson
Journal:  Epilepsia       Date:  2022-03-27       Impact factor: 6.740

10.  Seizure detection using wearable sensors and machine learning: Setting a benchmark.

Authors:  Jianbin Tang; Rima El Atrache; Shuang Yu; Umar Asif; Michele Jackson; Subhrajit Roy; Mahtab Mirmomeni; Sarah Cantley; Theodore Sheehan; Sarah Schubach; Claire Ufongene; Solveig Vieluf; Christian Meisel; Stefan Harrer; Tobias Loddenkemper
Journal:  Epilepsia       Date:  2021-07-15       Impact factor: 5.864

  10 in total

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