Wearable Devices Research Articles

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder.

A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children. 78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model. The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD. This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

Accuracy of the Apple Watch in Detecting Atrial Fibrillation Among Patients Undergoing 24-Hour Holter Monitoring: A Prospective, Pragmatic Study

BACKGROUND: As smartwatches with atrial fibrillation detection features gain popularity, it is important to assess the accuracy of these devices to guide decision-making. OBJECTIVES: Our study aimed to assess the sensitivity and specificity of the irregular rhythm notification and the electrocardiogram (ECG)–based detection features of a commonly used smart wearable device (Apple Watch) in detecting atrial fibrillation. METHODS: This was a prospective, pragmatic study conducted in Perpetual Succour Hospital–Cebu Heart Institute from August 2023 to January 2024. To assess the irregular rhythm notification feature, participants were asked to wear an Apple Watch alongside a 24-hour Holter monitor to verify notifications. For the ECG-based detection feature, participants had to tap the crown of the Apple Watch for 30 seconds to get a single-lead ECG similar to a lead I ECG tracing. They were instructed to get manual ECGs hourly, or more often while awake. Irregular rhythm notifications and ECG readings were then compared with that of the 24-hour Holter monitor. Sensitivity and specificity were then computed. RESULTS: A total of 140 participants consented to join after full study disclosure. The irregular rhythm notification feature of the Apple Watch exhibited a low sensitivity of 21.4% but achieved a high specificity of 100% in detecting atrial fibrillation. Meanwhile, the ECG-based detection feature, analyzed from 1295 manually taken ECGs with interpretable sinus rhythm or atrial fibrillation, demonstrated a high level of agreement with the Holter monitor, with a sensitivity of 100% and a specificity of 99.1%. CONCLUSION: The low sensitivity of the irregular rhythm notification feature of the Apple Watch in detecting atrial fibrillation cautions against relying on it as a primary screening tool. However, the high concordance of manually taken Apple Watch ECGs positions the device as a robust tool for detecting atrial fibrillation through manual ECG detection. KEYWORDS: Apple Watch, atrial fibrillation, smartwatches

Consumer Wearables-Advancing Atrial Fibrillation Care or Too Much Information?

This Viewpoint describes the diffusion of direct-to-consumer wearable devices capable of early atrial fibrillation detection and the unclear implications for initiation of anticoagulation therapy based on such findings.

(Re)defining Mobile Learning in the Post COVID-19 and GenAI Era

Mobile learning, thrust into the spotlight during the COVID-19 pandemic, has a rich 40-year history with a robust body of research predating 2020. This concise paper revisits mobile learning in light of the accelerated technological advancements and shifting educational practices post-pandemic. By examining the evolution of mobile learning frameworks and integrating recent advancements in AI and wearable technologies, we propose a contemporary redefinition that emphasises an accessible mobile ecology. We further explore the theoretical foundations and emerging scenarios in mobile learning, focusing on authentic learning experiences with current trends.

Toward Flexible and Stretchable Organic Solar Cells: A Comprehensive Review of Transparent Conductive Electrodes, Photoactive Materials, and Device Performance

AbstractFlexible and stretchable organic solar cells (FOSCs and SOSCs) hold immense potential due to their versatility and applicability in emerging areas such as wearable electronics, foldable devices, and biointegrated systems. Despite these promising applications, several challenges remain, primarily related to the mechanical durability, material performance, and scalability required for commercialization. This review comprehensively highlights recent advancements in the design and fabrication of FOSCs and SOSCs, with a particular emphasis on key functional layers, including transparent conductive electrodes, interfacial layers, photoactive materials, and top electrodes. Innovations in material design, such as active layers and transparent conductive electrodes with improved flexibility, are discussed alongside developments in device processes to achieve power conversion efficiencies exceeding 19%. Furthermore, the review addresses remaining challenges, including the need for scalable manufacturing techniques and enhanced mechanical robustness under strain. Finally, the prospects of FOSCs and SOSCs are analyzed, providing insights into how these technologies can contribute to the development of sustainable, high‐performance power sources for wearable electronic devices and other flexible electronics. This review offers valuable insights, bringing the commercialization of wearable, high‐performance FOSCs and SOSCs closer to reality.

Pulse-Driven MEMS NO2 Sensors Based on Hierarchical In2O3 Nanostructures for Sensitive and Ultra-Low Power Detection

As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the high operating temperature limits its application in some application scenarios such as mobile and wearable devices. At the same time, highly sensitive and low-power gas sensors are becoming more necessary and indispensable in response to the growth of the environmental problems and development of miniaturized sensing technologies. In this work, hierarchical indium oxide (In2O3) sensing materials were designed and the pulse-driven microelectromechanical system (MEMS) gas sensors were also fabricated. The hierarchical In2O3 assembled with the mass of nanosheets possess abundant accessible active sites. In addition, compared with the traditional direct current (DC) heating mode, the pulse-driven MEMS sensor appears to have the higher sensitivity for the detection of low-concentrations of nitrogen dioxide (NO2). The limit of detection (LOD) is as low as 100 ppb. It is worth mentioning that the average power consumption of the sensor is as low as 0.075 mW which is one three-hundredth of that in the DC heating mode. The enhanced sensing performances are attributed to loose and porous structures and the reducing desorption of the target gas driven by pulse heating. The combination of morphology design and pulse-driven strategy makes the MEMS sensors highly attractive for portable equipment and wearable devices.

A brief report: Lessons learned using wearable technology to collect data from adults receiving medications for opioid use disorder

ABSTRACT Background Incorporating wearable technology into substance use disorder research enables continuous, objective, and remote data collection, facilitating symptom monitoring to prevent relapse. However, implementation challenges may hinder data collection. This paper aims to assess adherence patterns to a wrist-worn wearable device, and to share lessons learned from incorporating wearable technology to collect physiological data from adults with opioid use disorder. Methods Employing a prospective observational design, we used convenience sampling to recruit adults receiving medications for opioid use disorder in outpatient settings. Wearable adherence was measured by days and nights worn over eight weeks, acceptability by a visual analog scale, and anecdotal notes tracked study activities and challenges. Results Twenty-three participants completed baseline, and 16 (70.0%) completed the study. Daytime and nighttime wearable adherence averaged 60.1% and 47.7%, respectively. Wearable adherence was highest at the earlier phase of the study (67.7%) and plateaued after overtime. User-related and technical challenges included digital illiteracy, protocol nonadherence, low motivation, measurement errors, and short battery life. Unique challenges included wearable design concerns and sleep problems. Conclusions This study demonstrates the feasibility of wearable use and highlights the potential to overcome challenges. Proactively sharing insights can facilitate wearable technology adoption for both participants and researchers.

Estimation of the Number of Active Sweat Glands Through Discrete Sweat Sensing

Sweat is a biomarker-rich fluid with potential for continuous patient monitoring via wearable devices. However, biomarker concentrations vary with the sweat rate per gland, posing a challenge for sweat sensing. To address this, we propose an algorithm to compute both the number of active sweat glands and their individual sweat rates. We developed models of sweat glands and a discrete sweat-sensing device to sense sweat volume. Our algorithm estimates the number of active glands by decomposing the signal into patterns generated by the individual sweat glands, allowing for the calculation of individual sweat rates. We assessed the algorithm’s accuracy using synthetic datasets for varying physiological parameters (sweat rate and number of active sweat glands) and device layouts. The results show that device layout significantly affects accuracy, with error rates below 0.2% for low and medium sweat rates (below 0.2 nL min−1 per gland). However, the method is not suitable for high sweat rates. The suitable sweat rate range can be adapted to different needs through the choice of device. Based on our findings, we provide recommendations for optimal device layouts to improve accuracy in estimating active sweat glands. This is the first study to focus on estimating the sweat rate per gland, which essential for accurate biomarker concentration estimation and advancing sweat sensing towards clinical applications.

Feasibility, usability and acceptability of a lifestyle-integrated multicomponent exercise delivered via a mobile health platform in community-dwelling pre-frail older adults: a short-term, mixed-methods, prospective pilot study

BackgroundPre-frailty is a window of opportunity for frailty reversal and can be enhanced by multicomponent exercise. The Lifestyle-integrated Functional Exercise (LiFE) program may be a promising alternative to overcome traditional exercise barriers. The latest advancements in mobile health (mHealth) technology have enabled individuals to retain the advantages of supervised exercise training within group settings while providing exercise programs that can be accessed and completed at home. This study aims to assess the feasibility, acceptability, and primary efficacy of the PF-Life program, which is the Lifestyle-Integrated Functional Exercise program for Pre-Frail supported by an mHealth platform.MethodsSixteen pre-frail adults aged ≥ 65 years were recruited from five community health centers in Fuzhou, China. All participants were prescribed the PF-Life program by geriatricians using the mhealth platform (web-based portal). Participants engaged in the customized exercises program following in-app video instructions and feedback on the mhealth platform (smartphone application). Physical activity (PA) and sedentary behavior (SB) were registered daily through wearable devices. Study endpoints were feasibility (retention rate, compliance rate, adverse events), usability (system usability scale), acceptability (qualitative interviews), changes in physical function (timed up and go (TUG), handgrip strength (HGS), and 30-second chair rise tests), PA and SB.ResultsThe intervention was feasible, 88% of participants adhered completely to the study protocol, and 95% had completed at least 75% of the prescribed experimental duration. System usability was high (85 out of 100 best imaginable). Changes were observed from baseline to follow-up for total HGS (21.41 ± 6.38 vs. 24.12 ± 6.62 kg, P < 0.05, d = 0.714), TUG (8.23 ± 1.33 vs. 7.48 ± 2.01 s, P < 0.01, d = 1.647) and 30-second chair rise test (17.13 ± 4.3 vs. 20.04 ± 4.54 repetitions, P < 0.01, d = 0.94). In addition, SB decreased by a mean of 33 min/day (P < 0.01), and low physical activity increased by 31 min/day (P < 0.01). No adverse events occurred. The majority of participants found the PF-Life program to be straightforward, adaptable, and easily integrable into their daily routines.ConclusionsPreliminary studies have demonstrated that the PF-life program exhibits good compliance, safety, and usability for the pre-frail elderly population. Large-scale randomized controlled trials are required to ascertain its long-term effectiveness.

Teledentistry Integration: Revolutionizing Dental Insurance Through Technological Innovation

This article examines the transformative impact of teledentistry on the dental insurance industry, exploring how telecommunications technology is revolutionizing dental care delivery, access, and insurance. It discusses the expansion of dental care accessibility, streamlining of insurance processes, enhancement of preventive care, and the generation of data-driven insights through teledentistry. The article also addresses privacy and security considerations, presents case studies of successful implementations, and explores future prospects and innovations in the field. By analyzing the integration of teledentistry with artificial intelligence and wearable technology, the article highlights the potential for more proactive, personalized, and accessible dental care, while emphasizing the need for continued research and development to fully realize these benefits.

Nanocellulose‐reinforced, anti‐freezing, highly conductive ionic organic hydrogels for flexible electronic devices

AbstractCurrently, flexible electronic devices based on ionic conductive hydrogels are receiving widespread attention in the field of human health detection. In this paper, a facile one‐pot method is proposed for the preparation of ionic organic hydrogels, in which polyvinyl alcohol (PVA), cellulose nanofiber (CNF), and graphene oxide (GO) are dissolved in dimethyl sulfoxide‐water (DMSO/H2O), and the organic hydrogel is obtained by a freezing–thawing process. The ionic organic hydrogel with excellent properties is then prepared by soaking the hydrogel in a calcium chloride (CaCl2) solution using a salting‐out strategy. The ionic organic hydrogel possesses good tensile (283%) and strength (0.3 MPa), super electrical conductivity (7.72 S/m), and high strain sensitivity [gauge factor (GF) up to 5.22]. Meanwhile, it has excellent anti‐freezing and moisturizing properties. In addition, the ionic organic hydrogels can be used in flexible strain sensors and triboelectric nanogenerators to realize real‐time monitoring of human motion, traceless writing, and energy conversion. It is foreseen that the prepared ionic organic hydrogels provide a feasible method for realizing the long‐term use of wearable electronic devices in extreme environments and daily life.

Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO2) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO2, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO2, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems.

Wearable sensors for cardiological monitoring in the SUS: regulatory challenges and impacts on Brazilian Public Health

Cardiovascular diseases are the leading cause of death in Brazil, with approximately 400,000 deaths annually, which corresponds to almost 30% of all deaths in the country. In addition to the impact on mortality, these conditions generate an enormous economic burden for the health system, with costs associated with the treatment of diseases such as heart attack and heart failure exceeding R$50 billion per year. In this context, the use of wearable devices for continuous monitoring of cardiovascular parameters emerges as an opportunity to improve the management and prevention of these diseases. This study conducts a scoping review of the regulatory challenges involved in the incorporation of wearables into the Unified Health System (SUS), with a focus on cardiological monitoring. Articles published between 2019 and 2024 were analyzed, discussing issues such as data accuracy, interoperability between systems, protection of sensitive data, and training of health professionals. In addition, the study examines the economic and social implications of the adoption of these technologies in a context of profound socioeconomic inequalities. The results indicate that, although wearables have the potential to transform cardiovascular care in Brazil, regulatory and technical barriers still limit their large-scale adoption in the SUS. Adequate regulation, aligned with the General Data Protection Law (LGPD), and investments in infrastructure and training are essential for the safe and effective integration of these technologies into public health.

Transcutaneous electrical nerve stimulation for fibromyalgia-like syndrome in patients with Long-COVID: a pilot randomized clinical trial

This study investigated the effect of Transcutaneous Electrical Nerve Stimulation (TENS) for fibromyalgia-like symptoms including chronic widespread musculoskeletal pain, fatigue, and/or gait impairment in twenty-five individuals with long-COVID. Participants were randomized to a high dose (intervention group, IG) or low dose (placebo group, PG) TENS device. Both groups received daily 3–5 h of TENS therapy for 4-weeks. The Brief Pain Inventory assessed functional interference from pain (BPI-I), and pain severity (BPI-S). The global fatigue index (GFI) assessed functional interference from fatigue. Wearable technology measured gait parameters during three 30-feet consecutive walking tasks. At 4-weeks, the IG exhibited a greater decrease in BPI-I compared to the PG (mean difference = 2.61, p = 0.008), and improved in gait parameters including stride time (4-8%, test condition dependent), cadence (4-10%, depending on condition), and double-support phase (12% in dual-task) when compared to baseline. A sub-group meeting the 2010 American College of Rheumatology Fibromyalgia diagnostic criteria undergoing high-dose TENS showed GFI improvement at 4-weeks from baseline (mean change = 6.08, p = 0.005). Daily TENS therapy showed potential in reducing functional interference from pain, fatigue, and gait alterations in long-COVID individuals. The study’s limited power could affect the confirmation of certain observations. Extending the intervention period may improve treatment effectiveness.

CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected "best-practice'' communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes.

Research on innovative design of intelligent wearable products based on human machine engineering and bionics

Designing intelligent wearable products entails integrating human factors, engineering, and bionics to develop ergonomic, efficient and convenient products. Human-machine engineering is a discipline that addresses the integration between the user wearing a particular system and improving the relationship between the user and the system. At the same time, bionics is an approach that aims to mimic biological systems and structures to enhance the performance of a particular product. This research explores the possibility of integrating these specializations to design new and enhanced wearable technology products with improved usability, convenience, and flexibility for practical usage. A detailed analysis of human biomechanics and ergonomic requirements established design parameters to ensure that wearable devices could be seamlessly integrated into daily life without hindering user movement. Bionic principles, such as the flexibility of animal joints and the energy-efficient movements of natural organisms, were applied to optimize the mechanical and structural aspects of the devices. This approach enabled the creation of products that mimic the natural dynamics of the human body, offering improved responsiveness and functionality. Prototypes were developed based on human-centred design principles and evaluated using simulation and testing environments. Wearables such as exoskeletons, bright clothing, and health-monitoring devices were examined for their ability to adapt to various physical conditions and environmental changes. Results demonstrate a significant increase in user comfort, reduction in mechanical strain, and enhanced performance, validating the effectiveness of integrating human-machine engineering and bionics in wearable design.

Matching combination of amorphous ionic hydrogel with elastic fabric enables integrated properties for wearable sensing

The combination of hydrogels and fabrics opens up significant opportunities for flexible materials, particularly in biomedicine and wearable technology. However, the weak interface caused by insufficient interactions between the different phases and the substantial modulus mismatch limit their broader application. In this research, flexible and amorphous polyvinyl alcohol (PVA) hydrogels were integrated with polar and elastic fabrics to create multifunctional composites via in-situ freeze-thawing technology. First, by incorporating antifreeze inorganic salts into the precursor solution, we effectively suppressed ice crystal growth during the cooling process, promoting a uniform and loosely aligned PVA chain structure. This led to the formation of an amorphous crosslinked network while simultaneously releasing free hydroxyl groups. These hydroxyl groups facilitate the formation of robust interfaces within the composite. In addition, an elastic fabric composed of a polyester-polyurethane fiber blend was selected. The polyester fibers, rich in carbonyl groups along their polymer chains, form strong hydrogen bonds with the free hydroxyl groups from the PVA hydrogel, creating a highly resilient interface. The polyurethane fibers contribute to a lower Young's modulus, as well as excellent elasticity and ductility, reducing the fabric's inherent stiffness and mitigating fiber pull-out failure. Further, the incorporation of CaCl2 not only created an environment rich in free ions, but also provided the amorphous structure with increasing spacing between adjacent polymer chains, facilitating the transportation of ions. Therefore, the composite exhibits adept sensing capabilities for intricate human body movements, fostering auspicious prospects in smart sensor applications.

Continuous heart rate monitoring in atrial fibrillation with multispectral photoplethysmography

Heart attacks and strokes account for more than four out of every five Cardiovascular Disease (CVD) deaths, with one-third occurring before age 70 years. Atrial fibrillation (AF), a common cause of ischemic strokes, can significantly reduce mortality rates through continuous monitoring, timely diagnosis, accurate confirmation, and early prognosis. This can be achieved by employing Multi-Spectral Photoplethysmography (MSPPG) technology as an alternative diagnostic tool to Electrocardiography (ECG). This investigation is implemented by determining the Heart Rate (HR) of AF-affected patients using a developed MSPPG-based wrist-worn device, keeping ECG as a standard gold reference. The Multispectral-based wrist-worn wearable Photoplethysmography device employs an optical bio-sensing, integrated BIOFY sensor to acquire the MSPPG signals using two green (526 nm), one red (660 nm) and one infrared (950 nm) LED and two photodetectors (Broadband detector, IR-cut detector). The MSPPG signals acquired using a microcontroller unit are stored in an On-device storage system simultaneously in the IoT Platform. Statistical analysis techniques were employed to compare the HR detected using the MSPPG technique with the HR measured from the gold-standard ECG. The accuracy paves the way for implementing the proposed MSPPG technology in detecting AF conditions in terms of HR, further laying the foundation for earlier detection of AF. Wearable PPG-based devices face a significant issue of motion artifacts that can be overcome using the MSPPG technique. The research advancements can potentially increase the accessibility of HR monitoring in AF patients, conveniently integrating into daily routines without restricting day-to-day activities.

Review and prospect on Metal-organic framework-based Separators for Lithium–Sulfur Battery

With the continuous advancement of technology, wearable smart devices and electric vehicles (EVs) have become an integral part of our daily lives. The increasing demand for energy from these devices and vehicles has driven the pursuit of high-performance rechargeable battery technology. In particular, the widespread adoption of electric vehicles has set higher standards for the energy density and cycle life of batteries. However, the energy density of the widely used lithium-ion batteries is currently limited by the theoretical capacity of commercial cathode materials, and there are certain challenges in terms of safety and cost-effectiveness. Therefore, the development of a new generation of rechargeable batteries with higher energy density, better safety, and more economical cost is particularly urgent. Lithium-sulfur batteries, due to their high energy density, high economic benefits, and high environmental friendliness, are considered to be one of the most promising successors to the widely used lithium-ion batteries. However, in practical applications, the electrochemical performance of lithium-sulfur batteries still needs to be improved. In particular, the poor conductivity of sulfur/lithium and the shuttle effect of polysulfides make the cycle life of the battery less than ideal, and lithium-sulfur batteries (LSB) are still far from achieving large-scale commercialization. The separator used in lithium-sulfur batteries plays a crucial role in its cycle performance and safety. The current commercial separator lacks the ability to effectively regulate the shuttle of polysulfides and is prone to thermal runaway at high temperatures. Therefore, to improve these issues, various functional materials have been used to modify the separator. Among them, as a new type of porous coordination polymer, metal organic frameworks (MOFs) have become a promising material for modifying separators due to their large specific surface area and highly ordered tunable nano-holes.At the same time, their rich inorganic nodes and designable organic ligands allow for the customization of pore chemistry at the molecular level, which can interact with the electroactive components in lithium-sulfur batteries in a tunable manner. This article reviews the reaction mechanism of lithium-sulfur batteries and the structural advantages of MOFs in terms of pore chemistry and morphology and briefly introduces the research progress of MOF-based interfacial layers for the functionalization of LSB separators in recent years. It elaborates on the mechanisms by which various modified separators improve electrochemical performance and provides a prospect for the development prospects of the field.

Wearable biosensors for pediatric hospitals: a scoping review.

As wearable biosensors are increasingly used in healthcare settings, this review aimed to identify the types of wearable biosensors used for neonate and pediatric patients and how these biosensors were clinically evaluated. A literature search was conducted using PubMed, CINAHL, Embase, Web of Science, and Cochrane. The studies published between January 2010 and February 2024 were included. Descriptive statistics were used to present counts and percentages of types, locations, clinical evaluation methods, and their results. Seventy-nine studies were included. 104 wearable sensors and 40 devices were identified. The most common type of biosensor was optoelectrical sensors (n = 40, 38.5%), and used to measure heart rate (n = 22, 19.0%). The clinical evaluation was tested by a combination of validity (n = 68, 86.1%) and reliability (n = 14, 17.7%). Only two-thirds of the wearable devices were validated or reported acceptable reliability. The majority of the biosensor studies (n = 51, 64.5%) did not report any complications related to wearable biosensors. The current literature has gaps regarding clinical evaluation and safety of wearable biosensor devices with interchangeable use of validity and reliability terms. There is a lack of comprehensive reporting on complications, highlighting the need for standardized guidelines in the clinical evaluation of biosensor medical devices. IMPACT: The most common types of biosensors in pediatric settings were optoelectrical sensors and electrical sensors. Only two-thirds of the wearable devices were validated or reported acceptable reliability, and more than half of the biosensor studies did not report whether they assessed any complications related to wearable biosensors. This review discovered significant gaps in safety and clinical validation reporting, emphasizing the need for standardized guidelines. The findings advocate for improved reporting clinical validation processes to enhance the safety of wearable biosensors in pediatric care.