Wearable Devices Research Articles

Smart watch applications in atrial fibrillation detection: Current state and future directions.

Atrial fibrillation (Afib) is a prevalent chronic arrhythmia associated with severe complications, including stroke, heart failure, and increased mortality. This review explores the use of smartwatches for Afib detection, addressing the limitations of current monitoring methods and emphasizing the potential of wearable technology in revolutionizing healthcare. Current Afib detection methods, such as electrocardiography, have limitations in sensitivity and specificity. Smartwatches with advanced sensors offer continuous monitoring, improving the chances of detecting asymptomatic and paroxysmal Afib. The review meticulously examines major clinical trials studying Afib detection using smartwatches, including the landmark Apple Heart Study and ongoing trials such as the Heart Watch, Heartline, and Fitbit Heart Study. Detailed summaries of participant numbers, smartwatch devices used, and key findings are presented. It also comments on the cost-effectiveness and scalability of smartwatch-based screening, highlighting the potential to reduce healthcare costs and improve patient outcomes. The integration of wearable technology into healthcare can lead to earlier diagnosis, improved patient engagement, and enhanced cardiac health monitoring. Despite ethical considerations and disparities, the potential benefits outweigh the challenges. This review calls for increased awareness, collaboration with insurance companies, and ongoing research efforts to optimize smartwatch accuracy and encourage widespread adoption of Afib detection. With insights from major trials, this review serves as a comprehensive reference for healthcare professionals and policymakers, guiding future strategies in the early diagnosis and management of atrial fibrillation.

Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006

PurposeCardiovascular disease (CVD) is one of the leading causes of death worldwide. Physical activity (PA) has previously been shown to be a prominent risk factor for CVD mortality. Traditionally, measurements of PA have been self-reported and based on various summary metrics. However, recent advances in wearable technology provide continuously monitored and objectively measured physical activity data. This facilitates a more comprehensive interpretation of the implications of PA in the context of CVD mortality by considering its daily patterns and compositions. MethodsThis study utilized accelerometer data from the 2003–2006 National Health and Nutrition Examination Survey (NHANES) on 2816 older adults aged 50–85 and mortality data from the National Death Index (NDI) in December 2019. A novel partially functional distributional analysis method was used to quantify and understand the association between daily distributional patterns of physical activity and cardiovascular mortality risk through a multivariable functional Cox model. ResultsA higher mean intensity of daily PA during the day was associated with a reduced hazard of CVD mortality after adjusting for other higher order distributional summaries of PA and age, gender, race, body mass index (BMI), smoking and coronary heart disease (CHD). A higher daily variability of PA during afternoon was associated with a reduced hazard of CVD mortality, after adjusting for the other predictors, particularly on weekdays. The subjects with a lower variability of PA, despite having same mean PA throughout the day, could have a lower reserve of PA and hence could be at increased risk for CVD mortality. ConclusionsOur results demonstrate that not only the mean intensity of daily PA during daytime, but also the variability of PA during afternoon could be an important protective factor against the risk of CVD-mortality. Considering circadian rhythm of PA as well as its daily compositions can be useful for designing time-of-day and intensity-specific PA interventions to protect against the risk of CVD mortality.

Wearable cuffless blood pressure monitoring devices: a commentary.

Hypertension is a growing public challenge as a leading risk factor for cardiovascular disease and all-cause mortality. Reducing overall cardiovascular risk through early screening, initiation of treatment and ongoing monitoring remains a priority in the comprehensive management of hypertension and its complications. Community nurses are ideally positioned to play a crucial role in the early detection of hypertension and providing support for its management. Wearable cuffless devices have the potential for continuous remote blood pressure monitoring. However, there is not enough literature on the validity and usability of wearable cuffless blood pressure devices to justify their use in clinical practice. This commentary critically appraises a systematic review designed to assess the validity, features and clinical usability of wearable cuffless devices, and expands on its findings and their relevance to community nursing and future research.

Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study

Background and Objectives: Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs). Methods: A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland–Altman plot analysis. Results: The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices. Conclusions: Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems.

LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors

In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model’s structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms’ most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of 96.52%.

Clinical Applications and Future Translation of Somatosensory Neuroprostheses.

Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.

Room-Temperature Flexible CNT/Fe2O3 Film Sensor for ppb-Level H2S Detection.

Carbon nanotubes (CNTs) had room temperature response, large surface area, and excellent mechanical properties, making them favorable for the design of flexible, wearable, and portable gas sensors. However, CNTs were lacking in response and selective response to different gases, such as H2S. Here, we demonstrated a flexible H2S ppb-level gas sensor based on a carbon nanotube/amorphous Fe2O3 (CNT/Fe2O3) film at room temperature, which was fabricated via a simple one-step solvent-thermal method. The CNT/Fe2O3 film gas sensor exhibited a high selective response to H2S (with a response of 55.1% to 100 ppb H2S), rapid reversible response at room temperature (with a response time of ∼127 s to 100 ppb H2S), and low limit of detection to about 2 ppb. Additionally, the CNT/Fe2O3 film maintained good sensing performance under various bending conditions and could be further fabricated into the fiber gas sensor device via wet stretching, retaining response at the ppb level (with a response of 18.6% to 100 ppb H2S). This research on a flexible gas sensor device based on the CNT film/fiber opened up new possibilities for wearable portable electronic device applications.

Legal Regulation of Smart Wearable Devices in China

Legal Regulation of Smart Wearable Devices in China

Exploring Pyrazine-Based Organic Redox Couples for Enhanced Thermoelectric Performance in Wearable Energy Harvesters.

Thermoelectric generators (TEGs) based on thermogalvanic cells can convert low-temperature waste heat into electricity. Organic redox couples are well-suited for wearable devices due to their nontoxicity and the potential to enhance the ionic Seebeck coefficient through functional-group modifications. Pyrazine-based organic redox couples with different functional groups is comparatively analyzed through cyclic voltammetry under varying temperatures.The results reveal substantial differences in entropy changes with temperature and highlight 2,5-pyrazinedicarboxylic acid dihydrate (PDCA) as the optimal candidate.How the functional groups of the pyrazine compounds impact the ionic Seebeck coefficient is examined, by calculating the electrostatic potential based on density functional theory.To evaluate the thermoelectric properties, PDCA is integrated in different concentrations into a double-network hydrogel comprising poly(vinyl alcohol) and polyacrylamide. The resulting champion device exhibits an impressive ionic Seebeck coefficient (Si) of 2.99mV K-1, with ionic and thermal conductivities of ≈67.6 µS cm-1 and ≈0.49W m-1 K-1, respectively.Finally, a TEG is constructed by connecting 36 pieces of 20× 10-3 m PDCA-soaked hydrogel in series. It achieves a maximum power output of ≈0.28 µW under a temperature gradient of 28.3 °C and can power a small light-emitting diode. These findings highlight the significant potential of TEGs for wearable devices.

Mechanoluminescent/Electric Dual-Mode Sensors Enabled by Trace Carbon Nanotubes.

Mechanoluminescence (ML)-based sensors are emerging as promising wearable devices, attracting attention for their self-powered visualization of mechanical stimuli. However, challenges such as weak brightness, high activation threshold, and intermittent signal output have hindered their development. Here, a mechanoluminescent/electric dual-mode strain sensor is presented that offers enhanced ML sensing and reliable electrical sensing simultaneously. The strain sensor is fabricated via an optimized dip-coating method, featuring a sandwich structure with a single-walled carbon nanotube (SWNT) interlayer and two polydimethylsiloxane (PDMS)/ZnS:Cu luminescence layers. The integral mechanical reinforcement framework provided by the SWNT interlayer improves the ML intensity of the SWNT/PDMS/ZnS:Cu composite film. Compared to conventional nanoparticle fillers, the ML intensity is enhanced nearly tenfold with a trace amount of SWNT (only 0.01wt.%). In addition, the excellent electrical conductivity of SWNT forms a conductive network, ensuring continuous and stable electrical sensing. These strain sensors enable comprehensive and precise monitoring of human behavior through both electrical (relative resistance change) and optical (ML intensity) methods, paving the way for the development of advanced visual sensing and smart wearable electronics in the future.

Self-Adhesive Electronic Skin with Bio-Inspired 3D Architecture for Mechanical Stimuli Monitoring and Human-Machine Interactions.

Recent development of wearable devices is revolutionizing the way of artificial electronic skins (E-skin), physiological health monitoring and human-machine interactions (HMI). However, challenge remains to fit flexible electronic devices to the human skin with conformal deformation and identifiable electrical feedback according to the mechanical stimuli. Herein, an adhesive E-skin is developed that can firmly attach on the human skin for mechanical stimuli perception. The laser-induced adhesive layer serves as the essential component to ensure the conformal attachment of E-skin on curved surface, which ensures the accurate conversion from mechanical deformation to precise electrical readouts. Especially, the 3D architecture facilitates the non-overlapping outputs that bi-directional joint bending and distinguishes strain/pressure. The optimized E-skin with bio-inspired micro-cilia exhibited significantly improved sensing performances with sensitivity of 0.652 kPa-1 in 0-4kPa and gauge factor of 8.13 for strain (0-15%) with robustness. Furthermore, the adhesive E-skin can distinguish inward/outward joint bending in non-overlapping behaviors, allowing the establishment of ternary system to expand communication capacity for logic outputs such as effective Morse code and intelligent control. It expects that the adhesive E-skin can serve as a functional bridge between human and electrical terminals for applications from daily mechanical monitoring to efficient HMI.

CONTACT: a non-randomised feasibility study of bluetooth-enabled wearables for contact tracing in UK care homes during the COVID-19 pandemic

BackgroundThe need for effective non-pharmaceutical infection prevention measures such as contact tracing in pandemics remains in care homes, but traditional approaches to contact tracing are not feasible in care homes. The CONTACT intervention introduces Bluetooth-enabled wearable devices (BLE wearables) as a potential solution for automated contact tracing. Using structured reports and reports triggered by positive COVID-19 cases in homes, we fed contact patterns and trends back to homes to support better-informed infection prevention decisions and reduce blanket application of restrictive measures. This paper reports on the evaluation of feasibility and acceptability of the intervention prior to a planned definitive cluster randomised trial of the CONTACT BLE wearable intervention.MethodsCONTACT was a non-randomised mixed-method feasibility study over 2 months in four English care homes. Recruitment was via care home research networks, with individual consent. Data collection methods included routine data from the devices, case report forms, qualitative interviews (with staff and residents), field observation of care, and an adapted version of the NoMaD survey instrument to explore implementation using Normalisation Process Theory. Quantitative data were analysed using descriptive statistical methods. Qualitative data were thematically analysed using a framework approach and Normalisation Process Theory. Intervention and study delivery were evaluated against predefined progression criteria.ResultsOf 156 eligible residents, 105 agreed to wear a device, with 102 (97%) starting the intervention. Of 225 eligible staff, 82% (n = 178) participated. Device loss and damage were significant: 11% of resident devices were lost or damaged, ~ 50% were replaced. Staff lost fewer devices, just 6%, but less than 10% were replaced. Fob wearables needed more battery changes than card-type devices (15% vs. 0%). Structured and reactive feedback was variably understood by homes but unlikely to be acted on. Researcher support for interpreting reports was valued. Homes found information useful when it confirmed rather than challenged preconceived contact patterns. Staff privacy concerns were a barrier to adoption. Study procedures added to existing work, making participation burdensome. Study participation benefits did not outweigh perceived burden and were amplified by the pandemic context. CONTACT did not meet its quantitative or qualitative progression criteria.ConclusionCONTACT found a large-scale definitive trial of BLE wearables for contact tracing and feedback-informed IPC in care homes unfeasible and unacceptable — at least in the context of shifting COVID-19 pandemic demands. Future research should co-design interventions and studies with care homes, focusing on successful intervention implementation as well as technical effectiveness.Trial registrationISRCTN registration: 11204126 registered 17/02/2021.

Fabric-based lamina emergent MXene-based electrode for electrophysiological monitoring

Commercial wearable biosignal sensing technologies encounter challenges associated with irritation or discomfort caused by unwanted objects in direct contact with the skin, which can discourage the widespread adoption of wearable devices. To address this issue, we propose a fabric-based lamina emergent MXene-based electrode, a lightweight and flexible shape-morphing wearable bioelectrode. This work offers an innovative approach to biosignal sensing by harnessing the high electrical conductivity and low skin-to-electrode contact impedance of MXene-based dry electrodes. Its design, inspired by Nesler’s pneumatic interference actuator, ensures stable skin-to-electrode contact, enabling robust biosignal detection in diverse situations. Extensive research is conducted on key design parameters, such as the width and number of multiple semicircular legs, the radius of the anchoring frame, and pneumatic pressure, to accommodate a wide range of applications. Furthermore, a real-time wireless electrophysiological monitoring system has been developed, with a signal-to-noise ratio and accuracy comparable to those of commercial bioelectrodes. This work excels in recognizing various hand gestures through a convolutional neural network, ultimately introducing a shape-morphing electrode that provides reliable, high-performance biosignal sensing for dynamic users.

Reversibly Compressible Silanated Cellulose Nanofibril Aerogel for Triboelectric Taekwondo Scoring Sensors.

The integration of bio-based materials into triboelectric nanogenerators (TENGs) for energy harvesting from human body motions has sparked considerable research attention. Here, a silanated cellulose nanofibril (SCNF) aerogel is reported for structurally reliable TENGs and reversely compressible Taekwondo scoring sensors under repeated impacts. The preparation of the aerogel involves silanizing cellulose nanofibers (CNFs) with vinyltrimethoxysilane (VTMS), following by freeze-drying and post-heating treatment. The SCNF aerogel with crosslinked physico-chemical bonding and highly porous network is found to exhibit superior mechanical strength and reversible compressibility as well as enhanced water repellency and electron-donating ability. The TENG having a tribo-positive SCNF layer exhibits exceptional triboelectric performances, generating a voltage of 270V, current of 11µA, and power density of 401.1mWm-2 under an applied force of 8N at a frequency of 5Hz. With its inherent merits in material composition, structural configuration, and device sensitivity, the SCNF TENG demonstrates the capability to seamlessly integrate into a Taekwondo protection gear, serving as an efficient self-powered sensor for monitoring hitting scores. This study highlights the significant potential of a facilely fabricated SCNF aerogel for the development of high-performance, bio-friendly, and cost-effective Bio-TENGs, enabling their application as self-powered wearable devices and sports engineering sensors.

Effects of wearable device training on upper limb motor function in patients with stroke: a systematic review and meta-analysis.

To evaluate the effect of wearable device training on improving upper limb motor function in patients who experienced strokes. The PubMed, Embase, Cochrane Library, Web of Science, MEDLINE, SCOPUS, China National Knowledge Infrastructure, WanFang, and VIP databases were searched for randomized controlled trials (RCTs) that assessed the effectiveness of wearable device training in improving upper limb motor function in patients with stroke. Two investigators independently screened studies by their titles and abstracts and cross-checked, downloaded, and evaluated the results. Disagreements were resolved by a third highly experienced researcher. Risk of bias was evaluated using the Cochrane risk-of-bias tool. This meta-analysis was registered in PROSPERO (registration No. CRD42023421633). This study comprised 508 patients from 14 RCTs. The experimental group assessed various wearable devices, including 3D-printed dynamic orthoses, inertial measurement unit (IMU) sensors, electrical stimulation devices, and virtual reality (VR) devices for virtual interactive training. The control group received traditional rehabilitation therapies, including physical and conventional rehabilitation. The experimental group scored better on the Fugl-Meyer Assessment (FMA-UE) scale (standardized mean difference [SMD] 0.26, 95% confidence interval [CI] 0.07, 0.45) and Box and Block Test (BBT) (SMD 0.43, 95% CI 0.17, 0.69) versus controls. No significant intergroup differences were observed in the Action Research Arm Test (SMD 0.20, 95% CI -0.15, 0.55), motor activity log (mean difference [MD] 0.32, 95% CI -0.54, 0.33), and modified Ashworth scale (MD -0.08, 95% CI -0.81, 0.64). The probability rankings of wearable devices that improved FMA-UE scores in patients with stroke were: orthotic devices, with the highest probability ranking of 0.45, followed by sensor devices at 0.23, electrical stimulation devices at 0.21, and VR devices at 0.11. Wearable device training was found to significantly improve upper limb motor function in patients with stroke, particularly for large-range movements. Improvements in FMA-UE and BBT scores reflected reduced impairment and enhanced manual dexterity, respectively. However, the training had no significant effect on hand movement frequency, fine motor skills, or spasticity. Among the different wearable devices tested, orthoses produced the most effective results.

Human behavior recognition method based on wearable devices

Abstract Human behavior recognition remains a prevalent subject of inquiry in contemporary scientific studies. Behavior recognition technology has penetrated into every aspect of our lives, mainly in video surveillance, health monitoring, and smart homes. Aiming at some people who need behavioral monitoring to prevent danger, a human behavior recognition method based on wearable devices is proposed, which first acquires the three-axis acceleration data of behavioral activities of such people through wearable devices and then performs sliding window segmentation and feature extraction on the acquired data and finally inputs the obtained features into the dual-directional long and short term memory framework (BiLSTM) model to complete the identification of human behavior. To affirm the method’s reliability, we performed activity detection tests on a dataset from UCI featuring a non-powered wearable sensor used by the senior population. The outcomes indicate that the method is proficient in identifying the routine daily activities of this demographic, demonstrating its practical utility.

Associations between wearable device metrics and performance status in adult patients with cancer.

427 Background: Wearable devices present a promising method for monitoring physical performance in cancer patients, potentially bridging the gap between patient experiences at home and clinical evaluations. This study assesses associations between performance status, home-based wearable device metrics, and patient-reported outcomes (PROs) among adults with cancer. (NCT03952767). Methods: This prospective cohort study enrolled 62 adults undergoing treatment for cancer, irrespective of primary site. Participants wore GENEActiv wrist accelerometer sensors (Activinsights Ltd., Kimbolton, UK) on the lumbar region and non-dominant wrist for 2 weeks, tracking gait speed and activity levels. Patients concurrently completed PRO questionnaires on physical function (PF) and fatigue (PROMIS Fatigue 7a & PROMIS Physical Function 10a). Study participants were stratified by baseline self-reported Karnofsky performance status (KPS; 100, 80-90, & ≤70). Linear regression models were used to assess associations between (1) KPS and device metrics and (2) device metrics and PROs. Covariates included age and a binary indicator for COVID period (pre/post 3/1/2020). Results: Average device wear time across the cohort was 22.2 hours/day. Significant differences in gait speed and moderate activity time were observed between the KPS≤70 group and the higher KPS groups ( Table ). Significant associations were also observed between device metrics and PROs. Each 1-hour increment moderate activity was associated with 5.5 point greater PROMIS PF score (95% CI: 3.3, 7.7; p<0.0001) and 4.6 point lower PROMIS Fatigue score (-6.7, -2.5; p<0.0001). Each 0.1 meter/sec increment in gait speed was associated with 4.6 point greater PF score (95% CI: 2.2, 7.1; p=0.0004) and 4.8 point lower Fatigue score (-6.9, -2.7; p<0.0001). Similar associations were observed between in-clinic assessments of gait speed and physical function scores. Conclusions: The associations between wearable device metrics and KPS indicate that wearable technology is both feasible and reliable for remote health monitoring in cancer patients. The associations between device metrics and PROs demonstrate that these metrics reflect patient experiences and outcomes relevant to both patients and regulatory authorities. The associations with physical function, a key PRO in the FDA’s Patient-Focused Drug Development guidance, are particularly noteworthy. Clinical trial information: NCT03952767 . Group 1(KPS 100) Group 2(KPS 80-90) Group 3(KPS ≤70) p (Group 1 vs 3) p (2 vs 3) Gait Speed (m/s) - adjusted mean, 95% CI 0.74 (0.70, 0.78) 0.72 (0.69, 0.74) 0.65 (0.60, 0.70) 0.0058 0.0210 Sedentary time (hours) 11.2 (9.8, 12.6) 10.2 (9.3, 11.0) 10.7 (9.1, 12.2) 0.5912 0.5647 Light activity (hours) 1.4 (1.0, 1.7) 1.3 (1.1, 1.5) 0.9 (0.5, 1.3) 0.0570 0.0474 Moderate activity (hours) 1.4 (1.0, 1.8) 1.2 (0.9, 1.5) 0.5 (0.0, 1.0) 0.0068 0.0159 Vigorous activity (hours) 0.1 (0.0, 0.2) 0.0 (0.0, 0.1) 0.0 (0.0, 0.1) 0.1201 0.2835

Flexible modular strategy for tailored, human–machine fusion wearable electronics

Over the past three decades, the field of wearable electronics has experienced significant growth, with future developments expected to lead to more seamless integration and a profound symbiosis between humans and electronic systems. However, current developments in wearable technology are constrained by traditional integrated circuit (IC) design paradigms and a limited range of form factors, predominantly manifesting as watches, glasses, and rings with fixed functionalities and performance capabilities. This study proposes a modular design method that leverages flexible electronics for tailoring wearable devices according to specific needs. This approach disperses and reinterprets the integrated device forms through the guidance of human body morphology and conformal design principles. The functional units within these wearable devices are constructed as physically isolated, flexible modules within a robust bus structure. These modules can be easily integrated using magnetic attachments and connectors, which allows for the creation of unconventional wearable devices with customised functionalities and forms. To validate this approach, a series of flexible functional modules were designed and prototyped. Demonstrations of some of these integrated wearable forms, including watches, headbands, and gloves, highlight the versatility of this approach. These devices can be rapidly assembled with selected flexible modules capable of monitoring vital signs, such as heart rate, peripheral capillary oxygen saturation (SpO2), barometric pressure, humidity, and temperature. The results suggest that the proposed modular method holds significant potential for advancing the development of future human–machine fusion wearable electronics.

Progress in the Application of Wearable Devices in Patients With Type 2 Diabetes Mellitus

Type 2 diabetes mellitus,a common metabolic disease,has become a global public health challenge due to its high morbidity and disability.With the rise of mobile healthcare,the advancement of emerging technologies such as artificial intelligence,and the popularization of the concept of personalized health,the field of smart wearable devices is growing rapidly.Wearable devices are categorized into two types:medical and fitness wearable devices,which have been applied in the monitoring and regulation of blood glucose and the modulation of healthy lifestyles of patients with type 2 diabetes mellitus.This article summarizes the research progress in the application of wearable devices in patients with type 2 diabetes mellitus in the past 8 years,with a view to promoting the application of wearable devices and realizing the whole life-cycle health management of patients with type 2 diabetes mellitus.

The Impact of Wearable Technologies on Blood Pressure Control in Hypertensive Patients: A Systematic Review and Meta-Analysis.

Hypertension is a major global health concern and a leading risk factor for cardiovascular disease and premature death. Despite effective treatments, blood pressure control remains suboptimal due to challenges in medication adherence, lifestyle modifications, and regular monitoring. Wearable technologies offer potential solutions by providing continuous monitoring and promoting healthier behaviors. This systematic review and meta-analysis evaluated the impact of wearable technologies on blood pressure control in hypertensive patients. A comprehensive search of databases identified six randomized controlled trials, which varied in sample size, interventions, and outcomes. The meta-analysis revealed small, non-significant effects on systolic (standardized mean difference (SMD) = -0.27, 95%CI -0.57, 0.04) and diastolic (SMD = -0.16, 95%CI -0.41, 0.08) blood pressure, with considerable heterogeneity across studies. While wearable technologies show promise in hypertension management, their effects on blood pressure control are inconsistent. Future research should focus on more tailored interventions, identifying patient subgroups that may benefit most, and integrating wearables with traditional care to enhance hypertension management.