Wearable Devices Research Articles

Flexible and highly selective NO2 gas sensor based on direct-ink-writing of eco-friendly graphene oxide for smart wearable application

Nitrogen dioxide (NO2) is a major cause of respiratory disorders in outdoor and indoor environments. Real-time NO2 monitoring using nonintrusive wearable devices can save lives and provide valuable health data. This study reports a room-temperature, wearable, and flexible smart NO2 gas sensor fabricated via cost-effective printing technology on a polyimide substrate. The sensor uses alkali lignin with edge-oxidised graphene oxide (EGO-AL) ink, demonstrating a sensitivity of 1.70% ppm⁻1 and a detection limit of 12.70 ppb, with excellent selectivity towards NO2. The high sensing properties are attributed to labile oxygen functional groups from GO and alkali lignin, offering abundant interacting sites for NO2 adsorption and electron transfer. The sensor fully recovers to the baseline after heat treatment at 150 °C, indicating its reusability. Integration into lab coats showcased its wearable application, utilising a flexible printed circuit board to wirelessly alert the wearer via cell phone to harmful NO2 levels (>3 ppm) in the environment. This smart sensing application underscores the potential for practical, real-time air quality monitoring, personal safety enhancement, and health management.

Threat Modeling for Enhanced Security in the Healthcare Industry with a Focus on Mobile Health and IoT

The advancement of mobile health and the Internet of Things (IoT) promises to enhance healthcare quality while reducing costs, particularly with the transition from inpatient to home and ambulatory care. This shift, driven by an aging population, financial pressures, and a shortage of skilled healthcare professionals, presents significant opportunities and challenges. While mobile health improves access and encourages self-management, it also raises serious concerns regarding security and interoperability, especially with wearable devices equipped with sensors in a patient's Body Area Network (BAN). This paper critically analyzes the security and interoperability risks associated with these technologies, emphasizing the need for robust measures such as configuration and asset management. Utilizing recommendations from ENISA (2016) and conducting a risk and vulnerability assessment, this study develops a comprehensive security model tailored for healthcare architectures. Additionally, it applies the STRIDE threat modeling approach to identify and mitigate potential threats, providing valuable insights for securing healthcare systems and prioritizing critical assets vital to organizational operations.

Automation and innovation in home hemodialysis machines.

In this review, we discuss the timeline of innovation and technologic development in home hemodialysis (HHD) in the United States and the legislative approvals that accompanied them. The most recently FDA-approved home hemodialysis devices provide features that include on-demand and batch dialysate generation, access disconnect for venous needle dislodgement, touchscreen interface with visual and auditory prompts and animations, drop-in sterilized cartridges with prestrung tubing, hot water disinfection of tubing allowing extended-use, dialysate flow rates as high as 500 ml/min, as well as remote treatment monitoring capabilities. Furthermore, wearable/portable dialysis devices are currently under development to simplify dialysis delivery to patients with end-stage kidney disease. Home hemodialysis devices providing longitudinal hemodialysis across different clinical settings, virtual reality headsets for more personalized training, automated patient support, as well as wearable device development and innovations give hope for a future where home hemodialysis is more accessible and seamless.

SMART JACKET PROTOTYPE FOR ONLINE OJEK DRIVERS BASED ON THE INTERNET OF THINGS (IOT) IN SURABAYA CITY

The research aims to design an automatic cooling jacket based on the Internet of Things (smart jacket) that can be used when riding motorbikes, including online motorcycle taxis. The methods used in this research include user needs analysis, system design, prototypes, and fan speed performance testing. In the user needs analysis stage, a survey was conducted among motorcyclists to identify problems faced when riding in hot weather conditions. Next, a system design consisting of a temperature sensor and an IoT communication module was carried out. After designing the system, an automatic cooling jacket prototype was developed. This prototype is equipped with a temperature sensor that will detect the rider's body temperature level, and an IoT communication module to allow users to control the jacket via an application on a smartphone. The results of this research show that an IoT-based automatic cooling jacket can effectively reduce the body temperature of motorcyclists when riding in hot weather. In speed performance testing, this jacket succeeded in reducing the rider's body temperature efficiently. This research is expected to contribute to the development of wearable technology that can increase comfort and safety for motorcyclists. Apart from that, this research can also be the basis for developing IoT-based automatic cooling jacket products that can be mass-produced and sold on the market. Keywords: jacket, fan cooling, IOT, motorbike, temperature sensor.

ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM

Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. As for the contribution of this research, a reliable method for stress identification has been suggested, using a random forest model to categorize stress levels from EEG signals. Data were filtered using a bandpass filter, Independent Component Analysis, and more so using the Z-score to remove outliers and poor signals. Data that has been cleaned from noise and outliers will go through a feature extraction process using Power Spectral Density (PSD). The result of PSD is the power of each frequency of the EEG signal. The number of features used is 20. Random Forest was chosen due to its high accuracy and robustness in handling complex, high-dimensional data, which is common in EEG analysis. Thus, the model obtained an accuracy level of 0.8571, thereby approving the tool’s efficiency in distinguishing between different degrees of stress. The computational efficiency of the model, with a classification time of 0.2762 seconds, demonstrates its feasibility for practical applications. Based on these findings, it can be concluded that the Random Forest algorithm can be used to integrate wearable technology and for offering suggestions and timely interventions for better mental health.

Enhancing patient education and engagement through digital intelligence tools in dermatology

This comprehensive digital health review examines the expanding landscape of digital intelligence tools within dermatology, specifically examining the innovative role of AI-powered applications aimed at educating and engaging patients in managing their skin health. Drawing upon a wide array of existing research, this review scrutinizes the evolution and impact of various digital tools. These include interactive apps that leverage AI algorithms to deliver tailored skincare guidance, considering factors such as skin type, concerns, and environmental factors. Additionally, the review explores the emerging use of immersive virtual reality experiences to deepen patients’ understanding of skin conditions, offering vivid simulations and educational content. These innovative approaches showcase significant promise in not only improving patient outcomes but also fostering greater treatment adherence and overall satisfaction with dermatological care. Looking ahead, future research should prioritize fine-tuning AI algorithms to further personalize patient recommendations, exploring the integration of wearable technology for real-time monitoring, and conducting longitudinal studies to evaluate the sustained effectiveness and scalability of digital intelligence tools in promoting patient education and engagement. Moreover, innovative strategies such as gamification techniques and social support platforms hold considerable potential for enhancing patient empowerment and involvement in managing their skin health. Leveraging digital intelligence tools will eventually redefine the landscape of dermatological care, empowering patients and optimizing treatment outcomes through enhanced education and engagement.

THE IMPACT OF USING SMART WATCHES ON STUDENTS’ EFFORT CAPACITY: A COMPARATIVE STUDY

The increasing use of smartwatch technology among university students has raised questions about its potential impact on physical health and effort capacity. This study aims to evaluate the influence of smartwatch usage on the physical performance and effort capacity of students, focusing on those who utilize these devices for monitoring physical activity and health functions. In this comparative study, we employed a questionnaire to assess the adoption and usage patterns of smartwatches among students. The research sample was divided into two groups: a control group and an experimental group. Both groups participated in weekly aerobic gymnastics classes. However, the experimental group, equipped with smartwatches, was tasked with achieving a daily goal of 13,000-15,000 steps or expending 600 kcal. At the conclusion of the experiment, we evaluated the exercise capacity of all students using the Ruffier and Harvard tests. The results revealed significant differences between the two groups, with the experimental group demonstrating notably higher effort capacity. This suggests that the regular use of smartwatches, combined with specific fitness targets, can enhance physical performance and overall health among students. Our findings underscore the potential benefits of integrating wearable technology into student lifestyles, particularly in promoting physical activity and improving exercise capacity. This study contributes to the growing body of literature on the impact of smartwatches on health and fitness, highlighting their role as valuable tools for monitoring and enhancing physical performance in an academic setting. In conclusion, smartwatches can be useful tools for students, but they must be used with discernment. It is important to strike a balance between the benefits and distractions these devices bring. Students' effort capacity may be influenced by how they integrate smartwatches into their daily routine.

Tailoring the properties for composites based on xGNP, TiC and nursing their biocompatibility for wearable technologies

Recently, the balance of stiffness, response time, output voltage, and nursing their biocompatibility are hot research topics and thus developed in the present work. Here, the configurations show the importance of harvesting mechanical energy into electric energy through the piezoelectric mechanism. Some basic strategies to achieve this work's goal involve using stretchable and dielectric elastomers such as silicone rubber (SR) as host matrix to fabricate the device. Then, the addition of 2-dimensional reinforcing and electrically conductive fillers such as exfoliated graphene nanoplatelets (xGNPs) or Titanium carbide (TiC) into the SR matrix was fabricated. Results show that at 5 phr of xGNP-filled composites, the compressive load was ∼275 N for 1 sample, ∼550 N for 2 replicas, and ∼ 650 N for 3 replicas. Similarly, the stretchability was 92 % (unfilled), and at 5 phr, it was 114 % (xGNP), 154 % (TiC), and 106 % (hybrid filler). For an electrode area of 235.5 mm2 (3 replicas), the output voltage was ∼1.6 mV (xGNP), ∼0.95 mV (TiC), and ∼ 0.45 mV (Hybrid). Similarly, for an electrode area of 235.5 mm2 (3 replicas), the response time was ∼283 ms (xGNP), ∼297 ms (TiC), and ∼ 307 ms (Hybrid). The durability tests were also performed and the results show that the voltage drop was negligible from initial to final cycles. Finally, the biocompatible tests were performed. The results show that the samples tested in the present work are biocompatible and can be useful for wearables or as implants for biomedical applications.

The Advantages of Health Recommending System with Wearable Devices

The health recommendation system is one of the critical application scenarios of machine learning technology. With the advantages of wearable and mobile devices to collect rich user health monitoring data, machine learning algorithms can accurately advise users’ health status and corresponding living habits. However, some technical and regulatory challenges still need to be solved at this stage. Specifically, to ensure real-time recommendation results, the health recommendation system needs to adopt the method of training computational learning algorithms on wearable devices. However, due to wearable devices’ limited computing resources and storage space, it is often difficult to obtain satisfactory results by changing the strategy. In addition, the issue of data privacy deserves attention because wearable devices collect a lot of user privacy information, but the data manager is often not the user himself. Finally, Bluetooth protocol usually connects wearable devices, making them vulnerable to intentional attacks. This paper investigates the above problems and expounds on the solutions to related problems.

Healthcare with Wearable Devices and Machine Learning

As health management continues to grow in importance globally, driven by the quest for improved health outcomes, longevity, and personalized care, machine learning (ML) has become a popular technique for detecting and analyzing health indicators, playing an essential role in multiple areas of health detection, such as continuous heart and blood pressure monitoring, blood sugar tracking for diabetics, sleep analysis to improve sleep quality, and even disease prediction. With wearable devices, a particular scale of health detection data has been accumulated at this stage. With this data, machine learning can be predictive analytics to assess real-time health conditions, enabling early intervention and personalized treatment plans. However, data quality remains the most critical issue for machine learning-based health detection systems, as inaccuracies in sensor readings can lead to misdiagnoses or incorrect treatment decisions. In addition, privacy concerns are significant, as the sensitive health information collected by these devices requires strong security measures to protect users’ data. Moreover, the interpretability of machine learning algorithms, especially those involving deep learning, can be limited, hindering clinicians’ understanding and trust. This paper addresses these challenges by providing a comprehensive overview of relevant data sets used in health monitoring and examining the latest machine learning algorithms tailored for wearables-based health detection. It further delves into the current limitations and challenges facing this emerging field, providing insights into potential solutions and future directions for research and development.

Diagnosis and Management of Obstructive Sleep Apnea: Updates and Review

Obstructive sleep apnea (OSA) is a heterogenous disease process that cannot be adequately categorized by AHI alone. There is a significant prevalence of OSA in the general population with ongoing efforts to evaluate the risk factors contributing to OSA and its associated clinical implications. Only by improving our understanding of OSA can we advance our methods in the diagnosis and treatment of OSA. For this article, the authors reviewed keywords of obstructive sleep apnea diagnosis and therapy in the databases of Embase, Medline, and Medline ePub over the past 3 years, excluding any articles that only addressed sleep apnea in children under age 17 years. This review article is divided into three main sections. First, we will investigate the use of novel screening tools, biomarkers, anthropometric measurements, and novel wearable technologies that show promise in improving the diagnosis of OSA. There is mention of comorbid conditions seen in OSA patients since certain disease combinations can significantly worsen health and should raise our awareness to diagnose and manage those concomitant disorders. The second section will look at the current and developing treatment options for OSA. These include positive airway therapy (PAP), mandibular advancement device (MAD), exciting new findings in certain medications, orofacial myofunctional therapy (OMT), hypoglossal nerve stimulation therapy (HGNS), and other surgical options. We will conclude with a section reviewing the current Clinical Practice Guidelines for Diagnostic Testing in Adults with Obstructive Sleep Apnea from 2017, which strongly advises polysomnography (PSG) or home sleep apnea testing (HSAT), along with comprehensive sleep evaluation for uncomplicated patients with a clinical presentation of OSA.

Hierarchical MoO2@MoS2 heterojunction nanosheets anchored with metal-organic framework-derived CoFe alloy as bifunctional catalysts for flexible zinc-air batteries

Crafting a robust, high-performance, non-precious metal-based flexible air electrode is crucial for realizing flexible zinc-air batteries (ZABs). Herein, hierarchical MoO2 and MoS2 heterojunction nanosheets grown on carbon cloth (CC) are prepared through a simple hydrothermal reaction followed by calcination. Afterward, through self-assembly and high-temperature carbonization of zeolitic imidazolate framework-67 (ZIF-67) and Materials of Institute Lavoisier-101 (MIL-101) on the heterojunction nanosheets, a hierarchical structure of nitrogen-doped carbon (NC) encapsulated CoFe alloy nanoparticles (NPs) supported on MoO2@MoS2 heterojunction nanosheets (CC/MoS2@MoO2@CoFe) is obtained. It exhibits remarkable activity and durability for both bifunctional oxygen evolution and reduction reactions (OER/ORR), attributed to the ternary heterojunction formed by NC-coated CoFe alloy NPs uniformly dispersed among the MoO2@MoS2 nanosheets, along with oxygen vacancies at heterogeneous interfaces between MoS2, MoO2, and Co0.72Fe0.28 alloy. As a binder-less self-supporting electrode, the assembled aqueous ZAB demonstrates a maximum power density of 113.6 mW cm–2, surpassing that of Pt/C + RuO2 (75.2 mW cm–2), along with excellent cycling stability. Furthermore, the CC/MoS2@MoO2@CoFe-based flexible ZABs achieve a maximum power density of 54.1 mW cm–2, exhibit good cycling stability for up to 120 cycles, and show outstanding flexibility. This study paves the way for the practical integration of flexible ZABs into wearable electronic devices, heralding a new era of portable power solutions.

Electrospinning as a Method for Fabrication of Nanofibrous Photocatalysts Based on Gallium Oxide

Photocatalysts are currently widely used in various research and industrial fields, from water and air purification to solar energy, from self‐cleaning surfaces to wearable biosensors and electronic devices. Among semiconductors, the growing interest is attracted to gallium oxide, especially β‐Ga2O3, due to the unique physical, chemical (especially acid‐resistance), physical–chemical, photochemical properties, and redox potential. These photocatalysts can be used not only as suspended mixture, but also in the form of films, nanoparticles, and nanofibers. The last one is more challenging due to the high level of surface‐to‐volume ratio, porousness, air and water permeability, and excellent morphological and physical–mechanical properties. Moreover, electrospinning techniques, in comparison with fabrication of nanoparticles, allow to obtain photocatalytic devices without usage of additional carrying base, which simplify the industrial process. However, to date there are limited publications related to the Ga2O3 electrospun nanofibers. The aim of this review is to summarize current results of gallium oxide electrospun nanofibers preparation. Special attention is given to the technological parameters of the electrospinning, the polymer solution receipts, and the properties of such nanomaterials and its potential application. Despite the limited number of publications related to Ga2O3 fibers fabrications, that data collected in this review article demonstrate great potential for practical applications.

One-Step Fabrication of 2.5D CuMoOx Interdigital Microelectrodes Using Numerically Controlled Electric Discharge Machining for Coplanar Micro-Supercapacitors

With the increasing market demands for wearable and portable electronic devices, binary metal oxides (BMOs) with a remarkable capacity and good structure stability have been considered as a promising candidate for fabricating coplanar micro-supercapacitors (CMSCs), serving as the power source. However, the current fabrication methods for BMO microelectrodes are complex, which greatly hinder their further development and application in BMO CMSCs. Herein, the one-step fabrication of 2.5D CuMoOx-based CMSCs (CuMoCMSCs) has been realized by numerically controlled electric discharge machining (NCEDM) for the first time. In addition, the controllable capacity of CuMoCMSCs has been achieved by adjusting the NCEDM-machining voltage. The CuMoCMSCs machined by a machining voltage of 60 V (CuMoCMSCs60) showed the best performance. The fabricated CuMoCMSCs60 with binary metal oxides could operate at an ultra-high scanning rate of 10 V s−1, and gained a capacity of 40.3 mF cm−2 (1.1 mA cm−2), which is more than 4 times higher than that of MoOx-based CMSCs (MoCMSCs60) with a single metal oxide. This is because CuMoOx BMOs materials overcome the poor electroconductivity problem of the MoOx single metal oxide. This one-step and numerically controlled fabrication technique developed in this research opens a new vision for preparing BMO materials, BMO microelectrodes, and BMO microdevices in an environmental, automatic, and intelligent way.

Improvement of Thermal Stability of Charges in Polylactic Acid Electret Films for Biodegradable Electromechanical Sensors.

Eco-friendly sensors fabricated from biocompatible and biodegradable materials are promising candidates for wearable and implantable electronics due to their environmental sustainability and biosafety. This article reports a fully biodegradable electromechanical sensor (FBES) utilizing a sandwich structure with macro ripple structured polylactic acid (PLA) electret films acting as sensitive layers and molybdenum (Mo) sheets serving as electrodes for a wearable device application. The stability of the space charge stored within the PLA film has been enhanced by introducing an internal cellular structure and improving the polarization process. A macro ripple structure of the PLA layer with higher deformation is a great guarantee for boosting the pressure sensitivity. The results indicate that inserting cell microstructures and optimizing the polarization process significantly improve the charge storage stability of PLA films by nearly 55%. This enhancement is attributed to several factors, including the extended charge drift path of the charges in cellular films, a synergy effect of surface charges, and "macroscopic" dipole charges distributed in the cells. The fabricated sensor achieves a high sensitivity of 1000 pC/kPa, a wide pressure detection range of 0.03-62.4 kPa, and satisfactory stability. Such sensors are not only sensitive to body movements but also to subtle physiological signals, satisfying the diverse needs of wearable healthcare. Importantly, all the composition materials of the sensor can be completely degraded after their service, aligning with the environmentally friendly principles of green development.

A photocurable ultra-tough eutectic gel with coordination crosslinking used for wearable sensors and TENG flexible electrodes

Flexible polymer molecular chains are of great interest to researchers in the field of flexible wearable electronics due to their unrivaled flexibility and ductility. However, achieving high toughness, high elasticity, environmental stability and easy machining of flexible electronic materials remains a challenge. In this study, we present a photocurable eutectic gel mainly composed of DMAPS, AAc, Zr4+ and deep eutectic solvents (DES). Due to the strong coordination between zirconium ions and polymer networks, the gel can be endowed with excellent properties, such as excellent tensile strength (1.14 MPa), low hysteresis (5 kJ·m−3) and good adhesion (above 40 kPa). DES can not only give the gel good electrical conductivity, but also maintain the stability of the gel to ensure that the leakage or volatilization of the solvent will not occur in use. The properties mentioned above allow the gel to achieve a sensitivity factor of up to 1.7 over a small strain range, demonstrating its potential applications in the field of flexible strain sensors and triboelectric flexible electrode materials. What’s more, the gel can be used to prepare complex geometric shapes with high precision through digital light processing (DLP) based 3D printing technology. Therefore, this work introduces a novel approach for the development of highly stable flexible wearable devices, which has the potential to expand the applications of eutectic gels.

Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning.

Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO2), and tin dioxide (SnO2) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO2, NO, and SO2 using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

DEVELOPMENT OF INNOVATIVE MEDICAL DEVICES IN NEONATOLOGY: LITERATURE REVIEW

The aim of this study is to review the development of medical devices in neonatology, exploring technological advances, identifying innovations and challenges, and proposing recommendations for future research. The methodology involved a narrative review of the literature, searching databases such as PubMed, EBSCO, Cochrane, and IEEE Xplore, focusing on studies from the last ten years on medical devices for neonatology. The results highlight that technologies such as artificial intelligence and machine learning have improved the monitoring and prognosis of neonatal conditions, allowing for more precise and personalized interventions. 3D printing emerges as a promising tool for creating personalized medical devices, meeting the specific needs of each patient. Wearable devices for continuous monitoring of vital signs and advanced enteral and parenteral feeding devices have demonstrated a positive impact on neonatal health. Mechanical ventilators with gentler and smarter modes have reduced serious complications, and catheters with antimicrobial coatings have helped to reduce the incidence of neonatal sepsis. The conclusions indicate that technological innovations are fundamental to improving neonatal care. The integration of artificial intelligence, 3D printing and continuous monitoring has the potential to transform neonatology, improving clinical outcomes and the quality of life of newborns. However, challenges such as the variability of neonatal pathologies, cost and accessibility of devices and the need for adequate training of health professionals need to be addressed. Investment in continuous research, clinical validation and interdisciplinary training is recommended to ensure the effective adoption of these innovative technologies in daily clinical practices.

A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.

For the current wearable devices in the application of cross-diversified user groups, it is common to face the technical difficulties of static sign language recognition accuracy attenuation, weak anti-noise ability, and insufficient system robustness due to the differences in the use of users. This paper proposes a novel static sign language recognition method enhanced by a self-attention mechanism. The key features of sign language gesture classification are highlighted by the weight function, and then the self-attention mechanism is combined to pay more attention to the key features, and the convolutional neural network is used to extract the features and classify them, which realizes the accurate recognition of different types of static sign language under standard gestures and non-standard gestures. Experimental results reveal that the proposed method achieves an average accuracy of 99.52% in the standard static sign language recognition task when tested against the standard 36 static gestures selected within the reference American Sign Language dataset. By imposing random angular bias conditions of ±(0°-9°] and ±(9°-18°], the average recognition rates in this range were 98.63% and 86.33%. These findings indicate that, compared to existing methods, the proposed method not only maintains a high recognition rate for standard static gestures but also exhibits superior noise resistance and robustness, rendering it suitable for static sign language recognition among diverse user populations.

Sociodemographic Factors Associated with the Utilization of Digital Health Technologies Among Informal Caregivers: A Nationwide Study in the USA, 2022.

Informal caregivers can leverage digital health technologies to support their own health while also assisting patients, particularly those with mental or physical challenges. This study investigated the sociodemographic factors associated with the use of digital health technology among informal caregivers. Data from the 2022 Health Information National Trends Survey (HINTS) were examined for this cross-sectional study. The survey identified key outcomes related to the use of online medical records, health apps, digital wearable health devices, and the digital sharing of health information with professionals, on social media, or with others facing similar health issues. Sociodemographic factors (gender, race/ethnicity, feelings of one's household income, education, and census division) were also analyzed. Weighted multivariable logistic regression models were employed. A total of 831 individuals were included, representing about 36,960,385 national informal caregivers in 2022. Caregivers with a high school education or less (vs. those with at least some college education) and non-Hispanic Black caregivers (vs. non-Hispanic White caregivers) were significantly less likely to be offered access to online medical records by their healthcare providers. Additionally, online medical record usage was lower among caregivers with high school education or less, but higher among caregivers aged 50-64 (vs. those aged 35-49). Caregivers with a high school education or less were less likely to use health apps and digital wearable health devices, but more likely to share personal health information on social media. Men caregivers, those aged 50-64 and over 65 (vs. the 35-49 age group), and caregivers who were dissatisfied with their income were less likely to use digital wearable health devices. The findings underscore disparities in the utilization of caregivers' digital health technology, particularly in digital wearable health devices. Recognizing and addressing these disparities are crucial for tailoring interventions to enhance equitable access to digital health technology among diverse informal caregiver populations.