Wearable and digital devices to monitor and treat metabolic diseases.

Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.

Utilizing Data from Wearable Technologies in the Era of Telemedicine to Assess Patient Function and Outcomes in Neurosurgery: Systematic Review and Time-Trend Analysis of the Literature.

BACKGROUND: The COVID-19 pandemic has driven the increased use of telemedicine and the adoption of wearable technology in neurosurgery. We reviewed studies exploring the use of wearables on neurosurgical patients and analyzed wearables' scientific production trends. METHODS: The review encompassed PubMed, EMBASE, Web of Science, and Cochrane Library. Bibliometric analysis was performed using citation data of the included studies through Elsevier's Scopus database. Linear regression was utilized to understand scientific production trends. All analyses were performed on R 4.1.2. RESULTS: We identified 979 studies. After screening, 49 studies were included. Most studies evaluated wearable technology use for patients with spinal pathology (n = 31). The studies were published over a 24-year period (1998-2021). Forty-seven studies involved wearable device use relevant to telemedicine. Bibliometric analysis revealed a compounded annual growth rate of 7.3%, adjusted for inflation, in annual scientific production from 1998 to 2021 (coefficient=1.3; 95% Confidence Interval = [0.7, 1.9], P < 0.01). Scientific production steadily increased in 2014 (n = 1) and peaked from 2019 (n = 8) to 2021 (n = 13) in correlation with the COVID-19 pandemic. Publications spanned 34 journals, averaged 24.4 citations per article, 3.0 citations per year per article, and 8.3 authors per article. CONCLUSION: Wearables can provide clinicians with objective measurements to determine patient function and quality of life. The rise in articles related to wearables in neurosurgery demonstrates the increased adoption of wearable devices during the COVID-19 pandemic. Wearable devices appear to be a key component in this era of telemedicine and their positive utility and practicality are increasingly being realized in neurosurgery.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare.

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

Validation of the optical Aktiia bracelet in different body positions for the persistent monitoring of blood pressure.

The diagnosis of hypertension and the adjustment of antihypertensive drugs are evolving from isolated measurements performed at the physician offices to the full phenotyping of patients in real-life conditions. Indeed, the strongest predictor of cardiovascular risk comes from night measurements. The aim of this study was to demonstrate that a wearable device (the Aktiia Bracelet) can accurately estimate BP in the most common body positions of daily life and thus become a candidate solution for the BP phenotyping of patients. We recruited 91 patients with BP ranging from low to hypertensive levels and compared BP values from the Aktiia Bracelet against auscultatory reference values for 4 weeks according to an extended ISO 81060-2 protocol. After initializing on day one, the observed means and standard deviations of differences for systolic BP were of 0.46 ± 7.75 mmHg in the sitting position, - 2.44 ± 10.15 mmHg in the lying, - 3.02 ± 6.10 mmHg in the sitting with the device on the lap, and - 0.62 ± 12.51 mmHg in the standing position. Differences for diastolic BP readings were respectively of 0.39 ± 6.86 mmHg, - 1.93 ± 7.65 mmHg, - 4.22 ± 6.56 mmHg and - 4.85 ± 9.11 mmHg. This study demonstrates that a wearable device can accurately estimate BP in the most common body positions compared to auscultation, although precision varies across positions. While wearable persistent BP monitors have the potential to facilitate the identification of individual BP phenotypes at scale, their prognostic value for cardiovascular events and its association with target organ damage will need cross-sectional and longitudinal studies. Deploying this technology at a community level may be also useful to drive public health interventions against the epidemy of hypertension.

Toward closed-loop drug delivery: Integrating wearable technologies with transdermal drug delivery systems.

The recent advancement and prevalence of wearable technologies and their ability to make digital measurements of vital signs and wellness parameters have triggered a new paradigm in the management of diseases. Drug delivery as a function of stimuli or response from wearable, closed-loop systems can offer real-time on-demand or preprogrammed drug delivery capability and offer total management of disease states. Here we review the key opportunities in this space for development of closed-loop systems, given the advent of digital wearable technologies. Particular considerations and focus are given to closed-loop systems combined with transdermal drug delivery technologies.

Effect of Viscosity on the Formation of Porous Polydimethylsiloxane for Wearable Device Applications.

Medical devices, which enhance the quality of life, have experienced a gradual increase in demand. Various research groups have attempted to incorporate soft materials such as skin into wearable devices. We developed a stretchable substrate with high elasticity by forming a porous structure on polydimethylsiloxane (PDMS). To optimize the porous structure, we propose a manufacturing process that utilizes a high-pressure steam with different viscosities (400, 800, 2100, and 3000 cP) of an uncured PDMS solution. The proposed method simplifies the manufacturing of porous structures and is cost-effective compared to other technologies. Porous structures of various viscosities were formed, and their electrical and mechanical properties evaluated. Porous PDMS (3000 cP) was formed in a sponge-like three-dimensional porous structure, compared to PDMS formed by other viscosities. The elongation of porous PDMS (3000 cP) was increased by up to 30%, and the relative resistance changed to less than 1000 times with the maximum strain test. The relative resistance increased the initial resistance (R0) by approximately 10 times during the 1500-times repeated cycling tests with 30% strain. As a result, patch-type wearable devices based on soft materials can provide an innovative platform that can connect with the human skin for robotics applications and for continuous health monitoring.

Twenty-four-hour patterns in electrodermal activity recordings of patients with and without epileptic seizures.

OBJECTIVE: Daytime and nighttime patterns affect the dynamic modulation of brain and body functions and influence the autonomic nervous system response to seizures. Therefore, we aimed to evaluate 24-hour patterns of electrodermal activity (EDA) in patients with and without seizures. METHODS: We included pediatric patients with (a) seizures (SZ), including focal impaired awareness seizures (FIAS) or generalized tonic-clonic seizures (GTCS), (b) no seizures and normal electroencephalography (NEEG), or (c) no seizures but epileptiform activity in the EEG (EA) during vEEG monitoring. Patients wore a device that continuously recorded EDA and temperature (TEMP). EDA levels, EDA spectral power, and TEMP levels were analyzed. To investigate 24-hour patterns, we performed a nonlinear mixed-effects model analysis. Relative mean pre-ictal (-30 min to seizure onset) and post-ictal (I: 30 min after seizure offset; II: 30 to 60 min after seizure offset) values were compared for SZ subgroups. RESULTS: We included 119 patients (40 SZ, 17 NEEG, 62 EA). EDA level and power group-specific models (SZ, NEEG, EA) (h = 1; P < .01) were superior to the all-patient cohort model. Fifty-nine seizures were analyzed. Pre-ictal EDA values were lower than respective 24-hour modulated SZ group values. Post hoc comparisons following the period-by-seizure type interaction (EDA level: χ2  = 18.50; P < .001, and power: χ2  = 6.73; P = .035) revealed that EDA levels were higher in the post-ictal period I for FIAS and GTCS and in post-ictal period II for GTCS only compared to the pre-ictal period. SIGNIFICANCE: Continuously monitored EDA shows a pattern of change over 24 hours. Curve amplitudes in patients with recorded seizures were lower as compared to patients who did not exhibit seizures during the recording period. Sympathetic skin responses were greater and more prolonged in GTCS compared to FIAS. EDA recordings from wearable devices offer a noninvasive tool to continuously monitor sympathetic activity with potential applications for seizure detection, prediction, and potentially sudden unexpected death in epilepsy (SUDEP) risk estimation.

Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring.

With the increasing prevalence of growing population, aging and chronic diseases continuously rising healthcare costs, the healthcare system is undergoing a vital transformation from the traditional hospital-centered system to an individual-centered system. Since the 20th century, wearable sensors are becoming widespread in healthcare and biomedical monitoring systems, empowering continuous measurement of critical biomarkers for monitoring of the diseased condition and health, medical diagnostics and evaluation in biological fluids like saliva, blood, and sweat. Over the past few decades, the developments have been focused on electrochemical and optical biosensors, along with advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have evolved gradually with a mix of multiplexed biosensing, microfluidic sampling and transport systems integrated with flexible materials and body attachments for improved wearability and simplicity. These wearables hold promise and are capable of a higher understanding of the correlations between analyte concentrations within the blood or non-invasive biofluids and feedback to the patient, which is significantly important in timely diagnosis, treatment, and control of medical conditions. However, cohort validation studies and performance evaluation of wearable biosensors are needed to underpin their clinical acceptance. In the present review, we discuss the importance, features, types of wearables, challenges and applications of wearable devices for biological fluids for the prevention of diseased conditions and real-time monitoring of human health. Herein, we summarize the various wearable devices that are developed for healthcare monitoring and their future potential has been discussed in detail.

Towards remote monitoring in pediatric care and clinical trials-Tolerability, repeatability and reference values of candidate digital endpoints derived from physical activity, heart rate and sleep in healthy children.

BACKGROUND: Digital devices and wearables allow for the measurement of a wide range of health-related parameters in a non-invasive manner, which may be particularly valuable in pediatrics. Incorporation of such parameters in clinical trials or care as digital endpoint could reduce the burden for children and their parents but requires clinical validation in the target population. This study aims to determine the tolerability, repeatability, and reference values of novel digital endpoints in healthy children. METHODS: Apparently healthy children (n = 175, 46% male) aged 2-16 were included. Subjects were monitored for 21 days using a home-monitoring platform with several devices (smartwatch, spirometer, thermometer, blood pressure monitor, scales). Endpoints were analyzed with a mixed effects model, assessing variables that explained within- and between-subject variability. Endpoints based on physical activity, heart rate, and sleep-related parameters were included in the analysis. For physical-activity-related endpoints, a sample size needed to detect a 15% increase was calculated. FINDINGS: Median compliance was 94%. Variability in each physical activity-related candidate endpoint was explained by age, sex, watch wear time, rain duration per day, average ambient temperature, and population density of the city of residence. Estimated sample sizes for candidate endpoints ranged from 33-110 per group. Daytime heart rate, nocturnal heart rate and sleep duration decreased as a function of age and were comparable to reference values published in the literature. CONCLUSIONS: Wearable- and portable devices are tolerable for pediatric subjects. The raw data, models and reference values presented here can be used to guide further validation and, in the future, clinical trial designs involving the included measures.

Frontiers in hemodialysis: Innovations and technological advances.

As increasing demand for hemodialysis (HD) treatment incurs significant financial burden to healthcare systems and ecological burden as well, novel therapeutic approaches as well as innovations and technological advances are being sought that could lead to the development of purification devices such as dialyzers with improved characteristics and wearable technology. Novel knowledge such as the development of more accurate kinetic models, the development of novel HD membranes with the use of nanotechnology, novel manufacturing processes, and the latest technology in the science of materials have enabled novel solutions already marketed or on the verge of becoming commercially available. This collaborative article reviews the latest advances in HD as they were presented by the authors in a recent symposium titled "Frontiers in Haemodialysis," held on 12th December 2019 at the Royal Society of Medicine in London.

FDA public workshop: Orthopaedic sensing, measuring, and advanced reporting technology (SMART) devices.

Traditional orthopaedic devices do not communicate with physicians or patients post-operatively. After implantation, follow-up of traditional orthopaedic devices is generally limited to episodic monitoring. However, the orthopaedic community may be shifting towards incorporation of smart technology. Smart technology in orthopaedics is a term that encompasses a wide range of potential applications. Smart orthopaedic implants offer the possibility of gathering data and exchanging it with an external reader. They incorporate technology that enables automated sensing, measuring, processing, and reporting of patient or device parameters at or near the implant. While including advanced technology in orthopaedic devices has the potential to benefit patients, physicians, and the scientific community, it may also increase the patient risks associated with the implants. Understanding the benefit-risk profile of new smart orthopaedic devices is critical to ensuring their safety and effectiveness. The 2018 FDA public workshop on orthopaedic sensing, measuring, and advanced reporting technology (SMART) devices was held on April 30, 2018, at the FDA White Oak Campus in Silver Spring, MD with the goal of fostering a collaborative dialogue amongst the orthopaedic community. Workshop attendees discussed four key areas related to smart orthopaedic devices: engineering and technology considerations, clinical and patient perspectives, cybersecurity, and regulatory considerations. The workshop presentations and associated discussions highlighted the need for the orthopaedic community to collectively craft a responsible path for incorporating smart technology in musculoskeletal disease care.

Wearable Devices Are Well Accepted by Patients in the Study and Management of Inflammatory Bowel Disease: A Survey Study.

BACKGROUND: Wearable devices are designed to capture health-related and physiological data. They may be able to improve inflammatory bowel disease management and address evolving research needs. Little is known about patient perceptions for their use in the study and management of inflammatory bowel disease. AIMS: The aim of this survey study is to understand patient preferences and interest in wearable technology. METHODS: Consecutive adult patients who self-reported having inflammatory bowel disease were approached at the Susan and Leonard Feinstein Inflammatory Bowel Disease Center at the Mount Sinai Hospital to complete a 28-question survey. Reponses were analyzed with descriptive statistics. The Pearson Chi-square test and Fischer's exact test were used to determine the association between demographic and disease-related features and survey responses. RESULTS: Four hundred subjects completed the survey. 42.7% of subjects reported prior or current use of wearable devices. 89.0% of subjects believed that wearable devices can provide important information about their health, while 93.8% reported that they would use a wearable device if it could help their doctor manage their IBD. Subjects identified wrist-worn devices as the preferred device type and a willingness to wear these devices at least daily. CONCLUSIONS: Patients with inflammatory bowel disease believe that wearable devices can provide important information about their health and report a willingness to wear them frequently in research studies and as part the routine management of inflammatory bowel disease.

Machine learning and wearable devices of the future.

Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.

Seizure forecasting and cyclic control of seizures.

Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.

Wearable electrochemical biosensors in North America.

Tremendous research and commercialization efforts around the world are focused on developing novel wearable electrochemical biosensors that can noninvasively and continuously screen for biochemical markers in body fluids for the prognosis, diagnosis and management of diseases, as well as the monitoring of fitness. Researchers in North America are leading the development of innovative wearable platforms that can comfortably comply to the human body and efficiently sample fluids such as sweat, interstitial fluids, tear and saliva for the electrochemical detection of biomarkers through various sensing approaches such as potentiometric ion selective electrodes and amperometric enzymatic sensors. We start this review with a historical timeline overviewing the major milestones in the development of wearable electrochemical sensors by North American institutions. We then describe how such research efforts have led to pioneering developments and are driving the advancement and commercialization of wearable electrochemical sensors: from minimally invasive continuous glucose monitors for chronic disease management to non-invasive sweat electrolyte sensors for dehydration monitoring in fitness applications. While many countries across the globe have contributed significantly to this rapidly emerging field, their contributions are beyond the scope of this review. Furthermore, we share our perspective on the promising future of wearable electrochemical sensors in applications spanning from remote and personalized healthcare to wellness.

Child and adolescent asynchronous technology competencies for clinical care and training: Scoping review.

Objective: Asynchronous technologies such as mobile health, e-mail, e-consult, and social media are being added to in-person and synchronous service delivery. To ensure quality care, clinicians need skills, knowledge, and attitudes related to technology that can be measured. This study sought out competencies for asynchronous technologies and/or an approach to define them. Methods: This 6-stage scoping review of Pubmed/Medline, APA PsycNET, PsycINFO and other databases was based on a broad research question, "What skills are needed for clinicians and trainees to provide quality care using asynchronous technologies for children and adolescents, and how can they be made measurable to implement, teach and evaluate?" The search focused on key words in 4 concept areas: (a) competencies; (b) asynchronous technology; (c) synchronous telepsychiatry, telebehavioral or telemental health; and (d) clinical. The screeners reviewed the full-text articles based on inclusion (mesh of the key words) and exclusion criteria. Results: From a total of 5,877 potential references, 2 authors found 509 eligible for full text review and found 110 articles directly relevant to the concepts. Clinical studies discuss clinical, technical and administrative workflow rather than competencies, though behavioral health professions' position statements advise on adapting care and training. Existing technology competencies for video, social media, mobile health, and other asynchronous technologies were used to build a framework. Training, faculty development, and organizational suggestions are suggested. Conclusions: Research is needed on how to implement and evaluate asynchronous competencies to ensure quality clinical care and training, which is a paradigm shift for participants. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Cellulose nanocrystal mediated fast self-healing and shape memory conductive hydrogel for wearable strain sensors.

Electro-conductive hydrogel (ECH) with self-healing, shape memory and biocompatible properties is highly urgent for wearable strain sensors to prolonging their lifespan, endowing programmable shape control property, and improving affinity to skin during service. However, most of synthetic polymer-based ECH usually involve potential toxicity, long healing and shape drive time. Herein, a fast healable and shape memory ECH with excellent biocompatibility is reported for the first time by incorporating cellulose nanocrystals grafted phenylboronic acid (CNCs-ABA) and multiwalled carbon nanotubes (MWCNTs) into polyvinyl alcohol (PVA). CNCs-ABA is designed as dispersant and crosslinker in hydrogel. pH-induced dynamic borate bonds give hydrogel excellent shape recovery and fixity ratio of 82.1% and 78.2%, respectively. Meanwhile, 97.1% healing efficiency is obtained within 2 min depending on remarkable photothermal effect of MWCNTs and reversible microcrystallization. Double crosslinking networks endow excellent mechanical properties to hydrogel, whose tensile strength, strain and elastic modulus reach 227.0 kPa, 395.0% and 9.0 kPa, respectively. Furthermore, the synergistic effect of MWCNTs and NaOH enhance the conductivity of hydrogel with value of 3.8×10-2 S/m. In addition, the hydrogel can act as strain sensor for detecting human motion with superior biocompatibility and fast resistance response to applied strain, which is suitable for human health management.

Telehealth in the time of Corona: 'doctor in the house'.

The COVID-19 pandemic has led to many physicians working from home whenever possible. Although the concept of 'remote' patient care has been around for decades, present circumstances have provided a grand impetus in that direction with a view to protecting both patient and caregiver. In this article, we discuss some of the various challenges to moving forward with telemedicine, drawing in part on our own experiences in dealing with the COVID-19 pandemic. Clinical, technical, financial and cultural barriers to telemedicine are identified, along with a discussion concerning anticipated benefits. We conclude that the COVID-19 pandemic will likely forever change how healthcare is conducted as telemedicine figures increasingly prominently in the clinical landscape.

Electronic skins and machine learning for intelligent soft robots.

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.