Solution For Remote Monitoring Research Articles

Cost impact of the use of a universal cardiac implantable electronic devices remote monitoring solution: results of the Evidence RM study

Abstract Background Remote monitoring (RM) is considered the standard of care for patients with cardiac implantable electronic devices (CIED). In 2023 the HRS/EHRA/APHPRS/LAHRS expert consensus highlighted the potential interest of alert-based monitoring and the use of a third-party platform for RM management. By lightening the RM workload for clinical staff, valuable time and resources can be redirected towards patient care. Purpose This study aims to assess the impact on healthcare expenditures of the adoption of a Universal, vendor-neutral, and alert-focused remote monitoring (RM) platform for CIED in France, as opposed to the Conventional RM conducted via device specific manufacturers' platforms. Methods This study utilizes the French National Health Database (SNDS) to evaluate the effectiveness of RM solutions among patients with implantable cardioverter defibrillators (ICD), including cardiac resynchronization therapy defibrillators (CRT-D). All patients under RM were categorized based on the type of monitoring, either the Universal RM or the Conventional RM. The analysis was conducted on year 2019 and included only patients maintaining consistent RM solutions and device types throughout the entirety of the study period. To mitigate potential biases, costs were adjusted according to age, gender, device type, year of first implantation, year of RM initiation, medical center experience with RM, and Elixhauser score for comorbidities. Results Study cohort consisted of 36,401 patients (age 67.3 ± 13.0 years / male 78.4% / CRT-D 40.1%), 1,482 patients followed using the Universal RM platform and 34,419 patients monitored with the Conventional RM solutions. The study findings revealed a 4% decrease in corrected total costs and a notable 17.8% reduction in hospital costs among patients utilizing the Universal RM. Analysis further identified that this decrease in hospital expenses was primarily influenced by a reduction of the costs associated with cardiovascular diseases. Conversely, the group utilizing the Universal RM experienced a 7.9% increase in total outpatient costs compared to Conventional RM, while ambulatory visit costs remained unchanged. As costs incurred by patients were not included, total costs may however be underestimated. Patients under the Universal RM solution may benefit of more proactive preventive measures delivered through outpatient care. By addressing issues preemptively, critical conditions may be averted, enhancing overall patient management, and diminishing hospital costs. The adoption of a third-party Universal platform may thus yield to cost savings, exemplified by a negative Incremental Cost-Effectiveness Ratio (ICER) of -103€ per Day Alive and Out of Hospital (DAOH) as observed in this study. Conclusions The use of a third-party Universal RM platform showed a positive impact in terms of costs reduction for the French healthcare system on this ICD population.

Current State of Connected Sensor Technologies Used During Rehabilitation Care: Protocol for a Scoping Review.

Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations. This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions. The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness. We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024. Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. Open Science Framework jys53; https://osf.io/jys53. DERR1-10.2196/60496.

FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications.

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the "FloatingBlue" architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE's low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, "FloatingBlue" demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight "FloatingBlue" as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.

Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings.

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.

Surveillance and Alert-based Multiparameter Monitoring to Reduce Worsening Heart Failure Events: Results From SCALE-HF 1

There is a need for better noninvasive remote monitoring solutions that prevent hospitalizations through the early prediction and management of heart failure (HF). SurveillanCe and Alert-Based Multiparameter Monitoring to ReducE Worsening Heart Failure Events (SCALE-HF 1) evaluated the performance of a novel congestion index that alerts to fluid accumulation preceding HF events. SCALE-HF 1 was a multicenter, prospective, observational study investigating HF event prediction using data from the cardiac scale. Participants with HF took measurements at home by standing barefoot on the scale for approximately 20 seconds each day. The congestion index was applied retrospectively, and an alert was generated when the index exceeded a fixed threshold established in prior studies. HF events were defined as unplanned administration of IV diuretics or admissions with a primary diagnosis of HF. Sensitivity was defined as the ratio of correctly identified HF events to the total number of HF events. We enrolled 329 participants (mean age 64 ± 14 years; 43% women; 32% Black; 56% with reduced ejection fraction) across 8 sites with 238 participant-years of follow-up and 69 usable HF events. The congestion index predicted 48 of the 69 HF events (70%) at 2.58 alerts per participant-year. In contrast, the standard weight rule (weight gain of >3 lb in 1 day or >5 lb in 7 days) predicted only 24 of the 69 HF events (35%) at 4.18 alerts per participant-year. The congestion index alerts had a significantly higher sensitivity (P < .01) at a lower alert rate than the standard weight rule. The congestion index alerts demonstrated sensitive prediction of HF events at a low alert rate, significantly exceeding the performance of weight-based monitoring. NCT04882449.

Assessing PV inverter efficiency degradation under semi-arid conditions: A case study in Morocco

The worldwide shift towards renewable energy sources, including solar and wind, is progressing rapidly. This change is driven by decreasing prices and technical advancements that are becoming more centralized. The objective is to develop a cutting-edge approach and technology that seamlessly incorporates photovoltaic (PV) energy sources into a power network while ensuring grid stability. This research evaluates the lifetime and degradation of PV inverters under real operating conditions, focusing on semi-arid climate scenarios. Current papers demonstrate a yearly failure rate of 1–15% for PV inverters, highlighting the need for a thorough reliability evaluation. This investigation research applied a unique technique that included continuous monitoring of PV inverter performance parameters by comparing the measured with the predicted normal operating output. The results show that Sandia's inverter performance model is highly suitable for accurately modeling normal PV inverter behavior. Furthermore, weighted efficiency dropped by 3.96%, 0.63% and 1.29% respectively for 7kW, 15kW and 20kW photovoltaic systems over the five years. These findings have implications for comprehensive maintenance strategies, including regular inspections, safety protocols, and remote monitoring solutions. Ultimately, this research paper sheds light on the causes of declining solar inverter performance and provides suggestions for enhancing PV plant maintenance and reliability. It also represents an outstanding reference given the literature shortage.

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

A new digital solution for remote monitoring of gait and daily life activities in orthopaedic patients: A validation study

A new digital solution for remote monitoring of gait and daily life activities in orthopaedic patients: A validation study

Telemonitoring of pediatric asthma in outpatient settings: A systematic review.

Telemonitoring technologies are rapidly evolving, offering a promising solution for remote monitoring and timely management of asthma acute episodes. We aimed to describe current pediatric asthma telemonitoring technologies. A systematic review was conducted until September 2023 on Medline, Scopus, and Web of Science. We included studies of children (0-18 years) with asthma or recurrent wheezing whose respiratory condition was telemonitored outside the healthcare setting. A narrative synthesis was performed. We identified 40 telemonitoring technologies described in 40 studies. The more frequently used technologies for telemonitoring were mobile applications (n = 21) and web-based systems (n = 14). Telemonitoring duration varied between 2 weeks and 32 months. Data collection included asthma symptoms (n = 30), patient-reported outcome measures (PROMs) (n = 11), spirometry/peak flow readings (n = 20), medication adherence (n = 17), inhaler technique (n = 3), air quality (n = 2), and respiratory sounds (n = 2). Both parents and children were the technology target users in most studies (n = 23). Technology training was reported in 23 studies of which 3 provided ongoing support. Automatic feedback was found in 30 studies, mostly related with asthma control. HCP were involved in data management in 27 studies. Technologies were tested in samples from 4 to 327 children, with most studies including school-aged children and/or adolescents (n = 38) and eight including preschool children. This review provides an overview of existing technologies for the outpatient telemonitoring of pediatric asthma. Specific technologies for preschool children represent a gap in the literature that needs to be specifically addressed in future research.

E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 3rd eNMD Congress: Pisa, Italy, 29-30 October 2021.

Neuromuscular diseases (NMDs), in their phenotypic heterogeneity, share quite invariably common issues that involve several clinical and socio-economical aspects, needing a deep critical analysis to develop better management strategies. From diagnosis to treatment and follow-up, the development of technological solutions can improve the detection of several critical aspects related to the diseases, addressing both the met and unmet needs of clinicians and patients. Among several aspects of the digital transformation of health and care, this congress expands what has been learned from previous congresses editions on applicability and usefulness of technological solutions in NMDs. In particular the focus on new solutions for remote monitoring provide valuable insights to increase disease-specific knowledge and trigger prompt decision-making. In doing that, several perspectives from different areas of expertise were shared and discussed, pointing out strengths and weaknesses on the current state of the art on topic, suggesting new research lines to advance technology in this specific clinical field.

Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis.

Implementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems-based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken. We aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations. A mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed. A total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology). The recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of "virtual wards." ClinicalTrials.gov NCT05321004; https://www.clinicaltrials.gov/study/NCT05321004.

MEMS Technology in Cardiology: Advancements and Applications in Heart Failure Management Focusing on the CardioMEMS Device.

Heart failure (HF) is a complex clinical syndrome associated with significant morbidity, mortality, and healthcare costs. It is characterized by various structural and/or functional abnormalities of the heart, resulting in elevated intracardiac pressure and/or inadequate cardiac output at rest and/or during exercise. These dysfunctions can originate from a variety of conditions, including coronary artery disease, hypertension, cardiomyopathies, heart valve disorders, arrhythmias, and other lifestyle or systemic factors. Identifying the underlying cause is crucial for detecting reversible or treatable forms of HF. Recent epidemiological studies indicate that there has not been an increase in the incidence of the disease. Instead, patients seem to experience a chronic trajectory marked by frequent hospitalizations and stagnant mortality rates. Managing these patients requires a multidisciplinary approach that focuses on preventing disease progression, controlling symptoms, and preventing acute decompensations. In the outpatient setting, patient self-care plays a vital role in achieving these goals. This involves implementing necessary lifestyle changes and promptly recognizing symptoms/signs such as dyspnea, lower limb edema, or unexpected weight gain over a few days, to alert the healthcare team for evaluation of medication adjustments. Traditional methods of HF monitoring, such as symptom assessment and periodic clinic visits, may not capture subtle changes in hemodynamics. Sensor-based technologies offer a promising solution for remote monitoring of HF patients, enabling early detection of fluid overload and optimization of medical therapy. In this review, we provide an overview of the CardioMEMS device, a novel sensor-based system for pulmonary artery pressure monitoring in HF patients. We discuss the technical aspects, clinical evidence, and future directions of CardioMEMS in HF management.

Wireless Patient Monitoring System with LoRa Communication for Remote Healthcare

This study presents a novel approach to remote healthcare through a wireless patient monitoring system using LoRa communication. The system consists of two main parts: a transmitter and receiver. The transmitter collects vital health data, such as body temperature, SpO2, accelerometer and gyroscope measurements, electrocardiogram (ECG) signals, air quality and heart rate. This information will be transmitted wirelessly using LoRa technology to the receiver section, which will transmit the data to the IoT cloud server. In addition, the receiver section is equipped with a GSM module to send an SMS alert in the event of abnormal health parameters. The aim of this system is to provide real-time monitoring of patients in remote areas, enabling timely interventions and improving accessibility to healthcare. The efficiency and reliability of the system make it a promising solution for remote patient monitoring in challenging environments.

Telehealth Monitoring System for Chronic Disease Management

Telehealth Monitoring System for Chronic Disease Management

A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation

This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.

A Robust and Secure Electronic Internet of Things-Cloud Healthcare Framework for Disease Classification Using Deep Learning

In the modern era, the Internet of Things (IoT) based nano-electronic devices are essential to provide practical patient information through monitoring performance in the healthcare system. Moreover, remote health monitoring is becoming necessary for lowering healthcare expenses and enhancing patient care due to the aging population and the rise in chronic illnesses. Recently, there has been a lot of interest in the IoT based nanoelectronics devices as a potential solution for remote health monitoring. An extensive range of physiological data, including heart rates, blood oxygen levels, body temperatures, ECG signals, etc., can be collected and analyzed by IoT-based sensors devices, giving medical practitioners real-time feedback so they can respond appropriately. Therefore, in this paper, a novel Functional neural network-based Aquila optimization (FNN-AO) algorithm is developed to classify the various types of diseases. Furthermore, an Identity-based Encryption (IbE) algorithm is adapted to secure the healthcare data. Initially, IoT based electronic sensors collect and store healthcare data in the cloud storage system. This process was done with the help of the MATLAB platform, and parameters were analyzed and compared with existing techniques in terms of performance metrics. From the comparison, the proposed framework has a 2.34% improvement over the other models.

Designing and Manufacturing an Affordable and Easy to Use Visual Bio Feedback Device to Fix Forward Head Posture: A Pilot Study Involving Female Students

Forward Head Posture (FHP) is when the head leans forward due to factors such as heavy backpacks or poor computer ergonomics. FHP can lead to neck strain and discomfort as well as potential long-term issues such as arthritis. Treatment options include specialized exercises, orthopedic devices, manual therapy, physical exercises, and visual feedback techniques, along with guidance from specialists in physical medicine and rehabilitation. In this study, a visual feedback-based approach was used to address FHP in female students. The study spanned ten days and included a visual feedback group and a control group. The results showed significant improvements in maximum head angle deviation in the visual feedback group compared to the control group; however, there was no significant change in the DFA number, indicating stability in policy control by the central nervous system. The study demonstrated that visual feedback sessions led to immediate benefits, with participants progressively acquiring skills involving the maintenance of proper head positioning. The test results indicated that the neck angle decreased to less than 15 degrees, indicating a return to a normal state. The versatility of the developed affordable and easy-to-use device and the potential for using smartphone motion sensors for similar visual feedback systems are discussed in this paper as well. The study suggests the promising potential of visual feedback in healthcare, including remote monitoring and smartphone-based solutions.

NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network

NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country’s VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R2) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles.

Recognition of Sleep Disorders using IoT-Based Wearables and Neutrosophic Data Analytics

In the dynamic landscape of healthcare technology, the amalgamation of Internet of Things (IoT) systems and Neutrosophic Data Analytics has heralded a paradigm shift. This study delves deep into this transformative synergy by presenting an innovative IoT-based wearable system design for the recognition of sleep disorders. Our meticulously crafted multilayer cellular system seamlessly integrates IoT devices, data acquisition, cloud computing, and machine learning to unlock a wealth of insights into sleep patterns, their anomalies, and the presence of sleep disorders. Through fair and rigorous experimental comparisons, we unveil the prowess of Long Short-Term Memory (LSTM) within the machine learning realm, showcasing its superior performance over baseline models. The results affirm LSTM's ability to detect sleep disorders with remarkable accuracy, precision, and recall, revolutionizing sleep medicine and healthcare practices. This research, at the crossroads of innovation and healthcare, not only illuminates the path to advanced sleep disorder diagnosis but also heralds a new era of personalized healthcare interventions and remote monitoring solutions. As we navigate the realm of IoT and data-driven healthcare, our findings hold the promise of improving the quality of life for countless individuals, reaffirming the pivotal role of technology in safeguarding one of the most fundamental aspects of human well-being – a peaceful and restorative night's sleep.

Digital Health for Oncological Care.

Digital health tools extend well beyond telemedicine, holding great potential to advance oncological care. We survey digital health and provide recommendations across the health continuum, tailoring them to oncology, including prevention, detection and diagnosis, and treatment and monitoring. Within the prevention realm, we review wellness technologies, cancer screening, mental health solutions, and digital biomarkers. For detection and diagnosis, we describe existing and emerging solutions for remote patient monitoring and various means to capture digital biomarkers, the "digital exam," and "digital outcomes." Treatment and monitoring solutions include telemedicine, chatbots, and digital therapeutics, which are also explored. We also discuss a host of technology enablers that are required for successful implementation and sustainment of digital health-enabled care. Our recommendations pertain to health care systems as well as companies that work with these systems or provide care to patients directly.