Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies

Highly Sensitive and Fast Responding Flexible Force Sensors Using ZnO/ZnMgO Coaxial Nanotubes on Graphene Layers for Breath Sensing

The authors report the fabrication of highly sensitive, rapidly responding flexible force sensors using ZnO/ZnMgO coaxial nanotubes grown on graphene layers and their applications in sleep apnea monitoring. Flexible force sensors are fabricated by forming Schottky contacts to the nanotube array, followed by the mechanical release of the entire structure from the host substrate. The electrical characteristics of ZnO and ZnO/ZnMgO nanotube-based sensors are thoroughly investigated and compared. Importantly, in force sensor applications, the ZnO/ZnMgO coaxial structure results in significantly higher sensitivity and a faster response time when compared to the bare ZnO nanotube. The origin of the improved performance is thoroughly discussed. Furthermore, wireless breath sensing is demonstrated using the ZnO/ZnMgO pressure sensors with custom electronics, demonstrating the feasibility of the sensor technology for health monitoring and the potential diagnosis of sleep apnea.

Online dissemination of Cochrane reviews on digital health technologies: a cross-sectional study

BACKGROUND

This cross-sectional study investigated the online dissemination of Cochrane reviews on digital health technologies.

METHODS

We searched the Cochrane Database of Systematic Reviews from inception up to May 2023. Cochrane reviews with any population (P), intervention or concept supported by any digital technology (I), any or no comparison (C), and any health outcome (O) were included. Data on review characteristics (bibliographic information, PICO, and evidence quality) and dissemination strategies were extracted and processed. Dissemination was assessed using review information on the Cochrane website and Altmetric data that trace the mentions of academic publications in nonacademic online channels. Data were analysed using descriptive statistics and binary logistic regression analysis.

RESULTS

Out of 170 records identified in the search, 100 Cochrane reviews, published between 2005 and 2023, were included. The reviews focused on consumers (e.g. patients, n = 86), people of any age (n = 44), and clinical populations (n = 68). All reviews addressed interventions or concepts supported by digital technologies with any devices (n = 73), mobile devices (n = 17), or computers (n = 10). The outcomes focused on disease treatment (n = 56), health promotion and disease prevention (n = 27), or management of care delivery (n = 17). All reviews included 1-132 studies, and half included 1-10 studies. Meta-analysis was performed in 69 reviews, and certainty of evidence was rated as high or moderate for at least one outcome in 46 reviews. In agreement with the Cochrane guidelines, all reviews had a plain language summary (PLS) that was available in 3-14 languages. The reviews were disseminated (i.e. mentioned online) predominantly via X/Twitter (n = 99) and Facebook (n = 69). Overall, 51 reviews were mentioned in up to 25% and 49 reviews in 5% of all research outputs traced by Altmetric data. Dissemination (i.e. higher Altmetric scores) was associated with bibliographic review characteristics (i.e. earlier publication year and PLS available in more languages), but not with evidence quality (i.e. certainty of evidence rating, number of studies, or meta-analysis performed in review).

CONCLUSIONS

Online attention towards Cochrane reviews on digital health technologies is high. Dissemination is higher for older reviews and reviews with more PLS translations. Measures are required to improve dissemination of Cochrane reviews based on evidence quality.

SYSTEMATIC REVIEW REGISTRATION

The study was prospectively registered at the Open Science Framework ( https://osf.io/mpw8u/ ).

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices

The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.

Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends

Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.

Tanzania's and Germany's Digital Health Strategies and Their Consistency With the World Health Organization's Global Strategy on Digital Health 2020-2025: Comparative Policy Analysis

BACKGROUND

In recent years, the fast-paced adoption of digital health (DH) technologies has transformed health care delivery. However, this rapid evolution has also led to challenges such as uncoordinated development and information silos, impeding effective health care integration. Recognizing these challenges, nations have developed digital health strategies (DHSs), aligning with their national health priorities and guidance from global frameworks. The World Health Organization (WHO)'s Global Strategy on Digital Health 2020-2025 (GSDH) guides national DHSs.

OBJECTIVE

This study analyzes the DHSs of Tanzania and Germany as case studies and assesses their alignment with the GSDH and identifies strengths, shortcomings, and areas for improvement.

METHODS

A comparative policy analysis was conducted, focusing on the DHSs of Tanzania and Germany as case studies, selected for their contrasting health care systems and cooperative history. The analysis involved a three-step process: (1) assessing consistency with the GSDH, (2) comparing similarities and differences, and (3) evaluating the incorporation of emergent technologies. Primary data sources included national eHealth policy documents and related legislation.

RESULTS

Both Germany's and Tanzania's DHSs align significantly with the WHO's GSDH, incorporating most of its 35 elements, but each missing 5 distinct elements. Specifically, Tanzania's DHS lacks in areas such as knowledge management and capacity building for leaders, while Germany's strategy falls short in engaging health care service providers and beneficiaries in development phases and promoting health equity. Both countries, however, excel in other aspects like collaboration, knowledge transfer, and advancing national DHSs, reflecting their commitment to enhancing DH infrastructures. The high ratings of both countries on the Global Digital Health Monitor underscore their substantial progress in DH, although challenges persist in adopting the rapidly advancing technologies and in the need for more inclusive and comprehensive strategies.

CONCLUSIONS

This study reveals that both Tanzania and Germany have made significant strides in aligning their DHSs with the WHO's GSDH. However, the rapid evolution of technologies like artificial intelligence and machine learning presents challenges in keeping strategies up-to-date. This study recommends the development of more comprehensive, inclusive strategies and regular revisions to align with emerging technologies and needs. The research underscores the importance of context-specific adaptations in DHSs and highlights the need for broader, strategic guidelines to direct the future development of the DH ecosystem. The WHO's GSDH serves as a crucial blueprint for national DHSs. This comparative analysis demonstrates the value and challenges of aligning national strategies with global guidelines. Both Tanzania and Germany offer valuable insights into developing and implementing effective DHSs, highlighting the importance of continuous adaptation and context-specific considerations. Future policy assessments require in-depth knowledge of the country's health care needs and structure, supplemented by stakeholder input for a comprehensive evaluation.

Virtual care in the health care system: A concept analysis

BACKGROUND

Health care providers need a better understanding of virtual care to recognise and use it for service delivery.

AIM

To provide a more comprehensive definition of the concept of virtual care.

METHOD

This study was conducted based on Walker and Avant's concept analysis method. A comprehensive review of the published texts in English from 2012 to 2022 was performed using the PubMed, Web of Science, Scopus, ProQuest, Science Direct, Ovid, CINAHL and Google Scholar databases.

RESULTS

The main aspects and attributes of virtual care, including the use of any information and communication technology in various formats such as platforms, telephone calls, messages, email consultation, remote monitoring, secure and two-way digital communication between health care providers and patients, the possibility of providing remote care synchronously or asynchronously, more interaction between patients and caregivers, the possibility of transferring information between patients and health care providers and within the teams themselves, symptom management, sending diagnostic results in the form of video visits, and providing follow-up care, are attributes that distinguish virtual care from telehealth, telemedicine and other methods of providing remote healthcare services.

CONCLUSION

Considering the positive and negative consequences of implementing virtual care, the findings of this study developed a basis for an operational definition of the concept so that providers can understand the meaning of virtual care and consider it when providing virtual care to patients. The findings of this study can be used in many international and national contexts in the health care system and in future studies on interventions to increase the use of virtual care.

Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI

This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring.

Niosomes: Composition, Formulation Techniques, and Recent Progress as Delivery Systems in Cancer Therapy

Niosomes are vesicular nanocarriers, biodegradable, relatively non-toxic, stable, and inexpensive, that provide an alternative for lipid-solid carriers (e.g., liposomes). Niosomes may resolve issues related to the instability, fast degradation, bioavailability, and insolubility of different drugs or natural compounds. Niosomes can be very efficient potential systems for the specific delivery of anticancer, antioxidant, anti-inflammatory, antimicrobial, and antibacterial molecules. This review aims to present an overview of their composition, the most common formulation techniques, as well as of recent utilizations as delivery systems in cancer therapy.

Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications

Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications.

Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis

As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Promoting healthy ageing in the Western Pacific: A mini review of good practices and policy responses

The Western Pacific Region is experiencing rapid population ageing, which has implications for almost all areas of society. Countries will need to prepare for population ageing by investing in health and optimizing living environments. This requires a whole-of-society approach to healthy ageing. Countries in the Western Pacific Region have been making significant progress in healthy ageing. Since the endorsement of the Regional Action Plan on Healthy Ageing, younger societies have also started preparing for population ageing, focusing on social and health systems transformation, community-based integrated care, social and technological innovations and research, monitoring and evaluation. As more countries are interested in healthy ageing and preparing for necessary social and health systems transformation, the case studies in this article can be an inspiration for Member States to transform their approaches to achieving a society where older adults are healthier and can participate fully.

Emerging Applications of Nanotechnology in Healthcare and Medicine

Knowing the beneficial aspects of nanomedicine, scientists are trying to harness the applications of nanotechnology in diagnosis, treatment, and prevention of diseases. There are also potential uses in designing medical tools and processes for the new generation of medical scientists. The main objective for conducting this research review is to gather the widespread aspects of nanomedicine under one heading and to highlight standard research practices in the medical field. Comprehensive research has been conducted to incorporate the latest data related to nanotechnology in medicine and therapeutics derived from acknowledged scientific platforms. Nanotechnology is used to conduct sensitive medical procedures. Nanotechnology is showing successful and beneficial uses in the fields of diagnostics, disease treatment, regenerative medicine, gene therapy, dentistry, oncology, aesthetics industry, drug delivery, and therapeutics. A thorough association of and cooperation between physicians, clinicians, researchers, and technologies will bring forward a future where there is a more calculated, outlined, and technically programed field of nanomedicine. Advances are being made to overcome challenges associated with the application of nanotechnology in the medical field due to the pathophysiological basis of diseases. This review highlights the multipronged aspects of nanomedicine and how nanotechnology is proving beneficial for the health industry. There is a need to minimize the health, environmental, and ethical concerns linked to nanotechnology.

Traditional surveys versus ecological momentary assessments: Digital citizen science approaches to improve ethical physical activity surveillance among youth

The role of physical activity (PA) in minimizing non-communicable diseases is well established. Measurement bias can be reduced via ecological momentary assessments (EMAs) deployed via citizen-owned smartphones. This study aims to engage citizen scientists to understand how PA reported digitally by retrospective and prospective measures varies within the same cohort. This study used the digital citizen science approach to collaborate with citizen scientists, aged 13-21 years over eight consecutive days via a custom-built app. Citizen scientists were recruited through schools in Regina, Saskatchewan, Canada in 2018 (August 31-December 31). Retrospective PA was assessed through a survey, which was adapted from three validated PA surveys to suit smartphone-based data collection, and prospective PA was assessed through time-triggered EMAs deployed consecutively every day, from day 1 to day 8, including weekdays and weekends. Data analyses included paired t-tests to understand the difference in PA reported retrospectively and prospectively, and linear regressions to assess contextual and demographic factors associated with PA reported retrospectively and prospectively. Findings showed a significant difference between PA reported retrospectively and prospectively (p = 0.001). Ethnicity (visible minorities: β = - 0.911, 95% C.I. = -1.677, -0.146), parental education (university: β = 0.978, 95% C.I. = 0.308, 1.649), and strength training (at least one day: β = 0.932, 95% C.I. = 0.108, 1.755) were associated with PA reported prospectively. In contrast, the number of active friends (at least one friend: β = 0.741, 95% C.I. = 0.026, 1.458) was associated with retrospective PA. Physical inactivity is the fourth leading cause of mortality globally, which requires accurate monitoring to inform population health interventions. In this digital age, where ubiquitous devices provide real-time engagement capabilities, digital citizen science can transform how we measure behaviours using citizen-owned ubiquitous digital tools to support prevention and treatment of non-communicable diseases.

Lignocellulosic Bionanomaterials for Biosensor Applications

The rapid population growth, increasing global energy demand, climate change, and excessive use of fossil fuels have adversely affected environmental management and sustainability. Furthermore, the requirements for a safer ecology and environment have necessitated the use of renewable materials, thereby solving the problem of sustainability of resources. In this perspective, lignocellulosic biomass is an attractive natural resource because of its abundance, renewability, recyclability, and low cost. The ever-increasing developments in nanotechnology have opened up new vistas in sensor fabrication such as biosensor design for electronics, communication, automobile, optical products, packaging, textile, biomedical, and tissue engineering. Due to their outstanding properties such as biodegradability, biocompatibility, non-toxicity, improved electrical and thermal conductivity, high physical and mechanical properties, high surface area and catalytic activity, lignocellulosic bionanomaterials including nanocellulose and nanolignin emerge as very promising raw materials to be used in the development of high-impact biosensors. In this article, the use of lignocellulosic bionanomaterials in biosensor applications is reviewed and major challenges and opportunities are identified.

Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a "neurofeedback" group, which performed motor imagery while receiving feedback, and a "control" group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual's ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.

The Gap Between AI and Bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice

BACKGROUND

Artificial intelligence (AI) and digital health technological innovations from startup companies used in clinical practice can yield better health outcomes, reduce health care costs, and improve patients' experience. However, the integration, translation, and adoption of these technologies into clinical practice are plagued with many challenges and are lagging. Furthermore, explanations of the impediments to clinical translation are largely unknown and have not been systematically studied from the perspective of AI and digital health care startup founders and executives.

OBJECTIVE

The aim of this paper is to describe the barriers to integrating early-stage technologies in clinical practice and health care systems from the perspectives of digital health and health care AI founders and executives.

METHODS

A stakeholder focus group workshop was conducted with a sample of 10 early-stage digital health and health care AI founders and executives. Digital health, health care AI, digital health-focused venture capitalists, and physician executives were represented. Using an inductive thematic analysis approach, transcripts were organized, queried, and analyzed for thematic convergence.

RESULTS

We identified the following four categories of barriers in the integration of early-stage digital health innovations into clinical practice and health care systems: (1) lack of knowledge of health system technology procurement protocols and best practices, (2) demanding regulatory and validation requirements, (3) challenges within the health system technology procurement process, and (4) disadvantages of early-stage digital health companies compared to large technology conglomerates. Recommendations from the study participants were also synthesized to create a road map to mitigate the barriers to integrating early-stage or novel digital health technologies in clinical practice.

CONCLUSIONS

Early-stage digital health and health care AI entrepreneurs identified numerous barriers to integrating digital health solutions into clinical practice. Mitigation initiatives should create opportunities for early-stage digital health technology companies and health care providers to interact, develop relationships, and use evidence-based research and best practices during health care technology procurement and evaluation processes.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Electromagnetic Sensing Techniques for Monitoring Atopic Dermatitis-Current Practices and Possible Advancements: A Review

Atopic dermatitis (AD) is one of the most common skin disorders, affecting nearly one-fifth of children and adolescents worldwide, and currently, the only method of monitoring the condition is through an in-person visual examination by a clinician. This method of assessment poses an inherent risk of subjectivity and can be restrictive to patients who do not have access to or cannot visit hospitals. Advances in digital sensing technologies can serve as a foundation for the development of a new generation of e-health devices that provide accurate and empirical evaluation of the condition to patients worldwide. The goal of this review is to study the past, present, and future of AD monitoring. First, current medical practices such as biopsy, tape stripping and blood serum are discussed with their merits and demerits. Then, alternative digital methods of medical evaluation are highlighted with the focus on non-invasive monitoring using biomarkers of AD-TEWL, skin permittivity, elasticity, and pruritus. Finally, possible future technologies are showcased such as radio frequency reflectometry and optical spectroscopy along with a short discussion to provoke research into improving the current techniques and employing the new ones to develop an AD monitoring device, which could eventually facilitate medical diagnosis.

Nanocellulose-based sensors in medical/clinical applications: The state-of-the-art review

In recent years, the considerable importance of healthcare and the indispensable appeal of curative issues, particularly the diagnosis of diseases, have propelled the invention of sensing platforms. With the development of nanotechnology, the integration of nanomaterials in such platforms has been much focused on, boosting their functionality in many fields. In this direction, there has been rapid growth in the utilisation of nanocellulose in sensors with medical applications. Indeed, this natural nanomaterial benefits from striking features, such as biocompatibility, cytocompatibility and low toxicity, as well as unprecedented physical and chemical properties. In this review, different classifications of nanocellulose-based sensors (biosensors, chemical and physical sensors), alongside some subcategories manufactured for health monitoring, stand out. Moreover, the types of nanocellulose and their roles in such sensors are discussed.

Evaluation and Analysis of Elderly Mental Health Based on Artificial Intelligence

Objective

The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedback the effect, so as to provide a reference for improving the mental health of the elderly.

Method

In order to make the output of different emotional data in LSTM more discriminative, a method to dynamically filter the output of LSTM is proposed. Combining the methods of Attention-LSTM, time-dimensional AI attention, and feature-dimensional AI attention, the best model in this paper is obtained. The multistage stratified cluster sampling method was used to conduct a questionnaire survey on the elderly aged 60 and above in a certain area, including the general demographic characteristics questionnaire of the elderly, the self-rating scale of mental health symptoms, and the health self-management ability of adults. All data were entered into a database using Excel software, and SPSS 19.0 statistical software was used for statistical analysis. Results/Discussion. The detection rate of depression (GDS ≥ 11 points) among the elderly in a community in a certain area was 39.38%. Multivariate logistic regression analysis showed that family history of mental illness, more negative life events, decreased ability of daily living, living alone, and suffering from physical diseases in the past six months were the risk factors for depression in the elderly. Community health education can partially alleviate depression in the elderly. The detection rate and degree of depression of the elderly in the comprehensive psychological intervention group were significantly lower than those in the control group, and the difference was statistically significant (P < 0.05).

Determinants of intention with remote health management service among urban older adults: A Unified Theory of Acceptance and Use of Technology perspective

Background

Although older adults health management systems have been shown to have a significant impact on health levels, there remains the problem of low use rate, frequency of use, and acceptance by the older adults. This study aims to explore the significant factors which serve as determinants of behavioral intention to use the technology, which in turn promotes actual use.

Methods

This study took a total of 402 urban older adults over 60 years to explore the impact of the use behavior toward remote health management (RHM) through an online questionnaire. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the author adds four dimensions: perceived risk, perceived value, perceived interactivity and individual innovation, constructed an extended structural equation model of acceptance and use of technology, and analyzed the variable path relationship.

Results

In this study, the factor loading is between 0.61 and 0.98; the overall Cronbach's Alpha coefficients are >0.7; The composite reliability ranges from 0.59 to 0.91; the average variance extraction ranges from 0.51 to 0.85, which shows the good reliability, validity, and discriminant validity of the constructed model. The influencing factors of the behavioral intention of the older adults to accept the health management system are: effort expectation, social influences, perceived value, performance expectation, perceived interactivity and perceived risk. Effort expectation has a significant positive impact on performance expectation. Individual innovation positively impacts performance expectation and perceived interactivity. Perceived interactivity and behavioral intention have a significant positive effect on the use behavior of the older adults, while the facilitating conditions have little effect on the use behavior.

Conclusions

This paper constructs and verifies the extended model based on UTAUT, fully explores the potential factors affecting the use intention of the older adult users. According to the research findings, some suggestions are proposed from the aspects of effort expectation, performance expectation, perceived interaction and perceived value to improve the use intention and user experience of Internet-based health management services in older adults.

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.

The Adoption of Digital Technologies and Artificial Intelligence in Urban Health: A Scoping Review

As more people live in cities, the impact of urban settings on population health has been increasing. One of the main strategies to cope with urbanization is adopting artificial intelligence (AI) and new digital technologies to develop new urban services that improve citizens’ health and well-being. The aim of this study is to review urban interventions and adopting digital technologies and AI-based algorithms to improve population health. A scoping review of the literature was conducted by querying MEDLINE, Web of Science, and Scopus databases. The included studies were categorized into one urban health area, suggested by the WHO, according to the type of intervention investigated. Out of 3733 records screened, 12 papers met all inclusion criteria. Four studies investigated the “outdoor and indoor pollution” area, one “climate change”, one “housing”, two “health and social services” and four “urban transport” areas. Only one article used a comprehensive approach to public health, investigating the use of AI and digital technologies both to characterize exposure conditions to health determinants and to monitor population health effects, while the others were limited to characterizing exposure conditions to health determinants, thus employing a preliminary public health perspective. From this point of view, countries should foster synergy for the development of research on digital technologies to address the determinants of health in the urban context. From a global health perspective, sharing results with the scientific community would also allow other countries to use those technologies that have been shown to be effective, paving the way for more sustainable living conditions worldwide.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections

In the past few years, remote monitoring technologies have grown increasingly important in the delivery of healthcare. According to healthcare professionals, a variety of factors influence the public perception of connected healthcare systems in a variety of ways. First and foremost, wearable technology in healthcare must establish better bonds with the individuals who will be using them. The emotional reactions of patients to obtaining remote healthcare services may be of interest to healthcare practitioners if they are given the opportunity to investigate them. In this study, we develop an artificial intelligence-based classification system that aims to detect the emotions from the input data using metaheuristic feature selection and machine learning classification. The proposed model is made to undergo series of steps involving preprocessing, feature selection, and classification. The simulation is conducted to test the efficacy of the model on various features present in a dataset. The results of simulation show that the proposed model is effective enough to classify the emotions from the input dataset than other existing methods.

Factors That Influence How Providers Assess the Appropriateness of Virtual Visits: A Qualitative Investigation (Preprint)

Background

The rapid implementation of virtual care (ie, telephone or video-based clinic appointments) during the COVID-19 pandemic resulted in many providers offering virtual care with little or no formal training and without clinical guidelines and tools to assist with decision-making. As new guidelines for virtual care provision take shape, it is critical that they are informed by an in-depth understanding of how providers make decisions about virtual care in their clinical practices.

Objective

In this paper, we sought to identify the most salient factors that influence how providers decide when to offer patients video appointments instead of or in conjunction with in-person care.

Methods

We conducted semistructured interviews with 28 purposefully selected primary and specialty health care providers from the US Department of Veteran’s Affairs health care system. We used an inductive approach to identify factors that impact provider decision-making.

Results

Qualitative analysis revealed distinct clinical, patient, and provider factors that influence provider decisions to initiate or continue with virtual visits. Clinical factors include patient acuity, the need for additional tests or labs, changes in patients’ health status, and whether the patient is new or has no recent visit. Patient factors include patients’ ability to articulate symptoms or needs, availability and accessibility of technology, preferences for or against virtual visits, and access to caregiver assistance. Provider factors include provider comfort with and acceptance of virtual technology as well as virtual physical exam skills and training.

Conclusions

Providers within the US Department of Veterans Affairs health administration system consider a complex set of factors when deciding whether to offer or continue a video or telephone visit. These factors can inform the development and further refinement of decision tools, guides, and other policies to ensure that virtual care expands access to high-quality care.

State of Industry 5.0—Analysis and Identification of Current Research Trends

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.

Are we there yet? Unbundling the potential adoption and integration of telemedicine to improve virtual healthcare services in African health systems

Since the outbreak of COVID-19, the attention has now shifted towards universal vaccination to gracefully lift strict COVID-19 restrictions previously imposed to contain the spread of the disease. Sub-Saharan Africa is experiencing an exponential increase of infections and deaths coupled with vaccines shortages, personal protective equipment, weak health systems and COVID-19 emerging variants. Some developed countries integrated telemedicine to reduce the impacts of the shortage of healthcare professionals and potentially reduce the risk of exposure, ensuring easy delivery of quality health services while limiting regular physical contact and direct hospitalization. However, the adoption of telemedicine and telehealth is still nascent in many sub-Saharan Africa countries. Therefore, this study reflects on progress made towards the use of telemedicine, virtual health care services, challenges encountered, and proffers ways to address them. We conducted a systematic literature review to synthesise literature on telemedicine in sub-Saharan Africa. The study revealed that telemedicine provides unprecedented benefits such as improving efficiency, effective utilization of healthcare resources, forward triaging, prevention of medical personnel infection, aiding medical students' clinical observation and participation, and assurance of social support for patients. However, the absence of policy on virtual care and political will, cost of sustenance of virtual health care services, inadequate funding, technological and infrastructural barriers, patient and healthcare personnel bias on virtual care and cultural barriers are identified as limiting factors to the adoption of virtual health care in many African health systems. To alleviate some of these barriers, we recommend the development of robust policies and frameworks for virtual health care, the inclusion of virtual care in the medical school curriculum, supporting virtual care research and development, increasing health funding, removing monopolisation of telecommunication services, developing of virtual health solutions that address eccentricities of African health systems.