Usage Of Wearable Devices Research Articles

Abstract 4138218: Meeting Physical Activity Recommendations Among Wearable Device Users: Insights from the 2022 Health Information National Trends Survey

Background: The benefits of physical activity for health and well-being are well documented. Promoting physical activity is crucial for enhancing population health. Wearable devices play a significant role in tracking health-related activities and have been shown to enhance engagement in physical activities. While studies have identified sociodemographic patterns in wearable device usage, little is known about their impact on real-world outcomes, such as increased physical activity. Research Question: Is there a variation in achieving the recommended level of physical activity among wearable device users? Aim: To examine factors associated with achieving the recommended weekly minimum of 150 minutes of moderate-intensity physical activity among wearable device users. Methods: Data from the 2022 Health Information National Trends Survey (HINTS 6) of wearable device users (n = 1,718) were analyzed. We reported adjusted odds ratios from multivariable logistic regression of meeting recommended levels of physical activity. Results: Among the 1,718 participants who utilized wearable devices, 64.3% were female with a mean (M) age of 48.2 ± 0.36 years. The majority were White (58.1%), followed by Asian (18.4%). Among the wearable users, 54.8% met the recommended weekly minimum of 150 minutes of moderate-intensity physical activity. The age group of 65 to 74 years (OR, 1.96; 95% CI, 1.04, 3.69; p=0.04) was more likely to achieve 150 minutes of physical activity per week. However, Asian wearable users were less likely to achieve physical activity goals compared to other racial and ethnic wearable users (OR, 0.55; 95% CI, 0.31, 0.96; p=0.04). Additionally, individuals with a body mass index ≥30 kg/m 2 (OR, 0.54; 95% CI, 0.36, 0.79; P<0.01) and those with high blood pressure (OR, 0.62; 95% CI, 0.41, 0.94; p=0.03) were less likely to meet physical activity recommendations. Conclusions: This study highlights variations in physical activity outcomes among wearable device users and underscores the importance of enhancing the public's ability to use these devices to increase physical activity. Future efforts should include exploring facilitators of effective wearable use, in various sociodemographic groups, especially for those at risk of obesity or hypertension.

Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study.

Although wearable technology has become increasingly common, comprehensive studies examining its ownership across different sociodemographic groups are limited. The aims of this study were to (1) measure wearable device ownership by sociodemographic characteristics in a cohort of US consumers and (2) investigate how these devices are acquired and used for health-related purposes. Data from the Rock Health Digital Health Consumer Adoption Survey collected from 2020 to 2022 with 23,974 US participants were analyzed. The sample was US Census-matched for demographics, including age, race/ethnicity, gender, and income. The relationship between sociodemographic factors and wearable ownership was explored using descriptive analysis and multivariate logistic regression. Of the 23,974 respondents, 10,679 (44.5%) owned wearables. Ownership was higher among younger individuals, those with higher incomes and education levels, and respondents living in urban areas. Compared to those aged 18-24 years, respondents 65 years and older had significantly lower odds of wearable ownership (odds ratio [OR] 0.18, 95% CI 0.16-0.21). Higher annual income (≥US $200,000; OR 2.27, 95% CI 2.01-2.57) and advanced degrees (OR 2.23, 95% CI 2.01-2.48) were strong predictors of ownership. Living in rural areas reduced ownership odds (OR 0.65, 95% CI 0.60-0.72). There was a notable difference in ownership based on gender and health insurance status. Women had slightly higher ownership odds than men (OR 1.10, 95% CI 1.04-1.17). Private insurance increased ownership odds (OR 1.28, 95% CI 1.17-1.40), whereas being uninsured (OR 0.41, 95% CI 0.36-0.47) or on Medicaid (OR 0.75, 95% CI 0.68-0.82) decreased the odds of ownership. Interestingly, minority groups such as non-Hispanic Black (OR 1.14, 95% CI 1.03-1.25) and Hispanic/Latine (OR 1.20, 95% CI 1.10-1.31) respondents showed slightly higher ownership odds than other racial/ethnic groups. Our findings suggest that despite overall growth in wearable ownership, sociodemographic divides persist. The data indicate a need for equitable access strategies as wearables become integral to clinical and public health domains.

Association between wearable device usage and engagement of strength activity among people with and without disabilities

There is a lack of literature examining the engagement in strength activities of people with disabilities (PWD) using and not using wearable devices. The objectives of the current study were to determine the prevalence of strength activity engagement among wearable device users with disabilities, and to compare strength activity engagement between wearable device users and non-users with and without disabilities. Wearable device users and non-users with and without disabilities from eight states of the CDC’s 2017 Behavioral Risk Factor Surveillance were included in the analysis. Linear regression was used to examine the association between wearable device use and disability status and the interaction term for frequency of strength activity per week, while logistic regression was used to determine the association between the two variables and the interaction term for strength activity engagement and meeting strength activity guidelines. 7055 wearable device users and non-users with and without disabilities were included in the analysis. No interaction effects were found between wearable device use and disability status. In unadjusted logistic regressions, wearable device users were more likely to engage in strength activity (OR = 1.16, 95% CI [1.16, 2.20]) and meet strength activity guidelines (OR = 1.50, 95% CI [1.07, 2.09]), whereas PWDs were less likely to engage in strength activity (OR = 0.57, 95% CI [0.44, 0.75]) and meet strength activity guidelines (OR = 0.72, 95% CI [0.53, 0.98]). The use of wearable devices could lead to engagement in strength activity. However, further research is needed to determine its effectiveness in PWD.

EPH90 Health Characteristics and Adherence Patterns in Electronic Wearable Device Users: A Study in Low- and Middle-Income Countries

EPH90 Health Characteristics and Adherence Patterns in Electronic Wearable Device Users: A Study in Low- and Middle-Income Countries

The use of wearable devices on physical activity levels among individuals living with diabetes: 2017 BRFSS

AimThis study aims to examine the association between wearing wearable devices and physical activity levels among people living with diabetes. Methods1298 wearable device users and nonusers living with diabetes from eight states of the 2017 Behavioral Risk Factors Surveillance System were included in the analysis. Unadjusted and adjusted linear regression was performed to determine the association between self-reported physical activity per week (min) and wearable device usage (users and nonusers) among people living with diabetes using survey analysis. Results84.97 % (95 % CI [80.39, 88.89]) of participants were nonusers of wearable devices, while 15.03 % (95 % CI [11.11, 19.61]) were users. Across the sample, the average weekly physical activity was 427.39 mins (95 % Cl [356.43, 498.35]). Nonusers had a higher physical activity per week with 433.83 mins (95 % CI [353.59, 514.07]), while users only had 392.59 mins (95 % CI [253.48, 531.69]) of physical activity per week. However, the differences between the two groups were non-statistically significant (p=.61). In both adjusted and unadjusted linear regressions between physical activity per week and wearable device usage, statistically significant associations were not found (unadjusted: β=-41.24, p=.62; adjusted: β=-56.41, p=.59). ConclusionFurther research is needed to determine the effectiveness of wearable devices in promoting physical activity among people with diabetes. Additionally, there is a need to determine how people with diabetes use wearable devices that could promote physical activity levels.

A Laguerre-Volterra network model based on ant colony optimization applied to evaluate EMG-force relationship in the muscle fatigue state.

With the accuracy and convenience improvement of electromyographic (EMG) acquired by wearable devices, EMG is gradually used to evaluate muscle force signal, a non-invasive evaluation method. However, the relationship between EMG and force is a complex nonlinear relationship, even which will change with different movements and different muscle states. Therefore, it is difficult to evaluate this nonlinear EMG-force relationship, especially when the muscle state gradually transits from non-fatigue to deep fatigue. For more accurate values of force in human fatigue state, this paper proposes a dual-input Laguerre-Volterra network (LVN) model based on ant colony optimization. First, the changes in 19 EMG features are discussed with increasing fatigue. We also consider two non-Gaussian features: kurtosis and negentropy in the 19 features. Later, 11 EMG fatigue features are picked out according to the fatigue test. Then, the preprocessed EMG and a composite signal of the 11 fatigue features are simultaneously input into the LVN model. Subsequently, the ant colony optimization algorithm is selected to train the model parameters. At the same time, a penalty term that we defined is introduced into the model cost function to adjust the weight of each feature adaptively. Finally, some experiments prove that the LVN model could quick fit the accurate force signal in five fatigue stages, such as non-fatigue, slight fatigue, mild fatigue, severe fatigue, and extreme fatigue. This LVN model can quickly transform EMG into strength signal in real time, which is suitable for people to observe muscle strength by a wearable device and makes it easy to detect the muscle current state. This model has good stability and can remain effective for a long time with training once, which provides convenience for the users of wearable devices.

Examining the Relationship Between E-Cigarette Status and Wearable Device Use on Physical Activity Levels in U.S. Adults.

The aim of this analysis is to investigate physical activity levels amongst e-cigarette users based on their wearable device use. Cross-sectional secondary data analysis using 2017 Behavioral Risk Factor Surveillance Survey (BRFSS). Data from the 2017 BRFSS were used. 5,562 U.S. adults (age 18+). Self-reported physical activity related variables from U.S. adults (age 18+). Separate unadjusted and adjusted linear regression models were performed for each of the dependent variables using survey analysis. Non-users of wearable devices and e-cigarettes account for 96.6% (95%CI [95.7, 97.6]) of the sample, whereas users of wearable devices and e-cigarettes account for 3.3% (95%CI [1.2, 5.4]) of the sample. Those who use e-cigarettes participate in almost 50% less vigorous physical activity minutes per week than nonusers, 46 (95%CI [0.43, 91.57]) and 93 (95%CI [80.59, 106.34]) minutes respectively. Individuals who use e-cigarettes and use wearable devices were found to spend significantly more time in total physical activity per week in both the unadjusted and adjusted linear regressions, P = =0.01 and P = =0.04 respectively. The use of e-cigarettes, wearable devices, or both technologies may influence the physical activity levels of its users. Additional research is needed to better understand the association between physical activity levels and the usage of these technologies.

Country differences in nocturnal sleep variability: Observations from a large-scale, long-term sleep wearable study

Study objectivesCountry or regional differences in sleep duration are well-known, but few large-scale studies have specifically evaluated sleep variability, either across the work week, or in terms of differences in weekday and weekend sleep. MethodsSleep measures, obtained over 50 million night's sleep from ∼220,000 wearable device users in 35 countries, were analysed. Each person contributed an average of ∼242 nights of data. Multiple regression was used to assess the impact country of residence had on sleep duration, timing, efficiency, weekday sleep variability, weekend sleep extension and social jetlag. ResultsNocturnal sleep was shorter and had a later onset in Asia than other regions. Despite this, sleep efficiency was lower and weekday sleep variability was higher. Weekend sleep extension was longer in Europe and the USA than in Asia, and was only partially related to weekday sleep duration. There were also cross-country differences in social jetlag although the regional differences were less distinct than for weekend sleep extension. ConclusionsIn addition to regional differences in sleep duration, cross-country differences in sleep variability and weekend sleep extension suggest that using the latter as an indicator of sleep debt may need to be reconsidered. In countries exhibiting both short sleep and high weekday sleep variability, a culturally different means of coping with inadequate sleep is likely. Country or region differences in culture, particularly those related to work, merit closer examination as factors influencing the variability in normative sleep patterns around the world.

Factors and Design Features Influencing the Continued Use of Wearable Devices.

The initial healthy uptake of wearable devices is not necessarily accompanied by sustained or continued use. Accordingly, this study investigates the factors influencing the continuous use of wearable devices with a particular emphasis on design features. We complemented the expectation-confirmation model (ECM) theoretical foundation with various design features such as trust, readability, dialogue support, personalization, device battery, appeal, and social support. The study employs a simultaneous mixed method research design denoted as QUANT + qual. The quantitative analysis leverages partial least squares structural equation modeling (PLS-SEM) using survey data collected from wearable device users. The qualitative analysis complements the quantitative focus of the research by providing insights into the results obtained from the quantitative analysis. We found that subjects tend to use wearables daily (60%) or several times a week (33%), and 91% plan to use them even more. Subjects indicated multiple usages for wearables. Most subjects were using wearables for healthcare and wellness (61%) or sports and fitness (54%) and had smartwatches wearable type (74%). The model explains 24.1% (p < 0.01) of the variance of continued intention to use. As a theoretical contribution, the findings support using the ECM as a theoretical foundation for explaining the continued use of wearables. Partial least squares (PLS) and qualitative data analysis highlight the relative importance that wearable users place on perceived usefulness. Most notable are tracking functions and design features such as device battery, integration with other apps/devices, dialogue support, and appeal.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020

Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities. To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020. This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022. Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking). Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey). Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care. Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

Parachute shape ultra‐wideband wearable antenna for remote health care monitoring

SummaryWireless body area networks (WBAN) is used to measure patients' health conditions continuously. Different kinds of sensors are required to measure health conditions. When such types of antennas are used on the human body, they are flexible with the movements. The usage of wearable devices is currently increasing in the biomedical field. The presented wearable antenna is suitable for biomedical applications. The presented ultra‐wideband (UWB) flexible parachute shape wearable antenna is fabricated on a jeans textile substrate. The prototype antenna has a −10 dB measured impedance bandwidth of 5800 MHz (7 to 12.8 GHz) with average radiation efficiency of 75.28%. The prototype antenna's size is 40 × 40 mm2 (1.32 × 1.32 at centre frequency 9.9 GHz) and a peak gain of 4.5 dB at 12.33 GHz. The fabricated antenna is suitable for biomedical applications in X‐band frequencies and can be implemented with a low‐cost manufacturing process. The radiating element is made by conductive copper tape. Muscle‐equivalent phantoms are used to analyze the body effect on antenna performance. The radiation effect emitted by the presented antenna on the human body is calculated by the specific absorption rate (SAR) value. The maximum SAR value of the proposed antenna is 1.84 W/kg at 12.33 GHz. This leads to promising results for wearable applications related to remote health care monitoring, such as biotelemetry and mobile health with a sensor‐driven approach.

On Monetizing Personal Wearable Devices Data: A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in Healthcare

Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.

Smart Wearable Technologies as Resources for Consumer Agency in Well-Being

Consumers are increasingly expected to be active in managing their personal well-being. Agency, meaning individuals’ ability to reflect on their circumstances, effect change, and act independently, constitutes an important means for consumer well-being. To a growing extent, consumers are using smart technologies, such as wearable devices and applications, to better manage their well-being. However, how interactions with wearables improve and affect consumer agency for well-being is underexamined. The aim of this study is to explore how consumers use smart wearable technologies as resources for agency in managing their well-being. Drawing on psychological and sociological literature on agency as well as qualitative data from users of various wearable devices, the authors distinguish individual and contextual levels of agency, in which knowing and acting constitute two types of smart wearable technology use. From these dimensions, they conceptualize a framework with four types of technology use for well-being: (1) self-improvement, (2) justification, (3) adaptation, and (4) activism. The authors discuss the theoretical and managerial implications of this framework with the aim of improving consumer agency for well-being through smart technology.

Wearable devices in palliative care for people 65 years and older: A scoping review.

The objective of this scoping review is to map existing evidence on the use of wearable devices in palliative care for older people. The databases searched included MEDLINE (via Ovid), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Google Scholar, which was included to capture grey literature. Databases were searched in the English language, without date restrictions. Reviewed results included studies and reviews involving patients aged 65 years or older who were active users of non-invasive wearable devices in the context of palliative care, with no limitations on gender or medical condition. The review followed the Joanna Briggs Institute's comprehensive and systematic guidelines for conducting scoping reviews. Of the 1,520 reports identified through searching the databases, reference lists, and citations, six reports met our inclusion criteria. The types of wearable devices discussed in these reports were accelerometers and actigraph units. Wearable devices were found to be useful in various health conditions, as the patient monitoring data enabled treatment adjustments. The results are mapped in tables as well as a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) chart. The findings indicate limited and sparse evidence for the population group of patients aged 65 years and older in the palliative context. Hence, more research on this particular age group is needed. The available evidence shows the benefits of wearable device use in enabling patient-centred palliative care, treatment adjustments and symptom management, and reducing the need for patients to travel to clinics while maintaining communication with healthcare professionals.

Addressing privacy concerns for mobile and wearable devices sensors: Small-group interviews with healthy adults and cancer survivors.

Mobile and wearable sensor technology is increasingly common and accessible. The aim of this study was to explore individuals' perceptions and acceptability of mobile and wearable sensors, as well as concerns. Purposive sampling was used to recruit non-patient adults (n = 22) and cancer survivors (n = 17) for face-to-face and virtual small-group interviews. Reflexive thematic analysis of the data focused on privacy concerns. Participants reported that privacy was generally not a concern for sensor adoptions for physical activity health interventions except for health insurer access. The patient perspectives as reported in the findings illustrate the need for transparency between potential adopters and users of mobile and wearable devices and health care practitioners, as well as secure privacy policies for health insurers. Older adults often are perceived as unwilling to adopt mHealth technologies for many reasons, including privacy concerns. This study examined an important patient population, cancer survivors, who are often overlooked yet may benefit from targeted health interventions using mHealth technologies, and compared their responses with a non-patient population for prevention purposes. Our findings suggest that one's lived health experiences (cancer survivorship) are more influential than one's age in adopting mHealth technologies.

Comparison of Participation in Strength Activity Between Wearable Device Users and Nonusers: 2017 Behavioral Risk Factors Surveillance System.

There are many benefits associated with engaging in strength physical activity. Many studies did not examine the engagement of strength activity among wearable device users. This study aimed to examine the association between wearable device usage and engagement of strength activity in free-living settings using nationally representative data. A total of 8250 adult wearable device users and nonusers from 8 states of the 2017 Behavioral Risk Factors Surveillance System were included in analysis. Multiple regression models were performed to determine the association between the dependent variables of strength activities and the independent variable of wearable devices. Wearable device users were 1.26 (95% confidence interval, 1.01-1.81) times the odds of nonusers in engaging in strength activity. Users also had higher odds of meeting both the strength and aerobic physical activity guidelines than nonusers (odds ratio = 1.49; 95% confidence interval, 1.07-2.06; adjusted odds ratio = 1.43; 95% confidence interval, 1.02-2.00). No associations were found between wearable device utilization and frequency of strength activity per week. Wearable device users were more likely to engage in strength activity than nonusers. However, additional studies are needed to determine the effectiveness of wearable devices in promoting strength activity.

Social Acceptability in Context: Stereotypical Perception of Shape, Body Location, and Usage of Wearable Devices

Assessing social acceptability is vital when designing body-worn mobile devices. Previous research found evidence that using stereotyping content model (SCM) mobile devices can systematically predict ratings of the warmth and competence of their wearers. However, it is currently unknown if other contextual dimensions of mobile device usage can also systematically affect those ratings. In two studies, we investigate if and how shape and body location of a body-worn mobile device as well as the activity in which the device is being used can systematically influence stereotypical ratings. Our results suggest that this is evident in some but not all cases. We conclude that people further differentiate between the placement of the device, particularly devices in the user’s hand, and during an activity in which the device can contextually be misused. This indicates that users further differentiate the context and that more contexual information is helpful while operationalizing the SCM as a measure for social acceptability.

Are individuals with disabilities using wearable devices? A secondary data analysis of 2017 BRFSS

Purpose The study aims to investigate the prevalence of individuals with disabilities who reported using wearable devices, to examine the association between wearable device usage and disability status, and to determine the characteristic of individuals with disabilities associated with wearable device usage using the 2017 Behavioural Risk Factor Surveillance System (BRFSS) through secondary data analysis. Materials and methods Data from the 2017 BRFSS of eight states were used in the analysis. Descriptive analysis, chi-square analysis, and multivariable logistic regressions were performed. Subsample analyses were also conducted for individuals with disabilities and different types of disability, including visual impairments, hearing impairments, cognitive disability, independent living disability, self-care disability, and mobility disability on wearable device usage. Results 14.6% (95% CI [11.7, 17.5]) of participants with disabilities were wearable device users. Individuals with disabilities were .63 (95% CI [.48, .83], p < 0.001) and .67 (95% CI [.50, .90], p = 0.007) times the odds of individuals without disabilities in using wearable devices, respectively, according to unadjusted and adjusted logistic regression. Individuals with mobility disability were less likely to utilise wearable devices than their counterparts. Among individuals with disabilities, those who were age 65 years or older had a lower odds of using wearable devices (OR = .55, 95% CI [0.35, 0.85), p = 0.007). Conclusion Individuals with disabilities are using wearable devices in collecting various health-related information. Further research is needed to determine reasons why individuals with disabilities are not using wearable devices and how individuals with disabilities are using wearable devices. IMPLICATIONS FOR REHABILITATION Wearable devices can track various health-related information such as physical activity levels, sleep patterns, calories intakes, and chronic health conditions. Using nationally represent data, individuals with disabilities have access and utilise wearable devices in free living setting. Compare to individuals without disabilities, individuals with disabilities are less likely to utilise wearable devices in free living setting. Further research is needed to determine the accessibility of wearable devices for individuals with disabilities and its usage in rehabilitation setting.

웨어러블 헬스케어 디바이스의 혁신 특성, 브랜드 및 이미지가 제품수용 의도에 미치는 영향 연구

The purpose of this study was to examine the effect of innovative values and purchase attitudes on product acceptance intention based on the effects of innovative characteristics (complexity, relative advantages) and brand, image (social image, brand resonance) of wearable healthcare device users on innovative values and purchasing attitudes. A survey was conducted on actual users who currently use wearable healthcare devices, and the final copies of 201 were analyzed using SPSS 25 and SmartPLS 3.0 programs. The summary of the research results is following here. First, it turned out that complexity among innovation characteristics had a negative (-) effect on innovative values and purchasing attitudes, and relative advantages did not affect innovative values, but on purchasing attitudes. Second, the social image and brand fairness among brands and images affect on innovative values. Third, among the innovation characteristics, complexity, relative advantages, and social images of brands images were showed to have an effect on purchasing attitudes. On the other hand, fairness of the brand and image did not affect on the purchase attitude. Fourth, it showed that innovative values and purchasing attitudes influence on product acceptance intention through this study. Based on the result of these research, theoretical, practical, and future research directions were suggested.

Study on wearable device users’ willingness to continue using—ECM-IS based on the expansion model

&lt;p&gt;Based on the Expectation Confirmation Model of Information System(ECM-IS), three personal characteristic factors of self-efficacy, privacy concerns, and innovation as well as two external environmental factors of subjective reference and switching costs were introduced to construct a model of factors affecting users’ continuance intention of wearable devices from the perspective of “technology-individual-environment”. 356 valid samples were collected through the questionnaire for empirical analysis. The results of the study show that self-efficacy, switching costs, and perceived usefulness in the ECM-IS model have a significant effect on users’ continuance intention at &lt;em&gt;p&lt;/em&gt;&amp;lt;0.001 level while innovativeness and subjective references affect users’ continuance intention at &lt;em&gt;p&lt;/em&gt;&amp;lt;0.05, but privacy concerns have no effect on continuance intention.&lt;/p&gt;