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Background: Out-of-hospital cardiac arrest (OHCA) is a major global health challenge, characterized by poor survival outcomes worldwide. Resource-limited settings are burdened with suboptimal emergency response and worse outcomes than high-resource areas. Engaging the community in the response to OHCA has the potential to improve outcomes, although an overview of community interventions in resource-limited settings has not been provided.

Objective: This review evaluated the scope of community-based OHCA interventions in resource-limited settings.

Methods: Literature searches in electronic databases (MEDLINE, EMBASE, Global Health, CINAHL, Cochrane Central Register of Controlled Clinical Trials) and grey literature sources were performed. Abstract screening, full-text review, and data extraction of eligible studies were conducted independently by two reviewers. The PCC (Population, Concept, and Context) framework was used to assess study eligibility. Studies that evaluated community-based interventions for laypeople (Population), targeting emergency response activation, cardiopulmonary resuscitation (CPR), or automated external defibrillator (AED) use (Concept) in resource-limited settings (Context) were included. Resource-limited settings were identified by financial pressures (low-income or lower-middle-income country, according to World Bank data on year of publication) or geographical factors (setting described using keywords indicative of geographical remoteness in upper-middle-income or high-income country).

Results: Among 14,810 records identified from literature searches, 60 studies from 28 unique countries were included in this review. Studies were conducted in high-income (n = 35), upper-middle-income (n = 2), lower-middle-income (n = 22), and low-income countries (n = 1). Community interventions included bystander CPR and/or AED training (n = 34), community responder programs (n = 8), drone-delivered AED networks (n = 6), dispatcher-assisted CPR programs (n = 4), regional resuscitation campaigns (n = 3), public access defibrillation programs (n = 3), and crowdsourcing technologies (n = 2). CPR and/or AED training were the only interventions evaluated in low-income, lower-middle-income, and upper-middle-income countries.

Conclusions: Interventions aimed at improving the community response to OHCA in resource-limited settings differ globally. There is a lack of reported studies from low-income countries and certain continental regions, including South America, Africa, and Oceania. Evaluation of interventions other than CPR and/or AED training in low- and middle-income countries is needed to guide community emergency planning and health policies.

Introduction Although the global survival rate of patients after out-of-hospital cardiac arrest (OHCA) has increased in the previous years, there still remain significant multifactorial public health challenges with many important aspects influencing the overall survival rate of these patients. The objective of this article is to analyze basic epidemiological parameters of OHCA in Serbia and to evaluate the influence of pre-hospitalization factors on the survival of OHCA patients. Methods Data on OHCA within the Eureca Serbia Registry was collected according to the EuReCa Study protocol during the period October 1, 2014 – December 31, 2019, and included basic demographic data of the patients, data related to OHCA prior to hospital arrival, as well as data regarding subsequent hospitalization. Results The study included 6,266 EuReCa events (54% males), with a median age of 73 years [interquartile range (IQR) 63-82]. Cardiac arrest was witnessed in 3,111 out of 6,266 cases (49.6%), of which 2,725 cases (87.6%) were witnessed by bystanders and 286 cases (12.4%) by the emergency medical service (EMS) team. Resuscitation measures were attempted in 2,097 of 3,111 (67.4%) witnessed OHCA cases. Bystander cardiopulmonary resuscitation (CPR) was initiated in 288 cases within the bystander-witnessed group of 2,725 cases (10.6%). An initial shockable rhythm was detected in 323 out of 3,111 witnessed cases (10.4%). Any return of spontaneous circulation (ROSC) prior to hospital arrival was observed in 441 out of 2,097 cases where CPR was initiated (21.0%). Within the group of 2,097 events where CPR was initiated, in 287 cases the patient was transported to the hospital with ROSC (13.7%). An automated external defibrillator (AED) was used by bystanders in three cases. The collapse in locations other than the place of residence [p < 0.01; odds ratio (OR) 3.928], attempt to initiate CPR by a bystander (p < 0.01; OR 2.169), and presence of initial shockable rhythm (p = 0.01; OR 2.070) were observed as significant predictors of any ROSC in OHCA patients. Out of 287 patients hospitalized with ROSC, 54 (18.8%) were discharged alive. Conclusion Collapse outside of residence place, bystander CPR initiation, and initially detected shockable rhythm are important predictors of ROSC prior to hospital arrival and overall survival. Key factors of CPR-providing performance observed in this study were witnessing OHCA, CPR initiated by a bystander, presence of initial shockable rhythm, and any ROSC prior to hospital arrival.

 

Objectives: Cardiac disease is the leading cause of death worldwide. If a proper diagnosis is made early, cardiovascular problems can be prevented. The ECG test is a diagnostic method used on the screen for heart disease. Based on a combination of multi-field extraction and nonlinear analysis of ECG data, this paper presents a framework for automated detection of heart disease. The main aim of this study is to develop a model for future diagnosis of cardiac vascular disease using ECG analysis and symptom-based detection.

Methods: Discrete wavelet transform and Nonlinear Vector Decomposed Neural Network methods are used to predict Cardiac disease. Here is the discrete wavelet transform used for preprocessing to remove unwanted noise or artifacts. The neural network was fed with thirteen clinical features as input which was then trained using a non-linear vector decomposition of the presence or absence of heart disease.

Results: The modules were implemented, trained, and tested using UCI and Physio net data repositories. The sensitivity, specificity and accuracy of this research work are 92.0%, 89.33% and 90.67% CONCLUSIONS: The proposed approach can discover complex non-linear correlations between dependent and independent variables without requiring traditional statistical training. The suggested approach improves ECG classification accuracy, allowing for more accurate cardiac disease diagnosis. The accuracy of ECG categorization in identifying cardiac illness is far greater than these other approaches.

 

Wearable technology is a promising and revolutionary technology that is changing some aspects of our standard of living to a great extent, including health monitoring, sport and fitness, performance tracking, education, and entertainment. This article presents a comprehensive literature review of over 160 articles related to state-of-the-art human wearable technologies. We provide a thorough understanding of the materials, power sources, sensors, and manufacturing processes, and the relationships between these to capture opportunities for enhancement and challenges to overcome in wearables. As a result of our review, we have determined the need for the development of a comprehensive, robust manufacturing system alongside specific standards and regulations that take into account wearables’ unique characteristics. Seeing the whole picture will provide a frame reference and road map for researchers and industries through the design, manufacturing, and commercialisation of effective, portable, self-powered, multi-sensing ultimate future wearable devices and create opportunities for new innovations and applications.

 

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.

 

Thin-film microfabrication-based bio-integrated sensors are widely used for a broad range of applications that require continuous measurements of biophysical and biochemical signals from the human body. Typically, they are fabricated using standard photolithography and etching techniques. This traditional method is capable of producing a precise, thin, and flexible bio-integrated sensor system. However, it has several drawbacks, such as the fact that it can only be used to fabricate sensors on a planar surface, it is highly complex requiring specialized high-end facilities and equipment, and it mostly allows only 2D features to be fabricated. Therefore, developing bio-integrated sensors via 3D-printing technology has attracted particular interest. 3D-printing technology offers the possibility to develop sensors on nonplanar substrates, which is beneficial for noninvasive bio-signal sensing, and to directly print on complex 3D nonplanar organ structures. Moreover, this technology introduces a highly flexible and precisely controlled printing process to realize patient-specific sensor systems for ultimate personalized medicine, with the potential of rapid prototyping and mass customization. This review summarizes the latest advancements in 3D-printed bio-integrated systems, including 3D-printing methods and employed printing materials. Furthermore, two widely used 3D-printing techniques are discussed, namely, ex-situ and in-situ fabrication techniques, which can be utilized in different types of applications, including wearable and smart-implantable biosensor systems.

 

The development of new biosensor technologies and their active use as wearable devices have offered mobility and flexibility to conventional western medicine and personal fitness tracking. In the development of biosensors, transducers stand out as the main elements converting the signals sourced from a biological event into a detectable output. Combined with the suitable bio-receptors and the miniaturization of readout electronics, the functionality and design of the transducers play a key role in the construction of wearable devices for personal health control. Ever-growing research and industrial interest in new transducer technologies for point-of-care (POC) and wearable bio-detection have gained tremendous acceleration by the pandemic-induced digital health transformation. In this article, we provide a comprehensive review of transducers for biosensors and their wearable applications that empower users for the active tracking of biomarkers and personal health parameters.

 

Smart electronic devices based on micro-controllers, also referred to as fashion electronics, have raised wearable technology. These devices may process physiological information to facilitate the wearer’s immediate biofeedback in close contact with the body surface. Standard market wearable devices detect observable features as gestures or skin conductivity. In contrast, the technology based on electrochemical biosensors requires a biomarker in close contact with both a biorecognition element and an electrode surface, where electron transfer phenomena occur. The noninvasiveness is pivotal for wearable technology; thus, one of the most common target tissues for real-time monitoring is the skin. Noninvasive biosensors formats may not be available for all analytes, such as several proteins and hormones, especially when devices are installed cutaneously to measure in the sweat. Processes like cutaneous transcytosis, the paracellular cell-cell unions, or even reuptake highly regulate the solutes content of the sweat. This review discusses recent advances on wearable devices based on electrochemical biosensors for biomarkers with a complex blood-to-sweat partition like proteins and some hormones, considering the commented release regulation mechanisms to the sweat. It highlights the challenges of wearable epidermal biosensors (WEBs) design and the possible solutions. Finally, it charts the path of future developments in the WEBs arena in converging/emerging digital technologies.

 

Recent advancements in wearable electrochemical biosensors have opened new avenues for on-body and continuous detection of biomarkers, enabling personalized, real-time, and preventive healthcare. While glucose monitoring has set a precedent for wearable biosensors, the field is rapidly expanding to include a wider range of analytes crucial for disease diagnosis, treatment, and management. In this review, recent key innovations are examined in the design and manufacturing underpinning these biosensing platforms including biorecognition elements, signal transduction methods, electrode and substrate materials, and fabrication techniques. The applications of these biosensors are then highlighted in detecting a variety of biochemical markers, such as small molecules, hormones, drugs, and macromolecules, in biofluids including interstitial fluid, sweat, wound exudate, saliva, and tears. Additionally, the review also covers recent advances in wearable electrochemical biosensing platforms, such as multi-sensory integration, closed-loop control, and power supply. Furthermore, the challenges associated with critical issues are discussed, such as biocompatibility, biofouling, and sensor degradation, and the opportunities in materials science, nanotechnology, and artificial intelligence to overcome these limitations.

 

Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.