Patients self-mastery of wearable devices for seizure detection: A direct user-experience
Open accessAbstract
Purpose wearable devices aimed at detecting seizures are rapidly emerging. Continuous collection and optimal data quality are paramount to guarantee the acquisition of clinically meaningful data. It is still unknown how successfully patients can self-manage new technologies and which factors have an impact on this. We assessed the performance of patients managing a wrist-worn device.Method patients wearing a wrist-worn device received a single training session to perform 5 tasks: (1) fitting the device correctly; (2) switching the device on and off; (3) charging the device on a daily basis; (4) pairing the device with a phone or tablet; (5) seeking assistance. Participants were then reviewed every 24 h and, at the end of the study, a Wearable technology Self-management Score (WSS) was calculated according to their performance in the different tasks (0-12). The association between WSS, seizure capture, demographics and clinical characteristics was analysed.Results Thirty patients were included. The mean WSS score was 9.4 ± 2.1 points. The task more often performed inaccurately was pairing the device with a phone or tablet, followed by performing charging procedures. A strong association was found between WSS and seizure capture (p = 0.019), with higher scores strongly associated with more seizures captured. A WSS of ≥9 was the minimum value of WSS that allowed the device to record all the seizures. Number of AEDs and illness-perception related factors (perceived disease timeline and personal control) were significantly associated with WSS.Conclusions Overall, participants demonstrated good performances in self-managing a wrist-worn device. Digital inequalities may extend to variations in how different individuals feel about their own disease and, consequently, manage the technology. These aspects should be addressed when technological solutions are delivered to users.
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Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.
“Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [38]
Study participants had the best compliance concerning wearing the device and correctly fitting it, while compliance was worst when it came to pairing the device to a Bluetooth companion device, with frequent connection issues.
“Study participants had the best compliance concerning wearing the device and correctly fitting it, while compliance was worst when it came to pairing the device to a Bluetooth companion device, with frequent connection issues.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [38]
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Retracted
- Authors
- Bruno, E., Biondi, A., Thorpe, S., & Richardson, M. P.
- Affiliations
- King's College London (GB)
- Corresponding author
- Bruno, E.
- Venue
- Seizure
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 81
- Pages
- 236-240
- ISSN
- 1059-1311 (ISSN-L)
- OpenAlex
- W3080454700
- Cited by
- 25
- References
- 23
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 1 · 2022: 7 · 2021: 5 · 2020: 1
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Seizure (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- Innovative Medicines Initiative 2 Joint Undertaking (115902)
- European Union’s Horizon 2020 research and innovation programme
- EFPIA
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- wearable devices, seizure detection, patient self-management, wrist-worn devices, user experience, device pairing, illness perception, perceived personal control, digital divide, epilepsy, automated seizure detection
- MeSH terms
- Wearable Electronic Devices, Humans, Seizures, Telephone
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