Understanding “Atmosome”, the Personal Atmospheric Exposome: Comprehensive Approach

Background Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the “atmosome.” The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health. Objective The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health. Methods An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access. Results The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality. Conclusions Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.

Using Medical Device Standards for Design and Risk Management of Immersive Virtual Reality for At-Home Therapy and Remote Patient Monitoring

Numerous virtual reality (VR) systems have received regulatory clearance as therapeutic medical devices for in-clinic and at-home use. These systems enable remote patient monitoring of clinician-prescribed rehabilitation exercises, although most of these systems are nonimmersive. With the expanding availability of affordable and easy-to-use head-mounted display (HMD)-based VR, there is growing interest in immersive VR therapies. However, HMD-based VR presents unique risks. Following standards for medical device development, the objective of this paper is to demonstrate a risk management process for a generic immersive VR system for remote patient monitoring of at-home therapy. Regulations, standards, and guidance documents applicable to therapeutic VR design are reviewed to provide necessary background. Generic requirements for an immersive VR system for home use and remote patient monitoring are identified using predicate analysis and specified for both patients and clinicians using user stories. To analyze risk, failure modes and effects analysis, adapted for medical device risk management, is performed on the generic user stories and a set of risk control measures is proposed. Many therapeutic applications of VR would be regulated as a medical device if they were to be commercially marketed. Understanding relevant standards for design and risk management early in the development process can help expedite the availability of innovative VR therapies that are safe and effective.

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Plasmonic and Hybrid Whispering Gallery Mode–Based Biosensors: Literature Review

Background The term “plasmonic” describes the relationship between electromagnetic fields and metallic nanostructures. Plasmon-based sensors have been used innovatively to accomplish different biomedical tasks, including detection of cancer. Plasmonic sensors also have been used in biochip applications and biosensors and have the potential to be implemented as implantable point-of-care devices. Many devices and methods discussed in the literature are based on surface plasmon resonance (SPR) and localized SPR (LSPR). However, the mathematical background can be overwhelming for researchers at times. Objective This review article discusses the theory of SPR, simplifying the underlying physics and bypassing many equations of SPR and LSPR. Moreover, we introduce and discuss the hybrid whispering gallery mode (WGM) sensing theory and its applications. Methods A literature search in ScienceDirect was performed using keywords such as “surface plasmon resonance,” “localized plasmon resonance,” and “whispering gallery mode/plasmonic.” The search results retrieved many articles, among which we selected only those that presented a simple explanation of the SPR phenomena with prominent biomedical examples. Results SPR, LSPR, tilted fiber Bragg grating, and hybrid WGM phenomena were explained and examples on biosensing applications were provided. Conclusions This minireview presents an overview of biosensor applications in the field of biomedicine and is intended for researchers interested in starting to work in this field. The review presents the fundamental notions of plasmonic sensors and hybrid WGM sensors, thereby allowing one to get familiar with the terminology and underlying complex formulations of linear and nonlinear optics.

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

Background A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements. Methods A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.

Point-of-Care Quantification of Serum Alpha-Fetoprotein for Screening Birth Defects in Resource-Limited Settings: Proof-of-Concept Study

Background Maternal serum alpha-fetoprotein (MSAFP) concentration typically increases during pregnancy and is routinely measured during the second trimester as a part of screening for fetal neural tube defects and Down syndrome. However, most pregnancy screening tests are not available in the settings they are needed the most. A mobile device–enabled technology based on MSAFP for screening birth defects could enable the rapid screening and triage of high-risk pregnancies, especially where maternal serum screening and fetal ultrasound scan facilities are not easily accessible. Shifting the approach from clinic- and laboratory-dependent care to a mobile platform based on our point-of-care approach will enable translation to resource-limited settings and the global health care market. Objective The objective of this study is to develop and perform proof-of-concept testing of a lateral flow immunoassay on a mobile platform for rapid, point-of-care quantification of serum alpha-fetoprotein (AFP) levels, from a drop of human serum, within a few minutes. Methods The development of the immunoassay involved the selection of commercially available antibodies and optimization of their concentrations by an iterative method to achieve the required detection limits. We compared the performance of our method with that of commercially obtained human serum samples, with known AFP concentrations quantified by the Abbott ARCHITECT chemiluminescent magnetic microparticle immunoassay (CMIA). Results We tested commercially obtained serum samples (N=20) with concentrations ranging from 2.2 to 446 ng/mL to compare the results of our point-of-care assay with results from the Abbott ARCHITECT CMIA. A correlation of 0.98 (P<.001) was observed on preliminary testing and comparison with the CMIA. The detection range of our point-of-care assay covers the range of maternal serum AFP levels observed during pregnancy. Conclusions The preliminary test results from the AFP test on the mobile platform performed in this study represent a proof of concept that will pave the way for our future work focused on developing a mobile device–enabled quad-screen point-of-care testing with the potential to enable the screening of high-risk pregnancies in various settings. The AFP test on the mobile platform can be applied to enable screening for high-risk pregnancies, within a few minutes, at the point of care even in remote areas where maternal serum tests and fetal ultrasound scans are not easily accessible; assessment of whether clinical follow-up and diagnostic testing may be needed after a positive initial screening evaluation; and development of surveillance tools for birth defects.

Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial

Background With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions. Objective This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients. Methods Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment–Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods. Results The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage. Conclusions Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation.

Personalized Monitoring Model for Electrocardiogram Signals: Diagnostic Accuracy Study

Background Due to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19. Objective The primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians. Methods In this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician. Results Our approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM. Conclusions Our proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.