Measurement of respiratory rate using wearable devices and applications to COVID-19 detection

Early Detection of COVID-19 in Female Athletes Using Wearable Technology

BACKGROUND

Heart rate variability (HRV), respiratory rate (RR), and resting heart rate (RHR) are common variables measured by wrist-worn activity trackers to monitor health, fitness, and recovery in athletes. Variations in RR are observed in lower-respiratory infections, and preliminary data suggest changes in HRV and RR are linked to early detection of COVID-19 infection in nonathletes.

HYPOTHESIS

Wearable technology measuring HRV, RR, RHR, and recovery will be successful for early detection of COVID-19 in NCAA Division I female athletes.

STUDY DESIGN

Cohort study.

LEVEL OF EVIDENCE

Level 2.

METHODS

Female athletes wore WHOOP, Inc. bands through the 2020 to 2021 competitive season. Of the athletes who tested positive for COVID (n = 33), 14 had enough data to be assessed (N = 14; 20.0 ± 1.3 years; 69.8 ± 7.2 kg; 172.0 ± 8.3 cm). Roughly 2 weeks of noninfected days were used to set baseline levels of HRV, RR, recovery, and RHR to compare with -3, -2, and -1 days before a positive COVID-19 result.

RESULTS

Increases in RR (P = 0.02) were detected on day -3. RHR (P < 0.01) and RR increased (P < 0.01), while HRV decreased (P < 0.05) on day -1, compared with baseline. Differences were noted in all variables on the day of the positive COVID-19 result: decreased HRV (P < 0.05) and recovery scores (P < 0.01), and increased RHR (P < 0.01) and RR (P < 0.01).

CONCLUSION

In female athletes, wearable technology was successful in predicting COVID-19 infection through changes in RR 3 days before a positive test, and also HRV and RHR the day before a positive test.

CLINICAL RELEVANCE

Wearable technology may be used, as part of a multifaceted approach, for the early detection of COVID-19 in elite athletes through monitoring of HRV, RR, and RHR for overall team health.

Mobile Diagnostic Clinics

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Noninvasive Temperature Measurements in Tissue-Simulating Phantoms Using a Solid-State Near-Infrared Sensor

The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature measurements are made at the skin surface, a measurement that is impacted by the ambient environment of the individual. The use of near-infrared spectroscopy provides the potential for a measurement below the epidermal layer of skin, thereby having the potential advantage of being more reflective of physiological conditions. The feasibility of noninvasive temperature measurements is demonstrated by using an in vitro model designed to mimic the near-infrared spectra of skin. A miniaturizable solid-state laser-diode-based near-infrared spectrometer was used to collect diffuse reflectance spectra for a set of seven tissue phantoms composed of different amounts of water, gelatin, and Intralipid. Temperatures were varied between 20-24 °C while collecting these spectra. Two types of partial least squares (PLS) calibration models were developed to evaluate the analytical utility of this approach. In both cases, the collected spectra were used without pre-processing and the number of latent variables was the only optimized parameter. The first approach involved splitting the whole dataset into separate calibration and prediction subsets for which a single optimized PLS model was developed. For this first case, the coefficient of determination (R2) is 0.95 and the standard error of prediction (SEP) is 0.22 °C for temperature predictions. The second strategy used a leave-one-phantom-out methodology that resulted in seven PLS models, each predicting the temperatures for all spectra in the held-out phantom. For this set of phantom-specific predicted temperatures, R2 and SEP values range from 0.67-0.99 and 0.19-0.65 °C, respectively. The stability and reproducibility of the sample-to-spectrometer interface are identified as major sources of spectral variance within and between phantoms. Overall, results from this in vitro study justify the development of future in vivo measurement technologies for applications as wearables for continuous, real-time monitoring of body temperature for both healthy and ill individuals.

A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates' determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman's coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman's coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Predicting stress in first-year college students using sleep data from wearable devices

Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user's experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation

The rapid diagnosis of respiratory virus infection through breath and blow remains challenging. Here we develop a wireless, battery-free, multifunctional pathogenic infection diagnosis system (PIDS) for diagnosing SARS-CoV-2 infection and symptom severity by blow and breath within 110 s and 350 s, respectively. The accuracies reach to 100% and 92% for evaluating the infection and symptom severity of 42 participants, respectively. PIDS realizes simultaneous gaseous sample collection, biomarker identification, abnormal physical signs recording and machine learning analysis. We transform PIDS into other miniaturized wearable or portable electronic platforms that may widen the diagnostic modes at home, outdoors and public places. Collectively, we demonstrate a general-purpose technology for rapidly diagnosing respiratory pathogenic infection by breath and blow, alleviating the technical bottleneck of saliva and nasopharyngeal secretions. PIDS may serve as a complementary diagnostic tool for other point-of-care techniques and guide the symptomatic treatment of viral infections.

Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring

Wrist-based respiratory rate (RR) measurement during sleep faces accuracy limitations. This study aimed to assess the accuracy of the RR estimation function during sleep based on the severity of obstructive sleep apnea (OSA) using the Samsung Galaxy Watch (GW) series. These watches are equipped with accelerometers and photoplethysmography sensors for RR estimation. A total of 195 participants visiting our sleep clinic underwent overnight polysomnography while wearing the GW, and the RR estimated by the GW was compared with the reference RR obtained from the nasal thermocouple. For all participants, the root mean squared error (RMSE) of the average overnight RR and continuous RR measurements were 1.13 bpm and 1.62 bpm, respectively, showing a small bias of 0.39 bpm and 0.37 bpm, respectively. The Bland-Altman plots indicated good agreement in the RR measurements for the normal, mild, and moderate OSA groups. In participants with normal-to-moderate OSA, both average overnight RR and continuous RR measurements achieved accuracy rates exceeding 90%. However, for patients with severe OSA, these accuracy rates decreased to 79.45% and 75.8%, respectively. The study demonstrates the GW's ability to accurately estimate RR during sleep, even though accuracy may be compromised in patients with severe OSA.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.

Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records

We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference (p < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.

Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection

This paper presents the Coswara dataset, a dataset containing diverse set of respiratory sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 individuals (1819 SARS-CoV-2 negative, 674 positive, and 142 recovered subjects). The respiratory sounds contained nine sound categories associated with variants of breathing, cough and speech. The rich metadata contained demographic information associated with age, gender and geographic location, as well as the health information relating to the symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test status. Our study is the first of its kind to manually annotate the audio quality of the entire dataset (amounting to 65 hours) through manual listening. The paper summarizes the data collection procedure, demographic, symptoms and audio data information. A COVID-19 classifier based on bi-directional long short-term (BLSTM) architecture, is trained and evaluated on the different population sub-groups contained in the dataset to understand the bias/fairness of the model. This enabled the analysis of the impact of gender, geographic location, date of recording, and language proficiency on the COVID-19 detection performance.

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

COVID-19 is a respiratory disease caused by the SARS-CoV-2 contagion, severely disrupted the healthcare infrastructure. Various countries have developed COVID-19 vaccines that have effectively prevented the severe symptoms caused by the virus to a certain extent. However, a small section of people continues to perish. Artificial intelligence advances have revolutionized healthcare diagnosis and prognosis infrastructure. In this study, we predict the severity of COVID-19 using heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors. This study extensively reviews various classifier architectures to predict the COVID-19 severity. We built and evaluated multiple pipelines entailing combinations of five state-of-the-art data-balancing techniques (Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic, Borderline SMOTE, SMOTE with Tomek links, and SMOTE with Edited Nearest Neighbor (ENN)) and twelve heterogeneous classifiers such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Xgboost, Extratrees, Adaboost, Light GBM, Catboost, and 1-D Convolution Neural Network. The best-performing pipeline consists of Random Forest trained on Borderline SMOTE balanced data that produced the highest recall of 83%. We deployed Explainable Artificial Intelligence tools such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, ELI5, Qlattice, Anchor, and Feature Importance to demystify complex tree-based ensemble models. These tools provide valuable insights into the significance of critical features in the severity prediction of a COVID-19 patient. It was observed that changes in respiratory rate, blood pressure, lactate, and calcium values were the primary contributors to the increase in severity of a COVID-19 patient. This architecture aims to be an explainable decision-support triaging system for medical professionals in countries lacking advanced medical technology and infrastructure to reduce fatalities.

A survey on computer vision based human analysis in the COVID-19 era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of ( i ) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and ( ii ) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

The Impact of SARS-CoV-2 Infection on Heart Rate Variability: A Systematic Review of Observational Studies with Control Groups

Autonomic nervous system (ANS) dysfunction can arise after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and heart rate variability (HRV) tests can assess its integrity. This review investigated the relationship between the impact of SARS-CoV-2 infection on HRV parameters. Comprehensive searches were conducted in four electronic databases. Observational studies with a control group reporting the direct impact of SARS-CoV-2 infection on the HRV parameters in July 2022 were included. A total of 17 observational studies were included in this review. The square root of the mean squared differences of successive NN intervals (RMSSD) was the most frequently investigated. Some studies found that decreases in RMSSD and high frequency (HF) power were associated with SARS-CoV-2 infection or the poor prognosis of COVID-19. Also, decreases in RMSSD and increases in the normalized unit of HF power were related to death in critically ill COVID-19 patients. The findings showed that SARS-CoV-2 infection, and the severity and prognosis of COVID-19, are likely to be reflected in some HRV-related parameters. However, the considerable heterogeneity of the included studies was highlighted. The methodological quality of the included observational studies was not optimal. The findings suggest rigorous and accurate measurements of HRV parameters are highly needed on this topic.

A caveat to using wearable sensor data for COVID-19 detection: The role of behavioral change after receipt of test results

BACKGROUND

Recent studies indicate that wearable sensors can capture subtle within-person changes caused by SARS-CoV-2 infection and play a role in detecting COVID-19 infections. However, in addition to direct effects of infection, wearable sensor data may capture changes in behavior after the receipt of COVID test results. At present, it remains unclear to what extent the observed discriminative performance of the wearable sensor data is affected by behavioral changes upon receipt of the test results.

METHODS

We conducted a retrospective study of wearable sensor data in a sample of medical interns who had symptoms and received COVID-19 test results from March to December 2020, and calculated wearable sensor metrics incorporating changes in step, sleep, and resting heart rate for interns who tested positive (cases, n = 22) and negative (controls, n = 83) after symptom onset. All these interns had wearable sensor data available for > 50% of the days in pre- and post-symptom onset periods. We assessed discriminative accuracy of the metrics via area under the curve (AUC) and tested the impact of behavior changes after receiving test results by comparing AUCs of three models: all data, pre-test-result-only data, and post-test-result-only data.

RESULTS

Wearable sensor metrics differentiated between symptomatic COVID-19 positive and negative individuals with good accuracy (AUC = 0.75). However, the discriminative capacity of the model with pre-test-result-only data substantially decreased (AUC from 0.75 to 0.63; change = -0.12, p = 0.013). The model with post-test-result-only data did not produce similar reductions in discriminative capacity.

CONCLUSIONS

Changes in wearable sensor data, especially physical activity and sleep, are robust indicators of COVID-19 infection, though they may be reflective of a person's behavior change after receiving a positive test result as opposed to a physiological signature of the virus. Thus, wearable sensor data could facilitate the monitoring of COVID-19 prevalence, but not yet replace SARS-CoV-2 testing.

Reducing chronic disease may just be a walk in the park

Wearable technology allows the collection of real-world granular data at scales that would be impossible using traditional collection methods. Master et al. demonstrate the power of this technology to estimate the risk of disease based on daily step counts.¹.

Wearable Devices in Veterinary Health Care

Wearables are an up-and-coming tool in veterinary health care. This article reviews the current and prospective wearable technology for veterinary patients and the future of wearables in veterinary medicine. These devices allow veterinarians to monitor a patient's vital signs remotely, in addition to other variables, and push the profession away from a reactive health-care system toward a proactive culture that is able to identify diseases earlier. Advances in this technology have the potential to profoundly change the way veterinarians obtain and use patient data to practice medicine.

Сardiorespiratory relationships in people of young age depending on the composite composition of the body

BACKGROUND: The decisive importance of the sympathetic and parasympathetic nervous system in maintaining vegetative homeostasis requires the determination of sensitive non-invasive parameters of multidimensional outpatient monitoring of cardiorespiratory adaptation under various physiological and clinical conditions, taking into account the function of external respiration (FER), compound body composition and heart rate variability (HRV).

AIM: To identify concomitant changes in HRV, HR and compound body composition in young people as markers of cardiorespiratory adaptation and rehabilitation.

MATERIALS AND METHODS: On the basis of the Kuban State Medical University, a single-centre, interventional, cross-sectional, single-sample, comparative, uncontrolled study of a general group of young people in which respiratory parameters and parameters of the compound body composition were determined. Some individuals in this group additionally underwent Holter monitoring of the electrocardiogram (ECG) at short intervals.

RESULTS: In young people, a change in the compound body composition with an increase in total fat mass, visceral and body fat is associated with a decrease in respiratory function (a decrease in the Tiffno index, a decrease in the maximum middle-expiratory flow — MMEF), manifested by a decrease in HRV (according to the TI indicator), the absence of an increase in the autonomic regulation circuit (according to SDNN indicator), a decrease in parasympathetic activity (in terms of rMSSD) and the absence of sympathetic activation (in terms of SDANN). Positive shifts in the form of an increase in trunk muscles, the total amount of water and a decrease in the total fat mass are accompanied by an increase in lung capacity, forced expiratory volume in the first second and a change in HRV with sympathetic (in terms of LF / HF, SDANN) and parasympathetic activation (in terms of rMSSD), an increase in HRV (in terms of TI) and an increase in the autonomic regulation circuit of the vegetative nervous system (in terms of SDNN).

CONCLUSION: Accurate and rapid diagnostics of vegetative homeostasis requires a comprehensive correlative analysis of the parameters characterizing HRV in short recordings, the compound composition of the human body and respiratory function.

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.

Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19

The COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become one of the worst global pandemics of the century. Wearable devices are well suited for continuously measuring heart rate. Here we show that the Resting Heart Rate is modified for several weeks following a COVID-19 infection. The Resting Heart Rate shows 3 phases: 1) elevated during symptom onset, with average peak increases relative to the baseline of 1.8% (3.4%) for females (males), 2) decrease thereafter, reaching a minimum on average ≈13 days after symptom onset, and 3) subsequent increase, reaching a second peak on average ≈28 days from symptom onset, before falling back to the baseline ≈112 days from symptom onset. All estimates vary with disease severity¹.

Best practices in the real-world data life cycle

With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.

Smartphone movement sensors for the remote monitoring of respiratory rates: Technical validation

Background

Mobile health (mHealth) offers potential benefits to both patients and healthcare systems. Existing remote technologies to measure respiratory rates have limitations such as cost, accessibility and reliability. Using smartphone sensors to measure respiratory rates may offer a potential solution to these issues.

Objective

The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure respiratory rates using movement sensors.

Methods

In Study 1, 15 participants simultaneously measured their respiratory rates with the app and a Food and Drug Administration-cleared reference device. A novel reference analysis method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their respiratory rates using the app, and these measures were compared to the novel reference. The usability of the app was also assessed in both studies.

Results

The app, when compared to the Food and Drug Administration-cleared and novel references, respectively, showed a mean absolute error of 1.65 (SD  =  1.49) and 1.14 (1.44), relative mean absolute error of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement = –3.27 to 4.89) and 0.08 (–3.68 to 3.51). Pearson correlation coefficients were 0.700 and 0.885. Ninety-three percent of participants successfully operated the app on their first use.

Conclusions

The accuracy and usability of the app demonstrated here in individuals with a normal respiratory rate range show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor respiratory rates. Further research should validate the benefits that this technology may offer patients and healthcare systems.