Monitoring Of Health Status Research Articles

Improving Participant Recruitment in Clinical Trials: Comparative Analysis of Innovative Digital Platforms.

Pharmaceutical product development relies on thorough and costly clinical trials. Participant recruitment and monitoring can be challenging. The incorporation of cutting-edge technologies such as blockchain and artificial intelligence has revolutionized clinical research (particularly in the recruitment stage), enhanced secure data storage and analysis, and facilitated participant monitoring while protecting their personal information. This study aims to investigate the use of novel digital platforms and their features, such as e-recruitment, e-consent, and matching, aiming to optimize and expedite clinical research. A review with a systematic approach was conducted encompassing literature from January 2000 to October 2024. The MEDLINE, ScienceDirect, Scopus, and Google Scholar databases were examined thoroughly using a customized search string. Inclusion criteria focused on digital platforms involving clinical trial recruitment phases that were in English and had international presence, scientific validation, regulatory approval, and no geographic limitations. Literature reviews and unvalidated digital platforms were excluded. The selected studies underwent meticulous screening by the research team, ensuring a thorough analysis of novel digital platforms and their use and features for clinical trials. A total of 24 digital platforms were identified that supported clinical trial recruitment phases. In general, most of them (n=22, 80%) are headquartered and operating in the United States, providing a range of functionalities including electronic consent (n=14, 60% of the platforms), participant matching, and monitoring of patients' health status. These supplementary features enhance the overall effectiveness of the platforms in facilitating the recruitment process for clinical trials. The analysis and digital platform findings refer to a specific time frame when the investigation took place, and a notable surge was observed in the adoption of these novel digital tools, particularly following the COVID-19 outbreak. This study underscores the vital role of the identified digital platforms in clinical trials, aiding in recruitment, enhancing patient engagement, accelerating procedures, and personalizing vital sign monitoring. Despite their impact, challenges in accessibility, compatibility, and transparency require careful consideration. Addressing these challenges is crucial for optimizing digital tool integration into clinical research, allowing researchers to harness the benefits while managing the associated risks effectively.

IT-based health event surveillance in mass gathering - Kumbh Mela 2021, Haridwar, Uttarakhand

The Kumbh Mela is a large religious mass gathering event organised in India. During April 01 to April 30, 2021, Kumbh Mela was organized in Haridwar, Uttarakhand, India in the amid of COVID-19 pandemic. To detect and respond to unusual health events, a special surveillance model was established during Kumbh Mela-2021, Haridwar. The Kumbh module of Integrated Health Information Platform (IHIP) is utilized for real time data capturing. An Emergency Operations Centre (EOC) established to monitor COVID-19 and other disease situations through IHIP. Capacity building of Rapid Response Teams (RRT) was conducted. The special surveillance monitored 24 diseases/ syndromes. Kumbh mela area was divided into 4 zones with total 75 health facilities. A total of 31021 cases were reported on IHIP, including 86% communicable diseases (CDs), 8% non-communicable diseases (NCDs) and 6% unusual syndrome. Among total CDs, 27% acute febrile illness (AFI), 25.3% acute respiratory illness (ARI), 19% water borne diseases (WBDs), 15% COVID-19 and 14% others encountered. Out of total NCDs, 67% trauma, 25% dog bite, 8% others. A total of 36 early warning signals generated and responded, of which 22% WBDs, 22% ARI, 17% COVID-19, 14% AFI, 11% unusual syndrome and 14% others. The special disease surveillance helped monitoring health event alerts and health condition alerts. EOC monitored early warning signals and facilitated early detection and quick response to public health threats. Acute febrile illnesses emerged as a leading cause of morbidity. Future Mass Gathering events like Kumbh Mela should include disease surveillance as part of planning and augment capacity to diagnose and management of acute febrile illness.

Near Real Time Crop Health Status Monitoring and Mapping using Sentinel Satellite Image in Menjar Shenkora District, Ethiopia

Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.

Mechanical Design and Testing of Mobile Monitoring Units for Security Systems

Mobile monitoring systems are currently used in many applications related to environmental applications or the monitoring of health status. However, security monitoring systems are usually chosen for a specific object, area or perimeter. The main goal of our article is to present the mechanical design of mobile monitoring units. These units create the basis of a developed mobile monitoring security system, which can be applied to monitor any area of interest, even in demanding weather conditions, involving, for example, windiness or wide operational temperature ranges. Therefore, this article is focused on the mechanical design of mobile monitoring units, which are constructed not only so that they can withstand challenging environmental conditions, but also with regard to their simple transportation, manufacturing process and, if necessary, possible repairs. During the design, emphasis was also placed on the vibrations of the mobile monitoring units and their temperature dependence, because vibrations can significantly affect the correct functioning of the mobile monitoring security system and cause false alarm situations. To confirm the correctness of the simulation models, experiments were performed on the mobile monitoring unit prototypes.

Revolutionizing Healthcare with Intelligent Remote Health Monitoring

Revolutionizing healthcare through intelligent remote health monitoring involves leveraging advanced technologies such as artificial intelligence, wearable devices, and IoT to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of possible wellness issues, and reducing the need for frequent hospital visits. It promotes personalized healthcare, improves patient outcomes, and enhances the efficiency of healthcare systems by allowing healthcare providers to make data-driven decisions. This transformation in healthcare delivery ensures better access to care, particularly for individuals in remote or underserved areas, while also addressing the growing demand for healthcare services globally. Revolutionizing healthcare through intelligent remote health monitoring involves leveraging Cutting-edge innovations like synthetic intelligence, wearable devices, and the Internet of Things (IoT) to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of emerging health issues, and reducing the need for frequent hospital visits. The integrationof machine learning algorithms allows for predictive analytics, which can anticipate health deteriorations and recommend timely interventions. It promotes personalized healthcare by tailoring treatments to individual needs, improves patient outcomes through constant monitoring and timely responses, and enhances healthcare systems by allowing healthcare providers to make data-driven decision.

Machine learning evaluation of a hypertension screening program in a university workforce over five years

The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous opportunities as a unique, easily accessible and practical avenue for early diagnosis and treatment of hypertension among the workforce class. The evaluation of such a Workplace Screening Strategy can give insight into its possible effects. Innovative machine learning approaches like k-means clustering are underutilized for such assessments. We set out to use this technology to analyze the results of our university’s yearly health checkup of the employees for hypertension. An anonymized dataset including the demographics and blood pressure monitoring information gathered from workers in various departments/units of a learning organization. The overall amount of samples or data values is 1, 723, and the supplied dataset includes six attributes, such as year group (2018, 2019, 2021, 2022), Department/Unit (academic and non-academic), and gender (male and female), with the intended output being the blood pressure status (low, normal, and high). The dataset was analyzed using machine learning approaches. In this longitudinal study, it was discovered that the average age for the workforce is 42. Similarly, it was revealed that hypertension was common among employees over the age of 40, regardless of gender or occupational type (academic or nonacademic). The data also found that there was a consistent drop in the prevalence of hypertension from 2018 to 2022. According to the study findings, the use of machine learning algorithms for periodic evaluations of workplace health status monitoring initiatives (particularly for hypertension) is feasible, realistic, and sustainable in diagnosing and controlling hypertension among those in the workforce.

DEN-HMM: Deep Emission Network Based Hidden Markov Model with Time-Evolving Multivariate Observations

A Hidden Markov Model (HMM) is a popular statistical modeling technique for system health state estimation, monitoring, and prognosis. However, most existing HMMs adopted some simple parametric probability distribution as the distribution of observations for a given state and thus cannot capture the intricate dependency of observations on state and possibly other covariates such as time. To address this, we propose a Deep Emission Network-based Hidden Markov Model (DEN-HMM) to capture the complex evolution of multivariate observations with respect to state and time. We also address the challenging issue of state nondiscrimination in DEN-HMM. To overcome this, we propose a regularized loss function that can prevent certain non-discriminative trivial solutions and enhance the state discriminative capabilities of DEN-HMM. The study further demonstrates extensive numerical studies to show the effectiveness of the proposed DEN-HMM, including a case study on steady-state estimation in ultrasonic cavitation-based dispersion processes.

Population Digital Health: Continuous Health Monitoring and Profiling at Scale.

This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices' sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions.

Porous Core-Shell Yarn in Wearable Electrochemical Sensors for Real-Time Sweat Monitoring.

Wearable sweat sensors have garnered substantial attention owing to their actual significance in the noninvasive and real-time monitoring of health conditions. However, it remains significantly challenging to efficiently construct a high-sensitivity sweat sensor with stable long-term sensing capability. Herein, we report an effective methodology based on wet-spinning/acid-etching technology to construct a porous core-shell yarn-based wearable electrochemical sensor. This strategy increases the inductive surface area of the ion concentration and facilitates signal transmission. As a result, the sensor demonstrates high sensitivity for monitoring K+ and pH in sweat (54.89 mV/dec for K+ and 40.2 mV/pH for pH). Furthermore, the sensors exhibit outstanding sensing stability, good long-term stability (>16 h), and satisfactory bending resistance (>1000 cycles). More importantly, the sensing yarns could be prepared at speeds of up to 500 m/h with a continuous preparation strategy, which enabled mass fabrication of the electrochemical sensor. Electrochemical sensors could serve as sweat-sensing systems for real-time health monitoring and hold great potential for the commercialization of health-detection technology.

Efficient Cross-Domain Fault Diagnosis via Distributed Multi-Source Domain Deep Transfer Learning

Abstract In the actual industrial production, the working conditions of rotating machinery are complex and changeable, and the health-state monitoring data are increasingly large and difficult to be labeled, which will seriously restrict the accuracy and efficiency of cross-domain fault diagnosis (CDFD) of rotating machinery. Therefore, an efficient multi-source domain deep transfer learning (MDDTL) method for CDFD of rotating machinery is proposed. Firstly, a MDDTL model is constructed to improve the accuracy of CDFD. In the model, a dual-phase domain alignment strategy is designed, which considers the alignment of feature distributions between each source and target domain pair in the feature space and that of prediction probabilities between domain-specific fault classifiers in the output space. The fault prediction results from multiple different fault classifiers are merged dynamically by the proposed imbalanced adaptive prediction strategy. Secondly, a data-parallel distributed training scheme for MDDTL model is proposed. Based on the idea of data parallelism, the distributed parallel training of MDDTL model is performed with Horovod-GPU platform, and the parameters are synchronously updated with the bandwidth-optimal Ring-AllReduce architecture. Under the premise of ensuring the accuracy of fault diagnosis (FD), the training time of MDDTL model is significantly reduced. Finally, extensive experiments are conducted to verify the effectiveness of the proposed MDDTL method. The results demonstrate that the proposed method not only effectively improves the accuracy of CDFD of rotating machinery, but also significantly improves the training efficiency of MDDTL model. After adopting the proposed method, the diagnosis accuracies achieved under two different cross-working condition scenarios reach 97.09% and 97.87% respectively, and the model training time is reduced by 73.62% when facing a large-scale rotating machinery training set.

A Pig Behavior-tracking Method Based on a Multi-Channel High-Efficiency Attention Mechanism

Because the behavior of pigs reflects their health status, continuous and precise monitoring of behavior is important for effective health management and welfare protection. To mitigate potential tracking failures during analysis of video footage, we introduced a novel multi-target pig tracking method that consisted of detection and tracking components. The detection model was enhanced with an efficient attention mechanism and a Cross Stage Partial Darknet backbone network, which significantly improved detection accuracy. The tracking component used the Bytetrack algorithm to accurately track the movement trajectories of individual pigs. Together, these components were combined into the Dual-YOLOX-Tiny-ByteTrack (DYTB) model, which demonstrated superior performance in automatic monitoring of pig behaviors compared with previously published approaches. We established multi-object pig tracking datasets with 180,321 images to evaluate this method. The DYTB method achieved a pig detection accuracy of 98.3% and tracking accuracies of 95.3% and 97.1%. Compared with the YOLOX-Tiny-ByteTrack base model, DYTB showed a 3.4% improvement in multiple object tracking accuracy, making it a robust method for non-contact, intelligent monitoring of pig health that can contribute to advances in precision livestock farming.

AGRICULTURAL DROUGHT ANALYSIS BASED ON REMOTE SENSING: THE CASE OF AYDIN, TURKIYE

Agricultural drought is a phenomenon that arises when there is a deficiency of moisture in the soil, which has a detrimental impact on the productivity of agricultural crops. In Aydın, Turkey, particularly in the fertile Söke Plain region, agricultural drought is a major problem for farmers. The use of satellite data based on remote sensing and the indices derived from them allows for the timely and spatially detailed monitoring of vegetation health and moisture conditions over large areas. This enables the early detection and monitoring of agricultural drought. The present study evaluates the occurrence of agricultural drought in Aydın province between 1995 and 2020. For this purpose, satellite images captured by the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) in August 1995 and 2020 and land cover maps produced by the European Space Agency (ESA) at the same dates were utilized. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) indices were produced using Landsat satellite images. Then, the Vegetation Temperature Condition Index (VTCI) was obtained to detect agricultural drought. Finally, the relationship between the VTCI and land cover (LC) was evaluated, as well as the changes in the VTCI index between 1995 and 2020. The study found that agricultural drought increased with rising land surface temperature and declining NDVI values in Aydın province between 1995 and 2020.

Recent Advances in Amperometric Biosensors for Medical Applications: A Mini-Review.

Amperometric biosensors have emerged as a cutting-edge technology in clinical diagnostics, thanks to their high level of sensitivity, rapid analytical results, compact size, and ability to monitor health parameters non-invasively and continuously using flexible and wearable sensors. This review explores the latest developments in the field of amperometric biosensing for medical applications. It discusses the materials used to construct these sensors and pays particular attention to biosensors designed to measure glucose, lactate, cholesterol, urea, and uric acid levels. The review also addresses the technological limitations and drawbacks of these devices. Furthermore, it presents the current status and identifies future trends in the development of flexible, wearable biosensors capable of providing continuous monitoring of a patient's health status.

On the measurement of piezoelectric d33 coefficient of soft thin films under weak mechanical loads: A rapid and affordable method

Thanks to their intrinsic flexibility, energy efficiency and high portability, soft piezoelectric thin films represent the most effective technological approach for wearable devices to monitor health conditions. In order to improve effectiveness and applicability, more and more innovative and high-performing soft piezoelectric materials are being developed and benchmarked through their piezoelectric d33 coefficient. However, most existing methods to measure the d33 were developed for ceramic or bulk materials and cannot be applied to soft materials because high force/pressure can deform and damage the material structure. This work introduces a simple, effective, and fast method to accurately measure the d33 of soft and thin piezoelectric films by applying weak sinusoidal forces to avoid any damage to the sample, and simultaneously measuring the charges produced by the direct piezoelectric effect. The approach is versatile as it can be used for different types of materials and sizes of the active area. This method represents an effective solution to speed up the process of material optimization, paving the way for the rapid development of novel wearable piezoelectric devices.

A noise-enhanced machine fault diagnosis method with multi-channel information fusion

ABSTRACT Condition health monitoring and fault diagnosis is able to safeguard against potential threats to machines. Nevertheless, the acquired signals by using a plenty of sensors are often accompanied by noise and meanwhile the useful information contained in the signals is not both complete and reliable, thereby together reducing the accuracy and stability of the fault diagnosis. Therefore, this paper proposes a noise-enhanced intelligent fault diagnosis method with multi-channel information fusion. Here, it would inject the varying levels of Gaussian noise into multi-channel input data as well as intelligent diagnostic models, with a view to elucidating the benefits of noise. Furthermore, the proposed method is validated through three experiments. The experimental results demonstrate that the diagnostic accuracy of the hydraulic motor and the two different bearings reaches 99.85%, 99.89%, and 99.85% by using the proposed method, respectively, which are increased by 2.50%, 4.09%, and 2.50%, respectively, compared with one without the injection of noise. In addition, by comparing the diagnostic accuracy of ones with single-channel and multi-channel information fusion under the optimal noise level in both cases, it is found that one with multi-channel information fusion shows an average of 3.20% increase than the single-channel one.

Maternity care coordinator's experiences at the department of Veteran Affairs.

The Veterans Health Administration (VHA) provides maternity care by paying for Veterans to receive pregnancy-related care in community settings and by utilizing maternity care coordinators (MCCs) at each medical facility. The purpose of this qualitative descriptive study was to understand the MCC's experiences performing their role across VA facilities. Thirty MCCs were recruited and interviewed virtually using Microsoft Teams. Interviews were recorded and transcribed verbatim. Using thematic analysis, transcripts were coded, and themes were derived. MCC's roles include being a liaison, care coordinator, and supporter. MCCs improve Veterans' care during pregnancy and postpartum by education, monitoring health status, and connecting Veterans to providers within VA and the community. Across VA facilities, there was variation in how MCCs engaged with Veterans and in the services provided. A challenge shared was the lack of dedicated time to the role. In the VA, MCCs are valuable in ensuring high-quality care coordination of pregnant/postpartum Veterans despite the fragmentation of care between VA and community providers. To improve inconsistencies in how the MCC program is implemented, systematic strategies such as ensuring dedicated time are needed.

Ultrasensitive Ultrasoft Buckled Crack‐Based Sensor for Respiration Measurement and Enhanced Human–Machine Interface

Wearable strain sensors have transformed the real‐time monitoring of health conditions and human–machine interactions. However, recently developed wearable strain sensors exhibit several limitations. For example, when a sensor is designed with high sensitivity to detect strain, it struggles to accurately measuring the deformation of low‐stiffness materials like skin. Additionally, finding the optimal balance between sensitivity, durability, hysteresis, and strain range in sensor design is challenging. To address these challenges, a Buckled, Ultrasoft, Crack‐based, Large strain, EpiDermal (BUCKLED) sensor is developed. This sensor integrates the benefits of soft structure engineering with high sensitivity of crack‐based sensing mechanisms to ensure optimal skin deformation measurements. The BUCKLED sensor exhibits significant improvements in compliance (18 500 mm N−1), stretchability (100%), hysteresis (2%), durability (10 000 cycles with 100% strain), and force sensitivity () owing to its buckled shape, confirming its ability to detect subtle movements with enhanced accuracy. The sensor's high compliance allows it to accurately measure low‐stiffness objects, ensuring reliable performance. Furthermore, the sensor's tunability is demonstrating its effectiveness in applications such as respiratory monitoring, facial expression recognition, and silent speech interfaces. Consequently, the proposed sensor is versatile and holds great potential for a wide range of sensing applications.

Quantification measurement of hemoglobin with large dynamic range and low detection limit via an optical fiber optofluidic laser with enzyme-catalyzed reaction

Quantitative measurement of hemoglobin (Hb) levels is an essential part of routine medical examinations, disease diagnosis and health status monitoring. In this study, we introduced optical fiber optofluidic laser (FOFL) technology combined with catalytic oxidation reaction to design a laser sensor for sensitive Hb detection. For the H2O2-rhodamine B (RhB) oxidation system, radially emitting FOFL was achieved with a thin-walled hollow optical fiber (HOF) as an optical microcavity and amplified the concentration change of RhB during oxidation by H2O2. Hb was employed as peroxide-mimicking enzyme to catalyze the oxidation system, which sped up the reaction, resulting in an earlier laser extinction time. With the laser extinction time as sensing signal, the Hb sensor achieved a dynamic range of five orders of magnitude and a limit of detection (LOD) of 46.0 pM at an assay time of 40 min. The developed method provides ideas for the exploitation of FOFL biosensing based on catalytic oxidation reactions.

Automated Digital Sfigmomanometer Prototype Testing

Abstract Blood pressure is the pressure of blood flow in the arteries generated by the heart. Abnormal blood pressure can lead to hypertension and hypotension. Hypotension, or low blood pressure, has a prevalence of 5% to 34% among individuals aged 17 to 19, and over 50% in the elderly. Hypertension can cause death in patients with heart disease. About 39.7% of people suffer from hypertension, especially those over the age of 60. Blood pressure measurement needs to be done regularly because it has impact such as early detection of blood pressure health conditions and monitoring of health conditions for those already diagnosed with abnormal blood pressure. Unfortunately, only 59% of the population in Indonesia regularly measure their blood pressure. Accurate blood pressure measurement can be performed using an invasive method. Another method is non-invasive, which can be done using a sphygmomanometer that applies the oscillometric method. This method uses pressure oscillations in the cuff influenced by the pulse in the blood vessels. When using this method, several factors need to be considered, such as body position, cuff size, and the technique used during the measurement. In this study, a fully automatic digital sphygmomanometer has been developed with a cuff attached to the device, equipped with a thermal printer to print out the measurement results and classify blood pressure categories. The advantage of the developed prototype is that it has a smaller relative error value, with 1.03% for systolic blood pressure and 1.45% for diastolic blood pressure, and has already been integrated with a thermal printer to directly print the measurement results.

Study on Fault Prediction Method of Hydropower Equipment Based on Equipment Health State Detection

Abstract With the continuous development of China’s electric power industry, the installed capacity of power plants is increasing, and hydropower units are developing in the direction of large capacity and high efficiency. As the complexity of the equipment is getting higher and higher, different equipment are more and more different, showing different characteristics when faults occur, which puts forward higher requirements for the safe and stable operation of hydropower units. In response to the above problems, this paper develops a set of human-machine closed-loop remote fault diagnosis reasoning machine system with self-learning function and a set of equipment health state monitoring and prediction system. This remote fault diagnosis system reasoning machine system can reduce the cost of unit equipment fault diagnosis, improve the diagnosis efficiency, and ensure the safe, stable and economic operation of the unit.