Wearable Health Technologies Research Articles

The future of wearable health technology: From monitoring to preventive healthcare

Wearable health technology has undergone rapid development, evolving from simple fitness trackers to sophisticated medical devices that provide real-time monitoring and data collection. As these technologies become increasingly integrated into healthcare systems, their potential to shift from reactive treatment to proactive preventive care is gaining attention. This review paper explores the current state of wearable health devices, including the key technologies that enable monitoring and data analysis, and discusses the latest advancements such as AI integration, smart fabrics, and implantable wearables. The paper also delves into the significant role these technologies play in preventive healthcare by enabling continuous monitoring, early disease detection, and personalized health interventions. Furthermore, it addresses the challenges facing wearables, including issues of data accuracy, privacy, and user compliance. Finally, the paper explores future directions and ethical considerations, emphasizing the potential for wearables to reshape healthcare by promoting more accessible, equitable, and preventive healthcare solutions.

Industry-Scalable Reusable Textile Electrodes for Neurostimulation Applications.

Neurostimulation delivers electrical pulses to modulate neuromuscular activity. Commonly used in medical interventions from pain relief to rehabilitation, neurostimulation typically uses manually placed hydrogel electrodes over the treated region. However, this method limits interventions requiring frequent, long-term daily use. To address this, novel fully textile electrodes are developed using industrial programmable knitting machines. These electrodes are designed to be washable, reusable, flexible, and breathable, with embedded interconnects. Textile electrodes are made of yarns with stainless steel and PEDOT-coated stainless steel conductive components. The electrodes' performance are compared with gel electrodes, characterizing impedance, sensorimotor stimulation thresholds, recruitment of induced movements, sensation levels, and perceived sensations. The effects of residential wash cycles and continuous use duration are also investigated. The proposed electrodes are found to perform similarly to hydrogel electrodes in all characterized metrics. No degradation in electrode performance is found after at least 30 wash cycles. Electrodes remained functionally intact after 1000 cycles of stretch loading at 50% of break strain. The textile electrodes consistently induced comfortable sensorimotor responses for at least six hours after donning. The proposed textile electrodes offer a novel and effective solution for neurostimulation interventions, paving the way for integration into smart garments and long-term wearable health technologies.

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope)

Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon.

Wearable health technology: integrating devices and health care systems for better monitoring

Wearable health technology has the potential to revolutionize the healthcare industry by allowing continuous monitoring, timely identification of health problems, and customized treatment. This paper presents a possible integration of wearable technology in a healthcare system, introducing a smartwatch, a fitness tracker, and biosensors in improving patient observation and care. The integrated wearable devices with electronic health records, artificial intelligence, and big data analytics lead toward better decision-making processes, improved patient outcomes, and cost efficiency in health care delivery. While discussing the wireless health technologies, the paper addresses the problems regarding data privacy, interoperability, and patient engagement in the implementation of wearable health technologies.

The Future of Wearable Health Technology: Advancing Continuous Patient Care through Data Management

The Future of Wearable Health Technology: Advancing Continuous Patient Care through Data Management

Inadvertent User Outcomes of Wearable Health Technology

Inadvertent User Outcomes of Wearable Health Technology

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies.

Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs. Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported. The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies. Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population.

Wearable health technologies are increasingly popular. Yet, wearable monitoring only works when devices are worn as intended, and adherence reporting lacks standardization. In this study, we aimed to explore the long-term adherence to a wrist-worn activity tracker in the prospective SafeHeart study and identify patient characteristics associated with adherence. This study enrolled 303 participants, instructed to wear a wrist-worn accelerometer day and night for 6 months. Long-term adherence was defined as valid days (≥22 h of wear time) divided by expected days, and daily adherence as mean hours of wear time per 24 h period. Optimal, moderate, and low long-term and daily adherence groups were defined as long-term adherence above or below 95 and 75% and daily adherence above or below 90 and 75%. Regression models were used to identify patient characteristics associated with long-term adherence. In total, 296 participants [median age 64 years; interquartile range (IQR) 57-72; 19% female] were found eligible, yielding 44 003 days for analysis. The median long-term adherence was 88.2% (IQR 74.6-96.5%). A total of 83 (28%), 127 (42.9%), and 86 (29.1%) participants had optimal, moderate, and low long-term adherence, and 163 (55.1%), 87 (29.4%), and 46 (15.5%) had optimal, moderate, and low daily adherence, respectively. Age and smoking habits differed significantly between adherence levels, and increasing changeover intervals improved the degree of long-term adherence. Long-term adherence to a wearable activity tracker was 88.2% over a 6-month period. Older age and longer changeover interval were positively associated with long-term adherence. This serves as a benchmark for future studies that rely on wearable devices. The National Trial Registration number: NL9218 (https://onderzoekmetmensen.nl/).

Wearable Technology for Health Monitoring and Diagnostics

Purpose: The general objective of this study was to investigate wearable technology for health monitoring and diagnostics. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to wearable technology for health monitoring and diagnostics. The advancement of wearable technology in health monitoring and diagnostics transformed the healthcare landscape by enabling continuous, real-time data collection and analysis, empowering individuals to manage their health proactively. Despite its benefits, challenges such as data privacy, device accuracy, and user adherence needed addressing. Ensuring robust data protection, validating device accuracy in diverse environments, and understanding barriers to sustained use were essential. Addressing the digital divide was also vital for equitable access. Overall, wearable technology held significant promise for preventive care and early diagnosis, but required ongoing research and collaboration to maximize its impact. Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Health Belief Model (HBM) and Unified Theory of Acceptance and Use of Technology (UTAUT) may be used to anchor future studies on wearable technology for health monitoring and diagnostics. The study recommended integrating technology acceptance and health behavior theories to provide a comprehensive understanding of adoption factors, emphasizing user-centered design for enhanced engagement, and advocating for stringent data privacy and security standards. It highlighted the importance of integrating wearable technology into healthcare systems for better clinical decisions, promoting equitable access, and using wearables in public health initiatives. The study also called for collaboration between technology developers, healthcare providers, and policymakers to address challenges and maximize the benefits of wearable health technology.

MEREC-MABAC method based on cumulative prospect theory for picture fuzzy sets: Applications to wearable health technology devices

Since there is a causal connection between the performance of the alternative with criteria removed and the assessment of the criteria weights, Method based on the Removal Effects of Criteria (MEREC) can reflect the objectivity of statistical data from this new perspective. Multi-Attributive Border Approximation area Comparison (MABAC) approach considers the subjective psychological characteristics of decision makers (DMs), particularly personal preferences and cognitive limitations, through expert scoring. Cumulative prospect theory (CPT) takes into account both potential gains and losses. We propose applying the MEREC method to picture fuzzy sets (PFS) for the first time to determine criteria weights. We suggest an alternate ranking approach for the CPT-MABAC algorithm based on the Hamming distance of the picture rejection membership degree that corresponds to four behavioral states: acceptance, opposition, abstention, and rejection. We develop the PFS-MEREC-CPT-MABAC evaluation framework to tackle the multiple attribute decision-making (MADM) problems while evaluating the performance of wearable health technology devices (WHTDs). The model achieves a compromise between objectivity and subjectivity as well as capturing the psychological behavior of the experts. Finally, in order to determine the soundness and effectiveness of this integrated research approach, sensitivity and comparison analysis are done to compare the results of its operation with those of existing models.

Creating a digital approach for promoting physical activity in pediatric pulmonary hypertension: A framework for future interventions.

Children with pulmonary hypertension (PH) often demonstrate limited exercise capacity. Data support exercise as an effective nonpharmacologic intervention among adults with PH. However, data on exercise training in children and adolescents are limited, and characteristics of the optimal exercise program in pediatric PH have not been identified. Exercise programs may have multiple targets, including muscle deficits which are associated with exercise limitations in both adult and pediatric PH. Wearable accelerometer sensors measure physical activity volume and intensity in the naturalistic setting and can facilitate near continuous data transfer and bidirectional communication between patients and the study team when paired with informatics tools during exercise interventions. To address the knowledge gaps in exercise training in pediatric PH, we designed a prospective, single arm, nonrandomized pilot study to determine feasibility and preliminary estimates of efficacy of a 16-week home exercise intervention, targeting lower extremity muscle mass and enriched by wearable mobile health technology. The exercIse Training in pulmONary hypertEnsion (iTONE) trial includes (1) semistructured exercise prescriptions tailored to the participant's baseline level of activity and access to resources; (2) interval goal setting fostering self-efficacy; (3) real time monitoring of activity via wearable devices; (4) a digital platform enabling communication and feedback between participant and study team; (5) multiple avenues to assess participant safety. This pilot intervention will provide information on the digital infrastructure needed to conduct home-based exercise interventions in PH and will generate important preliminary data on the effect of exercise interventions in youth with chronic cardiorespiratory conditions to power larger studies in the future.

Understanding older adults’ continuance intention toward wearable health technologies: an empowerment perspective

ABSTRACT Although wearable health technologies (WHTs) hold tremendous potential as an effective means for health management among older adults, their continued use of WHTs remains a big challenge. Drawing on the fit-viability model and empowerment theory, this study proposes a paradigm of empowerment enablers – empowerment outcomes – behaviour intention to explore the mechanism of older adults’ continuance intention toward WHTs from an empowerment perspective. We collected 381 valid survey responses from older adults in China who have experience with WHTs. The results show that continuance intention is positively influenced by four dimensions of empowerment: autonomy, competence, involvement, and impact on health. Task-technology fit has a positive and significant influence on autonomy and competence, and elderly-oriented fit positively and significantly influences involvement and impact on health positively. While intergenerational support is associated with competence, involvement, and impact on health. This study contributes to expanding knowledge on the continuous use of WHTs among older adults from a new perspective. It also provides effective strategies for practitioners to enhance the retention of older adults using WHTs.

The Intersection of Plastic Electronics and Medical Innovation

The aging global population has catalyzed a paradigm shift in healthcare, emphasizing disease prevention over treatment and underscoring the need for large-scale, long-term monitoring of physiological parameters. Wearable health monitoring devices have emerged as a pivotal solution, offering real-time physiological data collection across prevention, treatment, and rehabilitation phases, thus revolutionizing traditional health monitoring approaches. This paper explores the integration of advanced materials—conductive polymers, flexible carbon nanomaterial electrodes, and metal foil substrates—into wearable health technologies. These materials enhance device flexibility, biocompatibility, and non-intrusive monitoring capabilities, providing medical-grade data crucial for early diagnosis and treatment guidance. With the wearable device market projected to expand significantly, fueled by the Internet of Things (IoT) and device miniaturization, this study delves into the technological advancements in detection technologies for electrophysiological, physical, biochemical, and photoelectric signals. It also examines the challenges and future directions in wearable health devices, focusing on material innovation, device miniaturization, and the integration of plastic electronics to improve wearability, sustainability, and interactivity. Through systematic analysis, this paper aims to highlight the transformative impact of wearable health technologies on enhancing patient care, facilitating early disease detection, and offering personalized health management solutions.

Advancement in AI-Based Devices for Monitoring Heart Health in Patients with Cardiac Conditions

In the contemporary landscape of cardiac healthcare, the integration of Artificial Intelligence (AI) into monitoring devices represents a groundbreaking shift towards enhancing patient outcomes and streamlining the management of heart health. This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences. Through a meticulous selection and analysis of current studies, we uncover the transformative capabilities of AI technologies in detecting and predicting heart-related anomalies, offering a personalized approach to cardiac care. The findings underscore the paramount importance of AI in facilitating early intervention, reducing the incidence of heart-related morbacies, and ultimately, charting a course towards a more responsive and adaptive healthcare ecosystem. The significance of this review lies not only in its comprehensive synthesis of existing research but also in its exploration of future potentials, highlighting the ongoing evolution of AI as a pivotal ally in the quest for improved heart health monitoring. This paper aims to contribute to the academic and medical discourse surrounding AI applications in healthcare, offering insights that may guide future innovations and research directions. Keywords--- AI-driven Cardiac Monitoring, Wearable Health Technology, Remote Heart Health Monitoring, Smart Health Devices, Cardiovascular Disease Management, Machine Learning in, Cardiology, Digital Health Solutions, Real-time Heart Health Analytics, Sensor Technology in Cardiac Care, Patient-Centric Heart Monitoring, AI-based Cardiac Diagnostics, Continuous Heart Rate Monitoring, Mobile Health Applications, Personalized Cardiac Care, Telemedicine in Cardiology.

Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management.

This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges in monitoring and management due to the unpredictable nature of seizures. Wearable devices offer continuous monitoring and real-time data collection, providing insights into seizure patterns and trends. Wearable technology is important in epilepsy management because it enables early detection, prediction, and personalized intervention, empowering patients and healthcare providers. Key findings highlight the potential of wearable devices to improve seizure detection accuracy, enhance patient empowerment through real-time monitoring, and facilitate data-driven decision-making in clinical practice. However, further research is needed to validate the accuracy and reliability of these devices across diverse patient populations and clinical settings. Collaborative efforts between researchers, clinicians, technology developers, and patients are essential to drive innovation and advancements in wearable digital health technology for epilepsy management, ultimately improving outcomes and quality of life for individuals with this neurological condition.

Innovative Approaches to Menstruation and Fertility Tracking Using Wearable Reproductive Health Technology: Systematic Review.

Emerging digital health technology has moved into the reproductive health market for female individuals. In the past, mobile health apps have been used to monitor the menstrual cycle using manual entry. New technological trends involve the use of wearable devices to track fertility by assessing physiological changes such as temperature, heart rate, and respiratory rate. The primary aims of this study are to review the types of wearables that have been developed and evaluated for menstrual cycle tracking and to examine whether they may detect changes in the menstrual cycle in female individuals. Another aim is to review whether these devices are effective for tracking various stages in the menstrual cycle including ovulation and menstruation. Finally, the secondary aim is to assess whether the studies have validated their findings by reporting accuracy and sensitivity. A review of PubMed or MEDLINE was undertaken to evaluate wearable devices for their effectiveness in predicting fertility and differentiating between the different stages of the menstrual cycle. Fertility cycle-tracking wearables include devices that can be worn on the wrists, on the fingers, intravaginally, and inside the ear. Wearable devices hold promise for predicting different stages of the menstrual cycle including the fertile window and may be used by female individuals as part of their reproductive health. Most devices had high accuracy for detecting fertility and were able to differentiate between the luteal phase (early and late), fertile window, and menstruation by assessing changes in heart rate, heart rate variability, temperature, and respiratory rate. More research is needed to evaluate consumer perspectives on reproductive technology for monitoring fertility, and ethical issues around the privacy of digital data need to be addressed. Additionally, there is also a need for more studies to validate and confirm this research, given its scarcity, especially in relation to changes in respiratory rate as a proxy for reproductive cycle staging.

Artificial Intelligence in Remote Monitoring and Telemedicine

Telemedicine, remote monitoring coupled with Artificial Intelligence innovations are redesigning the face of the healthcare sector in record way and increasing satisfaction levels for patients’ clinical enhancements, reduced charges and increased effectiveness in the delivery of services. The applied and advanced AI technologies consist of machine learning, natural language processing, and predictive analytics used in applications for RPM, PT, and VC. In RPM, AI augments the processes of data aggregation and data analysis of patients’ real-time health data from wearable and other digital health technologies. The former capability makes it possible to check for the possible infections, caretaker interferences, and regular management of recurring diseases. For example, AI can help in anticipating incidents such as heart attacks based on previous data of patients, thus preventive care is implemented. On the other hand, AI is incorporated in telemedicine through applying virtual health assistance, diagnostic tools and even chats. With the help of AI, virtual assistants can filter patients’ complaints, give first-stages diagnoses, and suggest necessary treatments, which will decrease loads of clinicians and increase availability of medical services. This review analyses the current uses, advantages and disadvantages of AI in remote observation and m-telemedicine. Here the details what kind of AI technologies implied in different spheres of healthcare, diagnosing, treatment planning, and individual therapies. Also, it examines how the responsible AI should be implemented ethically in a healthcare setting. Moreover, it presents the further prospects of AI in telemedicine which underlines the importance of AI development and enhancement of patient treatment.

Pythagorean hesitant fuzzy rough multi-attribute decision-making method with application to wearable health technology devices

<p>Identifying the most optimal wearable health technology devices for hospitals is a crucial step in emergency decision-making. The multi-attribute group decision-making method is a widely used and practical approach for selecting wearable health technology devices. However, because of the various factors that must be considered when selecting devices in emergencies, decision-makers often struggle to create a comprehensive assessment method. This study introduced a novel decision-making method that took into account various factors of decision-makers and has the potential to be applied in various other areas of research. First, we introduced a list of aggregation operators based on Pythagorean hesitant fuzzy rough sets, and a detailed description of the desired characteristics of the operators under investigation were provided. The proposed operators were validated by a newly defined score and accuracy function. Second, this paper used the proposed approach to demonstrate the Pythagorean hesitant fuzzy rough technique for order of preference by similarity to ideal solution (TOPSIS) model for multiple attribute decision-making and its stepwise algorithm. We developed a numerical example based on suggested operators for the evaluation framework to tackle the multiple-attribute decision-making problems while evaluating the performance of wearable health technology devices. In the end, the sensitivity analysis has confirmed the performance and reliability of the proposed framework. The findings indicated that the models being examined demonstrated greater reliability and efficacy compared to existing methodologies.</p>

The Emerging Role of Wearable Health Technologies in Proactive Disease Prevention

The study gives a complete plan for lowering disease through the use of ICT in personal healthcare. The Health Pattern Recognition (HPR), Dynamic Risk Assessment (DRA), and Personalized Intervention Strategy (PIS) formulas are all parts of this method. They are used to collect, prepare, and use data. This research focuses on cybersecurity using health pattern recognition (HPR), dynamic risk assessment (DRA), and personalized intervention strategies (PIS). PIS offers a comprehensive disease prevention approach in personal healthcare that takes advantage of technological advancements. Because they integrate secure data processing with privacy-preserving machine learning, these aspects assure the safety and validity of health data collected from wearable devices. This option allows for the assessment of medical records. It may be helpful to analyze the technique's accuracy and adherence to established security standards in order to evaluate its application for disease prediction and preventive health management. The HPR program looks at each person's health information to find trends in diseases and other results using machine learning. This helps with early evaluation and healthcare management that avoids problems. DRA keeps a person's risk rating up to date so that it takes into account any changes in their health. After that, people are given choices based on the results and risks that PIS has predicted. Some of the tests that were used to compare the suggested method to industry standards were accuracy, sensitivity, specificity, precision, and the Matthews Correlation Coefficient. The suggested way seems to work because it has better predicting power, fewer fake positives, and more users who are involved in preventive health management.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.