Monitoring Of Respiratory Diseases Research Articles

Physicians' Views of a Digital Intervention to Support Respiratory Patients: Take-a-Breath Pre-Pilot Usability Study.

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end's usability. This study aims to evaluate the Healthcare Professional's (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.

Can aerosol optical depth unlock the future of air quality monitoring and lung cancer prevention?

AbstractThis “Policy Brief” explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom’s air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution’s impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs.

Electret-based flexible pressure sensor for respiratory diseases auxiliary diagnosis system using machine learning technique

Respiratory diseases have been increasingly affecting people worldwide, posing a major public health challenge due to rising morbidity and mortality rates. Subtle abnormal respiratory sounds s may present early in the pulmonary or respiratory tract diseases, therefore urging an instant intervention in critical clinical conditions. However, the current monitoring and signal analysis technologies pose significant challenges in achieving real-time, convenient, and accurate respiratory disease monitoring. Here, we propose a novel automatic auxiliary diagnosis system that utilizes a flexible electret-based self-powered sensor (FESS), signal processing technology, and machine learning algorithms. The FESS is based on a strain-enhanced laminated electret with an edge-to-edge hollow hemisphere array structure, which enables the system to detect and prevent various respiratory diseases with high accuracy rates of 99.43%, sensitivity of 98.30%, and specificity of 99.02%. Our system holds immense potential in reducing medical burden and improving the overall health of individuals.

Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming.

Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd's respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.

A Wearable Self‐Powered Multi‐Parameter Respiration Sensor

AbstractRespiratory disease can be early warned by many respiratory parameters such as intensity, rhythm, temperature, and breath molecules like acetone. It requires a complex process to measure these respiratory parameters simultaneously in clinical practice. Therefore, it is a hot topic to design portable devices especially wearable electronics to monitor respiratory conditions continually in daily life. Here, a flexible self‐powered multi‐parameter respiration sensor based on poly(vinylidene fluoride) (PVDF) thin film and flexible interdigital electrodes that is bonding with sodium‐doped zinc oxide nanoflowers is demonstrated , which can detect airflow, temperature, and acetone during breathing. The respiration sensor may provide some reference ways for the fabrication of miniaturized respiratory disease monitoring devices.

A Novel Approach to the Respiratory Disease Follow-up Based on Plethhysmography Signal Parameters

Aim: The aim of this paper is to present a novel approach for the monitoring of respiratory diseases based on the combined study of heart rate, respiratory rate and Pulse Oximetry (SpO2). Introduction: Since the start of the COVID-19 pandemic, respiratory diseases have increased its worldwide prevalence dramatically. Added to the impact of the pandemic is the already existing situation of a growing prevalence of diseases such as bronchial asthma and Chronic Obstructive Pulmonary Disease (COPD). On the other hand, it is known that the cardiovascular system is overloaded when there are respiratory insufficiencies and this condition can cause severe damage to health. Pulmonology and Cardiology are independent medical specialties that deal with these diseases and often do not cooperate with each other strongly, so a proposal is presented that combines respiratory and cardiovascular variables to deal with this health problem. Methods: A prototype was developed based on the STM32L073CZTx processor controlling the E305654 pulse oximetry module, which on demand delivers SpO2, photo plethysmography signal samples and pulse rate values. A vector was computed, every thirty minutes, to combine the respiratory rate obtained from the Plethysmography Signal (PPG), the mean value of SpO2 and the pulse rate. The vector module represents the cardiovascular and respiratory systems performance. Results: Twenty healthy volunteers, twenty people with COPD and five subjects suffering bronchial asthma, during periods without crisis, were studied. The dispersion within the group of each patient was not significant, but a notable difference can be observed between the healthy volunteers and the other people studied. A "normal region" can be set with clearly defined borders. Conclusion: The proposed solution seems promising for evaluating respiratory function even when it is compensated by a cardiac response, making it easier to identify people suffering from respiratory diseases and who may appear normal or healthy.

Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

BackgroundThe health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas.MethodsThe study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process.ResultsThe results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude.ConclusionsMultiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario.

Smart Speakers: The Next Frontier in mHealth.

The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection.

Videoconsulta en atención primaria: una ventana a la realidad. A propósito de 3 casos clínicos

ObjectivesDiscussion based on three clinical cases of the applicability of videoconsultation in the Primary Care practice. DescriptionVideoconsults are a modality of telemedicine that allows for an improved doctor-patient interaction and resembles in-person consultations. Despite overwhelming evidence at an international level, it is not until the arrival of the COVID-19 pandemic that it has found a place in our country.In the evaluation and follow-up of patients with mental health problems, it grants a climate of trust that enables the demonstration of emotions with more facial contact than in on-site consults and translates into a method that complements and expands the necessary clinical information.In the monitoring of respiratory diseases, videoconsults permit an improved education in management of inhaled therapy and the recording of the inhaling technique in a safe environment.In the management of COVID-19 it may be the consult of choice to discriminate patients with moderate affectation and indication of complementary tests. ConclusionThe main advantages to videoconsults are the greater facial interaction and the avoidance of unnecessary displacements.A need exists to resolve certain technical aspects, encourage training and creating a culture that facilitates its progressive implementation.

A narrative review of exploring potential salivary biomarkers in respiratory diseases: still on its way.

Saliva is abundant with proteins, metabolites, DNA, and a diverse range of bacterial species. During the past two decades, saliva has emerged as a novel diagnostic and evaluation medium for several diseases. Collection of saliva samples is simple, minimally invasive, and convenient even in infants, children, and patients with anxious. Furthermore, with the development of hypersensitive techniques [e.g., microsensor arrays, enzyme-labeled immunosensors, nanoparticle-labeled immunosensors, capacitive or impedimetric immunosensors, magneto immunosensors, field effect transistor immunosensors, and surface enhanced Raman spectroscopy (SERS)], the sensitivity and accuracy of saliva diagnostic procedures have been improved. Nowadays, saliva has been used as a potential medium for several disease diagnosis and assessment, such as periodontitis, caries, cancers, diabetes mellitus, and cardiovascular diseases. Saliva has been used widely for studying microbiomics, genomics, transcriptomics, proteomics, and metabolomics of respiratory diseases, however, the use of salivary biomarkers for the diagnosis, prognosis, and monitoring of respiratory disease is still in its infancy. Herein, we review the progress of research on salivary biomarkers related to several respiratory diseases, including bronchial asthma, chronic obstructive pulmonary disease (COPD), obstructive sleep apnea (OSA), pneumonia, tuberculosis (TB), Langerhans cell histiocytosis (LCH) and cystic fibrosis (CF). Furthermore, several limitations of saliva test such as the lack of standard protocol for saliva collection and reasonable reference values for saliva test are also mentioned in this review.

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of % and %. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Thoracic shape changes in newborns due to their position

The highly compliant nature of the neonatal chest wall is known to clinicians. However, its morphological changes have never been characterized and are especially important for a customised monitoring of respiratory diseases. Here, we show that a device applied on newborns can trace their chest boundary without the use of radiation. Such technology, which is easy to sanitise between patients, works like a smart measurement tape drawing also a digital cross section of the chest. We also show that in neonates the supine position generates a significantly different cross section compared to the lateral ones. Lastly, an unprecedented comparison between a premature neonate and a child is reported.

Flow Cytometry: From Experimental Design to Its Application in the Diagnosis and Monitoring of Respiratory Diseases.

Recent advances in the field of flow cytometry (FCM) have highlighted the importance of incorporating it as a basic analysis tool in laboratories. FCM not only allows the identification of cell subpopulations by detecting the expression of molecules in the cell membrane or cytoplasm, but it can also quantify and identify soluble molecules. The proper functioning of the FCM requires six fundamental systems, from those related to the transport of events to the systems dedicated to the analysis of information. In this review, we have identified the main considerations that every FCM user must know for an optimal antibody panel design, the quality systems that must govern the FCM protocols to guarantee reproducible results in research or clinical laboratories. Finally, we have introduced the current evidence that highlights the relevance of FCM in the investigation and clinical diagnosis of respiratory diseases, establishing important advances in the basic and clinical study of diseases as old as Tuberculosis along with the recent proposals for the monitoring and classification of patients infected with the new SARS-CoV2 virus.

A novel system for the comprehensive collection of nonvolatile molecules from human exhaled breath

Characterization of nonvolatile molecules in exhaled breath particles can be used for respiratory disease monitoring and diagnosis. Conventional methods for the collection of nonvolatile molecules in breath heavily rely on the physical properties of exhaled breath particles. Strategies taking advantage of their chemical properties have not yet been explored. In the present study, we developed a column system in which the surface chemistry between organic nonvolatile molecules and octadecyl carbon chain was exploited for the comprehensive collection of metabolites, lipids, and proteins. We demonstrated that the collection system had the capture efficiency of 99% and the capacity to capture representative nonvolatile molecules. The collection system was further evaluated using human subjects and proteins collected from human exhaled breath were characterized and identified using gel electrophoresis and bottom-up proteomics. The identified 303 proteins from mass spectrometry were further searched against reported bronchoalveolar lavage fluid proteomes and it was shown that 60 proteins have the tissue origin of lower respiratory airways. In summary, we demonstrate that our collection system can collect nonvolatile molecules from human exhaled breath in an efficient and comprehensive manner and has the potential to be used for the study of respiratory diseases.

Feasibility and accuracy of Tele-spirometry: a community based approach

The value of Spirometry in the detection and management of lung disease is well recognized in the management of lung health in the community. Spirometer is not easily available in primary health settings or in rural areas. Portable Tele-Spirometer can reduce this gap. The present study was conducted to find the usefulness of Tele-spirometry in community management of respiratory diseases. Remote monitoring of respiratory disease is possible with the help of Tele spirometry. Tele spirometer was made under the project with the help of our technology partner. Tele-Spirometer a portable machine that can easily be installed in an Ambulance and also be carried by a healthcare worker was used. Tele Spirometer recorded and stored data on web server and the doctor located at the Telemedicine centre can access it easily via Telemedicine application. We have done the clinical validation of Tele spirometry in 366 individuals (mean age 26.91±17.95 years) residing in the periphery of Chandigarh, India after taking their consent. The data were then transmitted to physicians in Postgraduate Institute of Medical Education and Research, Chandigarh, India. There was good correlation between the traditional Spirometer and portable Tele Spirometer. The portable Spirometer saved the time for diagnosis, reduced the cost involved and also prompted for immediate treatment. Our study had 80% good quality Spirometry and only 20% were of moderate quality. We also reported ∼95% patient satisfaction in this study. Thus, it can be safely concluded that tele-Spirometer is a portable, cost-effective, and convenient tool for diagnosis and monitoring the respiratory diseases and thus improves quality and accessibility, especially in rural areas. Furthermore, a home-based consultation for chronic respiratory disease patients might even be possible in the future.

Breathing process monitoring with a biaxially oriented polypropylene film based fiber Fabry–Perot sensor

The breathing process monitoring with a biaxially oriented polypropylene (BOPP) film based fiber Fabry–Perot (F–P) sensor is presented. The fiber F–P sensor is fabricated with 3-D printing technology and fixed on a medical respirator for breathing process monitoring. Due to the low Young’s modulus of the BOPP film and its thinner thickness, the breathing sensor shows the high pressure sensitivity of -0.581 nm/Pa. By monitoring its intensity response to the pressure induced by breathing, the breathing process can be monitored accurately, including respiratory waveform, respiratory rate, inspiratory duration and amplitude, expiratory duration and amplitude, and inspiratory and expiratory rhythm. Since the sensor is all fiber design with the inherent advantages of small size, anti-electromagnetic interference, green environmental protection, pollution-free and no harm to body, it is expected to be widely used in respiratory disease monitoring.

Combining Bioimpedance and Myographic Signals for the Assessment of COPD During Loaded Breathing.

Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic conditions. The current assessment of COPD requires a maximal maneuver during a spirometry test to quantify airflow limitations of patients. Other less invasive measurements such as thoracic bioimpedance and myographic signals have been studied as an alternative to classical methods as they provide information about respiration. Particularly, strong correlations have been shown between thoracic bioimpedance and respiratory volume. The main objective of this study is to investigate bioimpedance and its combination with myographic parameters in COPD patients to assess the applicability in respiratory disease monitoring. We measured bioimpedance, surface electromyography and surface mechanomyography in forty-three COPD patients during an incremental inspiratory threshold loading protocol. We introduced two novel features that can be used to assess COPD condition derived from the variation of bioimpedance and the electrical and mechanical activity during each respiratory cycle. These features demonstrate significant differences between mild and severe patients, indicating a lower inspiratory contribution of the inspiratory muscles to global respiratory ventilation in the severest COPD patients. In conclusion, the combination of bioimpedance and myographic signals provides useful indices to noninvasively assess the breathing of COPD patients.

Correlating Spirometric Parameters with Breath-Holding Time and Maximum Chest Expansion in Healthy Young Adults

The spirometric measurements are very sensitive, accurate and reliable parameters, which have diagnostic as well as prognostic values. We aimed to find the reliability of two simple measurements, namely chest expansion and voluntary breath holding, which are often suggested as tools for screening and monitoring of respiratory diseases. A cross-sectional descriptive study was conducted on students of Nepal Medical College. Measurements of spirometry (forced vital capacity, FVC in liter; forced expiratory volume in first second, FEV1 in liter; and peak expiratory flow rate, PEF in liter persecond), cirtometry (average of maximum chest expansion, CE in centimeter), and breath-holding time (maximum voluntary apnea at end-inspiration, MVAIT and maximum voluntary apnea at end expiration, MVAET in second) were performed. Degrees of correlation (Pearson’s r) were determined between different parameters; setting level of significance at 95%. Total 308 students (M=164, 53.25%;F=144, 46.75%) participated. Owing to very highly significant differences between males and females, gender-separate correlations were determined. In males, CE correlation was very highly significant (p=0.000) with FVC and FEV1 but not with PEF. MVAET correlated significantly with FVC, FEV1 and PEF; MVAIT correlation was not significant with any parameters. In females, CE correlation was significant with FVC and FEV1 but not with PEF; MVAET and MVAIT correlations were not significant with any of the parameters. In conclusion, the correlation of CE with different spirometric parameters is significant but not very strong (0.3<r<0.5). Also, gender differences exist. Therefore, using CE and breath-holding time may not be appropriate to assess respiratory ventilatory function.

Spirometry: basic concepts.

Spirometry is a pulmonary function test that allows screening, diagnosis and monitoring of respiratory diseases. This is a simple, non-invasive test that is easy to perform. By quantifying the respiratory volumes and flows, such as forced vital capacity (FVC) or forced expiratory volume in six seconds (FEV6), forced expiratory volume in the first second (FEV1), and the relationship between these parameters (FEV1/FVC or FEV1/FEV6 ratio), obstruction can be detected with high sensitivity and specificity; likewise, it is possible to classify the severity and response to the bronchodilator. This article presents indications, contraindications, and basic concepts for the interpretation of spirometry.

ДИНАМИКА И РЕГИОНАЛЬНЫЕ ГРАДИЕНТЫ ЗАБОЛЕВАЕМОСТИ НАСЕЛЕНИЯ БОЛЕЗНЯМИ ОРГАНОВ ДЫХАНИЯ НА ТЕРРИТОРИИ ДАЛЬНЕВОСТОЧНОГО ФЕДЕРАЛЬНОГО ОКРУГА

There was done analysis of epidemiological monitoring of respiratory diseases (RD) on the territory of the Far Eastern Federal District (FEFD), which allows to assess the levels, structure and regional features of the spread of pathology among different populations and territorial formations, the dynamics of the disease and its relationship with the environmental factors of the region. To organize the study, the methods of cluster, structural and comparative analysis, predictive modeling, dynamic series analysis, statistical and information-analytical methods, databases of the Ministry of Health of the Russian Federation, the Federal State Statistics Service of the Russian Federation were used.
 The epidemiological analysis of the main indicators of RD level and dynamics in the FEFD territory indicates the presence of regional differences in the registered morbidity, the degree of which is due to the natural-climatic and socio-demographic characteristics of the habitat. 77.8% of the region's subjects are in the zone of low morbidity. At the same time, the values of maximum and minimum levels of general and primary RD morbidity in the region differ by 2.2 and 2.4 times, respectively, becoming the most pronounced among the adult population (by 3.6 times). In chronic bronchitis and COPD, the regional gradients of the registered morbidity are 10.9 and 9.8 times, respectively. It was found that the values of the primary RD morbidity in children's population in the FEFD are 14.4% higher than at the federal level, and among the adult population the gradient of values has the opposite indicators: 24.9% lower. At the same time, if the incidence of pneumonia in the region is 15.2% higher than the incidence of pneumonia in the Russian Federation as a whole, the incidence of chronic forms of RD in the region is 17.3-28.9% lower than at the federal level.
 The level of primary RD morbidity in 2005-2016 in the Far Eastern Federal District increased by 23.3%, including bronchial asthma (by 20.3%) and COPD (by 47.7%), and the incidence of pneumonia significantly decreased. At the same time, the average annual rate of increase in primary RD morbidity is higher than in the Russian Federation as a whole. This is shown by the predictive model of morbidity dynamics, the linear trends of which are more progressive in the FEFD compared to the dynamics at the federal level. The rate of positive dynamics of morbidity, significantly exceeding the same rate of morbidity growth at the federal level, indicates an increase in the level of availability and quality of medical care for patients with pulmonological profile, timely and early detection of chronic forms of respiratory diseases, positive motivation of the population for treatment in medical organizations. These trends in the dynamics of the registered morbidity are due to the active introduction of effective methods of diagnosis and treatment of chronic respiratory diseases, preventive and educational programs implemented both at the federal and regional level.