Usage Of Wearable Devices Research Articles

Night-time voids, level of bother and sleep characteristics in a non-patient population of wearable devices users.

IntroductionNocturia is a highly prevalent and bothersome medical condition characterised mainly by the need to wake up to pass urine during the main sleep period. Using data from wearable devices, it is possible to examine the sleep of large cohorts in natural settings. This study seeks to use data from connected smartwatches combined with a one‐time survey to explore the presence of nocturia and associated level of bother and sleep characteristics in a non‐patient cohort of wearable device users representing a broad age range.MethodsThe data used come from a retrospective dataset containing sleep data from Withings watches of 250 000 users and a prospective dataset containing answers to a 10‐item questionnaire completed by a subset of users in the retrospective dataset.ResultsThe prospective dataset contained 6230 users. Overall, 6.0%, 15.3% and 38.9% of users in the age groups 18‐44 years, 45‐64 years and 65‐90 years, respectively, reported 2 or more nocturnal voids as their customary voiding pattern, corresponding to levels of nocturia consistent with previous literature. The level of bother associated with nocturia was higher among younger users with 27.8% of users aged 18‐44 years reporting that their daytime activity was highly affected versus just 14.1% among those aged 65‐90 years. A higher number of reported voids per night was associated with watch‐derived measures of a lower sleep efficiency, a longer awake duration at night and a shorter first uninterrupted sleep period.ConclusionThis study suggests not only that nocturia is present among the younger population but also that the younger are more bothered by this medical condition. Using data from wearables it was possible to establish that there is an association between the number of nocturnal voids and sleep characteristics.

Factors Influencing Continued Usage of Wearable Devices for Physical Activity and Weight Monitoring

Factors Influencing Continued Usage of Wearable Devices for Physical Activity and Weight Monitoring

Emotional Map : Building a Data Tool for Geolocation-related Product Design

This article introduces a solution of design simulation system based on data processing. It is to observe and analyze user behavior by reflecting a large number of user behavior data trajectories on city maps. We believe that under the technical background of big data and cloud technology, it is possible to build a tool that can help designers to predict user behavior to a certain extent. This article describes the design of the simulation system divided into five stages: data collection, local database establishment, cloud database establishment, user model simulation, and user model-based behavior prediction. We also introduce the experimental design for the first stage: data collection. This research proposes an experimental method of using sensor-based wearable data collecting devices to simulate the scenario with users of smart wearable devices and using the method of diary studies to create user profiles. The study believes that user’s emotion data, such as subjective feelings, which is difficult to measure directly through physical sensors, also plays an important role in the simulation analysis of the design process. This study collated the multi-dimensional information categories needed for user analysis of geolocation-based designs, including geographic data, biological data, and emotional data. In the experiment, a mechanism for collecting emotional data through user’s subjective selection was also introduced.

Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users

The Internet of Things (IoT) is increasingly empowering people with an interconnected world of physical objects ranging from smart buildings to portable smart devices, such as wearables. With recent advances in mobile sensing, wearables have become a rich collection of portable sensors and are able to provide various types of services, including tracking of health and fitness, making financial transactions, and unlocking smart locks and vehicles. Most of these services are delivered based on users’ confidential and personal data, which are stored on these wearables. Existing explicit authentication approaches (i.e., PINs or pattern locks) for wearables suffer from several limitations, including small or no displays, risk of shoulder surfing, and users’ recall burden. Oftentimes, users completely disable security features out of convenience. Therefore, there is a need for a burden-free (implicit) authentication mechanism for wearable device users based on easily obtainable biometric data. In this paper, we present an implicit wearable device user authentication mechanism using combinations of three types of coarse-grain minute-level biometrics: behavioral (step counts), physiological (heart rate), and hybrid (calorie burn and metabolic equivalent of task). From our analysis of over 400 Fitbit users from a 17-month long health study, we are able to authenticate subjects with average accuracy values of around .93 (sedentary) and .90 (non-sedentary) with equal error rates of .05 using binary SVM classifiers. Our findings also show that the hybrid biometrics perform better than other biometrics and behavioral biometrics do not have a significant impact, even during non-sedentary periods.

AI-enabled emotion-aware robot: The fusion of smart clothing, edge clouds and robotics

Mental health has become a severe problem that significantly influences people’s living quality. With the rapid development of science and technology, a completely new direction for mental health improving by using the interaction between robots and people has emerged. As an intelligent personal agent, a robot can be easily accepted in people’s daily life, meeting users’ behavior and mental demands to a certain extent. Nevertheless, the existing robot design is very limited, and a household personal robot is too large to be carried anywhere . The usage of wearable devices is simple, but these devices cannot offer diversified services. Therefore, this paper puts forward an emotion-aware system that integrates a personal robot, smart clothing, and cloud terminal. A new ’people-centered’ emotion-interaction mode is realized. Namely, personal robot and smart clothing supplement each other seamlessly and interact jointly with users . Artificial intelligence technology and knowledge graph are used to design emotion perception and interaction algorithms including intelligent recommendation, relation recognition, emotional expression recognition. Also, different scenarios are analyzed . Finally, a testbed is built to carry out relevant tests to verify the effectiveness of the proposed algorithms and emotion-aware system. According to the obtained test results, the system can be widely used to serve people and improve people’s mental health.

Gait-based identification for elderly users in wearable healthcare systems

The increasing scope of sensitive personal information that is collected and stored in wearable healthcare devices includes physical, physiological, and daily activities, which makes the security of these devices very essential. Gait-based identity recognition is an emerging technology, which is increasingly used for the access control of wearable devices, due to its outstanding performance. However, gait-based identity recognition of elderly users is more challenging than that of young adults, due to significant intra-subject gait fluctuation, which becomes more pronounced with user age. This study introduces a gait-based identity recognition method used for the access control of elderly people-centred wearable healthcare devices, which alleviates the intra-subject gait fluctuation problem and provides a significant recognition rate improvement, as compared to available methods. Firstly, a gait template synthesis method is proposed to reduce the intra-subject gait fluctuation of elderly users. Then, an arbitration-based score level fusion method is defined to improve the recognition accuracy. Finally, the proposed method feasibility is verified using a public dataset containing acceleration signals from three IMUs worn by 64 elderly users with the age range from 50 to 79 years. The experimental results obtained prove that the average recognition rate of the proposed method reaches 96.7%. This makes the proposed method quite lucrative for the robust gait-based identification of elderly users of wearable healthcare devices.

Wearable devices in healthcare: Privacy and information security issues.

Mobile health has provided new and exciting ways for patients to partake in their healthcare. Wearable devices are designed to collect the user's health data, which can be analysed to provide information about the user's health status. However, little research has been conducted that addresses privacy and information security issues of these devices. To investigate the privacy and information security issues to which users are exposed when using wearable health devices. The study used a cross-sectional survey approach to collect data from a convenience sample of 106 respondents. Half of the respondents did not understand the need to protect health information. There also appeared to be a general lack of awareness among respondents about the information security issues surrounding their data collected by wearable devices. Users were not knowledgeable about the privacy risks that their data are exposed to or how these data are protected once collected. Users of wearable devices that collect personal information about health need to be educated about privacy and information security issues to which they are exposed when using these devices.

Impact of Personal Health Records and Wearables on Health Outcomes and Patient Response: Three-Arm Randomized Controlled Trial.

BackgroundAlthough using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients’ response to a digital health app.ObjectiveThis study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement.MethodsWe developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app–only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA–related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant.ResultsWe collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app–only group showed more weight loss compared with the controls (P=.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices (P=.02). The average rate of data collection was not different in physical activity (P=.99), food intake (P=.98), sleep (P=.95), stress (P=.70), and weight (P=.90) in the app plus wearable and app-only groups, respectively.ConclusionsWe tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that a PHR–based intervention may be successful in losing body weight and improvement in lifestyle behavior.Trial RegistrationClinicalTrials.gov NCT03200223; https://clinicaltrials.gov/ct2/show/NCT03200223 (Archived by WebCite at http://www.webcitation.org/74baZmnCX).

Efficacy of Wearable Devices to Measure and Promote Physical Activity in the Management of Diabetes

Physical inactivity is a global health problem that contributes to the increasing risk of obesity and diabetes. Wearable devices are defined as technologies that can be comfortably worn on the body and that are designed to be effective at improving the health and overall fitness of the wearer. The aim of this narrative review is to summarise the current studies investigating the efficacy of wearable devices, especially mobile applications, for the management of diabetes. The use of pedometers or accelerometers has been shown to increase physical activity by approximately 1 hour per week; however, rates of obesity and diabetes were not affected. Although recent assessments of the effect of smartphone applications on physical activity and glycaemic control are sparse, they are useful for promoting physical activity and for treating diabetes. The interactions with patients using wearable devices for self-monitoring, education, and coaching are essential for the improvement of diabetes. However, there are currently no clinical trials investigating the efficacy of the ‘perfect’ wearable device, whereby patients with diabetes can accurately and comfortably monitor their physical activity, energy balance, blood pressure, heart rate, and blood glucose level. Analysis of big data from wearable device users will contribute to the development of effective wearable devices. Developments in wearable technology are anticipated and further studies will be required to assess the efficacy of wearable devices in measuring and promoting physical activity in the management of diabetes.

Socially-Motivated Cooperative Mobile Edge Computing

In this article we propose a novel paradigm of socially-motivated cooperative mobile edge computing, where the social tie structure among mobile and wearable device users is leveraged for achieving effective and trustworthy cooperation for collaborative computation task executions. We envision that a combination of local device computation and networked resource sharing empowers the devices with multiple flexible task execution approaches, including local mobile execution, D2D offloaded execution, direct cloud offloaded execution, and D2D-assisted cloud offloaded execution. Specifically, we propose a system model for cooperative mobile edge computing where a device social graph model is developed to capture the social relationship among the devices. We then devise a socially- aware bipartite matching based cooperative task offloading algorithm by integrating the social tie structure into the device computation and network resource sharing process. We evaluate the performance of socially-motivated cooperative mobile edge computing using both Erdos-Renyi and real-trace based social graphs, which corroborates the superior performance of the proposed socially-aware mechanism.

Sport technology consumers

PurposeThe development of wearable technology has significantly changed the way people participate in physical activities. The purpose of this paper is to segment users of sports wearable devices based on technology readiness (TR).Design/methodology/approachParticipants comprised a convenience sample of 356 participants using sports wearable devices in South Korea. Cluster analysis was performed to identify clusters of sports wearable users based on their TR (i.e. motivating and inhibiting beliefs regarding technologies). Analysis of variance and post hoc Tukey’s test were used to determine whether there were significant differences among the clusters.FindingsClustering identified three groups of users of sports wearable devices: Explorers (high motivation, low inhibition), Laggards (low motivation, high inhibition) and Pioneers (high motivation, high inhibition). Each group demonstrated significant differences in TR (i.e. optimism, innovativeness, discomfort and insecurity). It also found that Laggards are more likely to be female and older users (i.e. over 40 years old).Originality/valueThis study explores characteristics of possible market segments and provides a better understanding of user profiles of sports wearable devices. These findings provide insightful implications for marketers of sports wearable devices, who can tailor marketing strategies to each segment. Designers of sports wearable devices can benefit from the user profiles and develop more appropriate products for users.

Strategic Approach to Privacy Calculus of Wearable Device User Regarding Information Disclosure and Continuance Intention

The healthcare and fitness wearable-device market is considered as the driving force of the entire wearable device market. However, there are concerns with respect to information privacy because wearable devices constantly collect sensitive data such as individuals’ health information. Thus, there is a need for a comprehensive understanding from the perspective of information privacy concerns and related behavior. This study investigates factors considered in the privacy calculus of wearable fitness devices, and verifies differences obtained by the privacy calculus process according to the frequency of exercise. The results obtained from a survey of 248 undergraduate students in Korea revealed that service providers should consider users’ interests and exercise characteristics in order to mitigate their privacy concerns and encourage continuous use of wearable devices. This study provides useful insights pertaining to users of wearable fitness devices, and targets researchers and practitioners.

Wearable health information systems intermittent discontinuance

PurposeWearable 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.Design/methodology/approachThis 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.FindingsEmpirical 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.Originality/valueThis 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.

The open D1NAMO dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management

Abstract The usage of wearable devices has gained popularity in the latest years, especially for health-care and well being. Recently there has been an increasing interest in using these devices to improve the management of chronic diseases such as diabetes. The quality of data acquired through wearable sensors is generally lower than what medical-grade devices provide, and existing datasets have mainly been acquired in highly controlled clinical conditions. In the context of the D1NAMO project — aiming to detect glycemic events through non-invasive ECG pattern analysis — we elaborated a dataset that can be used to help developing health-care systems based on wearable devices in non-clinical conditions. This paper describes this dataset, which was acquired on 20 healthy subjects and 9 patients with type-1 diabetes. The acquisition has been made in real-life conditions with the Zephyr BioHarness 3 wearable device. The dataset consists of ECG, breathing, and accelerometer signals, as well as glucose measurements and annotated food pictures. We open this dataset to the scientific community in order to allow the development and evaluation of diabetes management algorithms.

Towards Wearability in Cryptographic Systems: A Novel Architecture Based on Rainbow Scheme for Wearable Devices

Wearable devices are examples of the Internet of Things, which often enable objects to exchange data with other connected devices, without requiring human intervention. Since quantum computers have been a potential threat to most of the public cryptographic systems, the data security has been the focuses of the users of wearable devices. Among public cryptographic systems, Multivariate Public Key Cryptography (MPKC) is one of the most popular candidates, which can resist quantum computer attacks. MPKC is based on a NP-hard problem, which includes Rainbow, UOV, enTTS schemes. We propose a novel architecture based on Rainbow scheme for wearable devices, which is one of the most efficient schemes of MPKC. In order to validate our design and verify its effectiveness, an experiment is carried out by using TSMC-0.18 μm standard cell CMOS Application Specific Integrated Circuit, which shows that the executing time of Rainbow is 3.8 ms and its area is 3500 gate equivalents. The results demonstrate that our design is well suit for wearable devices.

Tool, toy and tutor: Subjective experiences of digital self-tracking

Since the advent of the smartphone, users have become accustomed to alerts, notifications and reminders to interact with their internet-connected devices. But how do people make sense of prompts to exercise, eat or sleep? Digital self-tracking is a phenomenon that has grown substantially in recent years. However, despite some notable exceptions, there is still little sociological research into how users of wearable devices and apps subjectively experience self-tracking. This article draws on findings from a small qualitative study with 11 participants to reveal eminent themes in how users make sense of their self-tracking. Utilising and extending Lupton’s theorising of self-tracking, we argue for triple roles of self-tracking devices; ‘tool’, ‘toy’ and ‘tutor’. This trichotomy helps to characterise the use of self-tracking devices and apps, allowing us to reflect on the wider, ongoing implications of self-tracking.

Ethical Implications of User Perceptions of Wearable Devices.

Health Wearable Devices enhance the quality of life, promote positive lifestyle changes and save time and money in medical appointments. However, Wearable Devices store large amounts of personal information that is accessed by third parties without user consent. This creates ethical issues regarding privacy, security and informed consent. This paper aims to demonstrate users' ethical perceptions of the use of Wearable Devices in the health sector. The impact of ethics is determined by an online survey which was conducted from patients and users with random female and male division. Results from this survey demonstrate that Wearable Device users are highly concerned regarding privacy issues and consider informed consent as "very important" when sharing information with third parties. However, users do not appear to relate privacy issues with informed consent. Additionally, users expressed the need for having shorter privacy policies that are easier to read, a more understandable informed consent form that involves regulatory authorities and there should be legal consequences the violation or misuse of health information provided to Wearable Devices. The survey results present an ethical framework that will enhance the ethical development of Wearable Technology.

Rising Orthopedic Problems and Novel Home-Based Antidotes

Objectives: Today the health problems are increasing at an exponential rate due to our sedentary lifestyle and the nature of job. These problems are of varied nature, viz. respiratory, cardiac, muscular (or bones) etc. In this paper, we are developing an application to provide orthopedic recommendations to the users based on their activity status recorded from wearable device and to prescribe nutritional diet based on the symptoms observed in the people of different age groups. Statistical Analysis: Initially the activity status of wearable device users is stored in the android application and then, primary data is collected for the study using semi-structured questionnaire and in-depth interview with the senior orthopedician. Findings: We recommend customized novel home-based antidotes as immediate remedies to relieve the peoples’ pain. These heath care solutions are provided to the users of the application based either on their observed symptoms or on their age. The orthopedic recommendations are provided in the form of physical activities according to the age of individuals, while dietary solutions are prescribed based upon the user’s symptoms. Thus, our application adds value to the existing systems in such a way that it serves dual purposes, viz. general diet recommendations and orthopedic solutions in particular. Application: These recommendations, in general are precautionary measures. In future, the application should also cover endocrinal and respiratory disorders and must provide curative measures in the form of first-aid solution for all the covered health problems.

기술 및 개인적 특성이 헬스케어 웨어러블 디바이스 수용의도에 미치는 영향에 관한 연구

Purpose The purpose of this study is to examine the effect of the acceptance intention towards healthcare wearable device in Korea. Specifically, this study attempted to identify the relationships among the acceptance intention towards healthcare wearable device, technical characteristics and user's personal characteristic based on UTAUT(Unified Theory of Acceptance and Use of Technology) model. Design/methodology/approach For this purpose of research, we conducted against consumers who use the wearable devices and we collected data from 219 wearable device users. The analyses were conducted using SPSS and AMOS19.0 which is powerful structural equation modeling (SEM) software. Findings The main findings are as followed; First, functional diversity, wearability, trend sensitivity and health perception are significantly related to performance expectancy, while visual aesthetic and enjoyment are not significantly related. Second, performance expectancy and effort expectancy are significantly related to acceptance intention towards healthcare wearable device. Third, effort expectancy is significantly related to performance expectancy.

Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective

BackgroundWearable technology has shown the potential of improving healthcare efficiency and reducing healthcare cost. Different from pioneering studies on healthcare wearable devices from technical perspective, this paper explores the predictors of individuals’ adoption of healthcare wearable devices. Considering the importance of individuals’ privacy perceptions in healthcare wearable devices adoption, this study proposes a model based on the privacy calculus theory to investigate how individuals adopt healthcare wearable devices. MethodThe proposed conceptual model was empirically tested by using data collected from a survey. The sample covers 333 actual users of healthcare wearable devices. Structural equation modeling (SEM) method was employed to estimate the significance of the path coefficients. ResultsThis study reveals several main findings: (1) individuals’ decisions to adopt healthcare wearable devices are determined by their risk–benefit analyses (refer to privacy calculus). In short, if an individual’s perceived benefit is higher than perceived privacy risk, s/he is more likely to adopt the device. Otherwise, the device would not be adopted; (2) individuals’ perceived privacy risk is formed by health information sensitivity, personal innovativeness, legislative protection, and perceived prestige; and (3) individuals’ perceived benefit is determined by perceived informativeness and functional congruence. The theoretical and practical implications, limitations, and future research directions are then discussed.