KIJANI App to Promote Physical Activity in Children and Adolescents: Protocol for a Mixed Method Evaluation.

BACKGROUND: The prevalence of physical inactivity among children and adolescents is alarmingly high despite the well-documented and comprehensive benefits of regular physical activity (PA). Therefore, PA promotion should start early in childhood and adolescence. Although reducing recreational screen time in children and adolescents is an urgent concern, digital approaches have the potential to make activity promotion attractive and age appropriate for the target group. KIJANI is a mobile app approach to promote PA in children and adolescents via gamification and augmented reality. OBJECTIVE: This study protocol aims to describe the KIJANI intervention in detail, as well as the evaluation approach. METHODS: KIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of a collected step count. With these coins, in turn, blocks can be bought, which can be used to create virtual buildings and integrate them into the player's real-world environment via augmented reality. PA of users is detected via accelerometers integrated into the smartphones. KIJANI can be played at predefined play locations that were comprehensively identified as safe, child-friendly, and attractive for PA by the target group in a partner project. The evaluation process will be divided into 2 different stages. The phase-I evaluation will be a mixed methods approach with one-on-one semistructured interviews and questionnaires to evaluate the user experience and receive feedback from the target group. After the implementation of results and feedback from the target group, the phase-II evaluation will proceed in the form of a 2-arm randomized controlled trial, in which the effectiveness of KIJANI will be assessed via objectively measured PA as well as questionnaires. RESULTS: The study received ethical approval from the ethical board of the Technical University of Munich. Participants for the phase-I evaluation are currently being recruited. CONCLUSIONS: The study will help to determine the efficacy, applicability, and user experience of a gamified activity promotion application in children and adolescents. Overall, digital health approaches provide easy and wide reachability at low cost and are age appropriate and attractive for the target group of adolescents. Strategies have to be developed to apply digital health approaches in the best possible way for activity promotion. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55156.

KIJANI: Designing a Physical Activity Promoting Collaborative Augmented Reality Game.

BACKGROUND: There is an increased need for physical activity among children and adolescents. KIJANI, a mobile augmented reality game, is designed to increase physical activity through gamified exercises. OBJECTIVES: The primary aim of this study is to get feedback on the design and implementation of potentially physical activity-increasing features in KIJANI. METHODS: A mixed-method study (n=13) evaluates newly implemented game design features quantitatively through measuring physical activity and qualitatively through participant feedback. RESULTS: Preliminary results are limited and need further studies. Participants' feedback shows a positive trend and highlights the game's potential effectiveness. CONCLUSION: KIJANI shows potential for increasing physical activity among children and adolescents through gamified exercise. Future work will refine the game based on user feedback and findings presented in related work. The game's long-term impact is to be explored.

The German version of the mHealth App Usability Questionnaire (GER-MAUQ): Translation and validation study in patients with cardiovascular disease.

Objective: In Germany, only a few standardized evaluation tools for assessing the usability of mobile Health apps exist so far. This study aimed to translate and validate the English patient version for standalone apps of the mHealth App Usability Questionnaire (MAUQ) into a German version. Methods: Following scientific guidelines for translation and cross-cultural adaptation, the patient version for standalone apps was forward and back-translated from English into German by an expert panel. In total, 53 participants who were recruited as part of the beta testing process of the recently developed mHealth app HerzFit, answered the questions of the German version of the MAUQ (GER-MAUQ) and the System Usability Scale. Subsequently, a descriptive as well as a psychometric analysis was performed to test validity and reliability. Results: After conducting three cognitive interviews, five items were modified. The values for Cronbach alpha for the entire questionnaire and the three subscales (0.966, 0.814, 0.910, and 0.909) indicate strong internal consistency. The correlation analysis revealed that the scores of the GER-MAUQ, the subscales and the SUS were strongly correlated with each other. The correlation coefficient of the SUS and the GER-MAUQ overall score was r = 0.854, P < 0.001 and the coefficients of the subscales and the SUS were r = 0.642, P < 0.001; r = 0.866, P < 0.001 and r = 0.643, P < 0.001. Conclusions: We have developed a novel German version of the MAUQ and demonstrated it as a reliable and valid measurement tool for assessing the usability of standalone mHealth apps from the patients' perspective. The GER-MAUQ allows a new form of standardized assessment of usability of mHealth apps for patients with cardiovascular disease in Germany. Further research with a larger sample and other samples is recommended.

Guideline-Based Cardiovascular Risk Assessment Delivered by an mHealth App: Development Study.

BACKGROUND: Identifying high-risk individuals is crucial for preventing cardiovascular diseases (CVDs). Currently, risk assessment is mostly performed by physicians. Mobile health apps could help decouple the determination of risk from medical resources by allowing unrestricted self-assessment. The respective test results need to be interpretable for laypersons. OBJECTIVE: Together with a patient organization, we aimed to design a digital risk calculator that allows people to individually assess and optimize their CVD risk. The risk calculator was integrated into the mobile health app HerzFit, which provides the respective background information. METHODS: To cover a broad spectrum of individuals for both primary and secondary prevention, we integrated the respective scores (Framingham 10-year CVD, Systematic Coronary Risk Evaluation 2, Systematic Coronary Risk Evaluation 2 in Older Persons, and Secondary Manifestations Of Arterial Disease) into a single risk calculator that was recalibrated for the German population. In primary prevention, an individual's heart age is estimated, which gives the user an easy-to-understand metric for assessing cardiac health. For secondary prevention, the risk of recurrence was assessed. In addition, a comparison of expected to mean and optimal risk levels was determined. The risk calculator is available free of charge. Data safety is ensured by processing the data locally on the users' smartphones. RESULTS: Offering a risk calculator to the general population requires the use of multiple instruments, as each provides only a limited spectrum in terms of age and risk distribution. The integration of 4 internationally recommended scores allows risk calculation in individuals aged 30 to 90 years with and without CVD. Such integration requires recalibration and harmonization to provide consistent and plausible estimates. In the first 14 months after the launch, the HerzFit calculator was downloaded more than 96,000 times, indicating great demand. Public information campaigns proved effective in publicizing the risk calculator and contributed significantly to download numbers. CONCLUSIONS: The HerzFit calculator provides CVD risk assessment for the general population. The public demonstrated great demand for such a risk calculator as it was downloaded up to 10,000 times per month, depending on campaigns creating awareness for the instrument.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary. This allows for secure and controlled data exchange, while offering flexible datasharing options, considering trustworthiness, risk tolerance, and healthcare interoperability. Empirical studies and participatory research will be conducted to develop a trustworthy and effective data trust model.

Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles.

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world's population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple's ARKit technology to accurately measure the distance from the user's eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

Virtual reality in the diagnostic and therapy for mental disorders: A systematic review.

BACKGROUND: Virtual reality (VR) technologies are playing an increasingly important role in the diagnostics and treatment of mental disorders. OBJECTIVE: To systematically review the current evidence regarding the use of VR in the diagnostics and treatment of mental disorders. DATA SOURCE: Systematic literature searches via PubMed (last literature update: 9th of May 2022) were conducted for the following areas of psychopathology: Specific phobias, panic disorder and agoraphobia, social anxiety disorder, generalized anxiety disorder, posttraumatic stress disorder (PTSD), obsessive-compulsive disorder, eating disorders, dementia disorders, attention-deficit/hyperactivity disorder, depression, autism spectrum disorder, schizophrenia spectrum disorders, and addiction disorders. ELIGIBILITY CRITERIA: To be eligible, studies had to be published in English, to be peer-reviewed, to report original research data, to be VR-related, and to deal with one of the above-mentioned areas of psychopathology. STUDY EVALUATION: For each study included, various study characteristics (including interventions and conditions, comparators, major outcomes and study designs) were retrieved and a risk of bias score was calculated based on predefined study quality criteria. RESULTS: Across all areas of psychopathology, k = 9315 studies were inspected, of which k = 721 studies met the eligibility criteria. From these studies, 43.97% were considered assessment-related, 55.48% therapy-related, and 0.55% were mixed. The highest research activity was found for VR exposure therapy in anxiety disorders, PTSD and addiction disorders, where the most convincing evidence was found, as well as for cognitive trainings in dementia and social skill trainings in autism spectrum disorder. CONCLUSION: While VR exposure therapy will likely find its way successively into regular patient care, there are also many other promising approaches, but most are not yet mature enough for clinical application. REVIEW REGISTRATION: PROSPERO register CRD42020188436. FUNDING: The review was funded by budgets from the University of Bonn. No third party funding was involved.

mBalance: Detect Postural Imbalance with Mobile Devices.

BACKGROUND: Postural imbalance can be adopted for the early detection of age-related diseases or monitoring the course of the disease treatment; especially in monitoring, frequent balance measurement is crucial. This is mainly done through regular in-person examinations by a physician currently. Feedback in between examinations is often missing. OBJECTIVES: This paper proposes mBalance, a mobile application that uses the Romberg test to detect postural imbalance. mBalance provides a camera-based, low-cost approach to measure imbalance frequently at home using mobile devices. METHODS: Imbalance detection accuracy and usability was evaluated in two separate studies with 31 and 30 participants, respectively. RESULTS: mBalance correctly detected imbalance with a sensitivity of 80% and a specificity of 87%. The study found good usability with no significant problems. CONCLUSION: Overall, this study solves the problem of postural imbalance detection by digitizing a validated balance test into an easy-to-use mobile application.

Interrater reliability in the assessment of physiotherapy students.

BACKGROUND: Reliable and objective assessment of psychomotor skills in physiotherapy students' education is essential for direct feedback and skill improvement. The aim of this study is to determine the interrater reliability in the assessment process of physiotherapy students and to analyse the assessment behaviour of the examiners. METHODS: Physiotherapy teachers from two different schools assessed students from two different schools performing proprioceptive neuromuscular facilitation (PNF) patterns. An evaluation sheet with a 6-point rating scale and 20 evaluation criteria including an overall rating was used for assessment. The interrater reliability was determined calculating an intraclass-correlation coefficient (ICC) and Krippendorff's alpha. The assessment behaviour of the examiners was further analysed calculating the location parameters and showing the item response distribution over item in form of a Likert plot. RESULTS: The ICC estimates were mostly below 0.4, indicating poor interrater reliability. This was confirmed by Krippendorff's alpha. The examiners showed a certain central tendency and intergroup bias. DISCUSSION AND CONCLUSION: The interrater reliability in this assessment format was rather low. No difference between the two physiotherapy schools concerning the interrater reliability could be identified. Despite certain limitations of this study, there is a definite need for improvement of the assessment process in physiotherapy education to provide the students with reliable and objective feedback and ensure a certain level of professional competence in the students. TRIAL REGISTRATION: The study was approved by the ethics committee of the Medical Faculty RWTH Aachen University (EK 340/16).

Tracking of Mental Workload with a Mobile EEG Sensor.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

Mobile Motion Tracking for Disease Prevention and Rehabilitation Using Apple ARKit.

BACKGROUND: Physical activity helps improve the overall quality of life. The correct execution of physical activity is crucial both in sports as well as disease prevention and rehabilitation. Little to no automated commodity solutions for automated analysis and feedback exist. OBJECTIVES: Validation of the Apple ARKit framework as a solution for automatic body tracking in daily physical exercises using the smartphones' built-in camera. METHODS: We deliver insights into ARKit's body tracking accuracy through a lab experiment against the VICON system as Gold Standard. We provide further insights through case studies using apps built on ARKit. RESULTS: ARKit exposes significant limitations in tracking the full range of motion in joints but accurately tracks the movement itself. Case studies show that applying it to measure the quantity of execution of exercises is possible. CONCLUSION: ARKit is a light-weight commodity solution for quantitative assessment of physical activity. Its limitations and possibilities in qualitative assessment need to be investigated further.

Developing an App for Cardiovascular Prevention and Scientific Data Collection.

BACKGROUND: Mobile apps may encourage a lifestyle that avoids unhealthy behaviors, such as smoking or poor nutrition, which promotes cardiovascular diseases (CVD). Yet, little data is available on the utilization, perception, and long-term effects of such apps to prevent CVD. OBJECTIVES: To develop a mobile app concept to reduce the individual CVD risk and collect information addressing research questions on CVD prevention while preserving data privacy and security. METHODS: To validate the concept, a prototype will be built, and usability studies will be performed. RESULTS: We expect to determine whether it is possible to reach a broad user base and to collect scientific information while protecting user data sufficiently. CONCLUSION: To address CVD prevention, we propose a mobile coaching app. We expect high acceptance rates in validation studies.

Introducing a Navigation Algorithm for Reducing the Spread of Diseases in Public Transport Networks.

Reducing passenger flow through highly frequented bottlenecks in public transportation networks is a well-known urban planning problem. This issue has become even more relevant since the outbreak of the SARS-CoV-2 pandemic and the necessity for minimum distances between passengers. We propose an approach that allows to dynamically navigate passengers around dangerously crowded stations to better distribute the passenger load across an entire urban public transport network. This is achieved through the introduction of new constraints into routing requests, that enable the avoidance of specific nodes in a network. These requests consider walks, bikes, metros, subways, trams and buses as possible modes of transportation. An implementation of the approach is provided in cooperation with the Munich Travel Corporation (MVG) for the city of Munich, to simulate the effects on a real city's urban traffic flow. Among other factors, the impact on the travel time was simulated given that the two major exchange points in the network were to be avoided. With an increase from 26.5 to 26.8 minutes on the average travel time, the simulation suggests that the time penalty might be worth the safety benefits.

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status.

Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an automated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining.

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors' topology.

360-degree Delphi: addressing sociotechnical challenges of healthcare IT.

BACKGROUND: IT systems in the healthcare field can have a marked sociotechnical impact: they modify communication habits, alter clinical processes and may have serious ethical implications. The introduction of such systems involves very different groups of stakeholders because of the inherent multi-professionalism in medicine and the role of patients and their relatives that are often underrepresented. Each group contributes distinct perspectives and particular needs, which create specific requirements for IT systems and may strongly influence their acceptance and success. In the past, needs analysis, challenges and requirements for medical IT systems have often been addressed using consensus techniques such as the Delphi technique. Facing the heterogeneous spectrum of stakeholders there is a need to develop these techniques further to control the (strong) influence of the composition of the expert panel on the outcome and to deal systematically with potentially incompatible needs of stakeholder groups. This approach uses the strong advantages a Delphi study has, identifies the disadvantages of traditional Delphi techniques and aims to introduce and evaluate a modified approach called 360-Degree Delphi. Key aspects of 360-Degree Delphi are tested by applying the approach to the needs and requirements analysis of a system for managing patients' advance directives and living wills. METHODS: 360-Degree Delphi (short 360°D), as a modified Delphi process, is specified as a structured workflow with the optional use of stakeholder groups. The approach redefines the composition of the expert panel by setting up groups of different stakeholders. Consensus is created within individual stakeholder groups, but is also communicated between groups, while the iterative structure of the Delphi process remains unchanged. We hypothesize that (1) 360-Degree Delphi yields complementary statements from different stakeholders, which would be lost in classical Delphi; while (2) the variation of statements within individual stakeholder groups is lower than within the total collective. A user study is performed that addresses five stakeholder groups (patients, relatives, medical doctors, nurses and software developers) on the topic of living will communication in an emergency context. Qualitative open questions are used in a Delphi round 0. Answer texts are coded by independent raters who carry out systematic bottom-up qualitative text analysis. Inter-rater reliability is calculated and the resulting codes are used to test the hypotheses. Qualitative results are transferred into quantitative questions and then surveyed in round 1. The study took place in Germany. RESULTS: About 25% of the invited experts (stakeholders) agreed to take part in the Delphi round 0 (three patients, two relatives, three medical doctors, two qualified nurses and three developers), forming a structured panel of the five stakeholder groups. Two raters created a bottom-up coding, and 238 thematic codes were identified by the qualitative text analysis. The inter-rater reliability showed that 44.95% of the codes were semantically similar and coded for the same parts of the raw textual replies. Based on a consented coding list, a quantitative online-questionnaire was developed and send to different stakeholder groups. With respect to the hypotheses, Delphi round 0 had the following results: (1) doctors had a completely different focus from all the other stakeholder groups on possible channels of communications with the patient; (2) the dispersion of codes within individual stakeholder groups and within the total collective - visualized by box plots - was approximately 28% higher in the total collective than in the sub-collectives, but without a marked effect size. With respect to the hypotheses, Delphi round 1 had the following results: different stakeholder groups had highly diverging opinions with respect to central questions on IT-development. For example, when asked to rate the importance of access control against high availability of data (likert scale, 1 meaning restrictive data access, 6 easy access to all data), patients (mean 4.862, Stdev +/- 1.866) and caregivers (mean 5.667, Stdev: +/- 0.816) highly favored data availability, while relatives would restrict data access (mean 2.778, stdev +/- 1.093). In comparison, the total group would not be representative of either of these individual stakeholder needs (mean 4.344, stdev +/- 1.870). CONCLUSION: 360-Degree Delphi is feasible and allows different stakeholder groups within an expert panel to reach agreement individually. Thus, it generates a more detailed consensus which pays more tribute to individual stakeholders needs. This has the potential to improve the time to consensus as well as to produce a more representative and precise needs and requirements analysis. However, the method may create new challenges for the IT development process, which will have to deal with complementary or even contradictory statements from different stakeholder groups.

Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection.

Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 min long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.

Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial.

INTRODUCTION: Staphylococci are the most commonly identified pathogens in bloodstream infections. Identification of Staphylococcus aureus in blood culture (SAB) requires a prompt and adequate clinical management. The detection of coagulase-negative staphylococci (CoNS), however, corresponds to contamination in about 75% of the cases. Nevertheless, antibiotic therapy is often initiated, which contributes to the risk of drug-related side effects. We developed a computerised clinical decision support system (HELP-CDSS) that assists physicians with an appropriate management of patients with Staphylococcus bacteraemia. The CDSS is evaluated using data of the Data Integration Centers (DIC) established at each clinic. DICs transform heterogeneous primary clinical data into an interoperable format, and the HELP-CDSS displays information according to current best evidence in bacteraemia treatment. The overall aim of the HELP-CDSS is a safe but more efficient allocation of infectious diseases specialists and an improved adherence to established guidelines in the treatment of SAB. METHODS AND ANALYSIS: The study is conducted at five German university hospitals and is designed as a stepped-wedge cluster randomised trial. Over the duration of 18 months, 135 wards will change from a control period to the intervention period in a randomised stepwise sequence. The coprimary outcomes are hospital mortality for all patients to establish safety, the 90-day disease reoccurrence-free survival for patients with SAB and the cumulative vancomycin use for patients with CoNS bacteraemia. We will use a closed, hierarchical testing procedure and generalised linear mixed modelling to test for non-inferiority of the CDSS regarding hospital mortality and 90-day disease reoccurrence-free survival and for superiority of the HELP-CDSS regarding cumulative vancomycin use. ETHICS AND DISSEMINATION: The study is approved by the ethics committee of Jena University Hospital and will start at each centre after local approval. Results will be published in a peer-reviewed journal and presented at scientific conferences. TRIAL REGISTRATION NUMBER: DRKS00014320.

Author Correction: Visualizing flow in an intact CSF network using optical coherence tomography: implications for human congenital hydrocephalus.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study.

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.