Wearable Health Devices Research Articles

Recent Advances in Flexible Piezoresistive Arrays: Materials, Design, and Applications.

Spatial distribution perception has become an important trend for flexible pressure sensors, which endows wearable health devices, bionic robots, and human-machine interactive interfaces (HMI) with more precise tactile perception capabilities. Flexible pressure sensor arrays can monitor and extract abundant health information to assist in medical detection and diagnosis. Bionic robots and HMI with higher tactile perception abilities will maximize the freedom of human hands. Flexible arrays based on piezoresistive mechanisms have been extensively researched due to the high performance of pressure-sensing properties and simple readout principles. This review summarizes multiple considerations in the design of flexible piezoresistive arrays and recent advances in their development. First, frequently used piezoresistive materials and microstructures are introduced in which various strategies to improve sensor performance are presented. Second, pressure sensor arrays with spatial distribution perception capability are discussed emphatically. Crosstalk is a particular concern for sensor arrays, where mechanical and electrical sources of crosstalk issues and the corresponding solutions are highlighted. Third, several processing methods are also introduced, classified as printing, field-assisted and laser-assisted fabrication. Next, the representative application works of flexible piezoresistive arrays are provided, including human-interactive systems, healthcare devices, and some other scenarios. Finally, outlooks on the development of piezoresistive arrays are given.

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features.

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.

Personalized Medical Devices Connect Monitoring and Assistance: Emerging Wearable Soft Robotics.

As wearable health devices have the ability of intelligent monitoring, they are becoming cutting-edge technology in medical and health fields. However, the simplification of functions limits their further development. In addition, soft robotics with actuation functions can achieve therapeutic effects by doing external work, but their monitoring function is not sufficiently developed. The efficient integration of the two can guide future development. The functional integration of actuation and sensing can not only monitor the human body and surrounding environment but also realize actuation and assistance. Recent evidence shows that emerging wearable soft robotics can become the future of personalized medical treatment. In this Perspective, the comprehensive development in the field of actuators for simple structure soft robotics and the field of wearable application sensors are introduced, as well as their manufacturing processes and various potential medical applications. Furthermore, the challenges faced in this field are discussed, and future development directions are proposed.

Risk Analysis and Countermeasures for Wearable Mobile Medical Devices

With peoples attention to health, wearable mobile medical devices have gradually entered people's vision in recent years. With its convenient and fast functions, it is expected to im-prove the current medical environment. A wearable mobile health device is a portable de-vice that is worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not only hardware devices but also perform powerful functions through software support, data interaction, and cloud interaction. Wearable devices will bring great changes to our lives and perceptions. This chapter will explain the technology, development, device form, market performance, and other aspects of wearable devices.

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study.

Prevention of the risk factors for metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. We proposed a combined theoretical model based on the health belief model, the Unified Technology of Acceptance and Use of Technology 2, and perceived risk. We conducted a web-based survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable explaining the habitual use of wearable devices. The direct effect of the performance expectancy on habitual use of wearable devices was greater (β=.537, P<.001) than that of intention to continue use (β=.439, P<.001), and the total effect estimate of the performance expectancy was 0.909 (P<.001), including the indirect effect (β=.372, P=.03) on habitual use of wearable devices via intention to continue use. Furthermore, performance expectancy was influenced by health motivation (β=.497, P<.001), effort expectancy (β=.558, P<.001), and risk perception (β=.137, P=.02). Perceived vulnerability (β=.562, P<.001) and perceived severity (β=.243, P=.008) contributed to health motivation. The results suggest the importance of the users' performance expectations for wearable health devices for the intention of continued use for self-health management and habituation. Based on our results, developers and health care practitioners should find better ways to meet the performance expectations of middle-aged individuals with MetS risk factors. They also should generate device use easier and find a way to encourage users' health motivation, thereby reducing users' effort expectancy and resulting in a reasonable performance expectancy of the wearable health device, to induce users' habitual use behaviors.

Watch-Type Dual-Mode Wearable Health Device

Wearable devices can be found as numerous types of health devices ranging from monitoring hazardous environments to detecting physiological signals. Its utilities have also shown encouraging improvements toward personalized healthcare. Thus, many kinds of wearable health devices (WHD) have been reported and developed. Those reports highlight their efficiency in monitoring harmful environmental factors and various diseases for diagnostic and treatment purposes. This paper proposes a watch-type of dual-mode WHD that can perform gaseous phase detection to monitor dangerous environmental situations such as bad air quality and aqueous phase detection to measure physiological signals from human sweat or blood. The proposed system was fabricated on a printed circuit board and its size is 3 cm × 3 cm which is suitable as a wearable device. It also consumes very low power and has capabilities of wired/wireless charging and wired/wireless communication. Furthermore, customized windows and Android applications have been developed to visualize obtained data in a user’s computer or smartphone. The system was tested both electrically and chemically and showed promising results as the WHD.

IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives

The potential of the Internet of Health Things (IoHT), also identified in the literature as the Internet of Medical Things (IoMT), is enormous, since it can generate expressive impacts on healthcare devices, such as the capnograph. When applied to mechanical ventilation, it provides essential healthcare to the patient and helps save lives. This survey elaborates on a deep review of related literature about the most robust and effective innovative healthcare solutions using modern technologies, such as the Internet of Things (IoT), cloud computing, Blynk, Bluetooth Low Energy, Robotics, and embedded systems. It emphasizes that IoT-based wearable and smart devices that work as integrated systems can be a faster response to other pandemic crises, respiratory diseases, and other problems that may occur in the future. It may also extend the performance of e-Health platforms used as monitoring systems. Therefore, this paper considers the state of the art to substantiate research about sensors, highlighting the relevance of new studies, strategies, approaches, and novelties in the field.

Feasibility of conductive embroidered threads for I2C sensors in microcontroller-based wearable electronics

In recent years, the importance of flexible and textile electronics in the field of wearable devices has continuously increased, as they are expected to replace conventional wires that exhibit limited resistance to the mechanical stress occurring in on-body applications. Wearable health devices (WHDs) can provide physiological information about various body parts and employ distributed sensor networks. Among the sensors typically integrated within WHDs, those based on the I2C communication protocol are very common and exploit signals transmitted at frequencies up to hundreds of kilohertz. Therefore, robust communication is required to guarantee a proper transmission of the signal at those frequencies. In this context, we have realized embroidered conductive threads exhibiting a lower resistance, appositely designed to replace conventional wires in a microcontroller-based wearable device employing I2C sensors. A commercial conductive thread (silver coated polyamide) was used to embroider the conductive lines on to cotton fabric. Preliminary measurements were performed to characterize the response of these materials to signals typically operated within the I2C communication protocol at different path lengths. Resistive measurements have also been performed to stimulate different environmental conditions, that is, temperature, the effect of sweating, and repeated washing cycles, also apply mechanical stress, i.e. twisting, with promising results that validate our conductive paths for digital signal communication.

Behavioral Interpretation of Willingness to Use Wearable Health Devices in Community Residents: A Cross-Sectional Study

Wearable health devices (WHDs) have become increasingly advantageous in long-term health monitoring and patient management. However, most people have not yet benefited from such innovative technologies, and the willingness to accept WHDs and their influencing factors are still unclear. Based on two behavioral theories: the theory of planned behavior (TPB) and the diffusion of innovation (DOI), this study aims to explore the influencing factors of willingness to use WHDs in community residents from the perspective of both internal and external factors. A convenience sample of 407 community residents were recruited from three randomly selected Community Health Service Centers (CHSCs) in Nanjing, China, and were investigated with a self-developed questionnaires. The mean score of willingness to use WHDs was 17.00 (range 5-25). In the dimensions of TPB, perceived behavioral control (β = 1.979, p < 0.001) was the strongest influencing factor. Subjective norms (β = 1.457, p < 0.001) and attitudes (β = 0.651, p = 0.016) were also positively associated with willingness. In innovation characteristics of DOI, compatibility (β = 0.889, p < 0.001) and observability (β = 0.576, p = 0.003) had positive association with the willingness to wear a WHD. This study supports the applicability of the two behavioral theories to interpret the willingness to use WHDs in Chinese community residents. Compared with the innovative features of WHDs, individual cognitive factors were more critical predictors of willingness to use.

Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies.

The use of wearable devices has increased substantially in recent years. This, together with the rise of telemedicine, has led to the use of these types of devices in the healthcare field. In this work, we carried out a detailed study on the use of these devices (regarding the general trends); we analyzed the research works and devices marketed in the last 10 years. This analysis extracted relevant information on the general trend of use, as well as more specific aspects, such as the use of sensors, communication technologies, and diseases. A comparison was made between the commercial and research aspects linked to wearables in the healthcare field, and upcoming trends were analyzed.

Intention to use wearable health devices and its predictors among diabetes mellitus patients in Amhara region referral hospitals, Ethiopia: Using modified UTAUT-2 model

IntroductionThe burden of diabetes mellitus is increasing in Africa. Wearables have a proven track record of combating chronic diseases. However, little is known about patients’ intentions to use such technologies in resource-limited settings. Therefore, this study aimed to assess DM patients' intentions to use wearable health devices and its predictors in Ethiopia. MethodsAn institutional-based cross-sectional study was conducted among 924 diabetes mellitus patients from May 19 to June 23, 2022, at referral hospitals in the Amhara regional state of Ethiopia. An interviewer-administered questionnaire was used to collect the data using the Kobo Collect app. A systematic random sampling technique was used to select the study participants. Descriptive statistics were done using SPSS version 25 software and presented using tables and pie charts. Structural equation modeling analysis with SPSS AMOS version 26 software was employed to identify predictors associated with the intention to use wearable health devices in Ethiopia. ResultsA total of 883 diabetes mellitus patients, with a 95.56% response rate, participated in the study. The proportion of intention to use wearable health devices was 47.1%, 95% CI (43.7–50.5). Effort expectancy (β = 0.543, P < 0.01), performance expectancy (β = 0.306, P < 0.01), facilitating condition (β = 0.131, P < 0.05), and habit (β = 0.093, P < 0.05) had a positive direct relationship with intention to use wearable health devices. Age (β = 0.439, p < 0.001) was moderate performance expectancy, while gender (β = 0.780, p < 0.001) had moderate effort expectancy to the intention to use wearable health devices. ConclusionsOverall, diabetes mellitus patients’ intentions to use wearable health devices were promising. The intention to use wearables was positively related to effort expectancy, performance expectancy, facilitating conditions, and habit. Thus, increasing patient intention to utilize it could be achieved through capacity building, access to technology, and technical support. In addition, implementers should prioritize improving the availability of devices, patients' abilities, encouraging customers, to utilize the product on a regular basis by actively fostering their relationship with them, and create awareness about usefulness of wearable health devices.

중국 의료빅데이터의 프라이버시 정보 보호에 관한 고찰

A recent development of information technology (IT) has brought many changes to hospital treatment and medical administrative services. Healthcare using clinical data accumulated by electronic medical records is changing the industry paradigm. In addition, with the spread of mobile internet and wearable health devices, it has been possible to build an infrastructure for health data that can converge ‘health’ and ‘medical.’ It provides convenience and speed not experienced in traditional healthcare through medical institutions such as hospitals.&#x0D; On the other hand, the collection and utilization of medical big data in medicine and electronic medical records have brought about many changes in privacy projection. The privacy of patients is used in hospitals and other institutions in a way called ‘information privacy.’ In other words, privacy is used as a piece of ‘information’ and is used for disease treatment, medical research, and insurance product development. As medical big data is collected from various devices and reprocessed in various ways, however, the risk of personal privacy rights being infringed has increased. Although these risks are inherent, a legal basis is not perfectly established yet.&#x0D; Therefore, this study reviews the current status of medical big data utilization in China and analyzes various legal issues related to the privacy information of medical big data. Besides, the study examines the civil legal status and protection measureless of privacy inflammation on medical big data and then proposes legislative improvement measures.

Technology and Innovation in Global Ophthalmology: The Past, the Potential, and a Path Forward.

Technology and Innovation in Global Ophthalmology: The Past, the Potential, and a Path Forward.

Remote telemedicine strategy based on multi-risks intervention by intelligent wearable health devices in elderly comorbidities patients with coronary heart disease

Abstract Background Telemedicine based on wearable intelligent health devices becomes increasingly promissing for the elderly due to the accelerated aging population. Especially during COVID-19 pandemic, more elderly coronary heart disease patients with chronic comorbidities are in less secondary prevention management at home. Objective To explore the prevention effect on main cardiovascular risk factors and repeated hospitalization in elderly comorbidities patients by telemedicine intervention based on multi-parameter wearable monitoring devices. Methods Total of 337 patients with comorbidities of coronary heart disease, hypertension and diabetes, with age more than 65 years old were recruited in the study from October 2019 to January 2021. They were randomly divided into control group and telemedcine intervention group. The latter used remote multi-parameter wearable devices to measure blood pressure, glycemic and electrocardiograph at home every day. A real-time monitoring platform would alarm any abnormal data to the doctors. Both doctors and patients can read the measurement results on a real-time mobile phone APP and interact with each other remotely twice a week routinely. A medical team remotely indicated the medications, while offering guidance on lifestyle. In contrast, the control group adopted traditional outpatient medical strategy to manage diseases. Results A total of 306 patients were enrolled in the follow-up experiment finally: 153 in the intervention group and 153 in the control group. Patient characteristics at baseline were balanced between two groups. After 12 months, compared with the control group, the intervention group saw the following metrics significantly reduced: systolic blood pressure (SBP) (131.66±9.43 vs 137.20±12.02 mmHg, P=0.000), total cholesterol (TC) (3.65±0.79 vs 4.08±0.82 mmol/L, P=0.001), low density lipoprotein cholesterol (LDL-C) (2.06±0.53 vs 2.38±0.61 mmol/L, P=0.002), and fasting blood glucose (FBG) (6.26±0.75 vs 6.81±0.97 mmol/L, P=0.000), while the following metrics went up significantly: blood pressure control rate (77.3% vs 59.1%, P=0.039), blood lipid control rate(39.4% vs 21.2%, P=0.037), glycemic control rate (71.2% vs 51.5%, P=0.031), and medication adherence score (7.10±0.77 vs 6.80±0.73, P=0.020). Linear regression model analysis indicates that when interaction frequency ≥1.53, 2.47 and 1.15 times/week, the SBP, LDL-C and FBG levels would be controlled, respectively. Cox survival analysis finds that the hospitalization rate of intervention group is significantly lower than that of the control group (24.18% vs 35.29%, P=0.031). Conclusion The telemedicine interactive intervention based on multi-parameter wearable devices provides effectively improvement of cardiovascular risk controlling, medication adherence, while reducing the hospitalization rate of patients. A frequency of doctor-patient interactions more than 2 times/week is beneficial for disease management the elderly at home. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Key scientific research project of Health Commission

Wearable Health Technology Data Privacy; Investigating the Balance between the Benefits of Wearable Health Devices and the Privacy Concerns they Raise

Wearable Health Technology Data Privacy; Investigating the Balance between the Benefits of Wearable Health Devices and the Privacy Concerns they Raise

Remote telemedicine strategy based on multi-risks intervention by intelligent wearable health devices in elderly comorbidities patients with coronary heart disease

Abstract Background Telemedicine based on wearable intelligent health devices becomes increasingly promissing for the elderly due to the accelerated aging population. Especially during COVID-19 pandemic, more elderly coronary heart disease patients with chronic comorbidities are in less secondary prevention management at home. Objective To explore the prevention effect on main cardiovascular risk factors and repeated hospitalization in elderly comorbidities patients by telemedicine intervention based on multi-parameter wearable monitoring devices. Methods Total of 337 patients with comorbidities of coronary heart disease, hypertension and diabetes, with age more than 65 years old were recruited in the study from October 2019 to January 2021. They were randomly divided into control group and telemedcine intervention group. The latter used remote multi-parameter wearable devices to measure blood pressure, glycemic and electrocardiograph at home every day. A real-time monitoring platform would alarm any abnormal data to the doctors. Both doctors and patients can read the measurement results on a real-time mobile phone APP and interact with each other remotely twice a week routinely. A medical team remotely indicated the medications, while offering guidance on lifestyle. In contrast, the control group adopted traditional outpatient medical strategy to manage diseases. Results A total of 306 patients were enrolled in the follow-up experiment finally: 153 in the intervention group and 153 in the control group. Patient characteristics at baseline were balanced between two groups. After 12 months, compared with the control group, the intervention group saw the following metrics significantly reduced: systolic blood pressure (SBP) (131.66±9.43 vs 137.20±12.02 mmHg, P=0.000), total cholesterol (TC) (3.65±0.79 vs 4.08±0.82 mmol/L, P=0.001), low density lipoprotein cholesterol (LDL-C) (2.06±0.53 vs 2.38±0.61 mmol/L, P=0.002), and fasting blood glucose (FBG) (6.26±0.75 vs 6.81±0.97 mmol/L, P=0.000), while the following metrics went up significantly: blood pressure control rate (77.3% vs 59.1%, P=0.039), blood lipid control rate(39.4% vs 21.2%, P=0.037), glycemic control rate (71.2% vs 51.5%, P=0.031), and medication adherence score (7.10±0.77 vs 6.80±0.73, P=0.020). Linear regression model analysis indicates that when interaction frequency ≥1.53, 2.47 and 1.15 times/week, the SBP, LDL-C and FBG levels would be controlled, respectively. Cox survival analysis finds that the hospitalization rate of intervention group is significantly lower than that of the control group (24.18% vs 35.29%, P=0.031). Conclusion The telemedicine interactive intervention based on multi-parameter wearable devices provides effectively improvement of cardiovascular risk controlling, medication adherence, while reducing the hospitalization rate of patients. A frequency of doctor-patient interactions more than 2 times/week is beneficial for disease management the elderly at home. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Key scientific research project of Health Commission

WHILE YOU WERE SLEEPING

WHILE YOU WERE SLEEPING

Comparative study of a single lead ECG in a wearable device

BackgroundTechnological advances have led to electrocardiograph (ECG) functionality becoming increasingly accessible in wearable health devices, which has the potential to vastly expand the clinician's ability to monitor, diagnose, and manage cardiac health conditions. However, achieving the high signal quality necessary to make an accurate and confident diagnosis is inherently challenging on consumer device-acquired ECGs. Effective signal conditioning is crucial to make ECG data from wearable devices clinically actionable. ObjectiveThis study evaluates the heart rate (HR) performance of ECG data collected on the HeartKey® Test Watch, a single lead, dry electrode wrist wearable, against data acquired on two criterion devices: the Bittium® Faros 180, a gold standard wet electrode ambulatory monitoring device, and the HeartKey Chest Module. MethodsECG data was simultaneously acquired on three devices during a multi-stage protocol (sitting, walking, standing) designed to reflect the motion noise of real-life scenarios. Raw ECGs from the HeartKey Test Watch and HeartKey Chest Module were processed through HeartKey software, and the accuracy of the outputted heart rate data was compared to that of the criterion device at each stage of the protocol. A beat rejection analysis was performed to provide insight into the degree of high-frequency noise present in ECGs recorded on the HeartKey Test Watch. ResultsData acquired on the HeartKey Test Watch and processed by HeartKey software generated HR metrics that closely matched that of the criterion devices throughout the protocol. Bland-Altman analysis showed a mean absolute HR difference of 0.74, 1.21, 0.80 bpm during the sitting, walking, and standing stages respectively, which is within the ± 10% or ±5 bpm range required by ANSI EC13. ECG data from the HeartKey Test Watch had a higher beat rejection rate relative to the HeartKey Chest Module (8.5% vs ∼0%) due to the excessive high-frequency noise generated during the motion-based protocol. ConclusionHeartKey software demonstrated highly accurate HR performance, comparable to that of the criterion Faros device, when processing challenging ECG data acquired on a single lead, dry electrode wrist wearable during both non-motion and motion-based protocols.

Hydration Assessment Using the Bio-Impedance Analysis Method.

Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria.

Studying the Effect of Long COVID-19 Infection on Sleep Quality Using Wearable Health Devices: Observational Study.

BackgroundPatients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data.ObjectiveIn this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands.MethodsPatients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject’s total sleep time and sleep phases per night was calculated and compared between the 2 groups.ResultsThis study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO2). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO2. Increased light sleep time was correlated with increased RR and SpO2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO2. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P=.02).ConclusionsRegardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.