Wearable Sensors for Remote Health Monitoring

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Effects of using wearable devices to monitoring physical activity in pulmonary rehabilitation programs for chronic respiratory diseases: A systematic review protocol

INTRODUCTION

Pulmonary rehabilitation (PR) is an intervention aimed at the comprehensive care of individuals with chronic respiratory diseases. Patients with chronic obstructive pulmonary disease (COPD) and asthma present low levels of physical fitness because they avoid physical exercises due to the fear of triggering recurrent symptoms. Wearable devices have been integrated into behavioral modification interventions for physical activity in PR protocols. Therefore, this review aims to identify how wearable devices are being utilized for monitoring chronic respiratory diseases in pulmonary rehabilitation programs.

METHODS AND ANALYSIS

Searches will be conducted on Medline, Cochrane Central Register of Controlled Trials, Embase (CENTRAL), CINAHL and PEDro electronic databases, as well as a search in the grey literature. We will include baseline data from randomized clinical trials reporting the use of wearable devices for monitoring physical activity in protocols for pulmonary rehabilitation programs for chronic respiratory diseases. Studies that discuss only the development of algorithms or applications for the assessment of diseases or unavailable full texts will be excluded. The main reviewer will conduct the initial search and exclusion of duplicates, while two independent reviewers will select studies, extract data, and assess the methodological quality using the PEDro tool.

PROSPERO REGISTRATION NUMBER

CRD42024504137.

AI-Powered Patient-Centered Care: A Call to Action for Innovation

SUMMARY

The influential report Crossing the Quality Chasm: A New Health System for the 21st Century established six core objectives to enhance healthcare quality. It highlighted the necessity for healthcare to encompass safety, effectiveness, a patient-centered approach, timeliness, efficiency, and equity. This essay focuses on one of these six core objectives: a patient-centered approach. Healthcare leaders actively seek solutions to improve and ensure the delivery of high-quality care. The imperative to provide quality healthcare underscores the need for artificial intelligence (AI) to become an essential component in a patient-centered approach rather than merely an optional advantage. Despite the expansion of AI, there is a lack of understanding of how AI can improve patient-centered care. This essay examines the fundamental aspects of patient-centered care, as outlined by the Picker Institute, while also exploring the prospective role of AI in advancing the core principles of patient-centered care and proposing frameworks for applying AI in healthcare.

Nonwoven fiber meshes for oxygen sensing

Accurate oxygen sensing and cost-effective fabrication are crucial for the adoption of wearable devices inside and outside the clinical setting. Here we introduce a simple strategy to create nonwoven polymeric fibrous mats for a notable contribution towards addressing this need. Although morphological manipulation of polymers for cell culture proliferation is commonplace, especially in the field of regenerative medicine, non-woven structures have not been used for oxygen sensing. We used an airbrush spraying, i.e. solution blowing, to obtain nonwoven fiber meshes embedded with a phosphorescent dye. The fibers serve as a polymer host for the phosphorescent dye and are shown to be non-cytotoxic. Different composite fibrous meshes were prepared and favorable mechanical and oxygen-sensing properties were demonstrated. A Young's modulus of 9.8 MPa was achieved and the maximum oxygen sensitivity improved by a factor of ∼2.9 compared to simple drop cast film. The fibers were also coated with silicone rubbers to produce mechanically robust sensing films. This reduced the sensing performance but improved flexibility and mechanical properties. Lastly, we are able to capture oxygen concentration maps via colorimetry using a smartphone camera, which should offer unique advantages in wider usage. Overall, the introduced composite fiber meshes show a potential to significantly improve cell cultures and healthcare monitoring via absolute oxygen sensing.

Intensive Care Unit Without Walls

Critical illness is a continuum with different phases and trajectories. The "Intensive Care Unit (ICU) without walls" concept refers to a model whereby care is adjusted in response to the patient's needs, priorities, and preferences at each stage from detection, escalation, early decision making, treatment and organ support, followed by recovery and rehabilitation, within which all healthcare staff, and the patient are equal partners. The rapid response system incorporates monitoring and alerting tools, a multidisciplinary critical care outreach team and care bundles, supported with education and training, analytical and governance functions, which combine to optimise outcomes of critically ill patients, independent of location.

Introducing a Remote Patient Monitoring Usability Impact Model to Overcome Challenges

Telehealth and remote patient monitoring (RPM), in particular, have been through a massive surge of adoption since 2020. This initiative has proven potential for the patient and the healthcare provider in areas such as reductions in the cost of care. While home-use medical devices or wearables have been shown to be beneficial, a literature review illustrates challenges with the data generated, driven by limited device usability. This could lead to inaccurate data when an exam is completed without clinical supervision, with the consequence that incorrect data lead to improper treatment. Upon further analysis of the existing literature, the RPM Usability Impact model is introduced. The goal is to guide researchers and device manufacturers to increase the usability of wearable and home-use medical devices in the future. The importance of this model is highlighted when the user-centered design process is integrated, which is needed to develop these types of devices to provide the proper user experience.

Association of smart elderly care and quality of life among older adults: the mediating role of social support

BACKGROUND

In the current context of ageing, the field of smart elderly care has gradually developed, contributing to the promotion of health among older adults. While the positive impact on health has been established, there is a scarcity of research examining its impact on the quality of life (QoL). This study aims to investigate the mediating role of social support in the relationship between smart elderly care and QoL among older adults.

METHODS

A total of 1313 older adults from Zhejiang Province, China, participated in the study. Questionnaires were used to collect data on participants' basic demographic information, smart elderly care, social support, and QoL. The descriptive analyses of the demographic characteristics and correlation analyses of the three variables were calculated. Indirect effects were tested using bootstrapped confidence intervals (CI).

RESULTS

The analysis revealed a positive association between smart elderly care and social support (β = 0.42, p < 0.01), as well as a positive correlation between social support and QoL (β = 0.65, p < 0.01). Notably, social support emerged as an important independent mediator (effect size = 0.28, 95% bootstrap CI 0.24 to 0.32) in the relationship between smart elderly care and QoL.

CONCLUSIONS

The results of this study underscore the importance of promoting the utilization of smart elderly care and improving multi-faceted social support for older adults, as these factors positively contribute to the overall QoL.

Multi-Interface Engineering of MXenes for Self-Powered Wearable Devices

Self-powered wearable devices with integrated energy supply module and sensitive sensors have significantly blossomed for continuous monitoring of human activity and the surrounding environment in healthcare sectors. The emerging of MXene-based materials has brought research upsurge in the fields of energy and electronics, owing to their excellent electrochemical performance, large surface area, superior mechanical performance, and tunable interfacial properties, where their performance can be further boosted via multi-interface engineering. Herein, a comprehensive review of recent progress in MXenes for self-powered wearable devices is discussed from the aspects of multi-interface engineering. The fundamental properties of MXenes including electronic, mechanical, optical, and thermal characteristics are discussed in detail. Different from previous review works on MXenes, multi-interface engineering of MXenes from termination regulation to surface modification and their impact on the performance of materials and energy storage/conversion devices are summarized. Based on the interfacial manipulation strategies, potential applications of MXene-based self-powered wearable devices are outlined. Finally, proposals and perspectives are provided on the current challenges and future directions in MXene-based self-powered wearable devices.

Benchtop Performance of Novel Mixed Ionic-Electronic Conductive Electrode Form Factors for Biopotential Recordings

Background: Traditional gel-based (wet) electrodes for biopotential recordings have several shortcomings that limit their practicality for real-world measurements. Dry electrodes may improve usability, but they often suffer from reduced signal quality. We sought to evaluate the biopotential recording properties of a novel mixed ionic-electronic conductive (MIEC) material for improved performance. Methods: We fabricated four MIEC electrode form factors and compared their signal recording properties to two control electrodes, which are electrodes commonly used for biopotential recordings (Ag-AgCl and stainless steel). We used an agar synthetic skin to characterize the impedance of each electrode form factor. An electrical phantom setup allowed us to compare the recording quality of simulated biopotentials with ground-truth sources. Results: All MIEC electrode form factors yielded impedances in a similar range to the control electrodes (all <80 kΩ at 100 Hz). Three of the four MIEC samples produced similar signal-to-noise ratios and interfacial charge transfers as the control electrodes. Conclusions: The MIEC electrodes demonstrated similar and, in some cases, better signal recording characteristics than current state-of-the-art electrodes. MIEC electrodes can also be fabricated into a myriad of form factors, underscoring the great potential this novel material has across a wide range of biopotential recording applications.

ICT in Nursing and Patient Healthcare Management: Scoping Review and Case Studies

Over the past few decades, Information and Communication Technologies (ICT) have revolutionized the fields of nursing and patient healthcare management. This scoping review and the accompanying case studies shed light on the extensive scope and impact of ICT in these critical healthcare domains. The scoping review explores the wide array of ICT tools employed in nursing care and patient healthcare management. These tools encompass electronic health records systems, mobile applications, telemedicine solutions, remote monitoring systems, and more. This article underscores how these technologies have enhanced the efficiency, accuracy, and accessibility of clinical information, contributing to improved patient care. ICT revolution has revitalized nursing care and patient management, improving the quality of care and patient satisfaction. This review and the accompanying case studies emphasize the ongoing potential of ICT in the healthcare sector and call for further research to maximize its benefits.

Advancements in Polymer-Assisted Layer-by-Layer Fabrication of Wearable Sensors for Health Monitoring

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

Optical Camera Communications in Healthcare: A Wearable LED Transmitter Evaluation during Indoor Physical Exercise

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise.

Rational Design of a Surface Acoustic Wave Device for Wearable Body Temperature Monitoring

Continuous monitoring of vital signs based on advanced sensing technologies has attracted extensive attention due to the ravages of COVID-19. A maintenance-free and low-cost passive wireless sensing system based on surface acoustic wave (SAW) device can be used to continuously monitor temperature. However, the current SAW-based passive sensing system is mostly designed at a low frequency around 433 MHz, which leads to the relatively large size of SAW devices and antenna, hindering their application in wearable devices. In this paper, SAW devices with a resonant frequency distributed in the 870 MHz to 960 MHz range are rationally designed and fabricated. Based on the finite-element method (FEM) and coupling-of-modes (COM) model, the device parameters, including interdigital transducer (IDT) pairs, aperture size, and reflector pairs, are systematically optimized, and the theoretical and experimental results show high consistency. Finally, SAW temperature sensors with a quality factor greater than 2200 are obtained for real-time temperature monitoring ranging from 20 to 50 °C. Benefitting from the higher operating frequency, the size of the sensing system can be reduced for human body temperature monitoring, showing its potential to be used as a wearable monitoring device in the future.

Patient and Staff Experience of Remote Patient Monitoring-What to Measure and How: Systematic Review

BACKGROUND

Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience-measuring methods and tools exists.

OBJECTIVE

This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain.

METHODS

Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By "RPM interventions," we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique.

RESULTS

In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation.

CONCLUSIONS

At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.

Anti-freezing hydrogel regulated by ice-structuring proteins/cellulose nanofibers system as flexible sensor for winter sports

Conductive hydrogels are widely used as sensors in wearable devices. However, hydrogels cannot endure harsh low-temperature environments. Herein, a new regulatory system based on natural ice-structuring proteins (ISPs) and cellulose nanofibers (CNFs) is introduced into hydrogel network consisting of chemically crosslinked network of copolymerized acrylamide and 2-acrylamide-2-methylpropanesulfonic acid, and physically crosslinked polyvinyl alcohol chains, affording an anti-freezing hydrogel with high conductivity (2.63 S/m). These hydrogels show excellent adhesion behavior to various matrices (including aluminum, glass, pigskin, and plastic). Their mechanical properties are significantly improved with the increase in CNF content (tensile strength of 106.4 kPa, elastic modulus of 133.8 kPa). In addition, ISPs inhibit the growth of ice. This endows the hydrogels with anti-freezing property and allows them to maintain satisfactory mechanical properties, conductivity and sensing properties below zero degrees. Moreover, this hydrogel shows high sensitivity to tensile and compressive deformation (GF = 5.07 at 600-800 % strain). Therefore, it can be utilized to develop strain-type pressure sensors that can be attached directly to human skin for detecting various body motions accurately, reliably, and stably. This study proposes a simple strategy to improve the anti-freezing property of hydrogels, which provides new insights for developing flexible hydrogel electronic devices for application in winter sports.

Cardiovascular hardware simulator and artificial aorta-generated central blood pressure waveform database according to various vascular ages for cardiovascular health monitoring applications

This study presents a database of central blood pressure waveforms according to cardiovascular health conditions, to supplement the lack of clinical data in cardiovascular health research, constructed by a cardiovascular simulator. Blood pressure (BP) is the most frequently measured biomarker, and in addition to systolic and diastolic pressure, its waveform represents the various conditions of cardiovascular health. A BP waveform is formed by overlapping the forward and reflected waves, which are affected by the pulse wave velocity (PWV). The increase in vascular stiffness with aging increases PWV, and the PWV-age distribution curve is called vascular age. For cardiovascular health research, extensive data of central BP waveform is essential, but the clinical data published so far are insufficient and imbalanced in quantity and quality. This study reproduces the central BP waveform using a cardiovascular hardware simulator and artificial aortas, which mimic the physiological structure and properties of the human. The simulator can adjust cardiovascular health conditions to the same level as humans, such as heart rate of 40-100 BPM, stroke volume of 40-100 mL, and peripheral resistance of 12 steps. Also, 6 artificial aortas with vascular ages in the 20-70 were fabricated to reproduce the increase in vascular stiffness due to aging. Vascular age calculated from measured stiffness of artificial aorta and central BP waveform showed an error of less than 3 years from the clinical value. Through this, a total of 636 waveforms were created to construct a central BP waveform database according to controlled various cardiovascular health conditions.

Continuously monitoring the human machine? - A cross-sectional study to assess the acceptance of wearables in Germany

Background: Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. Methods: Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. Results: Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. Conclusion: The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.

Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study

BACKGROUND

Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. The Moveo device comprises 4 sensors positioned on the back of the hands and feet accompanied by a mobile application that gathers data and ML algorithms that are hosted on a cloud platform. The sensors measure movement signals, which are then transferred to the cloud through the mobile application. The cloud triggers a pipeline for feature extraction and subsequently feeds the ML model with these extracted features.

METHODS

The pilot study included 23 participants. Eleven patients with diabetes and suspected diabetic neuropathy were included in the experimental group. In the control group, 8 patients had suspected radiculopathy, and 4 participants were healthy. All participants underwent an electrodiagnostic examination (EDx) and a Moveo examination, which consists of sensors placed on the feet and back of the participant's hands and use of the mobile application. The participant performs six tests that are part of a standard neurological examination, and a ML algorithm calculates the probability of diabetic neuropathy. A user experience questionnaire was used to compare participant experiences with regard to both methods.

RESULTS

The total accuracy of the algorithm is 82.1%, with 78% sensitivity and 87% specificity. A high linear correlation up to 0.722 was observed between Moveo and EDx features, which underpins the model's adequacy. The user experience questionnaire revealed that the majority of patients preferred the less painful method.

CONCLUSIONS

Moveo represents an accurate, easy-to-use device suitable for home environments, showing promising results and potential for future usage.

E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin

The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.

Telehealth of cardiac devices for CVD treatment

This review covers currently available cardiac implantable electronic devices (CIEDs) as well as updated progress in real-time monitoring techniques for CIEDs. A variety of implantable and wearable devices that can diagnose and monitor patients with cardiovascular diseases are summarized, and various working mechanisms and principles of monitoring techniques for Telehealth and mHealth are discussed. In addition, future research directions are presented based on the rapidly evolving research landscape including Artificial Intelligence (AI).

Exploring the Potentials of Chitin and Chitosan-Based Bioinks for 3D-Printing of Flexible Electronics: The Future of Sustainable Bioelectronics

Chitin and chitosan-based bioink for 3D-printed flexible electronics have tremendous potential for innovation in healthcare, agriculture, the environment, and industry. This biomaterial is suitable for 3D printing because it is highly stretchable, super-flexible, affordable, ultrathin, and lightweight. Owing to its ease of use, on-demand manufacturing, accurate and regulated deposition, and versatility with flexible and soft functional materials, 3D printing has revolutionized free-form construction and end-user customization. This study examined the potential of employing chitin and chitosan-based bioinks to build 3D-printed flexible electronic devices and optimize bioink formulation, printing parameters, and postprocessing processes to improve mechanical and electrical properties. The exploration of 3D-printed chitin and chitosan-based flexible bioelectronics will open new avenues for new flexible materials for numerous industrial applications.

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.

Wearable technology use in long-term care facilities for older adults: a scoping review protocol

OBJECTIVE

The objective of this scoping review is to explore how wearable technology is being used to care for older adults in long-term care facilities.

INTRODUCTION

The use of digital health technologies to support care delivery in long-term care facilities for older adults has grown significantly in recent years, especially since the COVID-19 pandemic. Wearable technology refers to devices worn or attached to the body that can track a variety of health-related data, such as vital signs, falls, and sleep patterns. Despite the evidence that wearable devices are playing an increasing role in older adults' care, no review has been conducted on how wearable technology is being used in long-term care facilities.

INCLUSION CRITERIA

This review will consider studies that include people aged over 65, with any health condition or level of disability, who live in long-term care facilities. Primary and secondary studies using quantitative, qualitative, and mixed methods study designs will be included. Dissertations and policy documents will also be considered.

METHODS

Data sources will include comprehensive searches of electronic databases (MEDLINE, Embase, CINAHL, and Scopus), gray literature, and reference scanning of relevant studies. Two independent reviewers will screen titles, abstracts, and full texts of the selected studies. Data extraction will be performed using a tool developed by the researchers. Data will be mapped and analyzed. Descriptive frequencies and content analysis will be included, along with the tabulated results, which will be used to present the findings with regard to the review objectives.

REVIEW REGISTRATION

Open Science Framework https://osf.io/r9qtd.

A New Player in the Game: Can Exergame Be of Support in the Management of Atrial Fibrillation?

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, currently affecting 2-3% of the world's population. Traditional exercise and physical activity interventions have been successfully implemented in the management of AF, with the aim of improving patients' quality of life and their exercise capacity, as well as reducing their mortality rate. Currently, new technology-mediated approaches to exercise, defined as exergame, have been shown to be successful in the delivery of exercise home-based interventions in patients with cardiovascular diseases. However, data on the effects of exergame on AF are not yet available. In this paper, we summarise the current literature on the role of traditional exercise in AF and how it affects the pathophysiology of this condition. We also review the current literature on exergame and its employment in cardiac rehabilitation and suggest its potential role in the management of AF patients. A review of the evidence suggests that traditional exercise (of light-to-moderate intensity) is beneficial in patients with AF. Additionally, exergame seems to be a promising approach for delivering exercise interventions in patients with cardiovascular diseases. Exergame may be a promising tool to improve the quality of life and exercise capacity in patients with AF, with the additional advantage of being remotely delivered, and the potential to increase patients' engagement. Proper guidelines are required to prescribe exergame interventions, considering the principles of traditional exercise prescription and applying them to this new e-health approach. Further studies are needed to validate the use of exergame in patients with AF.

Key Factors Influencing the Operationalization and Effectiveness of Telemedicine Services in Henan Province, China: Cross-Sectional Analysis

BACKGROUND

Telemedicine has demonstrated its potential in alleviating the unbalanced distribution of medical resources across different regions. Henan, a province in China with a population of approximately 100 million, is especially affected by a health care divide. The province has taken a proactive step by establishing a regional collaborative platform for telemedicine services provided by top-tier provincial hospitals.

OBJECTIVE

We aim to identify the key factors that influence the current operationalization and effectiveness of telemedicine services in Henan province. The insights gained from this study will serve as valuable references for enhancing the efficient operation of telemedicine platforms in low- and middle-income regions.

METHODS

We analyzed service reports from the performance management system of telemedicine services in Henan province throughout 2020. Using descriptive statistics and graphical methods, we examined key influencing factors, such as management competency; device configuration; and hospital capability, capacity, and service efficacy, across hospitals at 2 different tiers. In addition, we used generalized linear models and multiple linear regression models to identify key operational factors that significantly affect the service volume and efficacy of 2 major telemedicine services, namely teleconsultation and tele-education.

RESULTS

Among the 89 tier 3 hospitals and 97 tier 2 hospitals connected to the collaborative telemedicine platform, 65 (73%) and 55 (57%), respectively, have established standardized management procedures for telemedicine services. As the primary delivery method for telemedicine services, 90% (80/89) of the tier 3 hospitals and 94% (91/97) of the tier 2 hospitals host videoconferencing consultations through professional hardware terminals rather than generic computers. Teleconsultation is the dominant service type, with an average annual service volume per institution of 173 (IQR 37-372) and 60 (IQR 14-271) teleconsultations for tier 3 and tier 2 hospitals, respectively. Key factors influencing the service volume at each hospital include available funding, management competency, the number of connected upper tiers, and the number of professional staff. After receiving teleconsultations from tier 3 (65/89, 73%) and tier 2 (61/97, 63%) hospitals, patients reported significant improvements in their medical conditions. In addition, we observed that service efficacy is positively influenced by management competency, financial incentives, the number of connected upper or lower tiers, and the involvement of participating medical professionals.

CONCLUSIONS

Telemedicine has become increasingly popular in Henan province, with a notable focus on teleconsultation and tele-education services. Despite its popularity, many medical institutions, especially tier 2 hospitals, face challenges related to management competency. In addition to enhancing the effectiveness of existing telemedicine services, health care decision-makers in Henan province and other low- and middle-income regions should consider expanding the service categories, such as including remote emergency care and telesurgery, which have promise in addressing crucial health care needs in these regions.

Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective

Digital health has transformed the healthcare landscape by leveraging technology to improve patient outcomes and access to medical services. The COVID-19 pandemic has highlighted the urgent need for digital healthcare solutions that can mitigate the impact of the outbreak while ensuring patient safety. In this chapter, we delve into how digital health technologies such as telemedicine, mobile apps, and wearable devices can provide personalized care, reduce healthcare provider burden, and lower healthcare costs. We also explore the creation of a greenway of digital healthcare that safeguards patient confidentiality, enables efficient communication, and ensures cost-effective payment systems. This chapter showcases the potential of digital health to revolutionize healthcare delivery while ensuring patient well-being and medical staff satisfaction.

Benefits and Challenges of Remote Patient Monitoring as Perceived by Health Care Practitioners: A Systematic Review

BACKGROUND

Remote patient monitoring (RPM), or telemonitoring, offers ways for health care practitioners to gather real-time information on the physiological conditions of patients. As telemedicine, and thus telemonitoring, is becoming increasingly relevant in today's society, understanding the practitioners' opinions is crucial. This systematic review evaluates the perspectives and experiences of health care practitioners with telemonitoring technologies.

METHODS

A database search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the selection of articles measuring health care practitioners' perspectives and experiences with RPM technologies published between 2017 and 2021. Only articles written in English were included. No statistical analysis was performed and thus this is a qualitative review.

RESULTS

A total of 1605 studies were identified after the initial search. After applying the inclusion and exclusion criteria of this review's authors, 13 articles were included in this review. In all, 2351 practitioners' perspectives and experience utilizing RPM technology in a variety of medical specialties were evaluated through close- and open-ended surveys. Recurring themes emerged for both the benefits and challenges. Common benefits included continuous monitoring of patients to provide prompt care, improvement of patient self-care, efficient communication, increased patient confidence, visualization of health trends, and greater patient education. Challenges comprised increased workload, higher patient anxiety, data inaccuracy, disorienting technology, financial issues, and privacy concerns.

CONCLUSION

Health care practitioners generally believe that RPM is feasible for application. Additionally, there is a consensus that telemonitoring strategies will become increasingly relevant. However, there are still drawbacks to the technology that need to be considered.

Recent advances in wearable iontronic sensors for healthcare applications

Iontronic sensors have garnered significant attention as wearable sensors due to their exceptional mechanical performance and the ability to maintain electrical performance under various mechanical stimuli. Iontronic sensors can respond to stimuli like mechanical stimuli, humidity, and temperature, which has led to exploration of their potential as versatile sensors. Here, a comprehensive review of the recent researches and developments on several types of iontronic sensors (e.g., pressure, strain, humidity, temperature, and multi-modal sensors), in terms of their sensing principles, constituent materials, and their healthcare-related applications is provided. The strategies for improving the sensing performance and environmental stability of iontronic sensors through various innovative ionic materials and structural designs are reviewed. This review also provides the healthcare applications of iontronic sensors that have gained increased feasibility and broader applicability due to the improved sensing performance. Lastly, outlook section discusses the current challenges and the future direction in terms of the applicability of the iontronic sensors to the healthcare.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

The Role of 3D Printing Technologies in Soft Grippers

Soft grippers are essential for precise and gentle handling of delicate, fragile, and easy-to-break objects, such as glassware, electronic components, food items, and biological samples, without causing any damage or deformation. This is especially important in industries such as healthcare, manufacturing, agriculture, food handling, and biomedical, where accuracy, safety, and preservation of the objects being handled are critical. This article reviews the use of 3D printing technologies in soft grippers, including those made of functional materials, nonfunctional materials, and those with sensors. 3D printing processes that can be used to fabricate each class of soft grippers are discussed. Available 3D printing technologies that are often used in soft grippers are primarily extrusion-based printing (fused deposition modeling and direct ink writing), jet-based printing (polymer jet), and immersion printing (stereolithography and digital light processing). The materials selected for fabricating soft grippers include thermoplastic polymers, UV-curable polymers, polymer gels, soft conductive composites, and hydrogels. It is conclude that 3D printing technologies revolutionize the way soft grippers are being fabricated, expanding their application domains and reducing the difficulties in customization, fabrication, and production.

The Role of Wearable Sensors in the Future Primary Healthcare - Preferences of the Adult Swiss Population: A Mixed Methods Approach

Wearable sensors have the potential to increase continuity of care and reduce healthcare expenditure. The user concerns and preferences regarding wearable sensors are the least addressed topic in related literature. Therefore, this study aimed first, to examine the preferences of the adult Swiss population regarding the use of wearable sensors in primary healthcare. Second, the study aimed to explain and learn more about these preferences and why such wearable sensors would or would not be used. An explanatory sequential design was used to reach the two aims. In the initial quantitative phase preferences of a nationwide survey were analyzed descriptively and a multivariable ordered logistic regression was used to identify key characteristics, that influence the preferences. In the second phase, eight semi-structured interviews were conducted. The cleaned study sample of the survey included 687 participants, 46% of whom gave a positive rating regarding the use of wearable sensors. In contrast, 44% gave a negative rating and 10% were neutral. The interviews showed that sensors should be small, not flashy and be compatible with everyday activities. Individuals without a current health risk or existing chronic disease showed lower preferences for using wearable sensors, particularly because they fear losing control over their own body. In contrast, individuals with increased risk or with an existing chronic disease were more likely to use wearable sensors as they can increase the personal safety and provide real-time health information to physicians. Therefore, an important deciding factor for and against the use of wearable sensors seems to be the perceived personal susceptibility for potential health problems.

Telemedicine in the management of patients with headache: current situation and recommendations of the Spanish Society of Neurology's Headache Study Group

INTRODUCTION

The COVID-19 pandemic has caused an unexpected boost to telemedicine. We analyse the impact of the pandemic on telemedicine applied in Spanish headache consultations, review the literature, and issue recommendations for the implementation of telemedicine in consultations.

METHOD

The study comprised 3 phases: 1) review of the MEDLINE database since 1958 (first reported experience with telemedicine); 2) Google Forms survey sent to all members of the Spanish Society of Neurology's Headache Study Group (GECSEN); and 3) online consensus of GECSEN experts to issue recommendations for the implementation of telemedicine in Spain.

RESULTS

COVID-19 has increased waiting times for face-to-face consultations, increasing the use of all telemedicine modalities: landline telephone (from 75% before April 2020 to 97% after), mobile telephone (from 9% to 27%), e-mail (from 30% to 36%), and video consultation (from 3% to 21%). Neurologists are aware of the need to expand the availability of video consultations, which are clearly growing, and other e-health and m-health tools.

CONCLUSIONS

The GECSEN recommends and encourages all neurologists who assist patients with headaches to implement telemedicine resources, with the optimal objective of offering video consultation to patients under 60-65 years of age and telephone calls to older patients, although each case must be considered on an individual basis. Prior approval and advice must be sought from legal and IT services and the centre's management. Most patients with stable headache and/or neuralgia are eligible for telemedicine follow-up, after a first consultation that must always be held in person.

A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place

Shortness of breath is often considered a repercussion of aging in older adults, as respiratory illnesses like COPD1 or respiratory illnesses due to heart-related issues are often misdiagnosed, under-diagnosed or ignored at early stages. Continuous health monitoring using ambient sensors has the potential to ameliorate this problem for older adults at aging-in-place facilities. In this paper, we leverage continuous respiratory health data collected by using ambient hydraulic bed sensors installed in the apartments of older adults in aging-in-place Americare facilities to find data-adaptive indicators related to shortness of breath. We used unlabeled data collected unobtrusively over the span of three years from a COPD-diagnosed individual and used data mining to label the data. These labeled data are then used to train a predictive model to make future predictions in older adults related to shortness of breath abnormality. To pick the continuous changes in respiratory health we make predictions for shorter time windows (60-s). Hence, to summarize each day's predictions we propose an abnormal breathing index (ABI) in this paper. To showcase the trajectory of the shortness of breath abnormality over time (in terms of days), we also propose trend analysis on the ABI quarterly and incrementally. We have evaluated six individual cases retrospectively to highlight the potential and use cases of our approach.

Optimized polarization-independent Chand-Bali nano-antenna for thermal IR energy harvesting

A novel, polarization-independent, wide-angle reception Chand-Bali nano-antenna is proposed. An adjoint-based optimization algorithm is used to create the same resonance at both linear polarizations of the incident radiation. The nano-antenna optimal parameters reveal that two hot spots with a strong field enhancement are created. These hot-spots could be integrated with metal-insulator-metal (MIM) diodes to form a rectenna for infrared (IR) energy harvesting. The metallic resonators allow for selecting several materials to facilitate the fabrication of the nano-antenna and the MIM diode. The Chand-Bali-based IR rectennas are investigated and simulations demonstrate an improvement of more than one order of magnitude in efficiency compared to ones using traditional nano-antennas.

QTT-DLSTM: A Cloud-Edge-Aided Distributed LSTM for Cyber-Physical-Social Big Data

Cyber-physical-social systems (CPSS), an emerging cross-disciplinary research area, combines cyber-physical systems (CPS) with social networking for the purpose of providing personalized services for humans. CPSS big data, recording various aspects of human lives, should be processed to mine valuable information for CPSS services. To efficiently deal with CPSS big data, artificial intelligence (AI), an increasingly important technology, is used for CPSS data processing and analysis. Meanwhile, the rapid development of edge devices with fast processors and large memories allows local edge computing to be a powerful real-time complement to global cloud computing. Therefore, to facilitate the processing and analysis of CPSS big data from the perspective of multi-attributes, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented in this article. First, a tensor is used to represent the multi-attributes CPSS big data, which will be decomposed into the QTT form to facilitate distributed training and computing. Second, a distributed cloud-edge computing model is used to systematically process the CPSS data, including global large-scale data processing in the cloud, and local small-scale data processed at the edge. Third, a distributed computing strategy is used to improve the efficiency of training via partitioning the weight matrix and large amounts of input data in the QTT form. Finally, the performance of the proposed QTT-DLSTM method is evaluated using experiments on a public discrete manufacturing process dataset, the Li-ion battery dataset, and a public social dataset.

A Deep Regression Approach for Human Activity Recognition Under Partial Occlusion

In real-life scenarios, Human Activity Recognition (HAR) from video data is prone to occlusion of one or more body parts of the human subjects involved. Although it is common sense that the recognition of the majority of activities strongly depends on the motion of some body parts, which when occluded compromise the performance of recognition approaches, this problem is often underestimated in contemporary research works. Currently, training and evaluation is based on datasets that have been shot under laboratory (ideal) conditions, i.e. without any kind of occlusion. In this work, we propose an approach for HAR in the presence of partial occlusion, in cases wherein up to two body parts are involved. We assume that human motion is modeled using a set of 3D skeletal joints and also that occluded body parts remain occluded during the whole duration of the activity. We solve this problem using regression, performed by a novel deep Convolutional Recurrent Neural Network (CRNN). Specifically, given a partially occluded skeleton, we attempt to reconstruct the missing information regarding the motion of its occluded part(s). We evaluate our approach using four publicly available human motion datasets. Our experimental results indicate a significant increase of performance, when compared to baseline approaches, wherein networks that have been trained using only nonoccluded or both occluded and nonoccluded samples are evaluated using occluded samples. To the best of our knowledge, this is the first research work that formulates and copes with the problem of HAR under occlusion as a regression task.

Continuous Remote Monitoring in Moderate and Severe COVID-19 Patients

Background COVID-19 steadily built up the pressure on healthcare systems worldwide, creating the need for novel methods to alleviate the burden. Continuous remote monitoring of vital parameters reduces morbidity and mortality in hospitals by providing real-time disease data that can be analyzed through web portals. It enables healthcare workers to identify which patients require prompt administration of healthcare. Patients remain under the purview of their doctors and can be notified early if there are any deteriorations in the parameters being monitored. Aims To evaluate the use of remote monitoring in moderate and severe COVID-19 patients and to correlate the Dozee Early Warning Score (DEWS) with severity and outcome in moderate and severe COVID-19 patients. Materials and methods We conducted a prospective study on adult (>18 years old) moderate and severe COVID-19 patients during the second wave of COVID-19. The vitals of the subjects were continuously monitored using Dozee, a contactless remote patient monitoring system enabled with DEWS that reflects the overall patient condition based on respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO2). We assessed the correlation of DEWS with patients' clinical outcomes: deteriorated or recovered. Results Thirty-nine COVID-19 patients were recruited for the study, of whom 29 were discharged after recovery and 10 deteriorated and died. Respiratory rate trend, respiratory rate DEWS, SpO2 DEWS, and total DEWS showed a significant reduction in recovered patients, while the same parameters showed a significant increase followed by consistently high scores in patients who deteriorated and died due to the disease. Total DEWS was proportional to the risk of mortality in a patient. Conclusion We concluded that continuous vitals monitoring and the resulting DEWS in moderate and severe COVID-19 patients were indicators of their improvement or deterioration. DEWS uses continuous remote monitoring of routinely collected vitals (HR, RR, and SpO2) to serve as a predictor of patient outcome.

Investigating the Accuracy of Wheelchair Push Counts Measured by Fitness Watches: A Systematic Review

Wheelchair users face an elevated risk of metabolic syndromes due to their sedentary lifestyles. One of the methods to prevent and treat various metabolic syndromes is regular physical activity, which varies among individuals based on their abilities. Monitoring physical activity among them can be performed by using wearable physical activity monitors (WPAMs), which utilize accelerometers and algorithms to track wheelchair push counts. However, the accuracy of push count detection varies among the devices due to technological limitations. The objective of this literature review was to evaluate the accuracy of WPAMs, specifically smartwatches, in measuring physical activity in the wheelchair population. This systematic literature review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The databases PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched in November 2022 for relevant articles. The initial search yielded 447 articles, seven of which were selected based on the inclusion criteria, which were as follows: participant ability to maneuver a wheelchair, arm- or wrist-worn WPAMs, and articles published after 2017. Among the devices studied, the Apple Watch was determined to be the most accurate calibration system for wheelchair users, with the lowest mean absolute percentage error (MAPE). Each succeeding generation of the Apple Watch (first to fourth) studied was more accurate than the previous. The review demonstrates that research on wheelchair fitness tracking remains scarce and further studies are required to address this issue.

Wearable Sensors for Respiration Monitoring: A Review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

The role of digital transformation in addressing health inequalities in coastal communities: barriers and enablers

Healthcare systems worldwide are striving for the "quadruple aim" of better population health and well-being, improved experience of care, healthcare team well-being (including that of carers) and lower system costs. By shifting the balance of care from reactive to preventive by facilitating the integration of data between patients and clinicians to support prevention, early diagnosis and care at home, many technological solutions exist to support this ambition. Yet few have been mainstreamed in the NHS. This is particularly the case in English coastal areas which, despite having a substantially higher burden of physical and mental health conditions and poorer health outcomes, also experience inequalities with respect to digital maturity. In this paper, we suggest ways in which digital health technologies (DHTs) can support a greater shift towards prevention; discuss barriers to digital transformation in coastal communities; and highlight ways in which central, regional and local bodes can enable transformation. Given a real risk that variations in digital maturity may be exacerbating coastal health inequalities, we call on health and care policy leaders and service managers to understands the potential benefits of a digital future and the risks of failing to address the digital divide.

Construction of Wearable Touch Sensors by Mimicking the Properties of Materials and Structures in Nature

Wearable touch sensors, which can convert force or pressure signals into quantitative electronic signals, have emerged as essential smart sensing devices and play an important role in various cutting-edge fields, including wearable health monitoring, soft robots, electronic skin, artificial prosthetics, AR/VR, and the Internet of Things. Flexible touch sensors have made significant advancements, while the construction of novel touch sensors by mimicking the unique properties of biological materials and biogenetic structures always remains a hot research topic and significant technological pathway. This review provides a comprehensive summary of the research status of wearable touch sensors constructed by imitating the material and structural characteristics in nature and summarizes the scientific challenges and development tendencies of this aspect. First, the research status for constructing flexible touch sensors based on biomimetic materials is summarized, including hydrogel materials, self-healing materials, and other bio-inspired or biomimetic materials with extraordinary properties. Then, the design and fabrication of flexible touch sensors based on bionic structures for performance enhancement are fully discussed. These bionic structures include special structures in plants, special structures in insects/animals, and special structures in the human body. Moreover, a summary of the current issues and future prospects for developing wearable sensors based on bio-inspired materials and structures is discussed.

Hydrogel Bioelectronics for Health Monitoring

Hydrogels are considered an ideal platform for personalized healthcare due to their unique characteristics, such as their outstanding softness, appealing biocompatibility, excellent mechanical properties, etc. Owing to the high similarity between hydrogels and biological tissues, hydrogels have emerged as a promising material candidate for next generation bioelectronic interfaces. In this review, we discuss (i) the introduction of hydrogel and its traditional applications, (ii) the work principles of hydrogel in bioelectronics, (iii) the recent advances in hydrogel bioelectronics for health monitoring, and (iv) the outlook for future hydrogel bioelectronics' development.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Trends and Challenges in the Development of 3D-Printed Heart Valves and Other Cardiac Implants: A Review of Current Advances

This article provides a comprehensive review of the current trends and challenges in the development of 3D-printed heart valves and other cardiac implants. By providing personalized solutions and pushing the limits of regenerative medicine, 3D printing technology has revolutionized the field of cardiac healthcare. The use of several organic and synthetic polymers in 3D printing heart valves is explored in this article, with emphasis on both their benefits and drawbacks. In cardiac tissue engineering, stem cells are essential, and their potential to lessen immunological rejection and thrombogenic consequences is highlighted. In the clinical applications section, the article emphasizes the importance of 3D printing in preoperative planning. Surgery results are enhanced when surgeons can visualize and assess the size and placement of implants using patient-specific anatomical models. Customized implants that are designed to match the anatomy of a particular patient reduce the likelihood of complications and enhance postoperative results. The development of physiologically active cardiac implants, made possible by 3D bioprinting, shows promise by eliminating the need for artificial valves. In conclusion, this paper highlights cutting-edge research and the promise of 3D-printed cardiac implants to improve patient outcomes and revolutionize cardiac treatment.

Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare

A rapidly expanding global population and a sizeable portion of it that is aging are the main causes of the significant increase in healthcare costs. Healthcare in terms of monitoring systems is undergoing radical changes, making it possible to gauge or monitor the health conditions of people constantly, while also removing some minor possibilities of going to the hospital. The development of automated devices that are either attached to organs or the skin, continually monitoring human activity, has been made feasible by advancements in sensor technologies, embedded systems, wireless communication technologies, nanotechnologies, and miniaturization being ultra-thin, lightweight, highly flexible, and stretchable. Wearable sensors track physiological signs together with other symptoms such as respiration, pulse, and gait pattern, etc., to spot unusual or unexpected events. Help may therefore be provided when it is required. In this study, wearable sensor-based activity-monitoring systems for people are reviewed, along with the problems that need to be overcome. In this review, we have shown smart detecting and versatile wearable electrical sensing mediums in healthcare. We have compiled piezoelectric-, electrostatic-, and thermoelectric-based wearable sensors and their working mechanisms, along with their principles, while keeping in view the different medical and healthcare conditions and a discussion on the application of these biosensors in human health. A comparison is also made between the three types of wearable energy-harvesting sensors: piezoelectric-, electrostatic-, and thermoelectric-based on their output performance. Finally, we provide a future outlook on the current challenges and opportunities.