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Internet of things (IoT) promises advantages in different sectors, especially the health-care sector. Due to its capabilities for chronic disease management, IoT has attracted the attention of researchers. Nowadays, there is research that focuses on the use of IoT for chronic disease management. However, the use of IoT in various contexts faces different barriers. This paper aims to explore Iranian experts’ conceptions of the barriers to using IoT in Iran regarding its application for chronic disease management.

This study follows a phenomenographic method to investigate Iranian experts’ conceptions of the barriers to using IoT in Iran regarding its application for chronic disease management.

The results show that there are four categories of description (governance, technical, economic and social barriers) that vary among experts’ conceptions.

The findings of the present work could provide valuable insights for managers and policymakers who want to address IoT barriers.

Digital transformation (DT) is a priority for the healthcare sector. In many countries, it is still considered in the early stages with an underestimation of its benefits and potentiality. Especially in Italy, little is known about the impact of digitalization – particularly of the Internet of Things (IoT) – on the healthcare sector, for example, in terms of clinician's jobs and patient's experience. Drawing from such premises, the paper aims to focus on an overlooked healthcare area related to the chronic heart diseases field and its relationship with DT. The authors aim at exploring and framing the main variables of remote Monitoring (RM) adoption as a specific archetype of healthcare digitalization, both on patients and medical staff level, by shedding some lights on its overall implementation.

The authors empirically inquiry the RM adoption within the context of the Cardiology Department of the Casilino General Hospital of Rome. To answer our research question, the authors reconstruct the salient information by using induction-type reasoning, direct observation and interviewees with 12 key informants, as well as secondary sources analysis related to the hospital (internal documentation, presentations and technical reports).

According to a socio-technical framework, the authors build a model composed of five main variables related to medical staff and patients. The authors classify such variables into an input-process-output (I-P-O) model. RM adoption driver represents the input; cultural digital divide, structure flexibility and reaction to change serve the process and finally, RM outcome stands for the output. All these factors, interacting together, contribute to understanding the RM adoption process for chronic disease management.

The authors' research presents two main limitations. The first one is related to using a qualitative method, which is less reliable in terms of replication and the interpretive role of researchers. The second limitation, connected to the first one, is related to the study's scale level, which focuses on a mono-centric consistent level of analysis.

The paper offers a clear understanding of the RM attributes and a comprehensive view for improving the overall quality management of chronic diseases by suggesting that clinicians carefully evaluate both hard and soft variables when undertaking RM adoption decisions.

RM technologies could impact on society both in ordinary situations, by preventing patient mobility issues and transport costs, and in extraordinary times (such as a pandemic), where telemedicine contributes to supporting hospitals in swapping in-person visits with remote controls, in order to minimize the risk of coronavirus disease (COVID-19) contagion or the spread of the virus.

The study enriches the knowledge and understanding of RM adoption within the healthcare sector. From a theoretical perspective, the authors contribute to the healthcare DT adoption debate by focusing on the main variables contributing to the DT process by considering both medical staff and patient's role. From a managerial perspective, the authors highlight the main issues for RM of chronic disease management to enable the transition toward its adoption. Such issues range from the need for awareness of the medical staff about RM advantages to the need for adapting the organizational structure and the training and education process of the patients.

In this chapter, we investigate the role of the Internet of Things (IoT) for a more sustainable future. The IoT is an umbrella term that refers to an interrelated network of devices connected to the internet. It also encompasses the technology that enables communication between these devices as well as between the devices and the cloud. The emergence of low-cost microprocessors, sensors and actuators, as well as access to high bandwidth internet connectivity, has led to the massive adoption of IoT systems in everyday life. IoT systems include connected vehicles, connected homes, smart cities, smart buildings, precision agriculture, among others. During the last decade, they have been impacting human activities in an unprecedented way. In essence, IoT technology contributes to the improvement of citizens' quality of life and companies' competitiveness. In doing so, IoT is also contributing to achieve the Sustainable Development Goals (SDGs) that were adopted by the United Nations in 2015 as an urgent call to action by all countries to eradicate poverty, tackle climate change and ensure that no one is left behind by 2030. The World Economic Forum (WEF) recognises that IoT is undeniably one of the major facilitators for responsible digital transformation, and one of its reports revealed that 84% of IoT deployments are presently addressing, or can potentially address the SDGs. IoT is closely interlinked with other emerging technologies such as Artificial Intelligence (AI) and Cloud Computing, for the delivery of enhanced and value-added services. In recent years, there has been a push from the IoT research and industry community together with international stakeholders, for supporting the deployment and adoption of IoT and AI technologies to overcome some of the major challenges facing mankind in terms of protecting the environment, fostering sustainable development, improving safety and enhancing the agriculture supply chain, among others.

Taking a business lens of telehealth, this article aims to review and provide a state-of-the-art overview of telehealth research.

This research conducts a systematic literature review using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) protocol and a collection of bibliometric analytical techniques (i.e. performance analysis, keyword co-occurrence, keyword clustering and content analysis).

Using performance analysis, this article unpacks the publication trend and the top contributing journals, authors, institutions and regions of telehealth research. Using keyword co-occurrence and keyword clustering, this article reveals 10 major themes underpinning the intellectual structure of telehealth research: design and development of personal health record systems, health information technology (HIT) for public health management, perceived service quality among mobile health (m-health) users, paradoxes of virtual care versus in-person visits, Internet of things (IoT) in healthcare, guidelines for e-health practices and services, telemonitoring of life-threatening diseases, change management strategy for telehealth adoption, knowledge management of innovations in telehealth and technology management of telemedicine services. The article proposes directions for future research that can enrich our understanding of telehealth services.

This article offers a seminal state-of-the-art overview of the performance and intellectual structure of telehealth research from a business perspective.

The Internet of Things (IoT), which enables smart objects to collect and exchange data, has a variety of application domains used in everyday life including healthcare. As a set of promising next-generation technologies in the healthcare domain, Healthcare-related Internet of Things (H-IoT) promises to facilitate better healthcare by offering data-driven insights. While effective in practice at large, emerging data concerns arise because of the inscrutable black-box systems. Inspired by the notion of human data interaction, this paper seeks to understand how people engage with the H-IoT data that is about and produced by themselves and to elucidate the main data issues and challenges involved in the development of H-IoT.

This work conducted a comprehensive survey and integrated the method of content analysis by systematically review the recently published H-IoT research work in the healthcare domain.

This study thoroughly surveyed more than 300 research studies published in the last decades and classified seven H-IoT end-user groups, and three H-IoT data types that are important to H-IoT comprehension. Attention to human data interaction, our study also highlights several critical issues associated with this notion in the context of H-IoT.

This study will support H-IoT research by characterizing the data issues and challenges exist in the context of H-IoT user and data interaction. The findings will provide insights in designing for effective interactions with data in the H-IoT.

This research focuses on the challenges of establishing a better medical system that can detect and diagnose diseases earlier. Using such cutting-edge health systems, healthcare practitioners may quickly and effectively manage patients’ medical issues by providing the appropriate data at the right time about the right people. The advancement of technology has increased the usefulness of devices that routinely analyse health measurements or monitoring time-sensitive health-related data. Medical professionals and patients alike are downloading health-related mobile apps to better track and manage their health. The research evidences how Internet of Things (IoT) technology may be used to support health care.

The purpose of this paper is to explore the benefits of the internet of things (IoT) technology on geriatric telehealth.

An exploratory case study approach is used to understand the applicability of the internet of medical things in geriatric telehealth. Data was collected from several managers who analyzed rates of re-hospitalizations for patients using telehealth services compared to those not using telehealth services and observations of patient satisfaction rates with telehealth services.

Benefits from the use of IoT included significant reductions in re-hospitalization rates for older adults and patients became more engaged in maintaining their health and wellness goals while becoming more tech-savvy, empowered and satisfied with the telehealth experience.

The present manuscript is among the few reports on the benefits of IoT on geriatric health care.

COVID-19 was indeed a global epidemic that revolutionized the way of life, especially health-care services. The way health care will be delivered will undergo a dramatic change in the future. The aim is to analyse the increasing usage of health care systems along with digital technology and IoT especially during pandemic.

This research paper deals with users’ perception and their recommendation status of IoT-based smart health-care monitoring devices based on their perception, experience and level of importance to enhance the quality of life. An effective artificial neural networking (ANN)-based predictive model is designed to classify the user’s perception of usage of IoT-based smart health-care monitoring wearables based on their experience and knowledge.

The model developed has 96.7% accuracy. Among the various predictors chosen as inputs for the model, the findings indicate that self-comfort and trusted data from the device are of high priority. The present study focused only on some common factors derived from previous studies.

Although the performance of the proposed system was noticed to be good, the size of the sample is also limited to a few responses. Implications for future research and practices are discussed.

This is a novel study that aims to develop an ANN model on analyzing the user’s perception of IoT-based smart health-care wearables with the effect of COVID-19 pandemic. This paper elaborates on the ongoing efforts to restart the health-care services for survivability in the new normal situations.

Diabetes is a chronic disease and the major types of diabetes are type 1 and type 2. On aging, people with diabetes tend to have long-term problems in hypertension, coronary artery disease, obesity, and nerves. Given the increasing number of complications in recent years, by 2040, 624 million people will have diabetes worldwide and l in 8 adults will have diabetes in the future. Machine learning (ML) is evolving rapidly, many aspects of medical learning use ML. In this study, tension-type headaches (TTH) were associated with diabetes using SPSS, Pearson correlation, and ANOVA tests. Data were collected from Delhi NCR Hospital. It contains 30 diabetic subjects. The purpose of this study was to correlate diabetes analysis from TTH and other diseases using the latest technologies to analyze the Internet of Things and Big Data and Stress Correlation (TTH) on human health. The authors used Pearson correlation to correlate study variables and see if there was any effect between them. There was an important relationship between the percent variable, the total number of individuals, the number of individuals, and the minimum variable. The age (field) of the number of individuals to one of the total number of individuals showed a strong correlation (1.000) with a significant value of p (1.000). Overall, cases of TTH increased with age in men and do not follow the pattern of change in diabetes with age, but in cases of TTH, patterns of headaches such as diabetes increase to age 60 and then tend to decrease.

The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital.

AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN.

Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN.

AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.