Wearables in epilepsy and Parkinson's disease—A focus group study

Ozanne, Anneli; Buvarp, Dongni; Graneheim, Ulla Hällgren; Malmgren, Kristina; Bergquist, Filip; Murphy, Margit Alt

doi:10.1111/ane.12798

Cited by 78 publications

(96 citation statements)

References 20 publications

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“…Adaptation Challenge 2 -Compliance: Participants in prior studies reported difficulty in using and charging the device regularly. It is difficult for a patient to charge the device, since it may involve taking the device off and wearing it on again [13]. Larger and bulky batteries help in increasing the lifetime of the device, but they also make the device uncomfortable to wear.…”

Section: Wearable Health: Challenges and Solutionsmentioning

confidence: 99%

“…However, clinical visits, which are typically weeks apart, capture only a snapshot of the symptoms [6]. This introduces complications in therapy decisions, since symptoms vary over time, and patients' recall accuracy is not reliable [13]. Moreover, access to highly trained specialists can be challenging in many parts of the world.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it enables evaluating the prescribed therapy on an individual basis [8], [10], [11]. Similarly, wearable sensors and smartphones have shown promising results in the diagnosis of ET [3] and detecting seizures in epilepsy [13]. Studies have This article has been accepted for publication in a future of IEEE Design & Test of Computers c 20XX IEEE.…”

Section: Introductionmentioning

confidence: 99%

“…also shown that both patients and health professionals (HPs) value the interactive information available from wearable monitoring. Hence, wearable sensors coupled with telepathic diagnostics can greatly improve health care [8], [13].…”

Section: Introductionmentioning

confidence: 99%

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OpenHealth: Open-Source Platform for Wearable Health Monitoring

2019

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Movement disorders are becoming one of the leading causes of functional disability due to aging populations and extended life expectancy. Wearable health monitoring is emerging as an effective way to augment clinical care for movement disorders. However, wearable devices face a number of adaptation and technical challenges that hinder their widespread adoption. To address these challenges, we introduce OpenHealth, an open source platform for wearable health monitoring. OpenHealth aims to design a standard set of hardware/software and wearable devices that can enable autonomous collection of clinically relevant data. The OpenHealth platform includes a wearable device, standard software interfaces and reference implementations of human activity and gesture recognition applications.

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Section: Wearable Health: Challenges and Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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OpenHealth: Open-Source Platform for Wearable Health Monitoring

2019

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“…With an attractive integrated design, they may fulfil the need of patients with epilepsy to have a better outpatient disease management, while avoiding social stigmatization [6]. Several commercially available devices with proprietary algorithms have been proposed to be effective for TCSs detection, including a wrist watch with built-in accelerometers [7], accelerometry sensors combined with audio recordings [8], or other modalities, such as electrodermal activity [9,10] and heart rate [11].…”

Section: Introductionmentioning

confidence: 99%

Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study

Buvarp

Ohlsson

Krýsl

et al. 2019

Seizure

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A B S T R A C TPurpose: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets. Methods: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set. Results: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h). Conclusions: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management. Recent advances in accelerometry technology have allowed the development of ambulatory monitoring systems able to detect changes in movement frequency and amplitude associated with motor seizures, and to differentiate these patterns from daily voluntary movements. Accelerometry-based sensors are usually small, portable and easy to use.

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User involvement in the design and development of medical devices in epilepsy: A systematic review

Ferreira,

Peixoto,

Lopes

et al. 2024

Epilepsia Open

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ObjectiveThis systematic review aims to describe the involvement of persons with epilepsy (PWE), healthcare professionals (HP) and caregivers (CG) in the design and development of medical devices is epilepsy.MethodsA systematic review was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Eligibility criteria included peer‐reviewed research focusing on medical devices for epilepsy management, involving users (PWE, CG, and HP) during the MDD process. Searches were performed on PubMed, Web of Science, and Scopus, and a total of 55 relevant articles were identified and reviewed.ResultsFrom 1999 to 2023, there was a gradual increase in the number of publications related to user involvement in epilepsy medical device development (MDD), highlighting the growing interest in this field. The medical devices involved in these studies encompassed a range of seizure detection tools, healthcare information systems, vagus nerve stimulation (VNS) and electroencephalogram (EEG) technologies reflecting the emphasis on seizure detection, prediction, and prevention. PWE and CG were the primary users involved, underscoring the importance of their perspectives. Surveys, usability testing, interviews, and focus groups were the methods used for capturing user perspectives. User involvement occurs in four out of the five stages of MDD, with production being the exception.SignificanceUser involvement in the MDD process for epilepsy management is an emerging area of interest holding a significant promise for improving device quality and patient outcomes. This review highlights the need for broader and more effective user involvement, as it currently lags in the development of commercially available medical devices for epilepsy management. Future research should explore the benefits and barriers of user involvement to enhance medical device technologies for epilepsy.Plain Language SummaryThis review covers studies that have involved users in the development process of medical devices for epilepsy. The studies reported here have focused on getting input from people with epilepsy, their caregivers, and healthcare providers. These devices include tools for detecting seizures, stimulating nerves, and tracking brain activity. Most user feedback was gathered through surveys, usability tests, interviews, and focus groups. Users were involved in nearly every stage of device development except production. The review highlights that involving users can improve device quality and patient outcomes, but more effective involvement is needed in commercial device development. Future research should focus on the benefits and challenges of user involvement.

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Wearables in epilepsy and Parkinson's disease—A focus group study

Cited by 78 publications

References 20 publications

OpenHealth: Open-Source Platform for Wearable Health Monitoring

OpenHealth: Open-Source Platform for Wearable Health Monitoring

Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study

User involvement in the design and development of medical devices in epilepsy: A systematic review

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