Optimizing Remote Health Monitoring for Digital Platforms: Evaluating the Efficacy of Gamification in Enhancing Data Collection.

In recent years, the integration of game-like elements into non-gaming contexts has shown promise in enhancing user engagement and motivation. This study assesses the impact of gamification elements on data collection efficacy in m-health applications. An ad-hoc mobile application was developed and used in a randomized two-arm pilot study. Participants interacted either with the gamified meal-logging application or with its non-gamified version for ten days. The results from this study emphasize the benefits of incorporating gamification techniques into health applications embedded in digital platforms. While both versions were well-received, reaching high System Usability Scale (SUS) scores (91 and 93.5) and generally positive feedback, the gamified app demonstrated a distinct advantage in promoting user engagement and consistent data logging. This highlights the importance of gamification in health research, suggesting its potential to ensure thorough and consistent data collection, which is essential for producing reliable research outcomes.

Empowering Ageing with Ingenuity: A Scoping Review on the Methods and Technologies Used into Elderly Health Monitoring.

With the rise in global life expectancy, ensuring healthier aging experiences for the older population becomes paramount. This scoping review delves into the technologies employed in the remote health monitoring of the elderly over the past 15 years. Exploring the concept of "Healthy Ageing" as proposed by the World Health Organization, this paper attempts to highlight the techniques and technologies used in health monitoring of the elderly population. The integration of wearable sensors in health monitoring presents promising avenues for enhancing healthcare delivery to older adults. However, challenges such as limited digital literacy and privacy concerns persist, necessitating innovative solutions for unobtrusive monitoring. This paper discusses the potential of passive and ambient sensors to address these challenges, offering insights into enhancing the well-being of the older population while preserving their independence and privacy.

Ambient energy harvesters in wearable electronics: fundamentals, methodologies, and applications.

In recent years, wearable sensor devices with exceptional portability and the ability to continuously monitor physiological signals in real time have played increasingly prominent roles in the fields of disease diagnosis and health management. This transformation has been largely facilitated by materials science and micro/nano-processing technologies. However, as this technology continues to evolve, the demand for multifunctionality and flexibility in wearable devices has become increasingly urgent, thereby highlighting the problem of stable and sustainable miniaturized power supplies. Here, we comprehensively review the current mainstream energy technologies for powering wearable sensors, including batteries, supercapacitors, solar cells, biofuel cells, thermoelectric generators, radio frequency energy harvesters, and kinetic energy harvesters, as well as hybrid power systems that integrate multiple energy conversion modes. In addition, we consider the energy conversion mechanisms, fundamental characteristics, and typical application cases of these energy sources across various fields. In particular, we focus on the crucial roles of different materials, such as nanomaterials and nano-processing techniques, for enhancing the performance of devices. Finally, the challenges that affect power supplies for wearable electronic products and their future developmental trends are discussed in order to provide valuable references and insights for researchers in related fields.

Development and validation of the UHPLC-MS/MS method for the quantitative determination of 25 PFAS in dried blood spots.

Per- and polyfluoroalkyl substances (PFAS) are anthropogenic fluorine-containing compounds largely used in industrial and consumer applications. They tend to bioaccumulate in the human body after intake from various sources in daily life. Following repeated exposure to PFAS, a broad range of adverse health outcomes has been reported. Consequently, monitoring PFAS levels in human blood is of paramount importance for public health policies. In contrast with traditional venipuncture, dried blood spots (DBS) constitute a reliable, cheap, and less invasive technique to allow microsampling by capillary blood collected on a specific device. This work aimed to develop and validate an innovative analytical method, combining quantitative DBS with UHPLC-MS/MS instrumentation to identify and quantify 25 PFAS. The extraction procedure was developed and optimized within the range 2-100 ng/mL. Specifically, fortified blood was applied on Capitainer®B devices providing 10 µL of blood volume through a microfluidic channel. After 3 h of drying, the extraction was performed by methanol under sonication, followed by centrifugation. Then, the extraction solvent was evaporated; the residue was reconstituted with the mobile phase solution. The validated method evidenced good sensitivity, with limits of detection ranging from 0.4 ng/mL (PFODA, PFOS) to 1.0 ng/mL (PFOA, 3,6-OPFHpA). The ± 20% acceptability criteria established for intra- and inter-day precision and accuracy were fulfilled for all analytes. High recovery-above 80%-was recorded, whereas significant matrix effect resulted in ion enhancement (> 50%) for 13 analytes. In conclusion, the proposed workflow proved to be reliable, fit for purpose, and easily adaptable in the laboratory routine.

Blood-based biomemristor for hyperglycemia and hyperlipidemia monitoring.

Thanks to its structural characteristics and signal patterns similar to those of human brain synapses, memristors are widely believed to be applicable for neuromorphic computing. However, to our knowledge, memristors have not been effectively applied in the biomedical field, especially in disease diagnosis and health monitoring. In this work, a blood-based biomemristor was prepared for in vitro detection of hyperglycemia and hyperlipidemia. It was found that the device exhibits excellent resistance switching (RS) behavior at lower voltage biases. Through mechanism analysis, it has been confirmed that the RS behavior is driven by Ohmic conduction and ion rearrangement. Furthermore, the hyperglycemia and hyperlipidemia detection devices were constructed for the first time based on memristor logic circuits, and circuit simulations were conducted. These results confirm the feasibility of blood-based biomemristors in detecting hyperglycemia and hyperlipidemia, providing new prospects for the important application of memristors in the biomedical field.

An intelligent garment for long COVID-19 real-time monitoring.

As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.

Recent Advances in Structure Design and Application of Metal Halide Perovskite-Based Gas Sensor.

Metal halide perovskites (MHPs) are emerging gas-sensing materials and have attracted considerable attention in gas sensors due to their unique bandgap structure and tunable optoelectronic properties. The past decade has witnessed significant developments in the gas-sensing field; however, their intrinsic structural instability and ambiguous gas-sensing mechanisms hamper their practical applications. Herein, we summarize the recent advances in MHP-based gas sensors. The physicochemical properties of MHPs are discussed at first. The structure design, including dimension design and engineering design, is overviewed as well as their fabrication methods, and we put forward our insights into the gas-sensing mechanism of MHPs. It is believed that enhanced understanding of gas-sensing mechanisms of MHPs are helpful for their application as gas-sensing materials, and structure design can enhance their stability, sensing sensitivity, and selectivity to target gases as gas sensors. Subsequently, the latest developments in MHP-based gas sensors are summarized according to their different application scenarios. Finally, we conclude with the current status and challenges in this field and propose future perspectives.

Portable bioimpedance analyzer for remote body composition monitoring: A clinical investigation under controlled conditions.

OBJECTIVES: In an era when telemedicine is becoming increasingly essential, the development and validation of miniaturized Bioelectrical Impedance Analysis (BIA) devices for accurate and reliable body composition assessment is crucial. This study investigates the BIA Metadieta, a novel miniaturized BIA device, by comparing its performance with that of standard hospital BIA equipment across a diverse demographic. The aim is to enhance remote health monitoring by integrating compact and efficient technology into routine healthcare practices. METHODS: A cross-sectional observational study was conducted with 154 participants from the Clinical Nutrition Unit. The study compared resistance (R), reactance (Xc), and phase angle (PhA) measurements obtained from the BIA Metadieta device and a traditional hospital-based BIA device. RESULTS: Analysis revealed strong positive correlations between the BIA Metadieta and the hospital-based device for R (r = 0.988, P < 0.001), Xc (r = 0.946, P < 0.001), and PhA (r = 0.929, P < 0.001), indicating the miniaturized device's high accuracy and reliability. These correlations were consistent across different genders and BMI categories, demonstrating the device's versatility. CONCLUSIONS: The BIA Metadieta device, with its miniaturized form factor, represents a significant step forward in the field of remote health monitoring, providing a reliable, accurate, and accessible means for assessing body composition.

Self-powered sensing platform for monitoring uric acid in sweat using cobalt nanocrystal-graphene quantum dot-Ti3C2TX monolithic film electrode with excellent supercapacitor and sensing behavior.

The synthesis of cobalt nanocrystal-graphene quantum dot-Ti3C2TX monolithic film electrode (Co-GQD-Ti3C2TX) is reported via self-assembly of Ti3C2TX nanosheets induced by protonated arginine-functionalized graphene quantum dot and subsequent reduction of cobalt (III). The resulting Co-GQD-Ti3C2TX shows good monolithic architecture, mechanical property, dispersibility and conductivity. The structure achieves excellent supercapacitor and sensing behavior. The self-charging supercapacitor produced by printing viscous Co-GQD-Ti3C2TX hydrogel on the back of flexible solar cell surface provides high specific capacitance (296 F g-1 at 1 A g-1), high-rate capacity (153 F g-1 at 20 A g-1), capacity retention (98.1% over 10,000-cycle) and energy density (29.6 W h kg-1 at 299.9 W kg-1). The electrochemical chip produced by printing Co-GQD-Ti3C2TX hydrogel on paper exhibits sensitive electrochemical response towards uric acid. The increase of uric acid between 0.01 and 800 µM causes a linear increase in differential pulse voltammetry signal with a detection limit of 0.0032 µM. The self-powered sensing platform integrating self-charging supercapacitor, electrochemical chip and micro electrochemical workstation was contentedly applied to monitoring uric acid in sweats and shows one broad application prospect in wearable electronic health monitoring device.

Recent Progress on Flexible Self-Powered Tactile Sensing Platforms for Health Monitoring and Robotics.

Over the past decades, tactile sensing technology has made significant advances in the fields of health monitoring and robotics. Compared to conventional sensors, self-powered tactile sensors do not require an external power source to drive, which makes the entire system more flexible and lightweight. Therefore, they are excellent candidates for mimicking the tactile perception functions for wearable health monitoring and ideal electronic skin (e-skin) for intelligent robots. Herein, the working principles, materials, and device fabrication strategies of various self-powered tactile sensing platforms are introduced first. Then their applications in health monitoring and robotics are presented. Finally, the future prospects of self-powered tactile sensing systems are discussed.

Flexible/Regenerative Nanosensor with Automatic Sweat Collection for Cytokine Storm Biomarker Detection.

The real-time monitoring of low-concentration cytokines such as TNF-α in sweat can aid clinical physicians in assessing the severity of inflammation. The challenges associated with the collection and the presence of impurities can significantly impede the detection of proteins in sweat. This issue is addressed by incorporating a nanosphere array designed for automatic sweat transportation, coupled with a reusable sensor that employs a Nafion/aptamer-modified MoS2 field-effect transistor. The nanosphere array with stepwise wettability enables automatic collection of sweat and blocks impurities from contaminating the detection zone. This device enables direct detection of TNF-α proteins in undiluted sweat, within a detection range of 10 fM to 1 nM. The use of an ultrathin, ultraflexible substrate ensures stable electrical performance, even after up to 30 extreme deformations. The findings indicate that in clinical scenarios, this device could potentially provide real-time evaluation and management of patients' immune status via sweat testing.

Developing Causality and Severity Assessment Frameworks for Food Safety Signals Using Social Media Reviews: A Technical Report Based on Data From an Urban Indian Suburb.

Social media reviews are a valuable data source, reflecting consumer experiences and interactions with businesses. This study leverages such data to develop a passive surveillance framework for food safety in urban India. By employing a Bidirectional Encoder Representations from Transformers (BERT)-powered Aspect-Based Sentiment Analysis tool, branded as Eat At Right Place (ERP), the study analyses over 100,000 reviews from 93 restaurants to identify and assess food safety signals. The Causality Assessment Index (CAI) and Severity Assessment Score (SAS) are introduced to systematically evaluate potential risks. The CAI uses pattern recognition and temporal relationships to establish causality while the SAS quantifies severity based on sub-aspects such as cleanliness, food handling, and unintended health outcomes. Results indicate that 40% of the restaurants had a CAI above 1, highlighting significant food safety concerns. The framework successfully prioritizes corrective actions by grading the severity of issues, demonstrating its potential for real-time food safety management. This study underscores the importance of integrating innovative data-driven approaches into public health monitoring systems and suggests future improvements in natural language processing algorithms and data source expansion. The findings pave the way for enhanced food safety surveillance and timely regulatory interventions.

Blade-Coated Porous 3D Carbon Composite Electrodes Coupled with Multiscale Interfaces for Highly Sensitive All-Paper Pressure Sensors.

Flexible and wearable pressure sensors hold immense promise for health monitoring, covering disease detection and postoperative rehabilitation. Developing pressure sensors with high sensitivity, wide detection range, and cost-effectiveness is paramount. By leveraging paper for its sustainability, biocompatibility, and inherent porous structure, herein, a solution-processed all-paper resistive pressure sensor is designed with outstanding performance. A ternary composite paste, comprising a compressible 3D carbon skeleton, conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate), and cohesive carbon nanotubes, is blade-coated on paper and naturally dried to form the porous composite electrode with hierachical micro- and nano-structured surface. Combined with screen-printed Cu electrodes in submillimeter finger widths on rough paper, this creates a multiscale hierarchical contact interface between electrodes, significantly enhancing sensitivity (1014 kPa-1) and expanding the detection range (up to 300 kPa) of as-resulted all-paper pressure sensor with low detection limit and power consumption. Its versatility ranges from subtle wrist pulses, robust finger taps, to large-area spatial force detection, highlighting its intricate submillimeter-micrometer-nanometer hierarchical interface and nanometer porosity in the composite electrode. Ultimately, this all-paper resistive pressure sensor, with its superior sensing capabilities, large-scale fabrication potential, and cost-effectiveness, paves the way for next-generation wearable electronics, ushering in an era of advanced, sustainable technological solutions.

Enhancing disease surveillance and preparedness: An early warning tool for disease occurrence in U.S. swine breeding herds.

Understanding regional disease risk is critical for swine disease prevention and control. Since 2011, the Morrison Swine Health Monitoring Project (MSHMP) has strengthened partnerships among practitioners and producers to report health events (e.g., porcine reproductive and respiratory syndrome (PRRS) virus outbreaks) at the U.S. national level. Using MSHMP data and PRRS as an example, an early regional occurrence warning tool to provide near-real-time alerts was developed. MSHMP-participating production systems were invited to enroll. An algorithm was developed to calculate the number of PRRSV-positive sites near each enrolled site, determined from site-specific radius. The radius was determined in three steps. First, an initial radius of 25 miles was set for sites in pig-dense states and 50 miles for others. Secondly, four variables were generated to account for the sites within the initial radius: A) Total number of PRRSV-positive sites; B) Number of PRRSV-positive sites from other production systems; C) Total number of sites enrolled, and D) Total number of sites monitored by MSHMP. Subsequently, the reporting radius was automatically increased when confidentiality concerns arose. Results were compiled into system-specific reports and shared weekly with each participant. Reports have been shared since May 9, 2023, representing 178 breeding sites, comprising approximately 565 K sows. Examples of how participants use these reports include adjusting biosecurity programs, frequency of supply introduction, and transportation routes. The early occurrence warning tool developed in this study enhances producers' ability to communicate effectively and respond quickly to health threats, mitigating regional disease while preparing for foreign disease introductions.

Fatigue Crack Monitoring Method Based on the Lamb Wave Damage Index.

For practical engineering structures, fatigue is one of the main factors affecting their safety and durability. Under long-term service conditions, the minor damage will be affected by fatigue loading and expand to macroscopic cracks, affecting the structure's service performance. Based on the sensitivity of Lamb waves to minor and initial damage, a damage monitoring method for fatigue crack propagation is proposed. By carrying out fatigue crack propagation tests under constant amplitude loading, the Paris equation of 316L steel and damage signals at different crack growth stages were obtained. Combined with damage monitoring tests and finite element analysis, the relationship between the phase damage index (PDI), amplitude damage index (ADI), signal correlation coefficient, and fatigue crack propagation length was studied. Compared with PDI and ADI, the signal correlation coefficient is more sensitive to crack initiation, which can be selected as the damage monitoring index in the initial stage of crack growth. With the increase of fatigue crack propagation length, the peak time of the direct wave signal gradually moves backward, which shows an obvious phase change. In the whole fatigue crack growth stage, PDI and crack length show a monotonically changing trend. By using the stress intensity factor as the conversion parameter, a prediction model of the fatigue crack propagation rate based on PDI was established. Compared to the fatigue crack propagation rate measured by experiments, the relative error of the predicted results is 10%, which verifies the accuracy of the proposed damage monitoring method.

Multimodal Signal Fusion for Heartbeat Monitoring on eScooters.

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time.

Study of factors affecting the magnetic sensing capability of shape memory alloys for non-destructive evaluation of cracks in concrete: Using response surface methodology (RSM) and artificial neural network (ANN) approaches.

Currently, the field of structural health monitoring (SHM) is focused on investigating non-destructive evaluation techniques for the identification of damages in concrete structures. Magnetic sensing has particularly gained attention among the innovative non-destructive evaluation techniques. Recently, the embedded magnetic shape memory alloy (MSMA) wire has been introduced for the evaluation of cracks in concrete components through magnetic sensing techniques while providing reinforcement as well. However, the available research in this regard is very scarce. This study has focused on the analyses of parameters affecting the magnetic sensing capability of embedded MSMA wire for crack detection in concrete beams. The response surface methodology (RSM) and artificial neural network (ANN) models have been used to analyse the magnetic sensing parameters for the first time. The models were trained using the experimental data obtained through literature. The models aimed to predict the alteration in magnetic flux created by a concrete beam that has a 1 mm wide embedded MSMA wire after experiencing a fracture or crack. The results showed that the change in magnetic flux was affected by the position of the wire and the position of the crack with respect to the position of the magnet in the concrete beam. RSM optimisation results showed that maximum change in magnetic flux was obtained when the wire was placed at a depth of 17.5 mm from the top surface of the concrete beam, and a crack was present at an axial distance of 8.50 mm from the permanent magnet. The change in magnetic flux was 9.50 % considering the aforementioned parameters. However, the ANN prediction results showed that the optimal wire and crack position were 10 mm and 1.1 mm, respectively. The results suggested that a larger beam requires a larger diameter of MSMA wire or multiple sensors and magnets for crack detection in concrete beams.

Highly Porous, Ultralight, Biocompatible Silk Fibroin Aerogel-Based Triboelectric Nanogenerator.

This study presents the fabrication of an ultralight, porous, and high-performance triboelectric nanogenerator (TENG) utilizing silk fibroin (SF) aerogels and PDMS sponges as the friction layer. The transition from two-dimensional film friction layers to three-dimensional porous aerogels significantly increased the specific surface area, offering an effective strategy for designing high-performance SF aerogel-based TENGs. The TENG incorporating the porous SF aerogel exhibited optimal output performance at a 3% SF concentration, achieving a maximum open circuit voltage of 365 V, a maximum short-circuit current of 11.8 µA, and a maximum power density of 7.52 W/m2. In comparison to SF-film-based TENGs, the SF-aerogel based TENG demonstrated a remarkable 6.5-fold increase in voltage and a 4.5-fold increase in current. Furthermore, the power density of our SF-based TENG surpassed the previously reported optimal values for SF-based TENGs by 2.4 times. Leveraging the excellent mechanical stability and biocompatibility of TENGs, we developed an SF-based TENG self-powered sensor for the real-time monitoring of subtle biological movements. The SF-based TENG exhibits promising potential as a wearable bioelectronic device for health monitoring.

Estimating the severity of obstructive sleep apnea during wakefulness using speech: A review.

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients. Additionally, patients often have to endure long waitlists before they can undergo PSG. As a result, other alternatives for screening OSA have gained attention. Speech, as an accessible modality, is generated by variations in the pharyngeal airway, vocal tract, and soft tissues in the pharynx, which shares similar anatomical structures that contribute to OSA. Consequently, in this study, we aim to provide a comprehensive review of the existing research on the use of speech for estimating the severity of OSA. In this regard, a total of 851 papers were initially identified from the PubMed database using a specified set of keywords defined by population, intervention, comparison and outcome (PICO) criteria, along with a concatenated graph of the 5 most cited papers in the field extracted from ConnectedPapers platform. Following a rigorous filtering process that considered the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, 32 papers were ultimately included in this review. Among these, 28 papers primarily focused on developing methodology, while the remaining 4 papers delved into the clinical perspective of the association between OSA and speech. In the next step, we investigate the physiological similarities between OSA and speech. Subsequently, we highlight the features extracted from speech, the employed feature selection techniques, and the details of the developed models to predict OSA severity. By thoroughly discussing the current findings and limitations of studies in the field, we provide valuable insights into the gaps that need to be addressed in future research directions.

Investigation of the Zero-Frequency Component of Nonlinear Lamb Waves in a Symmetrical Undulated Plate.

When an ultrasonic pulse propagates in a thin plate, nonlinear Lamb waves with higher harmonics and a zero-frequency component (ZFC) will be generated because of the nonlinearity of materials. The ZFC, also known as the static displacement or static component, has its unique application on the evaluation of early-stage damages in the elastic symmetrical undulated plate. In this study, analysis of the excitation mechanism of the ZFC and the second harmonic component (SHC) was theoretically and numerically investigated, and the material early-stage damage of a symmetrical undulated was characterized by studying the propagation of nonlinear Lamb waves. Both the ZFC and SHC can be effectively employed in monitoring the material damages of the undulated plate in its early stage. However, several factors must be considered for the propagation of the SHC in an undulated plate because of the geometric curvature and interference between the second harmonics during propagation, preventing efficient application of this technique. If the fundamental wave can propagate in the plate regardless of the plate boundary conditions, an accumulative effect always exists for the ZFC in a thin plate, indicating that the ZFC is independent of the structural geometry. This study reveals that the ZFC-based inspection technique is more efficient and powerful in characterizing the damages of a symmetrical undulated plate in the early stage of service compared to the second harmonic method.