Mobile Healthcare Research Articles

Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp.

Electroencephalography has garnered interest for applications in mobile healthcare, human-machine interfaces, and Internet of Things. Conventional electroencephalography relies on wet and dry electrodes. Despite favorable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes, and makes them unsuited for long-term mobile recording. In contrast, dry electrodes are better suited for long-term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non-adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Herein, a conical microstructure array (CMSA) based sensor made of carbon nanotube-polydimethylsiloxane composite is reported. The CMSA sensor is fabricated using the innovative, cost-effective, and scalable method of viscosity-controlled dip-pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical-grade wet gel electrodes.

Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases

BackgroundCardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. ObjectiveBased on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. MethodsThis paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF). ResultsRF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states. ConclusionsThe wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.

Implementation of the Early Warning Application Program for Pregnant Women to Support Health Services in Rural Areas

The development process of digital technology has affected many fields. One of them is health field. Number of health application appears. They can be accessed easily by smartphone or tablet device. For instance, in developed countries, doctors examine and diagnose children's ears using smartphone. He provides the appropriate treatment for their patients through the device. The growing number of mobile applications in health will certainly facilitate patient care. There are currently about 13,000 digital health applications. Nowadays, patients have gradually followed this trend. Rock Health, as a provider of digital health ecosystem services, explains the power of mobile health care as well as the health effects of technology. The digital health care industry has a significant impact on healthcare providers. In this research, the researchers design tools or web-based applications and or sms gate way. The main objective is to monitor early health for expectant mothers and infants who are less likely to receive direct medical care.

Early detection and prediction of acute exacerbation of chronic obstructive pulmonary disease.

Chronic Obstructive Pulmonary Disease (COPD) is characterized by persistent respiratory symptoms and airflow limitation. Acute exacerbation of COPD (AECOPD) is an acute worsening of respiratory symptoms, which needs additional treatment and can result in worsening health status, increasing risks of hospitalization and mortality. Therefore, it is necessary to early recognize and diagnose exacerbations of COPD. This review introduces the updated definition of COPD exacerbations, the current clinical assessment tools, and the current potential biomarkers. The application of mobile health care in COPD management for early identification and diagnosis is also included in this review.

KEdge: Fuzzy-Based Multi-AI Model Coalescence Solution for Mobile Healthcare System

In this work, we propose a condition-aware analytical framework—KEdge—for health condition recommendation in Internet of Things (IoT) based mobile healthcare systems. Procuring data from multiple sensors and making a singular assessment from them is a challenging task. KEdge overcomes such an issue by determining the severity of the patient by determining a condition index (CI) using a two-step analytical framework and a multiple rule fuzzy inference system (FIS). In the first step, KEdge detects the heart severity condition using a convolutional neural network model and, in the second step, it detects the respiratory condition using a random forest classification model. KEdge also utilizes auscultation sounds from SkopEdge (a digital stethoscope) for assessing the heartbeats. Through extensive experiments, we observe that KEdge identifies the arrhythmia condition with an accuracy of 98.53% and respiratory condition by 98.68%. KEdge considers the analytical predictions and analysis from SkopEdge to evaluate the CI for recommending the overall health condition using Mamdani FIS. We observe that KEdge is suitable for resource-constrained IoT devices providing memory consumption of 6.6%. On offloading the same to the fog nodes, we observe improved CPU utilization with data upload rates in the order of 25 kb/s (KEdge) and 5 Mb/s (SkopEdge).

보건소 모바일 헬스케어 서비스 참여가 초-중학생의 건강체력 변인에 미치는 영향

This study aims to investigate the nonimpact of time- and space-constrained physical activity devices and ICT-based non-face-to-face mobile health-care apps on elementary-middle school students, and to develop models and services of ICT-based non-face-to-face mobile health-care platforms for proper health care habits and health prevention. The 1,163 elementary-middle school students (585 men and 578 women) were participated in the study with the cooperation of 10 health centers nationwide. All participants measured body composition, physical fitness such as cardiovascular endurance (20m shuttle run), muscle strength (grip strength), power (long jump), and flexibility (sit-and reach). And individual physical activity information is automatically linked to the app from the physical activity devices that the activity information can be checked in real time on the smartphone. Male elementary-middle school students are height (p<.001), weight (p<.001) and BMI (p<.01), there was a significant difference before and after participation, and in female elementary-middle school students height (p<.In 001), there was a significant difference before and after participation. Male elementary-middle school students are muscle strength (p<.001), and power (p<.01), there was a significant difference before and after participation, and in female elementary-middle school students muscle strength (p<.01), there was a significant difference before and after participation. There was a significant difference in stpes (p<.01) for female elementary-middle school students before and after participation, but there was no significant difference in male elementary-middle school students. Participation in the “public health center mobile health care service for elementary-middle school students” for 24 weeks is expected to contribute not only to increasing the amount of physical activity of male and female elementary-middle school students but also to improving physical fitness and body composition. From this point of view that it can be used as one of various methods to improve the amount of physical activity of elementary-middle school students, and it is more advantageous in pandemic and non-face-to-face situations.

Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains

PurposeThe goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.Design/methodology/approachPotential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.FindingsThe study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.Practical implicationsPromoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.Originality/valueAt this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

Exploring Primary Healthcare Experiences and Interest in Mobile Technology Engagement Amongst an Urban Population Experiencing Barriers to Care.

Mobile phone-based engagement approaches provide potential platforms for improving access to primary healthcare (PHC) services for underserved populations. We held two focus groups (February 2020) with residents (n = 25) from a low-income urban neighbourhood (downtown Vancouver, Canada), to assess recent healthcare experiences and elicit interest in mobile phone-based healthcare engagement for underserved residents. Note-based analysis, guided by interpretative description, was used to explore emerging themes. Engagement in PHC was complicated by multiple, intersecting personal-level and socio-structural factors, and experiences of stigma and discrimination from care providers. Perceived inadequacy of PHC services and pervasive discrimination reported by participants indicate a significant and ongoing need to improve client-provider relationships to address unmet health needs. Mobile phone-based engagement was endorsed, highlighting phone ownership and client-provider text-messaging, facilitated by non-clinical staff such as peers, as helpful to strengthening retention and facilitating care team connection. Concerns raised included reliability, cost, and technology and language accessibility.

International Physician Migration: A Comparative Study of Healthcare in the US, UK, &amp;amp; India

Physician migration patterns are a phenomenon that affects the healthcare system within developing countries, but there is no strong evidence to show why doctors migrate to the West after getting an education from elite Indian medical institutions. This study aims to explain why physicians from India choose to leave given the different push and pull factors. Prior research shows that better opportunities for higher education, socioeconomic status, upward mobility, and equitable healthcare systems contribute to why physicians are attracted to the US and the UK. This leads to the next main question: which country do doctors prefer after moving away from India, the US or the UK? With further investigation through interviews, it becomes evident that there is no actual preference for one country. Each country has unique assets within the types of healthcare and lifestyles offered. Physicians note that social networks, support systems, and recruitment are all reasons for moving to the West. Female and lower caste/class doctors with adequate resources who left India discuss that they were able to feel more valued for their merit outside of the country. Some upper caste/class physicians were privileged enough to have more autonomy when deciding where to go; for them, the West was just another option. Upper-caste narratives also shed light on the politics they fled from, such as the medical education quota system for undergraduate and postgraduate institutions. To summarize: physicians didn’t worry too much about the destination. Rather, they just wanted to leave India.

Цифровые технологии в сфере здравоохранения как способ обеспечения качества человеческого капитала

Introduction. The article considers the relevance of the digital technologies introduction in healthcare organizations, accelerated by the coronavirus pandemic. The purpose of the publication is to clarify the role of digital technologies in the healthcare sector for solving applied problems and ensuring the quality of human capital as a factor of the sustainable development of the economy. Theoretical analysis. A review of Russian and foreign publications on the subject of digitalization in the field of healthcare is given. The problems of digital health are highlighted. The directions for the introduction of digital technologies in the domestic healthcare system based on the technologies of mobile healthcare, electronic medical records, medical analytics, telemedicine, and artificial intelligence systems are presented. The study is based on the analysis of statistical data on the federal districts of Russia. Results. The results of the study made it possible to identify the limiting factors for the introduction of digital technologies in healthcare organizations in Russia, to clarify the importance of digital technologies for ensuring the quality of human capital. Conclusions. The results and conclusions of the article can become a theoretical platform for further research to advance healthcare towards the implementation of the digital maturity model and the widespread use of digital technologies for the provision of medical care and services in the healthcare system.

Software-driven secure framework for mobile healthcare applications in IoMT

The traditional healthcare systems require more data storage and not a quick responsive mode. The new era of intelligent healthcare systems uses advanced frameworks and devices to work with Internet of things (IoT), Cloud Computing, edge computing devices, and the Internet of Medical Things (IoMT). These technology adoptions may drastically increase healthcare systems performance and are available anytime and anywhere to replace traditional medical methods – the organizations in the network exchange the patient data that has been gathered. The healthcare systems are therefore susceptible to a variety of threats. The attacker may exploit many attacks during the transmission. This paper introduces a novel mechanism of A Secure Robust Privacy-Preserving Authentication and Key Agreement Framework for Mobile Healthcare Applications. The proposed framework collects the patient’s data using wearable devices called sensors, and the collected data is shared with various entities using a secure mechanism. The proposed framework uses Elliptic Curve Cryptography (ECC). Digital sign is created and validated through the Elliptic Curve Digital Signature Algorithm (ECDSA). Security properties of the proposed framework are analyzed through standard model checking tool Automated Validation of Internet Security Protocols (AVISPA). The suggested protocol is simple to develop and can withstand network-related threats.

Health Care Utilization among Homeless-Experienced Adults Who Were Seen by a Mobile Addiction Health Clinic in Boston, Massachusetts: A Quasi-Experimental Study

Background: Mobile health clinics improve access to care for marginalized individuals who are disengaged from the healthcare system. This study evaluated the association between a mobile addiction health clinic and health care utilization among people experiencing homelessness. Methods: Using Medicaid claims data, we evaluated adults who were seen by a mobile addiction health clinic in Boston, Massachusetts from 1/16/18–1/15/19 relative to a propensity score matched control cohort. We evaluated both cohorts from four years before to one year after the index visit date with the mobile clinic. The primary outcome was the number of outpatient visits; secondary outcomes were the number of hospitalizations and emergency department (ED) visits. We used Poisson regression to compare changes in outcomes from before to after the index date in a quasi-experimental design. Results: 138 adults were seen by the mobile clinic during the observation period; 29.7% were female, 16.7% were Black, 8.0% Hispanic, 68.1% White, and the mean age was 40.4 years. The mean number of mobile clinic encounters was 3.1. The yearly mean number of outpatient visits increased from 11.5 to 12.1 (p = 0.43; pdiff-in-diff = 0.15), the number of hospitalizations increased from 2.2 to 3.0 (p = 0.04; pdiff-in-diff = 0.87), and the number of ED visits increased from 5.4 to 6.5 (p = 0.04; pdiff-in-diff = 0.40). Conclusions: The mobile addiction health clinic was not associated with statistically significant changes in health care utilization in the first year. Further research in larger samples using a broader set of outcomes is needed to quantify the benefits of this innovative care delivery model.

Hydrothermal synthesis of zinc oxide/PEDOT:PSS composite for flexible temperature sensor application

A flexible and printable temperature sensor was proposed for a fast detection of temperature measurements. A hybrid composite of zinc oxide (ZnO) and a conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonicacid) (PEDOT:PSS) was utilized as the temperature-sensing layer. An interdigitated electrodes structure based on silver (Ag) ink was used to electrically connect the composite through a facile drop-casting technique. A standout aspect of this work is the presentation of ZnO/PEDOT:PSS as a temperature-sensing layer. The PEDOT:PSS flakes were connected by hydrothermally prepared ZnO nanorods, which increased the composite sheets’ electrical conductivity. The linearity, sensitivity, stability and dynamic response of the flexible sensor were examined from a temperature of 29 °C–60 °C. The sensor has high sensitivity of 1.06% °C−1 with response and recovery times of 5 s and 12.7 s, respectively. This work clearly demonstrates the potential of ZnO/PEDOT:PSS composite for flexible temperature sensor and adds to the rapidly expanding field of personalized mobile healthcare.

Guest Editorial Innovations in Wearable, Implantable, Mobile, &amp; Remote Healthcare With IoT &amp; Sensor Informatics and Patient Monitoring

The papers presented in this special issue focus on technological innovations in wearable, implantable, mobile, and remote healthcare that include Internet of Things (IoT), sensor informatics, and patient monitoring applications. These new technologies are used to track the key signs of people’s health to improve their lifestyle and health disorders. Innovations in IoT devices play a vital role in assisting patients in managing their health conditions. Thanks to the advent of modern communication technologies and Internet of Things (IoT) paradigms that have made the implementation of biomedical devices nearly universal. Now with the evolving industrial revolution, patients and healthcare providers are expecting something more. Practically speaking, healthcare wearables have experienced tremendous growth in the past few years, and it is expected to grow even more shortly, making it an ideal space for the biomedical informatics research community to solve complex healthcare problems and more informed decision making to improve human health.

Energy Efficient and Secure Neural Network–based Disease Detection Framework for Mobile Healthcare Network

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.

A step in the right direction: the potential role of smartwatches in supporting chronic disease prevention in health care.

A step in the right direction: the potential role of smartwatches in supporting chronic disease prevention in health care.

Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.

Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients. This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations. A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team). A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022. This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

Facile and Controllable Ultrasonic Nebulization Method for Fabricating Ti3C2Tx‐Based Strain Sensor and Monitoring of Human Motion and Sound Wave

AbstractFlexible and wearable electronic devices hold great potential in electronic skins, health monitoring systems and soft robotics. Among them, flexible strain sensors with high performance are key components for wearable health monitoring devices. However, the facile and controllable preparation of highly sensitive sensors still faces significant challenges. By virtue of excellent conductivity of 2D transition metal carbids (MXenes), this work reports a facile and low‐cost fabrication strategy for large‐scale production of strain sensors. The sensitive layer is deposited on flexible interdigital electrodes by ultrasonic nebulization of Ti3C2Tx nanosheets. By controlling the nebulization time, different thicknesses of Ti3C2Tx films has a great influence on the performance of strain sensors. The Ti3C2Tx‐based strain sensor exhibits good sensing performances such as high GF (19.1) in the low strain range (≈0.25%–1.14%), short response time (0.7 s), and stable durability (over 1000 cycles). In practice, the potential applications of the strain sensor in sound frequency detection, human physiological signal monitoring and facial expression recognition are demonstrated. Finally, this work integrates the strain sensor with a miniaturized analyzer to assemble a wearable motion monitoring device for mobile healthcare. This study provides a facile strategy for fabricating flexible strain sensors in the field of wearable electronics.

The evolution of digital medicine. World and domestic experience

It is known that the healthcare system is currently under serious strain in all countries of the world. Despite the expansion of the number of services and the improvement of the quality of medical care, the industry cannot cope with the existing burden: due to the increase in the number and life expectancy of the population, the spread of epidemiological morbidity and the growth of public expectations for more personalized and comfortable services the population. A significant number of current problems of the healthcare system can be solved with the help of widespread introduction of digital technologies. The global COVID-19 pandemic has led to accelerated innovation and the transition of processes from informatization to digital transformation. Digital transformation of healthcare is an absolute global trend. The development of digital technologies is aimed both at solving national problems and at implementing the national strategy of the healthcare system. The digital healthcare market is growing due to the development of electronic medical records (EHR), mobile healthcare (mhealth) capabilities, telemedicine and medical analytics. According to the Statista agency, the volume of the global digital medicine market in 2019 amounted to $ 106 billion, analysts predict its growth to $639 billion, that is, more than 6 times. Th e introduction of digital technologies into the healthcare system is a process that concerns all participants: the state, medical professionals, developers of IT solutions and new medical technologies, citizens. The article presents an analysis of foreign and domestic experience of technological transformation of healthcare, depending on the level of development of telemedicine.

서비스 부합성과 이용자 보상추구 성향에 따른 게임화 서비스 몰입 연구

This study attempted to compare and analyze mobile banking, mobile healthcare, and virtual communities, which are representative service groups with gamification, in order to more accurately evaluate acceptance behavior on gamification. In addition, rather than simply comparing services with gamification elements, we focus on the congruence between the main service and the additional service (gamification elements). We sought to mature the academic discussion of gamification service reasearch by classifying users' characteristics as benefit-seeking tendencies and service congruence level. And we compared to service acceptance behaviors that vary depending on both levels.&#x0D; At a result of the verification, it was found that the level of engagement in gamification service depending on the service congruence and user's benefit-seeking tendencies. Specifically, in the case of mobile banking service, groups that highly perceive service congruence and seek to practical benefit showed the highest level of service engagement. And in the case of mobile healthcare service, groups that highly perceive service congruence and seek to self-actualization showed the highest level of service engagement. Last, in the case of virtual communities, groups that highly perceive service congruence and seek to social relationship showed the highest level of service engagement.