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Autoři originálního textu: C. Leclercq, H. Witt, G. Hindricks et al.Digitální technologie se již staly součástí medicíny. Nástroje pro vyhledávání, screening, diagnostiku a monitorování parametrů souvisejících se zdravím zlepšily péči o pacienty a jedincům umožnily seznamovat se s nejrůznějšími otázkami vedoucími k účinnější péči o vlastní zdraví. Přístrojová technika označovaná souhrnně slovem nositelná elektronika (wearables) má v sobě vestavěná čidla (senzory) a dokáže měřit tělesnou aktivitu, srdeční frekvenci a rytmus i hodnoty glykemie a elektrolytů. U jedinců s rizikem mohou být nositelná elektronika a další přístroje užitečné, protože umožňují časně odhalit fibrilace síní nebo subklinická stadia kardiovaskulárních onemocnění, ovlivňovat svůj zdravotní stav i svoje kardiovaskulární onemocnění, jako jsou hypertenze a srdeční selhání, i upravovat vlastní životosprávu. Údaje o zdraví lze získávat z nepřeberného množství zdrojů, konkrétně z klinického i laboratorního vyšetření, z vyšetření zobrazovacími metodami, z genetických profilů, z nositelné elektroniky, implantabilních přístrojů, různých měření spouštěných samotným pacientem, i ze sociálních sítí a zdrojů z vnějšího prostředí. Pro účinný sběr a výběr cenných informací z tohoto neustále se zvětšujícího objemu nejrůznějších údajů je nutná umělá inteligence, která rovněž pomáhá s jejich interpretací. Ve skutečnosti není problémem získávání digitálních informací, ale spíš racionální nakládání s nimi a jejich analýza. Existuje dlouhá řada zainteresovaných skupin a stran účastnících se vývoje a účinného využívání digitálních nástrojů. I když se potřeby těchto skupin a stran mohou lišit, mají i mnoho společného včetně přání zachovávat soukromý charakter a zabezpečení údajů, potřebu srozumitelných, důvěryhodných a transparentních systémů, standardizovaných procesů hodnocení systémů regulace a úhrad a dokonalejších způsobů rychlého posouzení hodnoty údajů.
Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wear- ables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.
Sleep medicine has been an expanding discipline during the last few decades. The prevalence of sleep disorders is increasing, and sleep centers are expanding in hospitals and in the private care environment to meet the demands. Sleep medicine has evidence-based guidelines for the diagnosis and treatment of sleep disorders. However, the number of sleep centers and caregivers in this area is not sufficient. Many new methods for recording sleep and diagnosing sleep disorders have been developed. Many sleep disorders are chronic conditions and require continuous treatment and monitoring of therapy success. Cost-efficient technologies for the initial diagnosis and for follow-up monitoring of treatment are important. It is precisely here that telemedicine technologies can meet the demands of diagnosis and therapy follow-up studies. Wireless recording of sleep and related biosignals allows diagnostic tools and therapy follow-up to be widely and remotely available. Moreover, sleep research requires new technologies to investigate underlying mechanisms in the regulation of sleep in order to better understand the pathophysiology of sleep disorders. Home recording and non-obtrusive recording over extended periods of time with telemedicine methods support this research. Telemedicine allows recording with little subject interference under normal and experimental life conditions.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
BACKGROUND: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. OBJECTIVE: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. METHODS: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. RESULTS: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. CONCLUSIONS: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.
- MeSH
- cvičení fyziologie MeSH
- fitness náramky trendy MeSH
- fotopletysmografie metody MeSH
- lidé MeSH
- mobilní aplikace trendy MeSH
- nositelná elektronika trendy MeSH
- srdeční frekvence fyziologie MeSH
- zápěstí MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.
- MeSH
- COVID-19 * MeSH
- lidé MeSH
- nositelná elektronika * MeSH
- pandemie MeSH
- reprodukovatelnost výsledků MeSH
- SARS-CoV-2 MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
Flexible supercapacitors (FSCs) have received a lot of interest as portable power sources for wearable electronics. The biocompatibility of electrodes and electrolytes in wearable FSCs is important to consider although research into these topics is still in its early stages. In this work, we developed a wearable FSC that uses MXene Ti3C2 nanosheets and polypyrrole-carboxymethylcellulose nanospheres composite (Ti3C2@PPy-CMC) as the active electrode material and sweat as the electrolyte. The electrochemical performances of Ti3C2@PPy-CMC FSC were analyzed using an artificial sweat solution and exhibited excellent specific capacitance, power density, cycling stability, and bending stability. To demonstrate a real application of Ti3C2@PPy-CMC FSC, a sweat-chargeable FSC patch has been developed that can be applied directly to human clothing and skin to power a portable electronic gadget when the wearer is exercising. A comprehensive electrochemical study of the FSC patch was also conducted in various sweat secretion body regions such as the finger, foot sole, and wrist. Ti3C2@PPy-CMC composite's outstanding electrochemical performance indicates its potential capabilities and biocompatibility in wearable energy storage devices.
The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download.
- MeSH
- bérec MeSH
- chodci * MeSH
- deep learning * MeSH
- lidé MeSH
- nositelná elektronika * MeSH
- pohyb těles MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
INTRODUCTION: Conflicting evidence exists on whether physical activity (PA) levels of humans have changed over the last quarter-century. The main objective of this study was to determine if there is evidence of time trends in PA, from cross-sectional studies that assessed PA at different time points using wearable devices (e.g., pedometers and accelerometers). A secondary objective was to quantify the rate of change in PA. METHODS: A systematic literature review was conducted of English-language studies indexed in PubMed, SPORTDiscus, and Web of Science (1960-2020) using search terms (time OR temporal OR secular) AND trends AND (steps per day OR pedometer OR accelerometer OR MVPA). Subsequently, a meta-analytic approach was used to aggregate data from multiple studies and to examine specific factors (i.e., sex, age-group, sex and age-group, and PA metric). RESULTS: Based on 16 peer-reviewed scientific studies conducted between 1995 and 2017, levels of ambulatory PA are trending downward in developed countries. Significant declines were seen in both males and females (P < 0.001) as well as in children (P = 0.020), adolescents (P < 0.001), and adults (P = 0.004). The average study duration was 9.4 yr (accelerometer studies, 5.3 yr; pedometer studies, 10.8 yr). For studies that assessed steps, the average change in PA was -1118 steps per day over the course of the study (P < 0.001), and adolescents had the greatest change in PA at -2278 steps per day (P < 0.001). Adolescents also had the steepest rate of change over time, expressed in steps per day per decade. CONCLUSIONS: Evidence from studies conducted in eight developed nations over a 22-yr period indicates that PA levels have declined overall, especially in adolescents. This study emphasizes the need for continued research tracking time trends in PA using wearable devices.
- MeSH
- aktigrafie přístrojové vybavení MeSH
- cvičení trendy MeSH
- lidé MeSH
- nositelná elektronika * MeSH
- vyspělé země MeSH
- zdravé chování * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- metaanalýza MeSH
- systematický přehled MeSH
In this paper, we describe a technical design of wearable multi-sensor systems for physiological data measurement and wide medical applications, significantly impacted in telehealth. The monitors are composed of three analog front-end (AFE) devices, which assist with interfacing digital electronics to the noise-, time-sensitive physiological sensors for measuring ECG (heart-rate monitor), RR (respiration-rate monitor), SRL (skin resistivity monitor). These three types of sensors can be used separately or together and allow to determine a number of parameters for the assessment of mental and physical condition. The system is designed based on requirements for demanding environments even outside the realm of medical applications, and in accordance with Health and Safety at Work directives (89/391/CE and Seveso-II 96/82/EC) for occupational hygiene, medical, rehabilitation, sports and fitness applications.
- MeSH
- automatizované zpracování dat metody přístrojové vybavení MeSH
- biomedicínské technologie metody přístrojové vybavení MeSH
- biomedicínský výzkum MeSH
- dechová frekvence MeSH
- duševní zdraví MeSH
- elektrokardiografie metody přístrojové vybavení MeSH
- lidé MeSH
- nositelná elektronika * klasifikace MeSH
- srdeční frekvence MeSH
- telemedicína metody přístrojové vybavení MeSH
- tělesná výkonnost MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- přehledy MeSH
BACKGROUND: Wearable devices with an ability to collect various type of physiological data are increasingly becoming seamlessly integrated into everyday life of people. In the area of electronic health (eHealth), many of these devices provide remote transfer of health data, as a result of the increasing need for ambulatory monitoring of patients. This has a potential to reduce the cost of care due to prevention and early detection. OBJECTIVE: The objective of this study was to provide an overview of available wearable sensor systems with data exchange possibilities. Due to the heterogeneous capabilities these systems possess today, we aimed to systematize this in terms of usage, where there is a need of, or users benefit from, transferring self-collected data to health care actors. METHODS: We searched for and reviewed relevant sensor systems (i.e., devices) and mapped these into 13 selected attributes related to data-exchange capabilities. We collected data from the Vandrico database of wearable devices, and complemented the information with an additional internet search. We classified the following attributes of devices: type, communication interfaces, data protocols, smartphone/PC integration, connection to smartphone health platforms, 3rd party integration with health platforms, connection to health care system/middleware, type of gathered health data, integrated sensors, medical device certification, access to user data, developer-access to device, and market status. Devices from the same manufacturer with similar functionalities/characteristics were identified under the same device family. Furthermore, we classified the systems in three subgroups of relevance for different actors in mobile health monitoring systems: EHR providers, software developers, and patient users. RESULTS: We identified 362 different mobile health monitoring devices belonging to 193 device families. Based on an analysis of these systems, we identified the following general challenges: CONCLUSIONS: Few of the identified mobile health monitoring systems use standardized, open communication protocols, which would allow the user to directly acquire sensor data. Use of open protocols can provide mobile health (mHealth) application developers an alternative to proprietary cloud services and communication tools, which are often closely integrated with the devices. Emerging new types of sensors, often intended for everyday use, have a potential to supplement health records systems with data that can enrich patient care.
- MeSH
- lidé MeSH
- mobilní aplikace MeSH
- nositelná elektronika * MeSH
- poskytování zdravotní péče MeSH
- srdeční arytmie MeSH
- telemedicína MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
