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The purpose of this paper is to investigate the factors associated with consumer’s intention to adopt wearable technology in healthcare, and to examine the moderating effects of product type on consumer’s adoption intention.

An integrated acceptance model was developed based on unified theory of acceptance and use of technology 2 (UTAUT2), protection motivation theory (PMT), and privacy calculus theory. The model was tested with 462 respondents using a survey.

Consumer’s decision to adopt healthcare wearable technology is affected by factors from technology, health, and privacy perspectives. Specially, fitness device users care more about hedonic motivation, functional congruence, social influence, perceived privacy risk, and perceived vulnerability, but medical device users pay more attention to perceived expectancy, self-efficacy, effort expectancy, and perceived severity.

This study is among the first to investigate healthcare wearable device from behavioral perspective. It also helps to comprehensively understand emerging health information technology (HIT) acceptance from technology, health, and privacy perspectives.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.

MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.

According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.

This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

Notwithstanding the novelty and importance of wearable fitness devices, few studies have focussed on comparing the drivers of adoption and usage of wearable fitness in the context of developing countries. This study aims to explore factors that drive overall acceptance of wearable fitness devices in developing countries (India and the Philippines) and whether the impact of these factors on the intention to adopt (INT) differs by country and gender.

The study extends the existing body of knowledge by developing a model that integrates the impact of various perceived benefits (health, autonomy, social, hedonic, symbolic), health self-efficacy (HEALTHSE) and individual characteristics (technological innovativeness [TI]) on the INT wearable fitness devices and the moderating impact of country and gender. The analysis was carried out using partial least square and data of 343 respondents.

This study finds that the INT wearable fitness devices by consumers in developing countries are positively impacted by hedonic, health and autonomy, HEALTHSE and TI. Symbolic and social factors do not have any significant impact on the overall INT wearable fitness devices. However, there are country and gender-specific differences that are consequential to the development of marketing strategies.

The framework and results are specific to the two countries and limited by convenience sampling. Future research can focus on replication across different countries and extend the model with additional contextual factors such as perceived risks.

To the best knowledge of the authors, this is one of the few studies to examine and compare the drivers of adoption of wearable fitness devices in lesser researched developing countries. Also, it is one of the few studies to compare the moderating impact of country and gender in the context of the INT wearable devices. The study provides a theoretical and methodological foundation for future research, as well as practical implications for global companies developing and promoting wearable fitness devices.

Adopting an interdisciplinary perspective, the purpose of this paper was to posit an industry-wide technological intervention for hotel housekeeper safety and health through the advancement of wearable technology.

Using the task-technology fit (TTF) model and examples of successful safety and health applications of wearable technologies in the health-care and construction management industries, interventions and future research directions are presented to address workplace hazards experienced by hotel housekeepers.

The fit between a variety of hotel housekeeper user requirements, task demands and wearable functions are explored with justification for the use of wearable devices to improve safety and health-related outcomes.

A research agenda is proposed for the adoption and use of wearables in the hospitality industry with the intention to generate meaningful interventions beyond corporate wellness, and the mitigation of employee privacy concerns to enhance wearable adoption.

Given the importance of consumer safety and health assurance in a post-pandemic business environment, hospitality and tourism organizations should place greater emphasis on protecting front line employees who will be essential in regaining economic viability.

Theoretical and practical foci should move beyond a simplistic view of hospitality and tourism worker safety and health that generally centers on wellness initiatives and other baseline strategies, toward a more holistic view benefitting the hospitality industry.

Extant concerns about hotel housekeeper safety and health, in addition to new concerns and threats in a post-pandemic work environment, are largely understudied and worthy of investigation.

The aim of this study was to develop a smart wearable mask designed for the prevention of respiratory infectious diseases by understanding consumer's preferences in designs and functions of the smart wearable masks.

To develop a smart mask design, a survey was conducted on Chinese consumers in their 20–40s and analyzed their mask wearing behaviors, preferences and caring aspects of masks. The collected data were analyzed to identify the demographic characteristics of the subjects surveyed by using the SPSS program, and technical statistical analysis was conducted. To identify differences in demographic characteristics, an independent samples t-test, one-way analysis of variance and Scheffe's ad hoc test were conducted.

Based on the research results, design guidelines for wearable masks were defined, and four wearable mask designs were developed and presented in 2D and 3D images based on the design guidelines. There were significant differences among people with different backgrounds.

It is significant that this research presents smart wearable mask design guidelines and designs through supplementation and improvement of existing mask. It is expected that this research provides basic empirical data for mask designs through the planning of smart wearable mask designs and surveys assessing consumer perceptions, attitudes and satisfaction.

Commercial entities (e.g. health and life insurance, airlines and supermarkets) in different countries have recently begun to introduce wearable technology as part of the consumer journey and as a means of enhancing the business value chain. While a firm’s decision to adopt such new technologies as wearable devices is often based on financial factors such as return on investment, costs and impact on profits, consumers may hold a different attitude toward the value of using smart wearables and sharing their personal data as part of their business-client relationships. The purpose of this paper is to investigate consumer perceptions of and reactions to persuaded self-tracking (PST) – a practice in which businesses actively encourage consumers to monitor, collect and share personal biometric data through wearable technologies in exchange for personalized incentives and rewards.

Using a qualitative research approach and a purposeful sampling method, the authors conducted personal in-depth interviews with 24 consumers (both users and non-users of wearable devices). Interviews were recorded and transcribed, resulting in 600 pages of transcripts comprising more than 203,000 words. Data coding and analysis were facilitated by using NVivo.

Consumers’ assessment of PST is based on perceived value-in-use, privacy/security concerns and perceived fairness/justice, resulting in four types of reactions to adopt or use PST (embracing, considering, debating and avoiding). Specifically, the authors identified two individual determinants (intrinsic motivation and extrinsic motivation) and four firm-related determinants (design of wearable device, assurance, transparency and controllability) that influence consumer perceptions of PST.

Results of this study have implications for both vendors of wearable devices and firms trying to leverage smart wearables in their value chains. Identifying consumers’ perceptions, as well as barriers and enablers of acceptance, will help firms to more effectively design and develop wearable device-based services, thus gaining consumer support for using fitness trackers. The primary limitation of the study is that using a thematic analysis method diminishes the generalizability of our findings.

This study addresses an under-researched area: the integration of wearable technologies in a firm’s value chain through the lens of the consumers. This study is one of the first, according to authors’ knowledge, to investigate consumer perceptions of PST.

Wearable health information systems (IS) open up a new era for personal health self-management, and bring about disruptive changes to individual lives. However, prior studies on IS post-adoptive behavior primarily focused on either continuance or discontinuance, neglecting the mutual transformation process between IS usage and rejection behavior. This behavior is expressed as intermittent discontinuance in the current study. By revising and extending the expectation-disconfirmation model, the purpose of this paper is to understand the factors affecting information system intermittent discontinuance in the context of wearable eHealth.

This study proposes a revised expectation-disconfirmation model by incorporating four new theoretical constructs, i.e. neutral disconfirmation, attitudinal ambivalence, neutral satisfaction, and intermittent discontinuance. An online survey was used to validate the research model, and 428 wearable health device users were recruited in this study.

Empirical results demonstrate that neutral disconfirmation exerts positive effects on neutral satisfaction and attitudinal ambivalence, both of which further have positive effects on intermittent discontinuance. In addition, attitudinal ambivalence also has a positive and significant effect on neutral satisfaction.

This study extends the current understanding on IS post-adoption usage behavior by introducing a new concept, i.e., intermittent discontinuance. In particular, post-adoption usage behavior is viewed as a dynamic process within a changing environment in this study. Some unique features specific to intermittent discontinuance are also identified. In this regard, neutral and conflicting constructs further add new values to IS adoption and diffusion literature. In addition, the integration of eHealth and wearable devices provides a fruitful research context for future research, and this study represents one of the first attempts to empirically explore the intermittent usage problems encountered within this emerging phenomenon.