Personal Health Devices Research Articles

Examination of Dominant Factors Affecting The Decision To Purchase Personal Medical Devices

This study aims to analyze the effect of product quality, price perception, advertising and distribution channels on purchasing decisions for personal health devices. In addition, this study also aims to determine the dominant factors that influence purchasing decisions for medical devices. This research was conducted in Denpasar City by taking a research sample of 100 (one hundred) people using purposive sampling technique. This study uses multiple linear regression analysis techniques, t test and F test and test of determination. The result of the research is product quality, price perception has a positive and significant effect on purchasing decisions, while distribution channels have no significant effect on purchasing decisions. The implication of this research is that manufacturers who provide medical devices must be able to carry out price strategies in improving purchasing decisions and controlling market share in the health industry.

Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management.

This study investigates the feasibility of accurately predicting adverse health events without relying on costly data acquisition methods, such as laboratory tests, in the era of shifting healthcare paradigms towards community-based health promotion and personalized preventive healthcare through individual health risk assessments (HRAs). We assessed the incremental predictive value of four categories of predictor variables-demographic, lifestyle and family history, personal health device, and laboratory data-organized by data acquisition costs in the prediction of the risks of mortality and five chronic diseases. Machine learning methodologies were employed to develop risk prediction models, assess their predictive performance, and determine feature importance. Using data from the National Sample Cohort of the Korean National Health Insurance Service (NHIS), which includes eligibility, medical check-up, healthcare utilization, and mortality data from 2002 to 2019, our study involved 425,148 NHIS members who underwent medical check-ups between 2009 and 2012. Models using demographic, lifestyle, family history, and personal health device data, with or without laboratory data, showed comparable performance. A feature importance analysis in models excluding laboratory data highlighted modifiable lifestyle factors, which are a superior set of variables for developing health guidelines. Our findings support the practicality of precise HRAs using demographic, lifestyle, family history, and personal health device data. This approach addresses HRA barriers, particularly for healthy individuals, by eliminating the need for costly and inconvenient laboratory data collection, advancing accessible preventive health management strategies.

HealthDote: A blockchain-based model for continuous health monitoring using interplanetary file system

The Healthcare industry demands increased privacy and security to protect confidential patient information and comply with regulations. Both these features can be incorporated into the existing systems using Blockchain technology. The only challenge faced here is the ease of users, but this can be quickly resolved by integrating the Internet of Things (IoT) and blockchain. IoT-based devices overcome limited computing capacity for personal intelligent health devices. Cloud-assisted IoT devices also require limited storage capacity for devices like wearable sensors. However, it must be considered that this system still has drawbacks, leading to its inefficiency. These problems include Data Privacy and Data sharing. This paper proposes an Interplanetary File System (IPFS) based solution to these problems. Here, the users continually upload the health data collected by IoT devices and add them to blockchain transactions that the other user nodes, such as physicians, pharmacists, insurance companies, hospital authorities, etc., can access. This system ensures the well-being of the users by monitoring the data gathered every 5 min and daily. It also facilitates the alarm feature in an emergency, making it reliable. The user receives daily notifications regarding his lifestyle, and the family members receive the notifications on his behalf if there are some chances of an emergency. The authorized doctors are also notified immediately in case an emergency is detected. Apart from this feature, the user can get consultations from doctors, prescriptions from the pharmacist, funds from insurance authorities, and hospital supplies, all through the transaction on the six blockchains of HealthDote using the cryptocurrency DoteCoins, which are designed specifically for this system.

Wireless standard-compliant e-health solution for elderly people with multiuser identification

ObjectiveOne of the main problems in e-health environments for developing an accurate multiuser identification is in the large number of patients, especially with itinerant medical devices and elderly people. This paper aims to contribute with two approaches to be included in ISO/IEEE 11073 family of standards as a standardized procedure for multiuser identification that can be used for a large variety of medical devices, regardless of their brand or model. From this contribution and to validate it, this work proposes a standardized e-health solution, including multiuser identification, implementing it in real health environments for elderly people, and evaluating their usability, interoperability, and adoption in daily living. Materials and methodsThis works implements the internationally recommended Personal Health Devices ISO/IEEE 11073 standards as a multiplatform environment (Windows/Linux/Mac OS) and fulfils the paradigms of scalability, modularity, portability, maintainability, and robustness. The standard e-health solution has been implemented in 26 health environments in several cities in Spain (Madrid, Barcelona, Sevilla, Zaragoza, etc.), consisting of 118 health professionals, 319 senior patients, and 18 technical professionals. ResultsThe proposed multiuser identification reduces the human error rate (from 13.3% to less than 5%) with positive evaluation: almost 70% of users are satisfied, with usability and time savings that are more than 50% in all the groups (nursing, medicine, and caregiving) and environments (residences, health centres, and hospitals). DiscussionThe use of e-health solutions within multiuser identification, as it is proposed through two standard-compliant approaches, permits advanced services and data analysis for a large variety of medical devices, regardless of their brand or model. ConclusionThis paper contributes an open interoperable e-health solution as alternative to the closed and commercial solutions and enables third-party developers to work collaboratively and extend the already implemented features, owing to the design based on plugins, value-added services, and multiple transport technologies and protocols.

RATE: A model‐based testing approach that combines model refinement and test execution

AbstractIn this paper, we present an approach to conformance testing based on abstract state machines (ASMs) that combines model refinement and test execution (RATE) and its application to three case studies. The RATE approach consists in generating test sequences from ASMs and checking the conformance between code and models in multiple iterations. The process follows these steps: (1) model the system as an abstract state machine; (2) validate and verify the model; (3) generate test sequences automatically from the ASM model; (4) execute the tests over the implementation and compute the code coverage; (5) if the coverage is below the desired threshold, then refine the abstract state machine model to add the uncovered functionalities and return to step 2. We have applied the proposed approach in three case studies: a traffic light control system (TLCS), the IEEE 11073‐20601 personal health device (PHD) protocol, and the mechanical ventilator Milano (MVM). By applying RATE, at each refinement level, we have increased code coverage and identified some faults or conformance errors for all the case studies. The fault detection capability of RATE has also been confirmed by mutation analysis, in which we have highlighted that, many mutants can be killed even by the most abstract models.

BNS: A Framework for Wireless Body Area Network Realistic Simulations.

Simulation is a useful and common technique to evaluate the performance of networks when the implementation of a real scenario is not available. Specifically for Wireless Body Area Networks (WBAN), it is crucial to perform evaluations in environments as close as possible to the real conditions of use. To achieve that, simulations must include different protocol layers involved in WBAN and models close to reality to create realistic simulation environments for e-health applications. To satisfy these needs, this work presents the BNS framework, a flexible tool for WBAN simulations. The proposal is an extension of the Castalia framework, which includes: (1) a new wireless channel model considering real radio-propagation over the human body; (2) an updated implementation of the WBAN MAC protocol in Castalia, with functionalities and requirements in accordance with the IEEE 802.15.6 standard; (3) a new comprehensive and configurable mobility model for simulating intra-WBAN communication; (4) a temperature module based on the Pennes bioheat transfer equation, to model the temperature of a WBAN node based on the activity of the node; and (5) a Healthcare Application Layer that implements data representation and a communication protocol between Personal Health Devices (PHD) following the ISO/IEEE 11073 standard. Three use cases are presented, where WBAN scenarios are simulated and evaluated using the proposed BNS framework. Results show that BNS is a valid and flexible tool to evaluate WBAN solutions through simulation.

A remote healthcare monitoring framework for diabetes prediction using machine learning.

Diabetes is a metabolic disease that affects millions of people each year. It is associated with an increased likelihood of vital organ failures and decreased quality of life. Early detection and regular monitoring are crucial for managing diabetes. Remote patient monitoring can facilitate effective intervention and treatment paradigms using current technology. This work proposes an end‐to‐end remote monitoring framework for automated diabetes risk prediction and management, using personal health devices, smart wearables and smartphones. A support vector machine was developed for diabetes risk prediction using the Pima Indian Diabetes Database, after feature scaling, imputation, selection and augmentation. This work achieved the performance metrics of accuracy, sensitivity and specificity scores at 83.20%, 87.20% and 79% respectively through the tenfold stratified cross validation method, which is competitive with existing methods. Patients can use multiple healthcare devices, smartphones and smartwatches to measure vital parameters, curb the progression of diabetes and close the communication loop with medical professionals. The proposed framework enables medical professionals to make informed decisions based on the latest diabetes risk predictions and lifestyle insights while attaining unobtrusiveness, reduced cost, and vendor interoperability.

The Role of Information Technology Mindfulness in the Postadoption Stage of Using Personal Health Devices: Cross-Sectional Questionnaire Study in Mobile Health.

BackgroundAlthough personal health devices (for example, smartwatches, fitness trackers and intelligent bracelets) offer great potential to monitor personal fitness and health parameters, many users discontinue using them after a few months. Thus, it is critical to study the postadoption behaviors of current users to enhance their engagement with personal health devices and use behaviors. However, there is little empirical research on the factors affecting users’ engagement in beneficial use behaviors. Mindfulness and identity are not new topics, but the applications of these concepts in the field of information systems are emerging themes. Information technology (IT) mindfulness has been conceptualized in previous studies; however, little is known about the antecedents and consequences of IT mindfulness in the mobile health (mHealth) context.ObjectiveThe main aim of this study is to explore both IT identity and IT mindfulness to develop a new ground for research in the domain of mHealth postadoption. Thus, we aim to explain why users should be fully mindful of their engagement with PHDs and what could be the consequences and implications.MethodsThis study proposes that IT mindfulness can play an important role in improving the use behaviors of users. Through a web-based survey with 450 current users of a personal health device, this paper tests the relationship between IT identity and IT mindfulness in the postadoption stage of using personal health devices.ResultsWe found that IT identity significantly shapes IT mindfulness associated with PHDs. Moreover, the IT identity–IT mindfulness relationship is negatively moderated by individuals’ perceived health status (P=.003). Finally, the results of this study show that IT mindfulness can significantly predict automatic use behaviors (eg, continued intention to use), active use behaviors (eg, feature use and enhanced use behaviors), and commitment behaviors in using personal health devices (eg, positive word-of-mouth intention).ConclusionsThe findings of this study provide implications for both research and practice. This study can contribute to our current understanding of IT mindfulness by developing and empirically testing a research model that explains the determinants and outcomes of the IT mindfulness construct in the context of personal health devices. The results imply that IT mindfulness significantly helps individuals express their alertness, awareness, openness, and orientation in the present in their postadoption interactions with smart devices used for health care purposes. Finally, our findings may assist practitioners and IT developers in designing mindfulness-supporting PHDs. Owing to the impact of IT mindfulness on postadoption behaviors, its 4 dimensions could be used for developing PHD technologies. Moreover, PHD developers may need to direct their efforts toward increasing IT mindfulness by reinforcing IT identity to serve and retain a wide range of target users.

Digital Health: Should We Be Concerned?

Type "health and fitness apps" into any search engine, and thousands of options will appear-so many, in fact, that people can now measure their own heart rate, sleep habits, and dozens of other health parameters that once required a doctor's visit. While anecdotes of a personal health device capturing early signs of disease may garner media attention, the increasing quantification of human physiology can have downsides. In essence, the enhanced ability to assess surrogate measures of health, such as temperature, minute-to-minute blood pressure, and genomics, are an expansion of anticipatory health-that is, an attempt to detect and avoid events that might happen in the future. While patients used to seek out doctors when they were sick, digital tools will send increasing numbers of people to their physicians before they are sick. In a Cartesian model of the human body, wherein one system could potentially be fixed independent of another system, having more data may be beneficial. The human body, however, is far more complex than any Cartesian model could explain. This article explores the limitations of digital devices to improve health.

IOT BASED REHABILITATION SYSTEM FOR PATIENTS AND ELDERLY PEOPLE- A SURVEY

During recent days the technological advancements of the Internet of Things (IoT) and rapid growing costs of various health care devices, the main vision of these connected e-health applications has been developed that constitute Personal Health Devices. To monitor the health of elderly people and developing a rehabilitation system is a challenging task. The incorporation of the communication technology in the healthcare industry have been explored the different possibilities to optimize the all the available medical resources and provide an efficient reliable healthcare services to the elderly people and patients with disabilities, chronic illness and other healthcare issues. This paper presents a comprehensive analysis of existing techniques which are developed in various healthcare applications using the IoT for the development of Rehabilitation system based on various parameters of disease by collecting patients as well as to inform doctors by sending early notifications. The system should be developed in such way that that collects all parameters of the patient and diagnose the disease as soon as possible. The network parameter are also considered suitably for the proposed solution for different environment are also presented, which improves efficiency of rehabilitation system developed for monitoring the health of elderly people.

How does IT identity affect individuals’ use behaviors associated with personal health devices (PHDs)? An empirical study

This study investigates the effects of information technology (IT) identity on the use of personal health devices (PHDs). We conducted an online survey of individuals who used a PHD to achieve a health-related goal. The results confirm that IT identity is a multidimensional construct with three reflective dimensions. The results exhibit that the identity verification process (IT characterizes through IT experience) fosters IT identity, which, in turn, encourages individuals to engage in beneficial post-adoption behaviors such as feature use and enhanced use. The findings also reveal that perceived health status moderates the relationship between IT identity and positive use behaviors.

Improvement of Wireless Connectivity and Efficiency in E-Healthcare Service System Using a Proxy in Body Area Device

E-health services have provided interoperability between personal health devices in personal area network, based the ISO/IEEE 11073 standard. In the healthcare system, the manager handles most agents concurrently through wireless communication. However, due to the distance limitation and the increased number of agents, it may be difficult to provide continuous connectivity. Recently, body area devices have been equipped with various applicable agents, which can even handle agents on behalf of the manager. A BAD may act as an intermediary device to increase system efficiency and performance. In this study, a device called “proxy”, which can be installed as software on BAD devices, is proposed. The data measured by an agent can be sent to the proxy first, and subsequently be sent to the manager again. Agents and the manager are not aware of the proxy existence and work normally without the proxy. Furthermore, a new smart proxy and modified manager are proposed. The smart proxy acts as one agent handling measurement data from several agents, which can transmit a significant amount of data at once. The proxy and smart proxy maintain compatibility with existing devices that conform to the 20601 standard. The proposed schemes are verified and the complexities of devices are analyzed. The analysis shows no significant difference among the proxy, smart proxy, and manager. Simulations exhibit that the proposed schemes can improve the system performance.

An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities

The use of the digital twin has been quickly adopted in industry in recent years and continues to gain momentum. The recent redefinition of the digital twin from the digital replica of a physical asset to the replica of a living or nonliving entity has increased its potential. The digital twin not only disrupts industrial processes, but also expands the domain of health and well-being towards fostering smart healthcare services in smart cities. In this paper, we propose an ISO/IEEE 11073 standardized digital twin framework architecture for health and well-being. This framework encompasses the process of data collection from personal health devices, the analysis of this data, and conveying the feedback to the user in a loop cycle. The framework proposes a solution to include not only X73 compliant devices, but also noncompliant health devices, by interfacing them with an X73 wrapper module as we explain in this paper. Besides, we propose a configurable X73 mobile application, designed to work with any X73 compliant device. We designed and implemented the proposed framework, and the X73 mobile app, and conducted an experiment as a proof of concept of the digital twin in the domain of health and well-being in smart cities. The experiment shows promising results and the potential of benefiting from the proposed framework, by gaining insights on the health and well-being of individuals, and providing feedback to the individual and caregiver.

Biosensors for Personal Mobile Health: A System Architecture Perspective.

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

Embracing machine learning and digital health technology for precision dermatology

Involvement from the dermatology community in the current revolution in sensors, personal health devices, computing, and machine learning algorithms can enable us to reach the promise of precision medicine in dermatology to deliver the ‘right treatment at the right time’. Machine learning combined with digital health technology offers enormous potential to expand access to dermatological services, engage patients in the management of their conditions, and provide tools that enable clinicians to be more effective in their delivery of health care. We offer our framework for how machine learning, rather than replacing dermatologists, will be essential in realizing the promise of precision dermatology.

Surveillance Capitalism, Datafication, and Unwaged Labour: The Rise of Wearable Fitness Devices and Interactive Life Insurance

This paper examines the relationship between interactive life insurance companies and their policyholders and the way in which wearable fitness devices are deployed by these companies as data-generating surveillance technologies instead of personal health and fitness devices. Working within an expanded framework of “surveillance capitalism” (Zuboff 2015), I argue that while the notion of self-care generally associated with wearable fitness devices is underpinned by neoliberal constructs, the incentivization of interactive life insurance programs works to obscure the immense value placed on information capital. This paper briefly considers the legal loopholes involved in the harvesting of sensitive health and fitness information from consumer wearables and suggests that the push toward fitness trackers has little to do with any real concerns for the health and fitness of consumers and policyholders. Lastly, I consider different forms of unwaged labour in the relationship between policyholders and interactive life insurance programs. I contend that policyholders do not recognise the free and immaterial labour that goes into sustaining the data-based business model that interactive life insurance companies and social media platforms use and rely on for profit. In so doing, they relinquish power and control over the data they work to produce, only so that the information can be commodified and used against them.

A Fog Computing Solution for Context-Based Privacy Leakage Detection for Android Healthcare Devices.

Intelligent medical service system integrates wireless internet of things (WIoT), including medical sensors, wireless communications, and middleware techniques, so as to collect and analyze patients’ data to examine their physical conditions by many personal health devices (PHDs) in real time. However, large amount of malicious codes on the Android system can compromise consumers’ privacy, and further threat the hospital management or even the patients’ health. Furthermore, this sensor-rich system keeps generating large amounts of data and saturates the middleware system. To address these challenges, we propose a fog computing security and privacy protection solution. Specifically, first, we design the security and privacy protection framework based on the fog computing to improve tele-health and tele-medicine infrastructure. Then, we propose a context-based privacy leakage detection method based on the combination of dynamic and static information. Experimental results show that the proposed method can achieve higher detection accuracy and lower energy consumption compared with other state-of-art methods.

ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards Compliant Systems: A Systematic Literature Review

Since the introduction of the ISO/IEEE 11073 personal health device (X73-PHD) standards, as part of ISO/IEEE 11073 family of standards, it has been applied to many health systems developed for personal use. In this systematic literature review, we review existing literature collected using three databases: Scopus, Pub Med, and Web of Science. We propose a classification for personal health systems based on the location in which they are used, the technology used to develop them, and the purpose which is determined by the targeted users. We found 51% of the devices used in such systems are standardized while approximately 40% are not and five systems did not specify the device status (9%). Various adaption techniques were used for standardization. Besides, the pulse oximeter is the most used device in such systems since it was used in 43% of them. In addition, we present the role of the X73-PHD standards in the Internet of Things (IoT) and tele-healthcare systems, discuss the challenges of utilizing this set of standards in health monitoring systems and converting the non-standardized devices into standardized ones. Finally, we propose the requirements of personal health systems based on our review of the literature.

The Conceptual Modeling of Interoperability Framework of Heart Sound Monitor in the Context of an Interoperable End-to-End Architecture.

Background: Heart sound monitor (HSM), a device suitable for home-use, can be used to acquire heart sounds. It enables the telemonitoring of cardiac function, which has been largely evolved and widely used in recent years. Nevertheless, the designers paid little attention to the consistency of information model and data interaction of HSM, thus the data could not be shared and aggregated among healthcare systems. Consequently, the device's development and its application in person-centered telehealth are hindered. Objective: To solve this problem and to build interoperability for HSM, this article proposes a HSM interoperability framework that is constructed by using standardized modeling methods. Methods: The authors collected the common device-output information of HSM involved in telemonitoring, leveraged the standardized interoperability framework defined in ISO/IEEE 11073 Personal Health Device (11073-PHD) standards to model the static data structure and dynamic interaction behaviors of HSM. Results: Via a meta-analysis, the HSM device-output information includes collected data (heart sound measurement), and derived data (e.g., device status). Based on such information, an 11073-PHD-compliant domain information model has been successfully created. This enables the interoperability between HSM and aggregation device, allowing inter-device plug-and-play using the service model and communication model. A prototype of this design has been implemented and validated via Continua Enabling Software Library. Conclusions: The ISO/IEEE 11073-PHD standard framework has the potential to accommodate the HSM, which implicate HSM can be integrated into the interoperable ecosystem to achieve holistic health solution. Findings in this article may be taken as a reference for standard developing organizations to establish a standardized interoperability framework for HSM.

Neural-network based adaptive context prediction model for ambient intelligence

To overcome the limitations of the conventional medical service in terms of ageing and chronic diseases, AmI-based precision medicine has drawn particular attention. Precision medicine is a customized personal medical service using various information technologies such as personal health device, AI algorithm, image recognition, voice recognition, and natural language processing. In particular, the information technologies for follow-up care services for patients, such as context awareness, context information, and inference rules, are required. In PHD, contexts such as variable data include blood pressure, BMI, blood sugar, weather, and food. It has time-series characteristics, meaning that it changes often with time. Other kinds of health-related information, such as age, family history, smoking, and residential area, are intermittently changed. For inference that is highly related to a user, the context collected through AmI is presented with ontology. Ontology consists of a user’s ambient data, weather data, and lifelog. Context is changed along with a user’s ambient conditions and time. An inference engine is used to create the knowledge base and predict a change. This study proposes a neural-network based adaptive context prediction model for ambient intelligence. This is a learning model using neural network to calculate the similarity for recommendation in a mining lifecare platform. In a conventional prediction procedure, an error is used to update a weight. The proposed model learns the similarity weight of the users to become adapted to the user’s ambient. Based on the knowledge base, user clustering and deviation from mean are applied to calculate the similarity weight. Collaborative filtering technology is used to predict a user’s context and learn the similarity weight repeatedly using a neural network. According to the performance evaluation, the proposed neural-network based similarity weight method had the highest accuracy of prediction when the learning rate was 0.001. Consequently, we found that AmI is a new added-value technology to maintain a healthy lifestyle and contributes to developing the healthcare industry and improving the quality of life.