Background Remote measurement technology (RMT) involves the use of wearable devices and smartphone apps to measure health outcomes in everyday life. RMT with feedback in the form of data visual representations can facilitate self-management of chronic health conditions, promote health care engagement, and present opportunities for intervention. Studies to date focus broadly on multiple dimensions of service users’ design preferences and RMT user experiences (eg, health variables of perceived importance and perceived quality of medical advice provided) as opposed to data visualization preferences. Objective This study aims to explore data visualization preferences and priorities in RMT, with individuals living with depression, those with epilepsy, and those with multiple sclerosis (MS). Methods A triangulated qualitative study comparing and thematically synthesizing focus group discussions with user reviews of existing self-management apps and a systematic review of RMT data visualization preferences. A total of 45 people participated in 6 focus groups across the 3 health conditions (depression, n=17; epilepsy, n=11; and MS, n=17). Results Thematic analysis validated a major theme around design preferences and recommendations and identified a further four minor themes: (1) data reporting, (2) impact of visualization, (3) moderators of visualization preferences, and (4) system-related factors and features. Conclusions When used effectively, data visualizations are valuable, engaging components of RMT. Easy to use and intuitive data visualization design was lauded by individuals with neurological and psychiatric conditions. Apps design needs to consider the unique requirements of service users. Overall, this study offers RMT developers a comprehensive outline of the data visualization preferences of individuals living with depression, epilepsy, and MS.

Background:Multiple sclerosis (MS), epilepsy, and depression are chronic central nervous system conditions in which remote measurement technology (RMT) may offer benefits compared with usual assessment. We previously worked with clinicians, patients, and researchers to develop 13 use cases for RMT: 5 in epilepsy (seizure alert, seizure counting, risk scoring, triage support, and trend analysis), 3 in MS (detecting silent progression, detecting depression in MS, and donating data to a biobank), and 5 in depression (detecting trends, reviewing treatment, self-management, comorbid monitoring, and carer alert). Objective:In this study, we aimed to evaluate the use cases and related implementation issues with an expert panel of clinicians external to our project consortium. Methods:We used a Delphi exercise to validate the use cases and suggest a prioritization among them and to ascertain the importance of a variety of implementation issues related to RMT. The expert panel included clinicians from across Europe who were external to the project consortium. The study had 2 survey rounds (n=23 and n=17) and a follow-up interview round (n=9). Data were analyzed for consensus between participants and for stability between survey rounds. The interviews explored the reasons for answers given in the survey. Results:The findings showed high stability between rounds on questions related to specific use cases but lower stability on questions relating to wider issues around the implementation of RMT. Overall, questions on wider issues also had less consensus. All 5 use cases for epilepsy (seizure alert, seizure counting, risk scoring, triage support, and trend analysis) were considered beneficial, with consensus among participants above the a priori threshold for most questions, although use case 3 (risk scoring) was considered less likely to facilitate or catalyze care. There was very little consensus on the benefits of the use cases in MS, although this may have resulted from a higher dropout rate of MS clinicians (50%). Participants agreed that there would be benefits for all 5 of the depression use cases, although fewer questions on use case 4 (triage support) reached consensus agreement than for depression use cases 1 (detecting trends), 2 (reviewing treatment), 3 (self-management), and 5 (carer alert). The qualitative analysis revealed further insights into each use case and generated 8 themes on practical issues related to implementation. Conclusions:Overall, these findings inform the prioritization of use cases for RMT that could be developed in future work, which may include clinical trials, cost-effectiveness studies, and the commercial development of RMT products and services. Priorities for further development include the use of RMT to provide more accurate records of symptoms and treatment response than is currently possible and to provide data that could help inform patient triage and generate timely alerts for patients and carers.

Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.

Background There is increasing interest in the potential uses of mobile health (mHealth) technologies, such as wearable biosensors, as supplements for the care of people with neurological conditions. However, adherence is low, especially over long periods. If people are to benefit from these resources, we need a better long-term understanding of what influences patient engagement. Previous research suggests that engagement is moderated by several barriers and facilitators, but their relative importance is unknown. Objective To determine preferences and the relative importance of user-generated factors influencing engagement with mHealth technologies for 2 common neurological conditions with a relapsing-remitting course: multiple sclerosis (MS) and epilepsy. Methods In a discrete choice experiment, people with a diagnosis of MS (n=141) or epilepsy (n=175) were asked to select their preferred technology from a series of 8 vignettes with 4 characteristics: privacy, clinical support, established benefit, and device accuracy; each of these characteristics was greater or lower in each vignette. These characteristics had previously been emphasized by people with MS and or epilepsy as influencing engagement with technology. Mixed multinomial logistic regression models were used to establish which characteristics were most likely to affect engagement. Subgroup analyses explored the effects of demographic factors (such as age, gender, and education), acceptance of and familiarity with mobile technology, neurological diagnosis (MS or epilepsy), and symptoms that could influence motivation (such as depression). Results Analysis of the responses to the discrete choice experiment validated previous qualitative findings that a higher level of privacy, greater clinical support, increased perceived benefit, and better device accuracy are important to people with a neurological condition. Accuracy was perceived as the most important factor, followed by privacy. Clinical support was the least valued of the attributes. People were prepared to trade a modest amount of accuracy to achieve an improvement in privacy, but less likely to make this compromise for other factors. The type of neurological condition (epilepsy or MS) did not influence these preferences, nor did the age, gender, or mental health status of the participants. Those who were less accepting of technology were the most concerned about privacy and those with a lower level of education were prepared to trade accuracy for more clinical support. Conclusions For people with neurological conditions such as epilepsy and MS, accuracy (ie, the ability to detect symptoms) is of the greatest interest. However, there are individual differences, and people who are less accepting of technology may need far greater reassurance about data privacy. People with lower levels of education value greater clinician involvement. These patient preferences should be considered when designing mHealth technologies.

BACKGROUND:Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures.OBJECTIVE:Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime.METHODS:An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation.RESULTS:In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data.CONCLUSIONS:We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.

Objective Movement-based wearable sensors are used for detection of convulsive seizures. The identification of the absence of motion following a seizure, known as post-ictal immobility (PI), may represent a potential additional application of wearables. PI has been associated with potentially life-threatening complications and with sudden unexpected death in epilepsy (SUDEP). We aimed to assess whether wearable accelerometers (ACCs) could be used as a digital marker of PI. Method Devices with embedded ACCs were worn by patients admitted to an epilepsy monitoring unit. Participants presenting with convulsive seizures were included in the study. PI presence and duration were assessed by experts reviewing video recordings. An algorithm for the automatic detection of post-ictal ACC silence and its duration was developed and the linear pairwise relationship between the automatically detected duration of post-ictal ACC silence and the duration of the expert-labeled PI was analyzed. Results Twenty-two convulsive seizures were recorded from 18 study participants. Twenty were followed by PI and two by agitation. The automated estimation of post-ictal ACC silence identified all the 20 expert-labeled PI. The regression showed that the duration of the post-ictal ACC silence was correlated with the duration of PI (Pearson r = .92; P < .001), with the age of study participants (Pearson r = .78; P < .001), and with the duration of post-ictal generalized electroencephalography suppression (PGES; Pearson r = .4; P = .033). Significance We highlight a novel application of wearables as a way to record post-ictal manifestations associated with an increased risk of SUDEP. The occurrence of a fatal seizure is unpredictable and the continuous, non-invasive, long-term identification of risk factors associated with each individual seizure may assume a great clinical importance.

PURPOSE:Wearable devices are progressively becoming an available tool for continuous seizure detection. Motivation to use wearables is not only driven by the accuracy and reliability of the performance but also by the form factor, comfort, and stability on the body. We collected direct feedback and device placement-related issues experienced by a cohort of people with epilepsy (PWE) to investigate to what extent available devices are nonintrusive, comfortable, and stable on the body.METHODS:Four models of wearable devices (E4 wrist band, Everion upper arm band, IMEC upper arm band, and Epilog scalp patch electrodes) were worn by PWE who were admitted to two epilepsy monitoring units (EMUs) in London and Freiburg. Participants were periodically reviewed, and accidental displacements of the devices were annotated. Participants' experience was assessed using the Technology Acceptance Model Fast Form (TAM-FF) plus two additional questions on comfort. A thematic analysis was also performed on the free text of the questionnaire.RESULTS:One hundred and fifteen participants were enrolled. The devices had a good stability on the body including during seizures. Overall, all the devices were considered comfortable to be worn, including during sleep. However, devices containing wires and patches demonstrated a lesser degree of stability on the body and were judged less positively. Participants age was correlated with TAM-FF mean scores, and older participants judged the devices less favorably compared with younger participants.DISCUSSION:Removable but securely fitted, wireless, and comfortable designs were considered more appropriate for a continuous monitoring aimed at seizure detection. Some caution may be required when patch electrodes and electrodes glued to the skin or to the scalp are used, as those evaluated in the present study demonstrated a lower level of acceptability and a lower degree of stability to the body, especially at night. These factors could limit a continuous monitoring decreasing the device performance for nocturnal, unsupervised seizures which are at higher risk of lethality.

Background In recent years, a growing body of literature has highlighted the role of wearable and mobile remote measurement technology (RMT) applied to seizure detection in hospital settings, whereas more limited evidence has been produced in the community setting. In clinical practice, seizure assessment typically relies on self-report, which is known to be highly unreliable. Moreover, most people with epilepsy self-identify factors that lead to increased seizure likelihood, including mood, behavior, sleep pattern, and cognitive alterations, all of which are amenable to measurement via multiparametric RMT. Objective The primary aim of this multicenter prospective cohort study is to assess the usability, feasibility, and acceptability of RMT in the community setting. In addition, this study aims to determine whether multiparametric RMT collected in populations with epilepsy can prospectively estimate variations in seizure occurrence and other outcomes, including seizure frequency, quality of life, and comorbidities. Methods People with a diagnosis of pharmacoresistant epilepsy will be recruited in London, United Kingdom, and Freiburg, Germany. Participants will be asked to wear a wrist-worn device and download ad hoc apps developed on their smartphones. The apps will be used to collect data related to sleep, physical activity, stress, mood, social interaction, speech patterns, and cognitive function, both passively from existing smartphone sensors (passive remote measurement technology [pRMT]) and actively via questionnaires, tasks, and assessments (active remote measurement technology [aRMT]). Data will be collected continuously for 6 months and streamed to the Remote Assessment of Disease and Relapse-base (RADAR-base) server. Results The RADAR Central Nervous System project received funding in 2015 from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. Ethical approval was obtained in London from the Bromley Research Ethics Committee (research ethics committee reference: 19/LO/1884) in January 2020. The first participant was enrolled on September 30, 2020. Data will be collected until September 30, 2021. The results are expected to be published at the beginning of 2022. Conclusions RADAR Epilepsy aims at developing a framework of continuous data collection intended to identify ictal and preictal states through the use of aRMT and pRMT in the real-life environment. The study was specifically designed to evaluate the clinical usefulness of the data collected via new technologies and compliance, technology acceptability, and usability for patients. These are key aspects to successful adoption and implementation of RMT as a new way to measure and manage long-term disorders. International Registered Report Identifier (IRRID) PRR1-10.2196/21840

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. (C) 2019 Elsevier Inc. All rights reserved.