Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

Momentary physiological indices related to eating disorders: A systematic and methodological review

Eating disorders (ED) are serious psychiatric illnesses, with no everyday support to intervene on the high rates of relapse. Understanding physiological indices that can be measured by wearable sensor technologies may provide new momentary interventions for individuals with ED. This systematic review, searching large databases, synthesises studies investigating peripheral physiological (PP) indices commonly included in wearable wristbands (heart rate [HR], heart rate variability [HRV], electrodermal activity [EDA], peripheral skin temperature [PST], and acceleration) in ED. Inclusion criteria included: (a) full peer-reviewed empirical articles in English; (b) human participants with active ED; and (c) containing one of five wearable physiological measures. Kmet risk of bias was assessed. Ninety-four studies were included (Anorexia nervosa [AN; N = 4418], bulimia nervosa [BN; N = 916], binge eating disorder [BED; N = 1604], other specified feeding and eating disorders [OSFED; N = 424], and transdiagnostic [N = 47]). Participants with AN displayed lower HR and EDA and higher HRV compared to healthy individuals. Those with BN showed higher HRV, and lower EDA and PST compared to healthy individuals. Other ED and Transdiagnostic samples showed mixed results. PP differences are indicated through various assessments in ED, which may suggest diagnostic associations, although more studies are needed to validate observed patterns. Results suggest important therapeutic potential for PP in ED, and larger studies including diverse participants and diagnostic groups are needed to fully uncover their role in ED.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges in monitoring and management due to the unpredictable nature of seizures. Wearable devices offer continuous monitoring and real-time data collection, providing insights into seizure patterns and trends. Wearable technology is important in epilepsy management because it enables early detection, prediction, and personalized intervention, empowering patients and healthcare providers. Key findings highlight the potential of wearable devices to improve seizure detection accuracy, enhance patient empowerment through real-time monitoring, and facilitate data-driven decision-making in clinical practice. However, further research is needed to validate the accuracy and reliability of these devices across diverse patient populations and clinical settings. Collaborative efforts between researchers, clinicians, technology developers, and patients are essential to drive innovation and advancements in wearable digital health technology for epilepsy management, ultimately improving outcomes and quality of life for individuals with this neurological condition.

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

Background/objective

Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy.

Methods

A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring.

Findings

Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies.

Conclusion

This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.

EEG seizure detection: concepts, techniques, challenges, and future trends

A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.

'No one's ever said anything about sleep': A qualitative investigation into mothers' experiences of sleep in children with epilepsy

INTRODUCTION

Sleep problems in children with epilepsy (CWE) are common. However, little is known about parental experiences and feelings about managing sleep in their CWE. To provide the most appropriate services' provision, it is essential that the lived experience of parents of this patient group and the issues and problems that they face in managing their child's sleep is understood.

METHOD

In 2018, nine mothers of CWE (aged 5-15 years) were interviewed about their perceptions and experiences around their child's sleep, sleep problems and their management, the impact of sleep difficulties on the child and their family and available support.

RESULTS

Four themes were identified that represented the nature of the child's sleep problems, including settling and night-waking issues, parasomnias and child anxiety around sleep. Seven themes represented mothers' experiences of managing their child's sleep and any associated problems, including the longstanding challenging nature of child sleep issues, management strategies adopted, challenges related to managing sleep over time, the link between sleep and seizures, the negative impact of poor sleep on daytime functioning, role of antiseizure medication and maternal concerns about child sleep. One theme represented the perceived lack of information, help and support available.

CONCLUSIONS

Findings suggest there are unmet needs in supporting parents to deal with sleep, sleep problems and their management in CWE.

PATIENT OR PUBLIC CONTRIBUTION

This individual study was conducted under the umbrella of the CASTLE research programme (see https://castlestudy.org.uk/). Parents who have lived experience of parenting a child with epilepsy were co-applicants for the programme and were involved in the original conception, aims, design and funding application for the research programme (including the project reported in this paper) and advised on project design. Mothers of CWE who have lived experience of managing sleep and sleep problems in their child were participants who shared their experiences through the interviews, which formed the data of the current study.

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

Health-Related Indicators Measured Using Earable Devices: Systematic Review

BACKGROUND

Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices.

OBJECTIVE

Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices.

METHODS

A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention.

RESULTS

A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each).

CONCLUSIONS

Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing.

Dysfunction of ABC Transporters at the Surface of BBB: Potential Implications in Intractable Epilepsy and Applications of Nanotechnology Enabled Drug Delivery

Epilepsy is a chronic neurological disorder affecting 70 million people globally. One of the fascinating attributes of brain microvasculature is the (BBB), which controls a chain of distinct features that securely regulate the molecules, ions, and cells movement between the blood and the parenchyma. The barrier's integrity is of paramount importance and essential for maintaining brain homeostasis, as it offers both physical and chemical barriers to counter pathogens and xenobiotics. Dysfunction of various transporters in the (BBB), mainly ATP binding cassette (ABC), is considered to play a vital role in hampering the availability of antiepileptic drugs into the brain. ABC (ATP-binding cassette) transporters constitute a most diverse protein superfamily, which plays an essential part in various biological processes, including cell homeostasis, cell signaling, uptake of nutrients, and drug metabolism. Moreover, it plays a crucial role in neuroprotection by out-flowing various internal and external toxic substances from the interior of a cell, thus decreasing their buildup inside the cell. In humans, forty-eight ABC transporters have been acknowledged and categorized into subfamilies A to G based on their phylogenetic analysis. ABC subfamilies B, C, and G, impart a vital role at the BBB in guarding the brain against the entrance of various xenobiotic and their buildup. The illnesses of the central nervous system have received a lot of attention lately Owing to the existence of the BBB, the penetration effectiveness of most CNS medicines into the brain parenchyma is very limited (BBB). In the development of neurological therapies, BBB crossing for medication delivery to the CNS continues to be a major barrier. Nanomaterials with BBB cross ability have indeed been extensively developed for the treatment of CNS diseases due to their advantageous properties. This review will focus on multiple possible factors like inflammation, oxidative stress, uncontrolled recurrent seizures, and genetic polymorphisms that result in the deregulation of ABC transporters in epilepsy and nanotechnology-enabled delivery across BBB in epilepsy.

Machine Learning for Healthcare Wearable Devices: The Big Picture

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it can play a great role in elderly care and patient's health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.

[Use of machine learning for the prediction of stress using the example of logistics]

Stress und seine komplexen Wirkungen werden bereits seit Anfang des 20. Jahrhunderts erforscht. Die vielfältigen psychischen und physischen Stressoren in der Arbeitswelt können in Summe zu Störungen des Organismus und zu Erkrankungen führen. Da die Ausprägung körperlicher und subjektiver Folgen von Stress individuell unterschiedlich ist, lassen sich keine absoluten Grenzwerte ermitteln. Zur Erforschung der systematischen Mustererkennung physiologischer und subjektiver Stressparameter sowie einer Stressvorhersage, werden in dem vorliegenden Beitrag Methoden des maschinellen Lernens (ML) eingesetzt. Als praktischer Anwendungsfall dient die Logistikbranche, in der Belastungsfaktoren häufig in der Tätigkeit und der Arbeitsorganisation begründet liegen. Ein Gestaltungselement bei der Prävention von Stress ist die Arbeitspause. Mit ML-Methoden wird untersucht, inwieweit Stress auf Basis physiologischer und subjektiver Parameter vorhergesagt werden kann, um Pausen individuell zu empfehlen. Im Beitrag wird der Zwischenstand einer Softwarelösung für ein dynamisches Pausenmanagement für die Logistik vorgestellt.

Praktische Relevanz: Das Ziel der Softwarelösung „Dynamische Pause“ besteht darin, Stress in Folge mentaler und physischer Belastungsfaktoren in der Logistik präventiv vorzubeugen und die Beschäftigten auf lange Sicht gesund, zufrieden, arbeitsfähig und produktiv zu halten. Infolge individualisierter Erholungspausen als Gestaltungselement, können Unternehmen unterstützt werden, Personalressourcen entsprechend der dynamischen Anforderungen der Logistik flexibler einzusetzen.

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms

Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.