Wearables in Medicine

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling

Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artificial intelligence (AI). It is possible to identify disease-specific biomarkers using real-time and sensitive molecular interactions. These technologies are precious for genetic, epigenetic, and proteomic changes critical to disease progression and treatment response. AI improves multi-omics profiling by analyzing large, diverse data sets and common patterns traditional methods overlook. Machine learning tools Biomarkers Discovery is revolutionizing, drug resistance is being understood, and medicine is being personalized as the combination of AI and nanosensors has advanced the detection of DNA methylation and proteomic signatures and improved our understanding of cancer, cardiovascular disease and vascular disease. Despite these advances, challenges still exist. Difficulties in integrating data sets, retaining sensors, and building scalable computing tools are the biggest obstacles. It also examines various solutions with advanced AI algorithms and innovations, including fabrication in nanosensor design. Moreover, it highlights the potential of nanosensor-assisted, AI-driven multi-omics profiling to revolutionize disease diagnosis and treatment. As technology advances, these tools pave the way for faster diagnosis, more accurate treatment and improved patient outcomes, offering new hope for personalized medicine.

Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition

Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.

Metal-Organic Framework-Based Tribovoltaic Textile for Human Body Signal Monitoring

The pursuit of sustainable and portable direct current (DC) energy suppliers has ignited considerable interest in tribovoltaic nanogenerators (TVNGs), devices that harvest mechanical energy from the surrounding environment. However, the predominant focus in TVNG research has centered on rigid and silicon-based semiconductors that lack flexibility and are thus ill-suited for integration into common fabrics. Herein, a fully-textile TVNG with a simple design is introduced that enables the real-time monitoring of human physiological signals. The utilization of copper-benzenehexathiol (Cu-BHT), a conductive 2D metal-organic framework is proposed as a p-type semiconductor grown on fabric surfaces. The developed tribovoltaic textile (TVT) consists of Cu-BHT-modified cotton and metallic aluminum textile producing pure DC output due to self-rectification. With excellent flexibility and stability, Cu-BHT TVT is seamlessly integrated into textile-based accessories for continuous monitoring of human motion and respiration.

Conformable Piezoelectric Devices and Systems for Advanced Wearable and Implantable Biomedical Applications

With increasing demands for continuous health monitoring remotely, wearable and implantable devices have attracted considerable interest. To fulfill such demands, novel materials and device structures have been investigated, since commercial biomedical devices are not compatible with flexible and conformable form factors needed for soft tissue monitoring and intervention. Among various materials, piezoelectric materials have been widely adopted for multiple applications including sensing, energy harvesting, neurostimulation, drug delivery, and ultrasound imaging owing to their unique electromechanical conversion properties. In this review, we provide a comprehensive overview of piezoelectric-based wearable and implantable biomedical devices. We first provide the basic principles of piezoelectric devices and device design strategies for wearable and implantable form factors. Then, we discuss various state-of-the-art applications of wearable and implantable piezoelectric devices and their design strategies. Finally, we demonstrate several challenges and outlooks for designing piezoelectric-based conformable biomedical devices.

Expert consensus document on artificial intelligence of the Italian Society of Cardiology

Artificial intelligence (AI), a branch of computer science focused on developing algorithms that replicate intelligent behaviour, has recently been used in patients management by enhancing diagnostic and prognostic capabilities of various resources such as hospital datasets, electrocardiograms and echocardiographic acquisitions. Machine learning (ML) and deep learning (DL) models, both key subsets of AI, have demonstrated robust applications across several cardiovascular diseases, from the most diffuse like hypertension and ischemic heart disease to the rare infiltrative cardiomyopathies, as well as to estimation of LDL cholesterol which can be achieved with better accuracy through AI. Additional emerging applications are encountered when unsupervised ML methodology shows promising results in identifying distinct clusters or phenotypes of patients with atrial fibrillation that may have different risks of stroke and response to therapy. Interestingly, since ML techniques do not analyse the possibility that a specific pathology can occur but rather the trajectory of each subject and the chain of events that lead to the occurrence of various cardiovascular pathologies, it has been considered that DL, by resembling the complexity of human brain and using artificial neural networks, might support clinical management through the processing of large amounts of complex information; however, external validity of algorithms cannot be taken for granted, while interpretability of the results may be an issue, also known as a "black box" problem. Notwithstanding these considerations, facilities and governments are willing to unlock the potential of AI in order to reach the final step of healthcare advancements while ensuring that patient safety and equity are preserved.

Self-Maintainable Electronic Materials with Skin-Like Characteristics Enabled by Graphene-PEDOT:PSS Fillers

Conventional devices lack the adaptability and responsiveness inherent in the design of nature. Therefore, they cannot autonomously maintain themselves in natural environments. This limitation is primarily because of using rigid and fragile material components for their construction, which hinders their ability to adapt and evolve in changing environments. Moreover, they often cannot self-repair after injuries or significant damage. Even devices with self-healing, soft, and responsive properties often fail to seamlessly integrate all these attributes into a single, scalable, and cohesive platform. In this study, a significant breakthrough is introduced by utilizing graphene-poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (graphene-PEDOT:PSS) fillers to transform a typically weak, insulating, and jelly-like material into a soft electronic material with properties akin to those of living organisms, such as skin tissue. The developed electronic materials exhibit a range of other capabilities attributed to the hierarchical organization originating from filler enhancement, which includes methods such as heat regulation, 3D printability, and multiplex sensing. The introduction of this new class of materials can facilitate the self-maintenance of life-like soft robots and bioelectronics that can be seamlessly integrated within dynamic environments, such as the human body, while demonstrating the ability to sense, respond, and adapt to challenging environments.

Artificial Intelligence for Clinical Trial Facilitation, Lessons for Inflammatory Bowel Disease: A Scoping Review

BACKGROUND & AIMS

Despite major advances in many fields of science and technology, pharmaceutical research and development continues to be inefficient across multiple diseases. Inflammatory bowel disease (IBD) trials are also subject to high failure rates. The aim of this review was to give an overview of the available literature on the use of artificial intelligence (AI) for clinical trial facilitation across a variety of medical specialties before trying to best position AI in IBD clinical trials.

METHODS

A scoping review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scoping reviews guidelines. The search strategy was performed using PubMed and combined the terms linked to "AI" and "clinical trials." We included studies assessing the use of AI in transforming the key steps of the drug development process and clinical trials. Studies evaluating the use of AI for clinical purposes were excluded.

RESULTS

In total, 80 articles were included. The rapid development of AI has great potential to transform the process of clinical development of new drugs in IBD, from screening of candidate molecules to study conception and execution, thereby enhancing trial efficiency. By improving the quality of trial design and facilitating patient selection, AI could help to decrease sample sizes and costs. AI may help to prescreen patients to facilitate-and possibly automate-trial enrollment. AI techniques could also help in patient monitoring by making adherence control more accurate, facilitating endpoint detection and real-time analysis.

CONCLUSIONS

Notwithstanding the ethical, regulatory, and practical limitations, incorporation of AI into clinical trials may accelerate and improve quality of drug development in IBD.

Universal Flexible Wearable Biosensors for Noninvasive Health Monitoring

Sweat, with its abundant biomarkers, is a highly appealing biofluid for personalized health monitoring and management. Noninvasive wearable sweat sensors hold great potential in this regard. However, developing an easy-to-prepare, highly sensitive, precise, and versatile wearable biosensor remains challenging. Herein, we report a universal electrochemical wearable biosensor for the accurate and sensitive detection of glucose, uric acid, and lactate in human sweat samples. A Pt nanoparticle-deposited nitrogen-doped mesoporous carbon/reduced graphene oxide composite (PNGO) was synthesized rapidly by using a simple multistage self-assembly strategy. The detection was carried out using electrodes modified with PNGO and enzyme-immobilized membranes, achieving high sensitivities (glucose: 15.33 μA mM-1 cm-2, uric acid: 103.2 μA mM-1 cm-2, lactate: 219.1 μA mM-1 cm-2), along with excellent selectivity, reproducibility, and stability. Based on the excellent performance of the biosensor, we investigated its reliability in detecting sweat targets during physical exercise and assessed its utility for monitoring human health status through glucose and purine dietary challenges, observing trends consistent with blood results. The integrated wearable flexible patch constructed in this work can provide periodic information related to sweat chemistry, and the low-cost electrode suggests the potential for large-scale manufacturing. Thus, it shows extraordinary promise for promotion and application in human health and training management.

Wearable data reveals distinct characteristics of individuals with persistent symptoms after a SARS-CoV-2 infection

Understanding the factors associated with persistent symptoms after SARS-CoV-2 infection is critical to improving long-term health outcomes. Using a wearable-derived behavioral and physiological dataset (n = 20,815), we identified individuals characterized by self-reported persistent fatigue and shortness of breath after SARS-CoV-2 infection. Compared with symptom-free COVID-19 positive (n = 150) and negative controls (n = 150), these individuals (n = 50) had higher resting heart rates (mean difference 2.37/1.49 bpm) and lower daily step counts (mean 3030/2909 steps fewer), even at least three weeks prior to SARS-CoV-2 infection. In addition, persistent fatigue and shortness of breath were associated with a significant reduction in mean quality of life (WHO-5, EQ-5D), even before infection. Here we show that persistent symptoms after SARS-CoV-2 infection may be associated with pre-existing lower fitness levels or health conditions. These findings additionally highlight the potential of wearable devices to track health dynamics and provide valuable insights into long-term outcomes of infectious diseases.

What Clinicians Should Tell Patients About Wearable Devices and Data Privacy: A Narrative Review

The recent growth of wearable medical device technology in fitness trackers, smartwatches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully "connected healthcare system." Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified. However, even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors. Still, most are unaware that data from these systems may be transmitted, stored, and even sold without the user's specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber villains. It is also important that efforts be made to make PGHD more secure since medical data may be of great value to telehealth applications and AI-physician assistants. Clinicians should actively inform patients about the risks and benefits of wearables and similar devices.

A single-fibre computer enables textile networks and distributed inference

Despite advancements in wearable technologies1,2, barriers remain in achieving distributed computation located persistently on the human body. Here a textile fibre computer that monolithically combines analogue sensing, digital memory, processing and communication in a mass of less than 5 g is presented. Enabled by a foldable interposer, the two-dimensional pad architectures of microdevices were mapped to three-dimensional cylindrical layouts conforming to fibre geometry. Through connection with helical copper microwires, eight microdevices were thermally drawn into a machine-washable elastic fibre capable of more than 60% stretch. This programmable fibre, which incorporates a 32-bit floating-point microcontroller, independently performs edge computing tasks even when braided, woven, knitted or seam-sewn into garments. The universality of the assembly process allows for the integration of additional functions with simple modifications, including a rechargeable fibre power source that operates the computer for nearly 6 h. Finally, we surmount the perennial limitation of rigid interconnects by implementing two wireless communication schemes involving woven optical links and seam-inserted radio-frequency communications. To demonstrate its utility, we show that garments equipped with four fibre computers, one per limb, operating individually trained neural networks achieve, on average, 67% accuracy in classifying physical activity. However, when networked, inference accuracy increases to 95% using simple weighted voting.

Play on Electrodes

Maximizing the efficiency of electrode usage is a crucial step in enhancing the integration of wearables. Currently, electrodes are combined in an additive manner to enable multiplexed sweat screening. The additive sensor requires significant space to accommodate single-function electrodes, which limits the integration of the wearable sensors. Here, we report that the versatility of a single electrode is achieved by assigning different roles to the electrode at different times, resulting in a flexible, disposable, epidermal sweat-sensing platform that integrates in situ iontophoresis and three electrochemical sensors on only four electrodes, while previous platforms required at least seven electrodes. For example, the iontophoresis electrode serves as the working electrode (WE) for chloride sensing and as the counter electrode (CE) for pH sensing after its controllable release of pilocarpine, and the sulfonated polyaniline (SPAN) modified glucose oxidase (GOx) serves as the WE for both pH and glucose sensing. All four functions are integrated into an 8 mm2 (1.8 × 4.45 mm) sensing area, requiring a sample volume of approximately 1 μL. These results open possibility for highly integrated wearable sweat sensors and multimodal sensors.

Microneedle electrodes: materials, fabrication methods, and electrophysiological signal monitoring-narrative review

Flexible, microneedle-based electrodes offer an innovative solution for high-quality physiological signal monitoring, reducing the need for complex algorithms and hardware, thus streamlining health assessments, and enabling earlier disease detection. These electrodes are particularly promising for improving patient outcomes by providing more accurate, reliable, and long-term electrophysiological data, but their clinical adoption is hindered by the limited availability of large-scale population testing. This review examines the key advantages of flexible microneedle electrodes, including their ability to conform to the skin, enhance skin-electrode contact, reduce discomfort, and deliver superior signal fidelity. The mechanical and electrical properties of these electrodes are thoroughly explored, focusing on critical aspects like fracture force, skin penetration efficiency, and impedance measurements. Their applications in capturing electrophysiological signals such as ECG, EMG, and EEG are also highlighted, demonstrating their potential in clinical scenarios. Finally, the review outlines future research directions, emphasizing the importance of further studies to enhance the clinical and consumer use of flexible microneedle electrodes in medical diagnostics.

Photonic Nanomaterials for Wearable Health Solutions

This review underscores the transformative potential of photonic nanomaterials in wearable health technologies, driven by increasing demands for personalized health monitoring. Their unique optical and physical properties enable rapid, precise, and sensitive real-time monitoring, outperforming conventional electrical-based sensors. Integrated into ultra-thin, flexible, and stretchable formats, these materials enhance compatibility with the human body, enabling prolonged wear, improved efficiency, and reduced power consumption. A comprehensive exploration is provided of the integration of photonic nanomaterials into wearable devices, addressing material selection, light-matter interaction principles, and device assembly strategies. The review highlights critical elements such as device form factors, sensing modalities, and power and data communication, with representative examples in skin patches and contact lenses. These devices enable precise monitoring and management of biomarkers of diseases or biological responses. Furthermore, advancements in materials and integration approaches have paved the way for continuum of care systems combining multifunctional sensors with therapeutic drug delivery mechanisms. To overcome existing barriers, this review outlines strategies of material design, device engineering, system integration, and machine learning to inspire innovation and accelerate the adoption of photonic nanomaterials for next-generation of wearable health, showcasing their versatility and transformative potential for digital health applications.

Stretchable Multimodal Photonic Sensor for Wearable Multiparameter Health Monitoring

Stretchable sensors that can conformally interface with the skins for wearable and real-time monitoring of skin deformations, temperature, and sweat biomarkers offer critical insights for early disease prediction and diagnosis. Integration of multiple modalities in a single stretchable sensor to simultaneously detect these stimuli would provide a more comprehensive understanding of human physiology, which, however, has yet to be achieved. Here, this work reports, for the first time, a stretchable multimodal photonic sensor capable of simultaneously detecting and discriminating strain deformations, temperature, and sweat pH. The multimodal sensing abilities are enabled by realization of multiple sensing mechanisms in a hydrogel-coated polydimethylsiloxane (PDMS) optical fiber (HPOF), featured with high flexibility, stretchability, and biocompatibility. The integrated mechanisms are designed to operate at distinct wavelengths to facilitate stimuli decoupling and employ a ratiometric detection strategy for improved robustness and accuracy. To simplify sensor interrogation, spectrally-resolved multiband emissions are generated upon the excitation of a single-wavelength laser, utilizing upconversion luminescence (UCL) and radiative energy transfer (RET) processes. As proof of concept, this work demonstrates the feasibility of simultaneous monitoring of the heartbeat, respiration, body temperature, and sweat pH of a person in real-time, with only a single sensor.

Triboelectric Mat Multimodal Sensing System (TMMSS) Enhanced by Infrared Image Perception for Sleep and Emotion-Relevant Activity Monitoring

To implement digital-twin smart home applications, the mat sensing system based on triboelectric sensors is commonly used for gait information collection from daily activities. Yet traditional mat sensing systems often miss upper body motions and fail to adequately project these into the virtual realm, limiting their specific application scenarios. Herein, triboelectric mat multimodal sensing system is designed, enhanced with a commercial infrared imaging sensor, to capture diverse sensory information for sleep and emotion-relevant activity monitoring without compromising privacy. This system generates pixel-based area ratio mappings across the entire mat array, solely based on the integral operation of triboelectric outputs. Additionally, it utilizes multimodal sensory intelligence and deep-learning analytics to detect different sleeping postures and monitor comprehensive sleep behaviors and emotional states associated with daily activities. These behaviors are projected into the metaverse, enhancing virtual interactions. This multimodal sensing system, cost-effective and non-intrusive, serves as a functional interface for diverse digital-twin smart home applications such as healthcare, sports monitoring, and security.

Highly Self-Adhesive and Biodegradable Silk Bioelectronics for All-In-One Imperceptible Long-Term Electrophysiological Biosignals Monitoring

Skin-like bioelectronics offer a transformative technological frontier, catering to continuous and real-time yet highly imperceptible and socially discreet digital healthcare. The key technological breakthrough enabling these innovations stems from advancements in novel material synthesis, with unparalleled possibilities such as conformability, miniature footprint, and elasticity. However, existing solutions still lack desirable properties like self-adhesivity, breathability, biodegradability, transparency, and fail to offer a streamlined and scalable fabrication process. By addressing these challenges, inkjet-patterned protein-based skin-like silk bioelectronics (Silk-BioE) are presented, that integrate all the desirable material features that have been individually present in existing devices but never combined into a single embodiment. The all-in-one solution possesses excellent self-adhesiveness (300 N m-1) without synthetic adhesives, high breathability (1263 g h-1 m-2) as well as swift biodegradability in soil within a mere 2 days. In addition, with an elastic modulus of ≈5 kPa and a stretchability surpassing 600%, the soft electronics seamlessly replicate the mechanics of epidermis and form a conformal skin/electrode interface even on hairy regions of the body under severe perspiration. Therefore, coupled with a flexible readout circuitry, Silk-BioE can non-invasively monitor biosignals (i.e., ECG, EEG, EOG) in real-time for up to 12 h with benchmarking results against Ag/AgCl electrodes.

Next-generation methods for precise pH detection in ocular chemical burns: a review of recent analytical advancements

Ocular burns due to accidental chemical spillage pose an immediate threat, representing over 20% of emergency ocular traumas. Early detection of the ocular pH is imperative in managing ocular chemical burns. Alkaline chemical burns are more detrimental than acidic chemical burns. Current practices utilize litmus, nitrazine strips, bromothymol blue, fluorescent dyes, and micro-combination glass probes to detect ocular pH. However, these methods have inherent drawbacks, leading to inaccurate pH measurements, less sensitivity, photodegradation, limited pH range, and longer response time. Hence, there is a tremendous necessity for developing relatively simple, accurate, precise ocular pH detection methods. The current review aims to provide comprehensive coverage of the conventional practices of ocular pH measurement during accidental chemical burns, highlighting their strengths and weaknesses. Besides, it delves into cutting-edge technologies, including pH-sensing contact lenses, microfluidic contact lenses, fluorescent scleral contact lenses, fiber optic pH technology, and pH-sensitive thin films. The study meticulously examines the reported work since 2000. The collected data have also helped propose future directions, and the research gap needs to be filled to provide a more rapid, sensitive, and accurate measurement of ocular pH in eye clinics. For the first time, this review consolidates current techniques and recent advancements in ocular pH detection, offering a strategic overview to propel ophthalmic-related research forward and enhance ocular burn management during a chemical spillage.

Integrated Wearable Flexible Hydrogel Patch Sensing System for the Detection of Physiological Markers

Conventional wearable flexible sensing systems typically comprise three components: a flexible substrate that contacts the skin, a signal processing module, and a signal output module. These components function relatively independently, resulting in a complex system that lacks sufficient integration. Therefore, developing an integrated wearable flexible sensing system by combining the flexible substrate, the signal processing module, and the signal output module not only enhances performance and comfort, but also reduces manufacturing costs and the risk of failure. Hydrogel substrates are particularly advantageous due to their excellent biocompatibility, flexibility, and encapsulation capabilities. Herein, we designed an integrated wearable flexible sensing system using an agarose hydrogel to encapsulate biological oxidative enzymes (e.g., glucose oxidase (GOx), lactate oxidase, and ethanol oxidase) and silver nanowires-polydopamine (Ag NWs-PB) as the signal processing module and a color-changing TMB probe as the signal output module. Additionally, we incorporated a polydimethylsiloxane-silicon dioxide patch to collect sweat for detecting physiological markers (e.g., glucose, lactate, and ethanol). An example of the application to facilitate visual detection of glucose in sweat was developed by encapsulating GOx as a biological oxidative enzyme in a sensing system. The system provides results within 3.5 min and operates within a linear range of 0.02 to 5.00 mmol/L, achieving a limit of detection of 0.011 mmol/L. This innovation not only presents a more integrated and portable solution for wearable hydrogel systems, but also introduces a new, feasible method for detecting human physiological markers through a straightforward detection process.

Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment. Traditional medical monitoring is not practical in these situations and, as such, wearable sensor technology may help support prehospital medicine. However, sensors alone are not sufficient in the prehospital setting where limited personnel without specialized medical training must make critical decisions based on physiological signals. Machine learning-based clinical decision support systems can instead be utilized to interpret these signals for diagnosing injuries, making triage decisions, or driving treatments. Here, we summarize the challenges of the prehospital medical setting and review wearable sensor technology suitability for this environment, including their use with medical decision support triage or treatment guidance options. Further, we discuss recommendations for wearable healthcare device development and medical decision support technology to better support the prehospital medical setting. With further design improvement and integration with decision support tools, wearable healthcare devices have the potential to simplify and improve medical care in the challenging prehospital environment.

Wearable Sensors and Motion Analysis for Neurological Patient Support

This work discusses the state of the art and challenges in using wearable sensors for the monitoring of neurological patients. The authors share their experience from their participation in numerous projects, ranging from drug trials to rehabilitation intervention assessment, and identify the obstacles in the way of the integrated adoption of wearable sensors in clinical and rehabilitation practices for neurological patients. Several highly promising developments are outlined and analyzed. It is considered that intelligent textiles are an attractive option, as they offer an esthetic outlook to and positive interaction with their users.

Circadian rhythms and cancer: implications for timing in therapy

Circadian rhythms, intrinsic cycles spanning approximately 24 h, regulate numerous physiological processes, including sleep-wake cycles, hormone release, and metabolism. These rhythms are orchestrated by the circadian clock, primarily located in the suprachiasmatic nucleus (SCN) of the hypothalamus. Disruptions in circadian rhythms, whether due to genetic mutations, environmental factors, or lifestyle choices, can significantly impact health, contributing to disorders such as sleep disturbances, metabolic syndrome, and cardiovascular diseases. Additionally, there is a profound link between the disruption of circadian rhythms and development of various cancer, the influence on disease incidence and progression. This incurred regulation by circadian clock on pathways has its implication in tumorigenesis, such as cell cycle control, DNA damage response, apoptosis, and metabolism. Furthermore, the circadian timing system modulates the efficacy and toxicity of cancer treatments. In cancer treatment, the use of chronotherapy to optimize the timing of medical treatments, involves administering chemotherapy, radiation, or other therapeutic interventions at specific intervals to enhance efficacy and minimize side effects. This approach capitalizes on the circadian variations in cellular processes, including DNA repair, cell cycle progression, and drug metabolism. Preclinical and clinical studies have demonstrated that chronotherapy can significantly improve the therapeutic index of chemotherapeutic agents like cisplatin and 5-fluorouracil by enhancing anticancer activity and reducing toxicity. Further research is needed to elucidate the mechanisms underlying circadian regulation of cancer and to develop robust chronotherapeutic protocols tailored to individual patients' circadian profiles, potentially transforming cancer care into more effective and personalized treatment strategies.

Improvement of an Edge-IoT Architecture Driven by Artificial Intelligence for Smart-Health Chronic Disease Management

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents' overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in R2, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context.

Breathable and Stretchable Epidermal Electronics for Health Management: Recent Advances and Challenges

Advanced epidermal electronic devices, capable of real-time monitoring of physical, physiological, and biochemical signals and administering appropriate therapeutics, are revolutionizing personalized healthcare technology. However, conventional portable electronic devices are predominantly constructed from impermeable and rigid materials, which thus leads to the mechanical and biochemical disparities between the devices and human tissues, resulting in skin irritation, tissue damage, compromised signal-to-noise ratio (SNR), and limited operational lifespans. To address these limitations, a new generation of wearable on-skin electronics built on stretchable and porous substrates has emerged. These substrates offer significant advantages including breathability, conformability, biocompatibility, and mechanical robustness, thus providing solutions for the aforementioned challenges. However, given their diverse nature and varying application scenarios, the careful selection and engineering of suitable substrates is paramount when developing high-performance on-skin electronics tailored to specific applications. This comprehensive review begins with an overview of various stretchable porous substrates, specifically focusing on their fundamental design principles, fabrication processes, and practical applications. Subsequently, a concise comparison of various methods is offered to fabricate epidermal electronics by applying these porous substrates. Following these, the latest advancements and applications of these electronics are highlighted. Finally, the current challenges are summarized and potential future directions in this dynamic field are explored.

A Paper-Based Wearable Moist-Electric Generator for Sustained High-Efficiency Power Output and Enhanced Moisture Capture

Disposable wearable electronics are valuable for diagnostic and healthcare purposes, reducing maintenance needs and enabling broad accessibility. However, integrating a reliable power supply is crucial for their advancement, but conventional power sources present significant challenges. To address that issue, a novel paper-based moist-electric generator is developed that harnesses ambient moisture for power generation. The device features gradients for functional groups and moisture adsorption and architecture of nanostructures within a disposable paper substrate. The nanoporous, gradient-formed spore-based biofilm and asymmetric electrode deposition enable sustained high-efficiency power output. A Janus hydrophobic-hydrophilic paper layer enhances moisture harvesting, ensuring effective operation even in low-humidity environments. This research reveals that the water adsorption gradient is crucial for performance under high humidity, whereas the functional group gradient is dominant under low humidity. The device delivers consistent performance across diverse conditions and flexibly conforms to various surfaces, making it ideal for wearable applications. Its eco-friendly, cost-effective, and disposable nature makes it a viable solution for widespread use with minimal environmental effects. This innovative approach overcomes the limitations of traditional power sources for wearable electronics, offering a sustainable solution for future disposable wearables. It significantly enhances personalized medicine through improved health monitoring and diagnostics.

Medication Management Initiatives Using Wearable Devices: Scoping Review

Background

Wearable devices (WDs) have evolved beyond simple fitness trackers to sophisticated health monitors capable of measuring vital signs, such as heart rate and blood oxygen levels. Their application in health care, particularly medication management, is an emerging field poised to significantly enhance patient adherence to treatment regimens. Despite their widespread use and increasing incorporation into clinical trials, a comprehensive review of WDs in terms of medication adherence has not been conducted.

Objective

This study aimed to conduct a comprehensive scoping review to evaluate the impact of WDs on medication adherence across a variety of diseases, summarizing key research findings, outcomes, and challenges encountered.

Methods

Adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a structured search was conducted across MEDLINE, Web of Science, and Embase databases, covering the literature from January 1, 2010, to September 30, 2022. The search strategy was based on terms related to WDs and medication adherence, specifically focusing on empirical studies to ensure the inclusion of original research findings. Studies were selected based on their relevance to medication adherence, usage of WDs in detecting medication-taking actions, and their role in integrated medication management systems.

Results

We screened 657 articles and identified 18 articles. The identified studies demonstrated the diverse applications of WDs in enhancing medication adherence across diseases such as Parkinson disease, diabetes, and cardiovascular conditions. The geographical distribution and publication years of these studies indicate a growing interest in this research area. The studies were divided into three types: (1) studies reporting a correlation between data from WDs or their usage and medication adherence or drug usage as outcomes, (2) studies using WDs to detect the act of medication-taking itself, and (3) studies proposing an integrated medication management system that uses WDs in managing medication.

Conclusions

WDs are increasingly being recognized for their potential to enhance medication management and adherence. This review underscores the need for further empirical research to validate the effectiveness of WDs in real-life settings and explore their use in predicting adherence based on activity rhythms and activities. Despite technological advancements, challenges remain regarding the integration of WDs into routine clinical practice. Future research should focus on leveraging the comprehensive data provided by WDs to develop personalized medication management strategies that can improve patient outcomes.

Design and Development of a Smart Fidget Toy Using Blockchain Technology to Improve Health Data Control

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.

Tear-Based Ocular Wearable Biosensors for Human Health Monitoring

Wearable tear-based biosensors have garnered substantial interest for real time monitoring with an emphasis on personalized health care. These biosensors utilize major tear biomarkers such as proteins, lipids, metabolites, and electrolytes for the detection and recording of stable biological signals in a non-invasive manner. The present comprehensive review delves deep into the tear composition along with potential biomarkers that can identify, monitor, and predict certain ocular diseases such as dry eye disease, conjunctivitis, eye-related infections, as well as diabetes mellitus. Recent technologies in tear-based wearable point-of-care medical devices, specifically the state-of-the-art and prospects of glucose, pH, lactate, protein, lipid, and electrolyte sensing from tear are discussed. Finally, the review addresses the existing challenges associated with the widespread application of tear-based sensors, which will pave the way for advanced scientific research and development of such non-invasive health monitoring devices.

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.

Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence

As an integral aspect of health care, digital technology has enabled modelling of complex relationships to detect, screen, diagnose, and predict patient outcomes. With massive data sets, artificial intelligence (AI) can have marked effects on 3 levels: for patients, clinicians, and health systems. In this review, we discuss contemporary AI-enabled wearable devices undergoing research in the field of cardiovascular medicine. These include devices such as smart watches, electrocardiogram patches, and smart textiles such as smart socks and chest sensors for diagnosis, management, and prognostication of conditions such as atrial fibrillation, heart failure, and hypertension as well as monitoring for cardiac rehabilitation. We review the evolution of machine learning algorithms used in wearable devices from random forest models to the use of convolutional neural networks and transformers. We further discuss frameworks for wearable technologies such as the V3-stage process of verification, analytical validation, and clinical validation as well as challenges of AI integration in medicine such as data veracity, validity, and security and provide a reference framework to maintain fairness and equity. Last, clinician and patient perspectives are discussed to highlight the importance of considering end-user feedback in development and regulatory processes.

FFF/FDM 3D-Printed Solid Polymer Electrolytes Based on Acrylonitrile Copolymers for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) are essential in modern electronics, particularly in portable devices and electric vehicles. However, the limited design flexibility of current battery shapes constrains the development of custom-sized power sources for advanced applications like wearable electronics and medical devices. Additive manufacturing (AM), specifically Fused Filament Fabrication (FFF), presents a promising solution by enabling the creation of batteries with customized shapes. This study explores the use of novel poly(acrylonitrile-co-polyethylene glycol methyl ether acrylate) (poly(AN-co-PEGMEA)) copolymers as solid polymer electrolytes for lithium-ion batteries, optimized for 3D printing using FFF. The copolymers were synthesized with varying AN:PEGMEA ratios, and their physical, thermal, and electrochemical properties were systematically characterized. The study found that a poly(AN-co-PEGMEA) 6:1 copolymer ratio offers an optimal balance between printability and ionic conductivity. The successful extrusion of filaments and subsequent 3D printing of complex shapes demonstrate the potential of these materials for next-generation battery designs. The addition of succinonitrile (SCN) as a plasticizer significantly improved ionic conductivity and lithium cation transference numbers, making these copolymers viable for practical applications. This work highlights the potential of combining polymer chemistry with additive manufacturing to provide new opportunities in lithium-ion battery design and function.

Semi-invasive wearable clinic: Solution-processed smart microneedle electronics for next-generation integrated diagnosis and treatment

The integrated smart electronics for real-time monitoring and personalized therapy of disease-related analytes have been gradually gaining tremendous attention. However, human tissue barriers, including the skin barrier and brain-blood barrier, pose significant challenges for effective biomarker detection and drug delivery. Microneedle (MN) electronics present a promising solution to overcome these tissue barriers due to their semi-invasive structures, enabling effective drug delivery and target-analyte detection without compromising the tissue configuration. Furthermore, MNs can be fabricated through solution processing, facilitating large-scale manufacturing. This review provides a comprehensive summary of the recent three-year advancements in smart MNs development, categorized as follows. First, the solution-processed technology for MNs is introduced, with a focus on various printing technologies. Subsequently, smart MNs designed for sensing, drug delivery, and integrated systems combining diagnosis and treatment are separately summarized. Finally, the prospective and promising applications of next-generation MNs within mediated diagnosis and treatment systems are discussed.

Effect of virtual reality-based biofeedback for depressive and anxiety symptoms: Randomized controlled study

BACKGROUND

The use of virtual reality (VR)-based biofeedback (BF), a relatively new intervention, is a non-pharmacological treatment of depressive and anxiety symptoms. However, studies on VR-based BF are lacking and inconclusive.

METHODS

A total of 131 adults were recruited from the community. Participants who scored ≥10 on the Patient Health Questionnaire-9 (PHQ-9) or ≥9 on the Panic Disorder Severity Scale (PDSS) were included in the group with depressive or anxiety symptoms (DAS group), and others as the healthy control group (HC group). Participants from the DAS group were randomly assigned to VR-based or conventional BF intervention. All individuals visited at three times (weeks 0, 2, and 4), and completed the Montgomery-Asberg Depression Rating Scale (MADRS), the State-Trait Anxiety Inventory (STAI), and a visual analog scale (VAS) before and after the intervention, and PHQ-9 at the beginning and final visit.

RESULTS

The analysis included a total of 118 participants (DAS/VR: 40, DAS/BF: 38, HC/VR: 40). There was no significant difference in demographic variables among the three groups. After the intervention, the DAS/VR group exhibited significant decreases in MADRS (70.0 %), PHQ-9 (64.1 %), STAI (29.5 %), and VAS (61.7 %) scores compared to the baseline (p <0.001). There were no significant differences between the effects of VR-based BF and conventional BF with a therapist. The HC group also showed significant decreases in the measures of depression and anxiety after receiving VR-based BF.

CONCLUSION

VR-based BF was effective in reducing depressive and anxiety symptoms, even for subthreshold depression and anxiety symptoms in the HC group.

How do gamified digital therapeutics work on obesity self-management?

Obesity management can effectively reduce the risks and complications associated with obesity and improve the quality of life of patients. After assessing the advantages and limitations of various obesity management approaches, self-management has been strongly recommended due to the advantages of minimal side effects and lower costs compared to treatment via drugs and surgery. However, successfully implementing lifestyle intervention strategies requires scientific guidance and strong determination. With the development of electronic and information technology, lifestyle intervention has transformed considerably. A new concept, called Gamified Digital Therapeutics (GDTx), represents a gaming format with Digital Therapeutics (DTx). It can effectively enhance patient compliance and accessibility to chronic disease management. Here, we review recent studies on the application of GDTx for the self-management of obesity and discuss three aspects surrounding its completion rates, satisfaction levels, and effectiveness. In contrast to traditional approaches to obesity self-management, implementing GDTx effectively corrects unhealthy dietary and lifestyle habits, markedly enhancing the dissemination of nutritional and exercise-related health knowledge. Of particular significance is the evident improvement in the adherence of obese patients to weight loss programs. Despite numerous studies indicating that GDTx may offer an effective solution for obesity self-management, there are still several limitations in the medicalization of GDTx for self-management of obesity. This review aimed to provide a reference for subsequent studies and promote the widespread application of GDTx in obesity self-management to help reduce the obesity rate and alleviate the burden on obese patients.

Evaluating the Correlation between Eyeglass-Type Wearable Device Measurements and Subjective Physical Activity Assessments

Introduction Wearable trackers are instrumental in monitoring various health indicators, notably daily physical activity, which is crucial for managing chronic diseases and improving overall health. This study examined the relationship between physical activity levels measured using JINS MEME, an eyeglass-type wearable device equipped with motion sensors, and subjective activity assessments reported through the International Physical Activity Questionnaire (IPAQ). Methods Healthy volunteers aged 20-60 were recruited for an observational study. Participants wore the JINS MEME throughout the day for one week, and data on walking activity were collected and analyzed alongside IPAQ responses to evaluate subjective physical activity levels. The correlation between the two sets of data was evaluated using the nonparametric Spearman's rho (ρ) correlation coefficient for both the assessed metabolic equivalents (METs) score of the JINS MEME and the IPAQ. Similarly, the relationship between the IPAQ questionnaire items and the measurements from the JINS MEME. Results The study included 42 participants and revealed a strong correlation (R=0.719, P<0.01) between the metabolic equivalents (METs) calculated from the JINS MEME and IPAQ scores, especially for walking activities. Similarly, a significant association was found between the IPAQ data and walking speed (R=0.129, P=0.02). METs showed significant relationships with all physical activities, except sitting or reclining time. Conclusion This study validated the use of eyeglass-type wearable devices, such as the JINS MEME, to accurately assess physical activity levels, demonstrating a strong correlation with subjective assessments using the IPAQ. This highlights the potential of wearable devices in comprehensive health monitoring and management strategies.

Nucleic acid-based wearable and implantable electrochemical sensors

The rapid advancements in nucleic acid-based electrochemical sensors for implantable and wearable applications have marked a significant leap forward in the domain of personal healthcare over the last decade. This technology promises to revolutionize personalized healthcare by facilitating the early diagnosis of diseases, monitoring of disease progression, and tailoring of individual treatment plans. This review navigates through the latest developments in this field, focusing on the strategies for nucleic acid sensing that enable real-time and continuous biomarker analysis directly in various biofluids, such as blood, interstitial fluid, sweat, and saliva. The review delves into various nucleic acid sensing strategies, emphasizing the innovative designs of biorecognition elements and signal transduction mechanisms that enable implantable and wearable applications. Special perspective is given to enhance nucleic acid-based sensor selectivity and sensitivity, which are crucial for the accurate detection of low-level biomarkers. The integration of such sensors into implantable and wearable platforms, including microneedle arrays and flexible electronic systems, actualizes their use in on-body devices for health monitoring. We also tackle the technical challenges encountered in the development of these sensors, such as ensuring long-term stability, managing the complexity of biofluid dynamics, and fulfilling the need for real-time, continuous, and reagentless detection. In conclusion, the review highlights the importance of these sensors in the future of medical engineering, offering insights into design considerations and future research directions to overcome existing limitations and fully realize the potential of nucleic acid-based electrochemical sensors for healthcare applications.

Biosensors for psychiatric biomarkers in mental health monitoring

Psychiatric disorders are associated with serve disturbances in cognition, emotional control, and/or behavior regulation, yet few routine clinical tools are available for the real-time evaluation and early-stage diagnosis of mental health. Abnormal levels of relevant biomarkers may imply biological, neurological, and developmental dysfunctions of psychiatric patients. Exploring biosensors that can provide rapid, in-situ, and real-time monitoring of psychiatric biomarkers is therefore vital for prevention, diagnosis, treatment, and prognosis of mental disorders. Recently, psychiatric biosensors with high sensitivity, selectivity, and reproducibility have been widely developed, which are mainly based on electrochemical and optical sensing technologies. This review presented psychiatric disorders with high morbidity, disability, and mortality, followed by describing pathophysiology in a biomarker-implying manner. The latest biosensors developed for the detection of representative psychiatric biomarkers (e.g., cortisol, dopamine, and serotonin) were comprehensively summarized and compared in their sensitivities, sensing technologies, applicable biological platforms, and integrative readouts. These well-developed biosensors are promising for facilitating the clinical utility and commercialization of point-of-care diagnostics. It is anticipated that mental healthcare could be gradually improved in multiple perspectives, ranging from innovations in psychiatric biosensors in terms of biometric elements, transducing principles, and flexible readouts, to the construction of 'Big-Data' networks utilized for sharing intractable psychiatric indicators and cases.

Label-Free Classification of L-Histidine Vs Artificial Human Sweat Using Laser Scribed Electrodes and a Multi-Layer Perceptron Neural Network

A challenge in wearable technology lies in the realtime monitoring of molecular biomarkers associated with human health. Electrochemical sensors are one of the most useful tools for this purpose and are commonly used in health monitoring devices. Electrochemical biosensing is particularly convenient when used in user-friendly, low-cost devices for testing noninvasive body fluids such as sweat and saliva. However, achieving high selectivity and specificity in measurements depends on the complexity of the biomarker and the stability of the biomarker capture molecule. In this study, laser-scribed electrodes (LSEs) were manufactured using a CO2 laser cutter on Polyimide for the label-free classification of sweat components. Cyclic voltammetry experiments were performed on artificial human sweat and the sweat component L-Histidine. The resulting voltammogram data served as input to train a Multi-Layer Perceptron Neural Network (MLP-NN) algorithm capable of classifying L-Histidine and artificial sweat.

Effects of Peer- or Professional-Led Support in Enhancing Adherence to Wearable Monitoring Devices Among Community-Dwelling Older Adults: Systematic Review of Randomized Controlled Trials

BACKGROUND

Despite the well-documented health benefits associated with wearable monitoring devices (WMDs), adherence among community-dwelling older adults remains low. By providing guidance on the purpose and benefits of using WMDs, facilitating goal-setting aligned with the device's features, promoting comprehension of the health data captured by the device, and assisting in overcoming technological challenges, peers and health care professionals can potentially enhance older adults' adherence to WMDs. However, the effectiveness of such support mechanisms in promoting adherence to WMDs among older adults remains poorly understood.

OBJECTIVE

The aims of this systematic review were to examine the effects of peer- or professional-led intervention programs designed to improve adherence to WMDs among community-dwelling older adults and to identify the intervention components that may positively influence the effects of the intervention.

METHODS

We conducted a comprehensive search across 7 electronic databases (Cochrane Central Register of Controlled Trials [CENTRAL], PubMed, EMBASE, PsycINFO, British Nursing Index, Web of Science, and CINAHL) to identify articles published between January 1, 2010, and June 26, 2023. We specifically targeted randomized controlled trials that examined the impact of peer- or professional-led interventions on enhancing adherence to WMDs among individuals aged 60 years and older residing in the community. Two independent reviewers extracted data from the included studies and assessed the potential risk of bias in accordance with the Cochrane Risk of Bias tool for randomized trials, version 2.

RESULTS

A total of 10,511 studies were identified through the database search. Eventually, we included 3 randomized controlled trials involving 154 community-dwelling older adults. The participants had a mean age of 65 years. Our review revealed that increasing awareness of being monitored and implementing the SystemCHANGE approach, a habit change tool focusing on personal goals and feedback, were effective strategies for enhancing adherence to WMDs among older adults. All of the included studies exhibited a low risk of bias.

CONCLUSIONS

By collaboratively designing specific goals related to WMDs with health care professionals, including nurses and physicians, older adults exhibited a higher likelihood of adhering to the prescribed use of WMDs. These goal-setting tools provided a framework for structure and motivation, facilitating the seamless integration of WMDs into their daily routines. Researchers should prioritize interventions that target awareness and goal-setting as effective approaches to enhance adherence to WMDs among older adults, thereby maximizing the realization of associated health benefits.

A reconfigurable integrated smart device for real-time monitoring and synergistic treatment of rheumatoid arthritis

Rheumatoid arthritis (RA) is a global autoimmune disease that requires long-term management. Ambulatory monitoring and treatment of RA favors remission and rehabilitation. Here, we developed a wearable reconfigurable integrated smart device (ISD) for real-time inflammatory monitoring and synergistic therapy of RA. The device establishes an electrical-coupling and substance delivery interfaces with the skin through template-free conductive polymer microneedles that exhibit high capacitance, low impedance, and appropriate mechanical properties. The reconfigurable electronics drive the microneedle-skin interfaces to monitor tissue impedance and on-demand drug delivery. Studies in vitro demonstrated the anti-inflammatory effect of electrical stimulation on macrophages and revealed the molecular mechanism. In a rodent model, impedance sensing was validated to hint inflammation condition and facilitate diagnosis through machine learning model. The outcome of subsequent synergistic therapy showed notable relief of symptoms, elimination of synovial inflammation, and avoidance of bone destruction.

Brave new world: expanding home care in stem cell transplantation and advanced therapies with new technologies

Hematopoietic stem cell transplantation and cell therapies like CAR-T are costly, complex therapeutic procedures. Outpatient models, including at-home transplantation, have been developed, resulting in similar survival results, reduced costs, and increased patient satisfaction. The complexity and safety of the process can be addressed with various emerging technologies (artificial intelligence, wearable sensors, point-of-care analytical devices, drones, virtual assistants) that allow continuous patient monitoring and improved decision-making processes. Patients, caregivers, and staff can also benefit from improved training with simulation or virtual reality. However, many technical, operational, and above all, ethical concerns need to be addressed. Finally, outpatient or at-home hematopoietic transplantation or CAR-T therapy creates a different, integrated operative system that must be planned, designed, and carefully adapted to the patient's characteristics and distance from the hospital. Patients, clinicians, and their clinical environments can benefit from technically improved at-home transplantation.

Biosensors for melanoma skin cancer diagnostics

Skin cancer is a critical global public health concern, with melanoma being the deadliest variant, correlated to 80% of skin cancer-related deaths and a remarkable propensity to metastasize. Despite notable progress in skin cancer prevention and diagnosis, the limitations of existing methods accentuate the demand for precise diagnostic tools. Biosensors have emerged as valuable clinical tools, enabling rapid and reliable point-of-care (POC) testing of skin cancer. This review offers insights into skin cancer development, highlights essential cutaneous melanoma biomarkers, and assesses the current landscape of biosensing technologies for diagnosis. The comprehensive analysis in this review underscores the transformative potential of biosensors in revolutionizing melanoma skin cancer diagnosis, emphasizing their critical role in advancing patient outcomes and healthcare efficiency. The increasing availability of these approaches supports direct diagnosis and aims to reduce the reliance on biopsies, enhancing POC diagnosis. Recent advancements in biosensors for skin cancer diagnosis hold great promise, with their integration into healthcare expected to enhance early detection accuracy and reliability, thereby mitigating socioeconomic disparities.

Effects of wearable physical activity tracking for breast cancer survivors: A systematic review and meta-analysis

PURPOSE

Breast cancer is the most common cancer type worldwide, with its survivors often experiencing physical and psychosocial health problems. Wearable device use is an innovative and effective way to promote physical activity and improve health-related outcomes in breast cancer survivors; however, the current evidence is unclear. We aimed to determine the effects of wearable devices on physical activity and health-related outcomes in breast cancer survivors.

METHODS

PubMed, Embase, Web of Science, and Cochrane Library databases were searched to identify eligible studies from inception to September 2022. Additional relevant studies were obtained from the reference lists of the identified studies. Two reviewers independently screened the eligible studies, appraised the risk of bias, and extracted the data. Meta-analysis was conducted using Review Manager version 5.3.

FINDINGS

Sixteen randomized controlled trials were included. Physical activity tracking and pedometer-based interventions improved moderate-intensity physical activity (standardized mean difference [SMD] = 0.32, 95% confidence interval [CI]: 0.17-0.46, p < 0.0001), moderate-to-vigorous physical activity (SMD = 0.85, 95%CI: 0.38-1.32, p = 0.0004), total physical activity (SMD = 0.51, 95%CI: 0.12-0.90, p = 0.01), quality of life (SMD = 0.17, 95%CI: 0.03-0.31, p = 0.01), physical function (SMD = 0.21, 95%CI: 0.04-0.38, p = 0.02), and mood state profiles (SMD = -0.58, 95%CI: -1.13 to 0.02, p = 0.04) in breast cancer survivors. However, the effects of low-intensity physical activity, vigorous-intensity physical activity, fatigue, anxiety, depression, and sleep quality could not be ascertained.

CONCLUSIONS

Physical activity tracking and pedometer-based interventions were effective in increasing physical activity and improving health-related outcomes in breast cancer survivors.

IMPLICATIONS FOR NURSING PRACTICE

This review offers availability of credible evidence supporting the potential usefulness and effectiveness of wearable physical activity trackers on physical activity and health-related outcomes in breast cancer survivors.

E-Tattoos: Toward Functional but Imperceptible Interfacing with Human Skin

The human body continuously emits physiological and psychological information from head to toe. Wearable electronics capable of noninvasively and accurately digitizing this information without compromising user comfort or mobility have the potential to revolutionize telemedicine, mobile health, and both human-machine or human-metaverse interactions. However, state-of-the-art wearable electronics face limitations regarding wearability and functionality due to the mechanical incompatibility between conventional rigid, planar electronics and soft, curvy human skin surfaces. E-Tattoos, a unique type of wearable electronics, are defined by their ultrathin and skin-soft characteristics, which enable noninvasive and comfortable lamination on human skin surfaces without causing obstruction or even mechanical perception. This review article offers an exhaustive exploration of e-tattoos, accounting for their materials, structures, manufacturing processes, properties, functionalities, applications, and remaining challenges. We begin by summarizing the properties of human skin and their effects on signal transmission across the e-tattoo-skin interface. Following this is a discussion of the materials, structural designs, manufacturing, and skin attachment processes of e-tattoos. We classify e-tattoo functionalities into electrical, mechanical, optical, thermal, and chemical sensing, as well as wound healing and other treatments. After discussing energy harvesting and storage capabilities, we outline strategies for the system integration of wireless e-tattoos. In the end, we offer personal perspectives on the remaining challenges and future opportunities in the field.

Accuracy of Apple Watch to Measure Cardiovascular Indices in Patients with Chronic Diseases: A Cross Sectional Study

Background

The validity of the Apple Watch to measure the heart rate (HR) and oxygen saturation (Spo2) for patients with chronic diseases such as diabetes mellitus (DM), dyslipidemia, and hypertension is still unclear. Therefore, this study aims to investigate the accuracy of the Apple Watch in measuring the Spo2 and HR in patients with chronic diseases.

Methods

Forty-one patients with chronic diseases, including 20 with hypertension, 10 with diabetes, and 11 with dyslipidemia, completed a cross-sectional study. All participants used the Apple Watch against the Polar chest strap and the pulse oximeter at rest and during moderate intensity exercise sessions to measure HR and the SpO2 at rest for 5 minutes, during exercise for 16 minutes, and followed by 3 minutes of rest. The HR was measured during all previous periods, but evaluation of the Spo2 included 5 measures, done only before and after exercise, with a minute interval between each measure.

Results

Overall, a strong correlation exists between measuring the SpO2 using the Apple Watch against the pulse oximeter (Contec) at rest (r = 0.92, p < 0.001) and after exercise (r = 0.86, p < 0.001) in all patients. The HR had a very strong correlation between the Apple Watch and the Polar chest strap (r = 0.99, p < 0.001) in all patients. There was no significant difference (p = 0.76) between the twenty-seven white and fourteen brown-skinned patients.

Conclusion

The Apple Watch is valid to measure the HR and SpO2 in patients with chronic diseases.

Clinical Trial Registration No

NCT05271864.

Contextualizing remote fall risk: Video data capture and implementing ethical AI

Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual's habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual's free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments.

Performance analysis of solution-processed nanosheet strain sensors-a systematic review of graphene and MXene wearable devices

Nanotechnology has led to the realisation of many potentialInternet of Thingsdevices that can be transformative with regards to future healthcare development. However, there is an over saturation of wearable sensor review articles that essentially quote paper abstracts without critically assessing the works. Reported metrics in many cases cannot be taken at face value, with researchers overly fixated on large gauge factors. These facts hurt the usefulness of such articles and the very nature of the research area, unintentionally misleading those hoping to progress the field. Graphene and MXenes are arguably the most exciting organic and inorganic nanomaterials for polymer nanocomposite strain sensing applications respectively. Due to their combination of cost-efficient, scalable production and device performances, their potential commercial usage is very promising. Here, we explain the methods for colloidal nanosheets suspension creation and the mechanisms, metrics and models which govern the electromechanical properties of the polymer-based nanocomposites they form. Furthermore, the many fabrication procedures applied to make these nanosheet-based sensing devices are discussed. With the performances of 70 different nanocomposite systems from recent (post 2020) publications critically assessed. From the evaluation of these works using universal modelling, the prospects of the field are considered. Finally, we argue that the realisation of commercial nanocomposite devices may in fact have a negative effect on the global climate crisis if current research trends do not change.

Skin temperature influence on transcutaneous carbon dioxide (CO2) conductivity and skin blood flow in healthy human subjects at the arm and wrist

Objective: present transcutaneous carbon dioxide (CO2)-tcpCO2-monitors suffer from limitations which hamper their widespread use, and call for a new tcpCO2 measurement technique. However, the progress in this area is hindered by the lack of knowledge in transcutaneous CO2 diffusion. To address this knowledge gap, this study focuses on investigating the influence of skin temperature on two key skin properties: CO2 permeability and skin blood flow. Methods: a monocentric prospective exploratory study including 40 healthy adults was undertaken. Each subject experienced a 90 min visit split into five 18 min sessions at different skin temperatures-Non-Heated (NH), 35, 38, 41, and 44°C. At each temperature, custom sensors measured transcutaneous CO2 conductivity and exhalation rate at the arm and wrist, while Laser Doppler Flowmetry (LDF) assessed skin blood flow at the arm. Results: the three studied metrics sharply increased with rising skin temperature. Mean values increased from the NH situation up to 44°C from 4.03 up to 8.88 and from 2.94 up to 8.11 m·s-1 for skin conductivity, and from 80.4 up to 177.5 and from 58.7 up to 162.3 cm3·m-2·h-1 for exhalation rate at the arm and wrist, respectively. Likewise, skin blood flow increased elevenfold for the same temperature increase. Of note, all metrics already augmented significantly in the 35-38°C skin temperature range, which may be reached without active heating-i.e. only using a warm clothing. Conclusion: these results are extremely encouraging for the development of next-generation tcpCO2 sensors. Indeed, the moderate increase (× 2) in skin conductivity from NH to 44°C tends to indicate that heating the skin is not critical from a response time point of view, i.e. little to no skin heating would only result in a doubled sensor response time in the worst case, compared to a maximal heating at 44°C. Crucially, a skin temperature within the 35-38°C range already sharply increases the skin blood flow, suggesting that tcpCO2 correlates well with the arterial paCO2 even at such low skin temperatures. These two conclusions further strengthen the viability of non-heated tcpCO2 sensors, thereby paving the way for the development of wearable transcutaneous capnometers.

Stretchable, flexible fabric heater based on carbon nanotubes and water polyurethane nanocomposites by wet spinning process

Wearable heaters are essential for people living in cold regions, but creating heaters that are low-cost, lightweight, and high air permeability poses challenges. In this study, we developed a wearable heater using carbon nanotube/water polyurethane (CNT/WPU) nanocomposite fibers that achieve high extension rate and conductivity. We produced low-cost and mass-produced fibers using the wet spinning. With heat treatment, we increased the elongation rate of the fibers to 1893.8% and decreased the resistivity to 0.07 Ω*m. then wove the fibers into a heating fabric using warp knitting, that resistance is 493 Ω. Achieved a uniform temperature of 58 °C at voltage of 36 V, with a thermal stability fluctuation of -5.0 °C to +6.3 °C when bent from 0° to 360°. Our results show that wearable heaters have excellent flexibility and stretchability, due to nanocomposite fibers and special braided structure, which offer a novel idea for wearable heaters.

The Psychedelic Future of Post-Traumatic Stress Disorder Treatment

Post-traumatic stress disorder (PTSD) is a mental health condition that can occur following exposure to a traumatic experience. An estimated 12 million U.S. adults are presently affected by this disorder. Current treatments include psychological therapies (e.g., exposure-based interventions) and pharmacological treatments (e.g., selective serotonin reuptake inhibitors (SSRIs)). However, a significant proportion of patients receiving standard-of-care therapies for PTSD remain symptomatic, and new approaches for this and other trauma-related mental health conditions are greatly needed. Psychedelic compounds that alter cognition, perception, and mood are currently being examined for their efficacy in treating PTSD despite their current status as Drug Enforcement Administration (DEA)- scheduled substances. Initial clinical trials have demonstrated the potential value of psychedelicassisted therapy to treat PTSD and other psychiatric disorders. In this comprehensive review, we summarize the state of the science of PTSD clinical care, including current treatments and their shortcomings. We review clinical studies of psychedelic interventions to treat PTSD, trauma-related disorders, and common comorbidities. The classic psychedelics psilocybin, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine (DMT) and DMT-containing ayahuasca, as well as the entactogen 3,4-methylenedioxymethamphetamine (MDMA) and the dissociative anesthetic ketamine, are reviewed. For each drug, we present the history of use, psychological and somatic effects, pharmacology, and safety profile. The rationale and proposed mechanisms for use in treating PTSD and traumarelated disorders are discussed. This review concludes with an in-depth consideration of future directions for the psychiatric applications of psychedelics to maximize therapeutic benefit and minimize risk in individuals and communities impacted by trauma-related conditions.