Health monitoring through wearable technologies for older adults: Smart wearables acceptance model

Exposure Detection Applications Acceptance: The Case of COVID-19

The pandemic's context is rife with numerous dangerous threats and high fear levels, influencing human decision-making. Such characteristics are identified by investigating the acceptance of exposure detection apps from the technology acceptance model (TAM) perspective. This study purposed a model to investigate protection technology acceptance, specifically exposure detection apps in the context of COVID-19. Quantitative study approach and a cross-section design targeted 586 participants from Saudi Arabia. As the study model is complex, the study hypotheses were analysed using the structural equation modelling-partial least squares (SEM-PLS3) approach. The findings support the entire model hypothesis except the link between social media awareness and exposure detection apps' intention. Mediation of COVID-19 anxiety and influence was confirmed as well. The current paper contributes to the technologies acceptance domain by developing a context-driven model comprising the major pandemic characteristics that lead to various patterns of technology acceptance. This study also fills the literature gap regarding mediating effects of social influence and COVID-19 anxiety in the relationship between trust in government and exposure detection apps implementation, and between COVID-19 anxiety and exposure detection apps implementation, respectively. The results may assist government agencies, health policymakers, and health organisations in the wide world and specifically Saudi Arabia, in their attempts to contain the COVID-19 pandemic spread.

Impact of the Moderating Effect of National Culture on Adoption Intention in Wearable Health Care Devices: Meta-analysis

Background

Wearable health care devices have not yet been commercialized on a large scale. Additionally, people in different countries have different utilization rates. Therefore, more in-depth studies on the moderating effect of national culture on adoption intention in wearable health care devices are necessary.

Objective

This study aims to explore the summary results of the relationships between perceived usefulness and perceived ease of use with adoption intention in wearable health care devices and the impact of the moderating effect of national culture on these two relationships.

Methods

We searched for studies published before September 2021 in the Web of Science, EBSCO, Engineering Village, China National Knowledge Infrastructure, IEEE Xplore, and Wiley Online Library databases. CMA (version 2.0, Biostat Inc) software was used to perform the meta-analysis. We conducted publication bias and heterogeneity tests on the data. The random-effects model was used to estimate the main effect size, and a sensitivity analysis was conducted. A meta-regression analysis was used to test the moderating effect of national culture.

Results

This meta-analysis included 20 publications with a total of 6128 participants. Perceived usefulness (r=0.612, P<.001) and perceived ease of use (r=0.462, P<.001) positively affect adoption intention. The relationship between perceived usefulness and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.008, P<.001) and indulgence/restraint (β=.005, P<.001), and negatively moderated by uncertainty avoidance (β=-.005, P<.001). The relationship between perceived ease of use and adoption intention is positively moderated by individualism/collectivism (β=.003, P<.001), masculinity/femininity (β=.006, P<.001) and indulgence/restraint (β=.009, P<.001), and negatively moderated by uncertainty avoidance (β=-.004, P<.001).

Conclusions

This meta-analysis provided comprehensive evidence on the positive relationship between perceived usefulness and perceived ease of use with adoption intention and the moderating effect of national culture on these two relationships. Regarding the moderating effect, perceived usefulness and perceived ease of use have a greater impact on adoption intention for people in individualistic, masculine, low uncertainty avoidance, and indulgence cultures, respectively.

Wearable devices for continuous monitoring of biosignals: Challenges and opportunities

The ability for wearable devices to collect high-fidelity biosignals continuously over weeks and months at a time has become an increasingly sought-after characteristic to provide advanced diagnostic and therapeutic capabilities. Wearable devices for this purpose face a multitude of challenges such as formfactors with long-term user acceptance and power supplies that enable continuous operation without requiring extensive user interaction. This review summarizes design considerations associated with these attributes and summarizes recent advances toward continuous operation with high-fidelity biosignal recording abilities. The review also provides insight into systematic barriers for these device archetypes and outlines most promising technological approaches to expand capabilities. We conclude with a summary of current developments of hardware and approaches for embedded artificial intelligence in this wearable device class, which is pivotal for next generation autonomous diagnostic, therapeutic, and assistive health tools.

Secure data collection and transmission for IoMT architecture integrated with federated learning

Purpose

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

Design/methodology/approach

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

Findings

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

Originality/value

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

A Regulatory Game Analysis of Smart Aging Platforms Considering Privacy Protection

Privacy and information protection are important issues in the era of big data. At present, China’s elderly care industry is gradually adopting the supply model of smart elderly care to alleviate the contradiction between supply and demand. However, the low level of regulation of smart aging platforms may lead to a low level of privacy protection on the platforms. Therefore, in this paper, based on the evolutionary game and Lyapunov theory, we discuss the willingness of elderly people to participate in regulation, the privacy protection status of platform service providers, and the degree of government regulation, as well as the key factors affecting the equilibrium of the three-party game system, and conduct simulation analysis and game system optimization using MATLAB. The simulation results show that A1(0,0,1) and A5(0,0,0) can be transformed to A8(1,1,0) by adjusting the parameters, i.e., the optimal ESS is participation, high-quality protection, and low investment supervision; the service income of the elderly, the loss of privacy leakage, the investment cost of service providers, and the amount of government rewards and punishments are the key factors affecting the tripartite game system. By analyzing the impact of factors, such as benefits and costs, on privacy protection and the regulation of smart senior care platforms, the level of privacy protection of smart senior care platforms can be improved and the process of the comprehensive regulation of domestic senior care services can be promoted.

Home-care robots - Attitudes and perceptions among older people, carers and care professionals in Ireland: A questionnaire study

Many countries face major challenges to ensure that their health and social care systems are ready for the growing numbers of older people (OP). As a way of realising ageing in place, assistive technologies such as home-care robots are expected to play a greater role in the future. In Asia and Europe, robots are gradually being adopted as a public policy solution to the workforce shortage. Yet, there is still a strongly held belief that such technologies should not be part of human and personal care services such as OP's care. However, there has been little research into attitudes and perceptions of potential users regarding home-care robots which can provide companionship and support with activities of daily living. To explore these in more detail, a questionnaire study was carried out in Finland, Ireland and Japan. This study reports findings from the Irish cohort (114 older people [OP], 8 family carers and 56 Health and Social Care Professionals [HSCPs]). Seventy per cent of the total respondents (N = 178) reported being open to the use of home-care robots, and only one quarter had a negative image of robots. People with care responsibilities in their private capacity expressed more interest in, and readiness to use, home-care robots, while stressing the importance of 'privacy protection' and 'guaranteed access to human care'. Both OP and HSCPs identified observation and recording of OP's mental and physical condition as desirable functions of such robots, whereas practical functions such as fall prevention and mobility support were also deemed desirable by HSCPs. There is generally positive interest in home-care robots among Irish respondents. Findings strongly suggest that the interest is generated partly by great need among people who deliver care. Should such robots be developed, then careful consideration must be given to user-centred design, ethical aspects and national care policy.

What Factors Predict the Adoption of Type 2 Diabetes Patients to Wearable Activity Trackers-Application of Diffusion of Innovation Theory

Background: Globally, diabetes has brought an enormous burden to public health resources, and the situation of disease burden caused by diabetes in China is especially severe. China is currently facing the dual threat of aging and diabetes, and wearable activity trackers could promote elderly diabetic patients' physical activity levels and help them to manage blood glucose control. Therefore, examining the influencing factors of elderly patients' adoption intention is critical as wearing adoption determines actual wearing behaviors. Objective: This study aims to explore the predicting factors of Chinese elderly type 2 diabetic patients' adoption intention to wearable activity trackers and their actual wearing behavior, using diffusion of innovation theory as the theoretical framework. We hope to provide insights into future interventions using wearable activity trackers as tools to improve the outcome of patients. Methods: Wearable activity trackers were freely distributed to type 2 diabetic patients in Beijing, China. A questionnaire survey was conducted to examine predicting factors of adoption intention after a week's try-on. Actual wearing behavior for 3-month was obtained from the exclusive cloud. Data were analyzed with structural equation modeling. Results: A total of 725 patients completed the questionnaire. Patients had a mean age of 60.3 ± 7.6 years old and the educational level was generally lower. The results indicated that observability was the primary influencing factor of patients' adoption intention (β = 0.775, P < 0.001). Relative advantage (β = 0.182, P = 0.014) and perceived social image (β = 0.080, P = 0.039) also had a positive influence while perceived risk (β = -0.148, P < 0.001) exerted a negative influence. In addition, results showed that the more intention led to the better actual wearing behavior (β = 0.127, P = 0.003). Observability (β = 0.103, P = 0.005), perceived ease (β = 0.085, P = 0.004), and relative advantage (β = 0.041, P = 0.009) also indirectly influenced the wearing behavior. Conclusion: The intentions of Chinese elderly type 2 diabetic patients to wearable activity trackers directly influenced the actual wearing behavior. In addition, their adoption intention to wearable activity trackers was mainly influenced by observability, perceived ease to use, relative advantage, perceived risk, and social image.

The acceptance and use of smartphones among older adults: differences in UTAUT determinants before and after training

Purpose

This article aims at a Unified Theory of Acceptance and Use of Technology (UTAUT) model framework that was used to investigate the impact of a 16-h smartphone training program on the correlations among different constructs of smartphone use in a sample of older adults.

Design/methodology/approach

A total of 208 participants aged 60–78 (mean: 65.4) years completed a questionnaire that collected information on demographic variables and the frequency and duration of smartphone use as well as the answers to questions on the six UTAUT constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention and usage behavior. The data were analyzed using partial least squares analysis.

Findings

This study was the first to compare post-training changes in the correlations among UTAUT constructs. The results revealed significant post-training changes in all construct correlations. Behavioral intention and facilitating conditions were shown to significantly impact usage behavior both before and after training and performance expectancy was shown to impact behavioral intention before training. After training, both effort expectancy and social influence were found to impact behavioral intention significantly. Moreover, the impact of facilitating conditions on usage behavior was significantly increased after training.

Originality/value

To date, no study published in the literature has investigated the impact of technological training on the technology-use intentions and behaviors of older adults. The findings of this study suggest that, for older adults, the results of the acceptance and use model for smartphones change significantly and positively between pre-smartphone training and post-smartphone training time points. The findings support that technology training has a positive impact on smartphone use in older adults.

Prioritizing and Overcoming Barriers to e-Health Use among Elderly People: Implementation of the Analytical Hierarchical Process (AHP)

Rise in the aging population brings new challenges to modern societies. Old age is associated with several morbidities and usual issues related to health. Therefore, the provision of healthy and timely care has become the dire need to maintain their quality of life and wellbeing. The evolution of the e-health care system put pressure on societies to implement it successfully to ensure a safe and prompt provision of care services to the most vulnerable population successfully. Therefore, the provision and implementation of the e-health care system is a challenge for the health industry in terms of multi-objective decision-making. Multicriteria decision-making is a generalizable approach to making decisions with dependence and feedback and is known as an effective tool in decision-making processes, particularly in the healthcare sector. The present study aims to present an e-healthcare framework by identifying and prioritizing potential barriers towards the use of e-health by the elderly population. The analytical hierarchy process approach is adopted to calculate weights of identified potential barriers, respectively, and then rank them based on their degree of significance. The findings show that health and the ability-related barrier is ranked highest, followed by socio-environmental and attitudinal barriers. This research contributes to healthcare decision-making regarding e-health usage by implementing MCDA techniques. Our study will assist the public health practitioners and policymakers in drawing decisions on the best strategy to minimize the risks in using the e-healthcare system by the aging population, which significantly contributes to the smart healthcare system.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

[Systematic review of digital technology acceptance in older adults. Perspective of TAM models]

In this study, a systematic review of researches published about the determining factors in the intention of the use of devices and digital technology in older adults has been carried out. The technology acceptance model is the framework used. The main result of the analysis of 60 scientific articles published between 2010 and 2020 has been summarized after a bibliographic research and a subsequent selection process in the most important bibliographic databases: Web of Science (n=45), Scopus (n=66) and Google Scholar (n=224). It has been proved the predominance of quantitative studies with a prevalence of study factors that associate the intention to use technology with: Facilitating conditions, experience, health, social support, emotions, perceived enjoyment, perceived satisfaction in using technology, anxiety, perceived risk, motivation, cost, subjective norm and social influence. The results of the systematic review allow us to conclude the urgent need to incorporate new research on the identification and influence of these factors that stand between older adults and technology, as possible barriers or facilitators of use.

The Investigation of Adoption of Voice-User Interface (VUI) in Smart Home Systems among Chinese Older Adults

Driven by advanced voice interaction technology, the voice-user interface (VUI) has gained popularity in recent years. VUI has been integrated into various devices in the context of the smart home system. In comparison with traditional interaction methods, VUI provides multiple benefits. VUI allows for hands-free and eyes-free interaction. It also enables users to perform multiple tasks while interacting. Moreover, as VUI is highly similar to a natural conversation in daily lives, it is intuitive to learn. The advantages provided by VUI are particularly beneficial to older adults, who suffer from decreases in physical and cognitive abilities, which hinder their interaction with electronic devices through traditional methods. However, the factors that influence older adults' adoption of VUI remain unknown. This study addresses this research gap by proposing a conceptual model. On the basis of the technology adoption model (TAM) and the senior technology adoption model (STAM), this study considers the characteristic of VUI and the characteristic of older adults through incorporating the construct of trust and aging-related characteristics (i.e., perceived physical conditions, mobile self-efficacy, technology anxiety, self-actualization). A survey was designed and conducted. A total of 420 Chinese older adults participated in this survey, and they were current or potential users of VUI. Through structural equation modeling, data were analyzed. Results showed a good fit with the proposed conceptual model. Path analysis revealed that three factors determine Chinese older adults' adoption of VUI: perceived usefulness, perceived ease of use, and trust. Aging-related characteristics also influence older adults' adoption of VUI, but they are mediated by perceived usefulness, perceived ease of use, and trust. Specifically, mobile self-efficacy is demonstrated to positively influence trust and perceived ease of use but negatively influence perceived usefulness. Self-actualization exhibits positive influences on perceived usefulness and perceived ease of use. Technology anxiety only exerts influence on perceived ease of use in a marginal way. No significant influences of perceived physical conditions were found. This study extends the TAM and STAM by incorporating additional variables to explain Chinese older adults' adoption of VUI. These results also provide valuable implications for developing suitable VUI for older adults as well as planning actionable communication strategies for promoting VUI among Chinese older adults.

Zozus M, Jiang X, Brimhall BB, Windham AK, Schmidt S, Visweswaran S, Ye Y, Goodrum H, Ling Y, Barapatre S, Becich MJ. Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.

Design of an Integrated Acceptance Framework for Older Users and eHealth: Influential Factor Analysis

Background

eHealth and telehealth play a crucial role in assisting older adults who visit hospitals frequently or who live in nursing homes and can benefit from staying at home while being cared for. Adapting to new technologies can be difficult for older people. Thus, to better apply these technologies to older adults’ lives, many studies have analyzed the acceptance factors for this particular population. However, there is not yet a consensual framework that can be used in further development and to search for solutions.

Objective

This paper aims to present an integrated acceptance framework (IAF) for older users’ acceptance of eHealth based on 43 studies selected through a systematic review.

Methods

We conducted a 4-step study. First, through a systematic review in the field of eHealth from 2010 to 2020, the acceptance factors and basic data for analysis were extracted. Second, we conducted a thematic analysis to group the factors into themes to propose an integrated framework for acceptance. Third, we defined a metric to evaluate the impact of the factors addressed in the studies. Finally, the differences among the important IAF factors were analyzed according to the participants’ health conditions, verification time, and year.

Results

Through a systematic review, 731 studies were found in 5 major databases, resulting in 43 (5.9%) selected studies using the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) methodology. First, the research methods and acceptance factors for eHealth were compared and analyzed, extracting a total of 105 acceptance factors, which were grouped later, resulting in an IAF. A total of 5 dimensions (ie, personal, user–technology relational, technological, service-related, and environmental) emerged, with a total of 23 factors. In addition, we assessed the quality of evidence and then conducted a stratification analysis to reveal the more appropriate factors depending on the health condition and assessment time. Finally, we assessed the factors and dimensions that have recently become more important.

Conclusions

The result of this investigation is a framework for conducting research on eHealth acceptance. To elaborately analyze the impact of the factors of the proposed framework, the criteria for evaluating the evidence from the studies that have the extracted factors are presented. Through this process, the impact of each factor in the IAF has been presented, in addition to the framework proposal. Moreover, a meta-analysis of the current status of research is presented, highlighting the areas where specific measures are needed to facilitate eHealth acceptance.

Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

Towards a Wearable Technology Model

This purpose of this study is to develop a wearable technology model (WTM) by identifying key determinants of wearable technology adoption (comfort, safety, privacy, data accuracy, portability, and design, perceived usefulness, and perceived ease of use). Wearable technologies (WT) are Internet enabled technologies that are rapidly emerging and gaining popularity. WT refers to clothing or accessories that are enabled with minicomputers and sensors and are meant to be worn on the human body for an extended period of time to enhance their daily lives. Results indicate the most important factors that users care about when intending to use wearable technology ranked from most to least important are design, data accuracy, portability, comfortability, PU, PEOU, safety, and privacy of the device to be worn. This study develops the WTM, a novel model that is tested and validated specifically to examine user’s intentions to adopt WT, and to extend the traditional technology acceptance models. This study proves the WTM can be applied successfully to examine user’ s intentions to adopt WT.

Elderly's intention to use technologies: A systematic literature review

The population aging and an increased life expectancy are widely recognized social changes. Technologies are believed to improve the elderly's daily lives and maintain their health efficiently. Despite the advantage of adopting technologies, the elderly are slower to adopt new technologies compared to younger adults. This paper presents a Systematic Literature Review (SLR) to identify the different antecedents prevailing in the literature on elderly technology adoption. The SLR classifies and analyzes 26 relevant articles on elderly's technology adoption. Our findings revealed that quantitative approach and cross-sectional studies predominate in this field, building fundamentally upon the technology-driven theories. We identify seven categories of antecedents influencing elderly's use of technology, namely, technology, psychological, social, personal, cost, behavior, and environment antecedents. A conceptual framework for elderly's technology adoption and recommendations were presented. Particular attention is given to the need for in depth study for the antecedents, development of new measurement scales and investigation on the effectiveness of the proposed benefits after technology adoption.

Is a Smarter Generation in the offing? Empirical Evidence about the Use of Smart Wearable Devices among Indian Youth

The study explored the critical determinants of behavioral intention and willingness to use smart wearable devices among Indian youth. Data were collected using a survey instrument on the youth aged between 20-30 years (N=262) in India. The results showed, performance expectancy, hedonic motivation, and habits impacted the behavioural intention of the youth. Facilitating conditions impacted the youth’s willingness to use. Behavioural intentions of the youth impacted their willingness to use smart wearable devices. Male youth moderated the effect of performance expectancy on behavioural intentions. Gender and Purpose of-Use moderated the effect of hedonic motivation and habits on behavioural intention and behavioural intention on willingness to use. Only health and fitness moderated the effect of facilitating condition on willingness to use smart wearable devices among youth. Though the study is limited to the Indian context, its implications can benefit the wearable commerce providers in designing, positioning, and marketing the products in developing economies.

Factors Affecting the Adoption of Mobile Government by Older People

Increases in the population's average age require government services to offer high-efficiency, low-cost services aimed at the elderly. An extended technology acceptance model (ETAM) embedded in a contextualized perspective is proposed to analyze elderly users' mobile government behavioral intentions. In this study, a questionnaire survey was conducted on 800 participants aged 60 and over to identify crucial influencing factors. One dependent variable, three independent variables, four intervening variables, and four control variables were used for the questionnaire design. The proposed model was tested based on structural equation modeling (SEM) under eight constructs. The empirical results showed that elderly users' attitudes, perceived external support, orientation level, and perceived ease-of-use have a significant positive impact on elderly users' acceptance of mobile government, while perceived risk has a relatively negative impact. Among these factors, both users' attitudes and perceived external support exhibit the strongest effect on behavioral intention.

Determinants of the adoption of AI wearables - practical implications for marketing

Wearables have become a natural element of human life, determining our way of perceiving, understanding and experiencing the world. Enriched with elements of artificial intelligence, they will change our habits and draw us into the digital dimension of the world - a space of uninterrupted interaction between people and technology. As a result, there are still new ideas for the effective use of AI wearables in the consumer space. The main aim of the article is to examine the determinants behind the acceptance of the AI wearables, with particular emphasis on the strength and nature of the relationship between the consumer and technology. The UTAUT2 model is used for this purpose. The article is a continuation of the previous reflections and analyses in this area; at the same time it constitutes an initial stage of research on the issues related to the adoption of AI wearables.

Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India

Purpose

The purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.

Design/methodology/approach

Data was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the standardized direct and indirect effects in the model.

Findings

The study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.

Research limitations/implications

The research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.

Practical implications

The study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.

Social implications

The use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.

Originality/value

This was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India.

Digital Health and Care Study on Elderly Monitoring

Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.

Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers

Wearable healthcare devices offer tremendous promise to effectively track and improve the well-being of older adults. Yet, little is known about the use of wearable devices by older adults. Drawing upon a national survey in US with 1481 older adults, we examine the use of wearable healthcare devices and the key predictors of use viz. sociodemographic factors, health conditions, and technology self-efficacy. We also examine if the predictors are associated with elders' willingness to share health data from wearable devices with healthcare providers. We find low level of wearable use (17.49%) among US older adults. We find significant positive associations between technology self-efficacy, health conditions, and demographic factors (gender, race, education, and annual household income) and use of wearable devices. Men were less likely (OR = 0.62, 95% CI 0.36-1.04) and Asians were more likely (OR = 2.60, 95% CI 0.89-7.64) to use wearables, as did healthy adults (OR = 1.98, 95% CI 1.37-2.87). Those who electronically communicated with their doctors (OR = 1.86, 95% CI 1.16-2.97), and those who searched online for health information (OR = 1.79, 95% CI 1.03-3.10) were more likely to use wearables. Though 80.15% of wearable users are willing to share health data with providers, those with greater technology self-efficacy and favorable attitudes toward exercise are more willing.

Technology Acceptance in Healthcare: A Systematic Review

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.

A Dual-Stage SEM-ANN Analysis to Explore Consumer Adoption of Smart Wearable Healthcare Devices

Advances in information technology have included the development of smart wearable healthcare (SWH) devices that have potential benefits for consumer health. The adoption of SWH devices is limited, however, compared with other established digital technologies. This study examines the determinants of consumers’ adoption of SWH devices. A conceptual model is proposed that incorporates health (health beliefs and health information accuracy), and technology (compatibility and functional congruence) attributes into the technology acceptance model framework. The proposed model was tested in two steps. Structural equation modelling (SEM) was performed with 473 usable responses to test the hypothesized relationships. The artificial neural network (ANN) approach was then applied to validate the outcomes of Step 1. The SEM analysis indicates that all the hypothesized relationships are supported. The ANN analysis further validates the outcomes of the SEM. The findings of this study and the dual-stage SEM-ANN methodology will have a strong impact on the existing literature regarding SWH devices.

User acceptance of a personalised home lighting system based on wearable technology

Light/dark cycles play an essential role in people's performance, mood and daily rhythm. This study aimed to evaluate an early prototype of a personalised home lighting system including body-worn loggers. A convenience sample (N = 28) wore the devices for 23 h in the field and were given a demonstration of the lighting system components. Participants reported on questionnaires their acceptance of the lighting system and experience of physical comfort and visual appearance of the body-worn loggers. Semi-structured interviews were also held. In a hierarchical linear regression, physical comfort of the loggers explained 35.8% of the variance of 'the behavioural intention to use the system in the future', and 'performance expectancy' explained 50.6%. The interview data helped understand the reasons for the physical and psychological discomfort of wearing the devices. Many participants reported they were not comfortable about using a mobile phone for presence detection in the home.

VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors

After affecting the world in unexpected ways, the virus has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available, but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of smart sensors integrated with the Internet of Medical Things to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the VIrus Resistance Framework using the Internet of Medical Things (VIRFIM) to tie all the individual modules in a unified architecture. Furthermore, we validate the realization of VIRFIM framework with two case studies related to physical activity monitoring and stress detection services. We envision that VIRFIM would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed VIRFIM.

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

BACKGROUND

Considering the increasing demand for health services by older people and the ongoing COVID-19 pandemic, digital health is commonly viewed to offer a pathway to provide safe and affordable health services for older adults, thus enabling self-management of their health while health care systems are struggling. However, several factors cause older people to be particularly reluctant to adopt digital health technologies such as mobile health (mHealth) tools. In addition to previously studied technology acceptance factors, those related to perceived risks of mHealth use (eg, leakage of sensitive information or receiving incorrect health recommendations) may further diminish mHealth adoption by older adults.

OBJECTIVE

The aim of this study was to explore the relationship between perceived risks of using mHealth applications and the intention to use these applications among older adults.

METHODS

We designed a cross-sectional study wherein a questionnaire was used to collect data from participants aged 65 years and older in the Netherlands. Perceived risk was divided into four constructs: privacy risk, performance risk, legal concern, and trust. Linear regression analyses were performed to determine the associations between these perceived risk constructs and the intention to use mHealth applications.

RESULTS

Linear regression per perceived risk factor showed that each of the four constructs is significantly associated with the intention to use mobile medical applications among older adults (adjusted for age, sex, education, and health status). Performance risk (β=-.266; P=<.001), legal concern (β=-.125; P=.007), and privacy risk (β=-.100; P=.03) were found to be negatively correlated to intention to use mHealth applications, whereas trust (β=.352; P=<.001) was found to be positively correlated to the intention to use mHealth applications.

CONCLUSIONS

Performance risk, legal concern, and privacy risk as perceived by older adults may substantially and significantly decrease their intention to use mHealth applications. Trust may significantly and positively affect this intention. Health care professionals, designers of mHealth applications, and policy makers can use these findings to diminish performance risks, and tailor campaigns and applications to address legal and privacy concerns and promote mHealth uptake and health care access for older adults, especially during the COVID-19 pandemic.

Exploring older adults' adoption and use of a tablet computer during COVID-19: Longitudinal qualitative study (Preprint)

Background

As mobile computing technology evolves, such as smartphones and tablet computers, it increasingly offers features that may be particularly beneficial to older adults. However, the digital divide exists, and many older adults have been shown to have difficulty using these devices. The COVID-19 pandemic has magnified how much older adults need but are excluded from having access to technologies to meet essential daily needs and overcome physical distancing restrictions.

Objective

This study sought to understand how older adults who had never used a tablet computer learn to use it, what they want to use it for, and what barriers they experience as they continue to use it during social isolation caused by the COVID-19 pandemic.

Methods

We conducted a series of semistructured interviews with eight people aged 65 years and older for 16 weeks, investigating older novice users’ adoption and use of a tablet computer during the nationwide lockdown due to COVID-19.

Results

Participants were gradually yet successfully accustomed to using a tablet computer to serve various daily needs, including entertainment, social connectedness, and information-seeking. However, this success was not achieved through developing sufficient digital skills but rather by applying the methods they were already familiar with in its operation, such as taking and referring to instruction notes.

Conclusions

Our findings imply that older adults without digital literacy can still benefit from a digital device for quality of later life if proper traditional methods they are already familiar with are offered in its use.

Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis

Due to rapid advancements in the field of information and communication technologies, mobile health (mHealth) has become a significant topic in the delivery of healthcare. Despite the perceived advantages and the large number of mHealth initiatives, the success of mHealth ultimately relies on whether these initiatives are used; their benefits will be diminished should people not use them. Previous literature has found that the adoption of mHealth by users is not yet widespread, and little research has been conducted on this problem. Therefore, this study identifies the antecedents of the intention to use mHealth and proposes a general model that might prove beneficial in explaining the acceptance of mHealth. The authors performed a quantitative meta-analysis of 49 journal papers published over the past 10 years and systematically reviewed the evidence regarding the most commonly identified factors that may affect the acceptance of mHealth. The findings indicate that the proposed model includes the seven most commonly used relationships in the selected articles. More specifically, the model assumes that perceived usefulness positively affects perceived ease of use and user behavioral intention to use mHealth is commonly influenced by five factors: perceived usefulness, perceived ease of use, attitude toward behavior, subjective norms, and facilitating conditions. The results of this work provide important insights into the predictors of mHealth acceptance for future researchers and practitioners.

Older Adults' Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis

BACKGROUND

Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use.

OBJECTIVE

This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices.

METHODS

We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a "line-of-argument" was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible.

RESULTS

In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user's life. The user's needs and the support structure around the device-aspects that are often overlooked-seem to play a crucial role in long-term adoption. Our "line-of-argument" model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user's life, which subsequently predicts whether the device will be integrated into the user's life.

CONCLUSIONS

The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user's preferences.

Energy-efficient IoT based improved health monitoring system for sports persons

Nowadays, wearable technology and the Internet of Things (IoT) are transforming the healthcare sector by refining the way how devices, applications, and people connect and interact with each other. IoT applications in sports are tremendously useful to monitor health and reduce the risk factor. The battery life of wearable and accurate monitoring has been considered a significant challenge in sports medicine. Hence, in this paper, Energy Efficient IoT based Improved Health Monitoring system (EEIoT-IHMS) has been proposed for accurate and continuous sports person’s health monitoring system. This paper determines the optimal set of clusters based on sensor features, in which power usage has been minimized by duty cycling with optimized prediction accuracy. The experimental results demonstrate that the proposed (EEIoT-IHMS) enhances accuracy ratio, improves battery life, and reduces energy consumption compared to other popular methods.

Wearable sensors for continuous oral cavity and dietary monitoring toward personalized healthcare and digital medicine

Oral monitoring plays an essential role in preventing and diagnosing systemic diseases through saliva in the mouth. Dietary monitoring is also crucial to reduce the likelihood of chronic diseases such as hypertension and diabetes by analyzing food types, amounts and diet patterns. Therefore, the oral cavity and dietary monitoring are vital for accurate personalized healthcare management that can improve healthcare. To perform continuous oral cavity and dietary monitoring in real-time, a wearable sensing system capable of continuous analysis is necessary. In this review, we classify chemical and physical biosensing methods and summarize recent progress in wearable sensor development for oral cavity and dietary monitoring for personalized healthcare and digital medicine. We also discuss future perspectives and the potential of wearable sensors to provide robust data for food-intake monitoring and the saliva analysis of super-aged/aging societies, non-face-to-face social life, and global pandemic disease issues. We believe that this review will result in a paradigm shift toward personalized healthcare and digital medicine using wearable sensors through the analysis of massively parallel healthcare data.

Exploring the acceptance of tablets usage for cognitive training among older people with cognitive impairments: A mixed-methods study

This study investigated the acceptance of tablets technology among cognitively impaired older adults from individual and contextual levels when used in cognitive training. A convergent parallel mixed-methods design, comprising a post-usage questionnaire survey and focus groups, was used for data collection. A number of 57 community-dwelling cognitively impaired older people in Hong Kong completed an eight-week, home-based cognitive training using tablets delivered by older volunteers. The acceptance of the tablet usage for cognitive training was evaluated using questionnaire survey. Focus groups were conducted with participants, volunteers, and social workers to explore their experiences of tablet usage for cognitive training. Results indicated that attitudes toward tablets and facilitating conditions were predictors of intention to use tablets at the individual level. Tablets were perceived as beneficial on cognition, enjoyment, learning, social relationships, and communication. Contextual level factors that can encourage tablets usage include capacity building, empowerment, supports from the organization, and trust.

Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

Commonly classified as individual, task or environmental, constraints are boundaries which shape the emergence of functional movement solutions. In applied sport, an ongoing challenge is to improve the measurement, analysis and understanding of constraints to key stakeholders. Methodological considerations for furthering these pursuits should be centred around an interdisciplinary approach. This integration of methodology and knowledge from different disciplines also encourages the sharing of encompassing principles, concepts, methods and data to generate new solutions to existing problems. This narrative review discusses how a number of rapidly developing fields are positioned to help guide, support and progress an understanding of sport through constraints. It specifically focuses on examples from the fields of technology, analytics and perceptual science. It discusses how technology is generating large quantities of data which can improve our understanding of how constraints shape the movement solutions of performers in training and competition environments. Analytics can facilitate new insights from numerous and complex data through enhanced non-linear and multivariate analysis techniques. The role of the perceptual sciences is discussed with respect to generating outputs from analytics that are more interpretable for the end-user. Together, these three fields of technology, analytics and perceptual science may enable a more comprehensive understanding of constraints in sports performance.

Informal Dementia Caregivers: Current Technology Use and Acceptance of Technology in Care

(1) Background: Given the increased social isolation caused by the COVID-19 pandemic, the challenges faced by informal dementia caregivers have increased. An increasing use of technology, both in care and dementia clinical trials, depends upon caregivers' abilities as a user. Accordingly, the aim of our study was to verify the current technology (smartphone and computer) use and acceptance in care, regarding socio-demographic variables; (2) Methods: Questionnaires were distributed to 102 dementia caregivers, mostly of patients with moderate dementia; (3) Results: The majority of participants were women (63%), and large number of them used technological devices such as a smartphone (91%) or computer (81%). Results revealed differences between age, gender, and education level on technology acceptance. Interestingly, smartphone use and acceptance seemed to be feasible, regardless of age, whereas computer use was negatively correlated with age. Technology was perceived by respondents as most useful for patients' activities including locomotion, toileting, and meals; (4) Conclusions: The future of technology use in dementia care should indicate solutions tailored to individual characteristics such as new technology solutions (GPS trackers, smartphone apps, dietary intervention, and meal planning apps).

Acceptance of a Virtual Reality Headset Designed for Fall Prevention in Older Adults: Questionnaire Study

BACKGROUND

Falls are a common phenomenon among people aged 65 and older and affect older adults' health, quality of life, and autonomy. Technology-based intervention programs are designed to prevent the occurrence of falls and their effectiveness often surpasses that of more conventional programs. However, to be effective, these programs must first be accepted by seniors.

OBJECTIVE

Based on the technology acceptance model, this study aimed to examine the acceptance among older adults before a first use of a virtual reality headset (VRH) used in an intervention program designed to prevent falls.

METHODS

A sample of 271 French older adults (mean age 73.69 years, SD 6.37 years) voluntarily and anonymously filled out a questionnaire containing the focal constructs (perceived usefulness, perceived enjoyment, perceived ease of use, intention to use, fall-related self-efficacy, and self-avoidance goals) adapted to the VRH, which was designed to prevent falls.

RESULTS

The results of the structural equation modeling analysis showed that intention to use the VRH was positively predicted by perceived usefulness, perceived enjoyment, and perceived ease of use. Perceived usefulness of the VRH was also negatively predicted by fall-related self-efficacy (ie, the perceived level of confidence of an individual when performing daily activities without falling) and positively predicted by self-avoidance goals (ie, participating in a physical activity to avoid physical regression).

CONCLUSIONS

A better understanding of the initial acceptance among older adults of this VRH is the first step to involving older adults in intervention programs designed to prevent falls using this kind of device.

Predicting healthcare professionals' intention to use poison information system in a Malaysian public hospital

PURPOSE

The main objective of this paper is to determine the predicting factors that influence the intention to use Poison Information System (PIS) among healthcare professionals.

METHODS

A quantitative approach was applied, using a five-point Likert scale questionnaire, adapted from previous studies. Data were collected from 167 healthcare professionals working for Malaysian Public Hospitals in Penang. Smart Partial Least Square (PLS) version 3.2.7 were used to analyse the proposed relationships.

RESULTS

The results indicated that attitude and computer anxiety had a significant positive relation to the intention to use PIS among healthcare professionals wherein computer knowledge was found to have had a significant relationship with attitude and computer habit. Apart from that, technical support and training had a positive relationship with perceived ease of use. Surprisingly, computer habit, perceived usefulness, perceived ease of use, compatibility and facilitating condition did not significantly influence intention to use PIS.

CONCLUSION

The results of this study provided useful insights for healthcare agencies to understand the underlying elements that could improve the poison information management. The results proved that attitude and computer anxiety were critical factors among healthcare professionals managing poisoning cases in a highly stressful and unpredictable work environment. These factors must, therefore, be considered before implementing PIS in managing poisoning cases. The study also provided an understanding of how to improve system development by utilising the end user's expectation on the implementation of the system.

Understanding the Drivers of Wearable Health Monitoring Technology: An Extension of the Unified Theory of Acceptance and Use of Technology

The market for wearable health monitoring technology is promising globally and in Saudi Arabia particularly. The country has a very high prevalence of chronic diseases that can be managed using wearable health monitoring technology. However, wearable devices are not fully advantageous if people do not accept them. Due to the parsimony of studies on the acceptance of wearable health monitoring technology, understanding the key drivers of using wearable health monitoring technology remains uncertain. This cross-sectional study extends the extended unified theory of acceptance and use of technology (UTAUT2) to explain the variance in the adoption intention of wearable health monitoring technology. A total of 256 responses were analyzed using the partial least squares structural equation modeling technique, in addition to the importance-performance map analysis. The results indicate that performance expectancy (PE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM) and habit (HA) significantly impact users’ behavioral intention (BI) to adopt wearable health monitoring technology. The results also demonstrate that effort expectancy (EE), price value (PV), government health policy (GHP) and trust (TR) are not important. Based on the findings, this research presents a set of recommendations for decisions makers, managers and system developers in the healthcare sector to enhance the use and quality of wearable technology.

The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review

Wearable smart sensors are emerging technology for daily monitoring of vital signs with the reducing discomfort and interference with normal human activities. The main objective of this study was to review the applied wearable smart sensors for disease control and vital signs monitoring in epidemics outbreaks. A comprehensive search was conducted in Web of Science, Scopus, IEEE Library, PubMed and Google Scholar databases to identify relevant studies published until June 2, 2020. Main extracted specifications for each paper are publication details, type of sensor, disease, type of monitored vital sign, function and usage. Of 277 articles, 11 studies were eligible for criteria. 36% of papers were published in 2020. Articles were published in 10 different journals and only in the Journal of Medical Systems more than one article was published. Most sensors were used to monitor body temperature, heart rate and blood pressure. Wearable devices (like a helmet, watch, or cuff) and body area network sensors were popular types which can be used monitoring vital signs for epidemic trending. 65% of total papers (n = 6) were conducted by the USA, Malaysia and India. Applying appropriate technological solutions could improve control and management of epidemic disease as well as the application of sensors for continuous monitoring of vital signs. However, further studies are needed to investigate the real effects of these sensors and their effectiveness.

An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID-19

The novel coronavirus disease 2019 (COVID-19) global pandemic has shifted how many patients receive outpatient care. Telehealth and remote monitoring have become more prevalent, and measurements taken in a patient's home using biometric monitoring technologies (BioMeTs) offer convenient opportunities to collect vital sign data. Healthcare providers may lack prior experience using BioMeTs in remote patient care, and, therefore, may be unfamiliar with the many versions of BioMeTs, novel data collection protocols, and context of the values collected. To make informed patient care decisions based on the biometric data collected remotely, it is important to understand the engineering solutions embedded in the products, data collection protocols, form factors (physical size and shape), data quality considerations, and availability of validation information. This article provides an overview of BioMeTs available for collecting vital signs (temperature, heart rate, blood pressure, oxygen saturation, and respiratory rate) and discusses the strengths and limitations of continuous monitoring. We provide considerations for remote data collection and sources of validation information to guide BioMeT use in the era of COVID-19 and beyond.

The development of a mobile user interface ability evaluation system for the elderly

This research aimed to develop a comprehensive evaluation of the mobile user interface abilities of the elderly so that technology can be designed to meet individualized needs. A total of 135 older adults were evaluated with the developed system, the Elderly Mobile User Interface Ability Evaluation System (EMUIAES). The prediction of age and the use of technology on elderly mobile interface usage were investigated based on the findings of the evaluation. The relationship between performance on Fitts' task and elderly mobile user interface ability (EMUIA) was also examined. The findings showed a strong effect of age on the elderly's use of mobile user interfaces. Previous experience with personal and tablet computers also contributed to the use of mobile user interfaces. In addition, this research demonstrated the application of Fitts' law to describe the elderly mobile user interface behaviors, particularly for tasks involving fast tapping and pointing. The EMUIAES can provide future researchers and designers a comprehensive tool to describe the elderly's diverse behaviors and changes in their ability to use mobile interfaces. Individualized interface designs for elderly users can be developed based on these findings to improve the elderly users' experiences of using technology.

Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey

BACKGROUND

Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults.

OBJECTIVE

The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults.

METHODS

Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis.

RESULTS

About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health.

CONCLUSIONS

The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis

Background

At present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas.

Methods

We use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003–2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis.

Results

The field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research.

Conclusions

This study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.

Factors Affecting the Acceptability of Technology in Health Care Among Older Korean Adults with Multiple Chronic Conditions: A Cross-Sectional Study Adopting the Senior Technology Acceptance Model

Purpose

Older adults experience challenges employing technology in their health-care management due to changes in cognitive and physical functions. This study aimed to investigate the acceptance of technology among older Korean adults with multiple chronic health conditions and examine factors associated with technology acceptance, adopting the senior technology acceptance model (STAM).

Patients and Methods

In total, 226 community-dwelling older adults with more than two chronic conditions participated in this study. We conducted a survey that covered demographics, gerontechnology self-efficacy, gerontechnology anxiety, facilitating conditions, self-reported health conditions, cognitive ability, social relationships, attitude toward life and satisfaction, physical functioning, and technology acceptance.

Results

Older Korean adults with multiple chronic health conditions scored moderately high for technology acceptance (25.36±5.28). There were significant differences in technology acceptance according to age (r=−0.241), cognitive ability (r=0.225), gerontechnology self-efficacy (r=0.323), and facilitating conditions (r=0.288). Only age and education were significant factors predicting technology acceptance (Adjusted R²=0.151, p<0.001).

Conclusion

Although older Korean adults with multiple chronic conditions displayed good technology acceptance, their age and education level predicted the level of acceptance. Given that some components of the STAM model have social and cultural relevance, it is necessary to conduct research across various cultures to better understand technology acceptance by older adults.

Enabling Older Adults' Health Self-Management through Self-Report and Visualization-A Systematic Literature Review

Aging is associated with a progressive decline in health, resulting in increased medical care and costs. Mobile technology may facilitate health self-management, thus increasing the quality of care and reducing costs. Although the development of technology offers opportunities in monitoring the health of older adults, it is not clear whether these technologies allow older adults to manage their health data themselves. This paper presents a review of the literature on mobile health technologies for older adults, focusing on whether these technologies enable the visualization of monitored data and the self-reporting of additional information by the older adults. The systematic search considered studies published between 2009 and 2019 in five online databases. We screened 609 articles and identified 95 that met our inclusion and exclusion criteria. Smartphones and tablets are the most frequently reported technology for older adults to enter additional data to the one that is monitored automatically. The recorded information is displayed on the monitoring device and screens of external devices such as computers. Future designs of mobile health technology should allow older users to enter additional information and visualize data; this could enable them to understand their own data as well as improve their experience with technology.