Search

Viewing brain function through the lens of other physiological processes has critically added to our understanding of human cognition. Further advances though may need a closer look at the interactions between these physiological processes themselves. Here we characterize the interplay of the highly periodic, and metabolically vital respiratory process and fluctuations in arousal neuromodulation, a process classically seen as nonperiodic. In the data from three experiments ( N  = 56 / 27 / 25 women and men), we tested for covariations in respiratory and pupil size (arousal) dynamics. After substantiating a robust coupling in the largest dataset, we further show that coupling strength decreases during task performance compared with rest and that it mirrors a decreased respiratory rate when participants take deeper breaths. Taken together, these findings suggest a stronger link between respiratory and arousal processes than previously thought. Moreover, these links imply a stronger coupling during periods of rest, and the effect of respiratory rate on the coupling suggests a driving role. As a consequence, studying the role of neuromodulatory arousal on cortical function may also need to consider respiratory influences., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Kluger et al.)

Breathing patterns (respiratory kinematics) contain vital prognostic information. This dimension of physiology is not captured by conventional vital signs. We sought to determine the feasibility and utility of quantifying respiratory kinematics. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We extracted 34 features based on an explainable correspondence with well-established breathing patterns. K-means clustering revealed that respiratory kinematics had three dimensions apart from the respiratory rate. We represented these dimensions using respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of the kinematic measures revealed two profiles consistent with the established clinical constructs of labored and unlabored breathing. In logistic regression, the labored breathing profile improved model discrimination for critical illness beyond the Sequential Organ Failure Assessment (SOFA) score (AUROC 0.77 v/s 0.72; p = 0.02). These findings quantitatively confirm the prior understanding that the respiratory rate alone does not adequately represent the complexity of respiratory kinematics; they demonstrate that high-dimensional signatures of labored breathing can be quantified in routine practice settings, and they can improve predictions of clinical deterioration., Competing Interests: Declarations Competing interests SMG, WBA, RDW, SJR, and JRM are co-inventors in United States Patent Application Serial No. 18/018,469 for “METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR ANALYZING RESPIRATORY KINEMATICS”. Other authors declare that they have no competing interests. Ethical approval This study was funded by the Ivy foundation COVID-19 translational research fund. It was approved by University of Virginia Health Sciences Institutional Review Board (study number 20844)., (© 2024. The Author(s).)

Aim: To investigate if tachypneic response to hypoxia is decreased in older patients., Methods: We included all patients ≥65 years of age attending 52 Spanish emergency departments (EDs) for whom peripheral arterial oxygen saturation (SatO 2 ) measured by pulsioxymetry and respiratory rate (RR) were registered at ED arrival. We assessed the relationship between SatO2 and RR in different models, and with the best-fitting model, we independently analyzed this relationship in four subgroups according to patient age (65-69, 70-79, 80-89, and ≥90 years). Five sensitivity analyses using different subsets of patients were carried out to check for the consistency of the results., Results: We included 7126 patients, with medians for SatO 2 and RR of 97% (interquartile range [IQR]: 94-98) and 15 bpm (IQR: 15-16), respectively. We found significant associations (P < 0.001) between SatO 2 and RR in every model tested (P < 0.001 for all), with the quadratic model obtaining the best fit (R 2 : 0.098) over those obtained with linear (R 2 : 0.096) and logarithmic (R 2 : 0.092) models. The same was observed in sensitivity analyses, with R 2 for quadratic models ranging from 0.069 in patients with low comorbidity and 0.102 in patients breathing room air. The mean RR for 100% SatO 2 was 15 bpm and increased as SatO 2 decreased, although with a progressive slowing of the slope, with a mean RR of 27 at 50% SatO 2 . We detected a decreased RR response to increasing hypoxemia according to age and, while the RR curve was higher and with a progressively steepening slope in the 972 patients aged 65-69 (mean RR of 42 bpm with 50% SatO 2 ), a progressive slowing of slope was observed in the 2693 patients aged 70-79 (mean RR of 28 with 50% SatO2), the 2582 aged 80-89 (mean RR of 25) and the 879 aged ≥90 (mean RR of 23). Sensitivity analyses provided very similar results., Conclusion: Tachypneic response to hypoxemia in older patients decreases as age advances, regardless of the reason leading to hypoxemia. Geriatr Gerontol Int 2024; 24: 1120-1129., (© 2024 Japan Geriatrics Society.)

The neurovisceral integration model proposes that information flows bidirectionally between the brain and the heart via the vagus nerve, indexed by vagally mediated heart rate variability (vmHRV). Voluntary reduction in breathing rate (slow-paced breathing, SPB, 5.5 Breathing Per Minute (BPM)) can enhance vmHRV. Additionally, prefrontal transcranial direct current stimulation (tDCS) can modulate the excitability of the prefrontal region and influence the vagus nerve. However, research on the combination of SPB and prefrontal tDCS to increase vmHRV and other cardiac (heart rate (HR) and blood pressure) and peripheral (skin conductance) indices is scarce. We hypothesized that the combination of 20 min of SPB and prefrontal tDCS would have a greater effect than each intervention in isolation. Hence, 200 participants were divided into four groups: active tDCS with SPB, active tDCS with 15 BPM breathing, sham tDCS with SPB, and sham tDCS with 15 BPM breathing. Regardless of the tDCS condition, the 5.5 BPM group showed a significant increase in vmHRV over 20 minutes and significant decreases in HR at the first and second 5-min epochs of the intervention. Regardless of breathing condition, the active tDCS group exhibited higher HR at the fourth 5-min epoch of the intervention than the sham tDCS group. No other effects were observed. Overall, SPB is a robust technique for increasing vmHRV, whereas prefrontal tDCS may produce effects that counteract those of SPB. More research is necessary to test whether and how SPB and neuromodulation approaches can be combined to improve cardiac vagal tone., (© 2024 Society for Psychophysiological Research.)

Purpose: To evaluate the agreement between the two Gas Exchange Thresholds (GETs = GET1 and GET2), identified by the conventional V-Slope method, and two Respiratory Frequency Thresholds (f R Ts = f R T1 and f R T2) obtained from a novel, low-cost, and simple method of breakpoint determination., Methods: Fifty middle-aged males (age: 50-58 years; V ˙ o 2peak : 37.5 ± 8.6 mL·Kg -1 ·min -1 ), either healthy or with chronic illnesses, underwent an incremental cycle exercise test to determine maximal oxygen uptake ( V ˙ o 2max / V ˙ o 2peak ), GETs and f R Ts., Results: There were no statistical differences [P > 0.05; ES: 0.17 to 0.32, small] between absolute and relative (56-60% V ˙ o 2peak ) oxygen uptake ( V ˙ o 2 ) values at GET1 with those obtained at f R T1, nor between V ˙ o 2 values at GET2 with those at f R T2 (76-78% V ˙ o 2peak ). Heart rate (HR) at f R T1, and V ˙ o 2 and HR at f R T2 showed very large correlations (r = 0.75-0.82; P < 0.001) and acceptable precision (SEE < 7-9%) in determination of their corresponding values at GET1 and GET2. The precision in the estimation of V ˙ o 2 at GET1 from f R T1 was moderate (SEE = 15%), while those of power output at GET1 (SEE = 23%) and GET2 (SEE = 12%) from their corresponding f R Ts values were very poor to moderate., Conclusion: HR at f R T1 and V ˙ o 2 and HR at f R T2, determined using a new objective and portable approach, may potentially serve as viable predictors of their respective GETs. This method may offer a simplified, cost-effective, and field-based approach for determining exercise threshold intensities during graded exercise., (© 2024. The Author(s).)

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model's superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development.

Objective: Non-contact respiratory rate estimation (RR) is highly desirable for infants because of their sensitive skin. We propose a novel RGB video-based RR estimation method for infants in the neonatal intensive care unit (NICU) that can accurately measure the RR contact-less., Methods and Procedures: We utilize Eulerian video magnification (EVM) method and develop an adaptive peak prominence threshold value estimation method to address challenges of RR estimation (e.g., dark environments, shallow breathing, babies swaddled or under blankets). We recruited 13 infants recorded for 4 consecutive hours per case. We then evaluate the performance of the algorithm for several (i.e., 19 to 25) randomly selected videos, each lasting 1 minute, for each case., Results: Intraclass correlation coefficients of the proposed method over manually and automatically selected ROIs are 0.91 (95%CI: [Formula: see text]) and 0.88 (95%CI: [Formula: see text]), indicating excellent and good reliability, respectively. The Bland-Altman analysis of the proposed algorithm shows higher agreement between the estimated values via the proposed method and visually counted RR than the agreement between the RR obtained from the impedance sensors and reference RR, and agreement between a former EVM-based method and reference RR values., Conclusion: Our algorithm shows promising results for RR estimation in a real-life NICU environment under various conditions that can confound the estimation., Clinical Impact: We present a robust algorithm for non-contact neonatal respiratory rate monitoring, capable of performing well under various environmental lighting conditions in NICU, even when the infant is clothed or covered., (© 2024 The Authors.)

Capnography, routinely used in operating rooms and intensive care units, reveals essential information on lung ventilation and ventilation-perfusion matching. Mainstream capnography directly measures CO 2 in the breathing circuit for accurate analysis and is considered a reference technique. Sidestream capnography, however, analyzes gas away from the patient leading to potentially less accurate measures. While these methodological differences impact the capnogram indices in mechanically ventilated patients, such assessments during spontaneous breathing are essentially lacking. Accordingly, we aimed to compare mainstream and sidestream capnography in spontaneously breathing subjects, focusing on differences in capnogram shape and dead space indices at various respiratory rates. Simultaneous mainstream and sidestream time and volumetric capnography were performed on spontaneously breathing adults (n = 35). Measurements were performed during controlled low (10/min), medium (12/min), and high (20/min) breathing rates as a challenge. Correlation and Bland-Altman analyses were used to assess trends and agreements between time and volumetric capnography indices obtained by the mainstream and sidestream techniques, including end-tidal CO 2 (ETCO 2 ), shape factors reflecting the slopes of phases 2 and 3, and anatomical and physiological dead space fractions. ETCO 2 and physiological dead space measured by mainstream and sidestream techniques showed excellent correlations (r > 0.90, p < 0.001 for all breathing rates) and agreements. While strong correlations and moderate agreements were evidenced in the parameters reflecting the late phase of expiration (phase 3 slope and exhaled CO 2 volume), these relationships were weaker for indices related to the early phase of expiration (phase 2 slope, anatomical dead space). Changing breathing frequency caused significant alterations in all capnography parameters, which were detectable by both mainstream and sidestream techniques. Sidestream capnography cannot substitute the more accurate mainstream technique for measuring the absolute values of shape factors and ventilation dead space fractions. However, sidestream capnography is also able to detect and track changes in uneven alveolar emptying, ventilation-perfusion matching and ventilation dead space fraction in spontaneously breathing subjects., (© 2024. The Author(s).)

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas.

Background: As Japan is the world's fastest-aging society with a declining population, it is challenging to secure human resources for care providers. Therefore, the Japanese government is promoting digital transformation and the use of nursing care equipment, including nonwearable devices that monitor heart and respiratory rates. However, the feasibility of monitoring heart and respiratory rates with nonwearable devices and the consistency of the rates measured have not been reported., Objective: In this study, we focused on a sheet-type nonwearable device (Safety Sheep Sensor) introduced in many nursing homes. We evaluated the feasibility of monitoring heart rate (HR) and respiratory rate (RR) continuously using nonwearable devices and the consistency of the HR and RR measured., Methods: A sheet-type nonwearable device that measured HR and RR every minute through body vibrations was placed under the mattress of each participant. The participants in study 1 were healthy individuals aged 20-60 years (n=21), while those in study 2 were older adults living in multidwelling houses and required nursing care (n=20). The HR was measured using standard methods by the nurse and using the wearable device (Silmee Bar-type Lite sensor), and RR was measured by the nurse. The primary outcome was the mean difference in HR and RR between nonwearable devices and standard methods., Results: The mean difference in HR was -0.32 (SD 3.12) in study 1 and 0.04 (SD: 3.98) in study 2; both the differences were within the predefined accepted discrepancies (<5 beats/min). The mean difference in RR was -0.98 (SD 3.01) in study 1 and -0.49 (SD 2.40) in study 2; both the differences were within the predefined accepted discrepancies (3 breaths/min)., Conclusions: HR and RR measurements obtained using the nonwearable devices and the standard method were similar. Continuous monitoring of vital signs using nonwearable devices can aid in the early detection of abnormal conditions in older people., (© Kasumi Ikuta, Miya Aishima, Maiko Noguchi-Watanabe, Sakiko Fukui. Originally published in JMIR Human Factors (https://humanfactors.jmir.org).)

Successful implementation of remote monitoring of vital signs outside of the hospital setting hinges on addressing three crucial unmet needs: longer-term wear, skin comfort and signal quality. Earlier, we developed a Health Patch research platform that uses self-adhesive dry electrodes to measure vital digital biomarkers. Here, we report on the analytical validation for heart rate, heart rate variability and respiration rate. Study design included n = 25 adult participants with data acquisition during a 30-minute exercise protocol involving rest, squats, slow, and fast cycling. The Shimmer3 ECG Unit and Cosmed K5, were reference devices. Data analysis showed good agreement in heart rate and marginal agreement in respiratory rate, with lower agreement towards higher respiratory rates. The Lin's concordance coefficient was 0.98 for heart rate and 0.56 for respiratory rate. Heart rate variability (RMSSD) had a coefficient of 0.85. Participants generally expressed a positive experience with the technology, with some minor irritation from the medical adhesive. The results highlighted potential of this technology for short-to-medium term clinical use for cardiorespiratory health, due to its reliability, accuracy, and compact design. Such technology could become instrumental for remote monitoring providing healthcare professionals with continuous data, remote assessment and enhancing patient outcomes in cardiorespiratory health management., (© 2024. The Author(s).)

Thermal videos provide a privacy-preserving yet information-rich data source for remote health monitoring, especially for respiration rate (RR) estimation. This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. A detection transformer (DeTr) first finds the subject's facial region of interest in each thermal frame. A respiratory signal is estimated from a dynamically cropped thermal video using 3D convolutional neural networks and bi-directional long short-term memory stages. To account for the expected phase shift between the respiration measured using a respiratory effort belt vs. a facial video, a novel loss function based on negative maximum cross-correlation and absolute frequency peak difference was introduced. Thermal recordings from 22 subjects, with simultaneous gold standard respiratory effort measurements, were studied while sitting or standing, both with and without a face mask. The RR estimation results showed that our proposed method outperformed existing models, achieving an error of only 1.6 breaths per minute across the four conditions. The proposed method sets a new State-of-the-Art for RR estimation accuracy, while still permitting real-time RR estimation.

(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO 2 ). This study assessed the validity and reliability of two types of wearable sensors, based on electrocardiogram or photoplethysmography, compared with continuous monitoring of patients recovering from trauma surgery at the postanesthesia care unit. (2) Methods: In a prospective observational study, HR, RR, SpO 2 , and temperature of patients were simultaneously recorded with the VitalPatch and Radius PPG and compared with reference monitoring. Outcome measures were formulated as correlation coefficient for validity and mean difference with 95% limits of agreement for reliability for four random data pairs and 30-min pairs per vital sign per patient. (3) Results: Included were 60 patients. Correlation coefficients for VitalPatch were 0.57 to 0.85 for HR and 0.08 to 0.16 for RR, and for Radius PPG, correlation coefficients were 0.60 to 0.83 for HR, 0.20 to 0.12 for RR, and 0.57 to 0.61 for SpO 2 . Both sensors presented mean differences within the cutoff values of acceptable difference. (4) Conclusions: Moderate to strong correlations for HR and SpO 2 were demonstrated. Although mean differences were within acceptable cutoff values for all vital signs, only limits of agreement for HR measured by electrocardiography were considered clinically acceptable.

Objective : Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods : In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results : Compared with electrocardiography, the present work achieves limits of agreement of [-2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [-0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions : These results showcased Speckle Vibrometry's potential in vital sign monitoring in a clinical setting. Significance : This is the first human clinical study for Speckle Vibrometry.

Introduction: Substance abuse is a widespread problem, with high rates of treatment dropout. Stress plays a crucial role in this problem, so innovative interventions with stressed patients can assist them in completing treatment., Methods: This study is a randomized controlled trial with 60 participants who have substance abuse disorder undergoing detoxification at a residency facility in Tehran, Iran. Participants were randomly assigned to one of three groups: watching a 360° video of nature, a 360° video of a city environment, or no virtual experience. The intervention was performed only for one session. Psychological stress was measured using the Positive and Negative Affect Schedule and Perceived Stress Scale before and after the intervention. Physiological stress was assessed through respiratory rate, skin conductance, and heart rate recordings during the virtual reality (VR) experience. Data analysis was performed using R software (version 4.2)., Results: Paired t-test results indicated significant psychological differences before and after virtual nature experiences, but not in the control and city groups. The repeated measure ANOVA showed a significant reduction in skin conductance (p < 0.01) and respiratory rate (p < 0.01) scores in the nature group., Conclusion: The findings suggest that VR relaxation could be a potentially beneficial intervention for reducing stress in patients during detoxification., (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Background: This study examines the psychophysiological differences between virtual reality (VR) and real archery. It explores whether VR archery induces heart rate (HR), heart rate variability (HRV), and breathing rates similar to those experienced in real archery. Additionally, the study assesses differences in perceived anxiety, difficulty, confidence, rate of perceived exertion (RPE), and shooting performance between the two modalities, providing insights into the efficacy of VR as a training tool for archery., Methods: Twenty-two (women: 8) individuals aged 20-24 participated in the study. We first recorded individuals' resting HR, HRV, and breathing rates during baseline. Afterward, participants shot 10 real and virtual arrows from 18 m, whereas their HR, HRV, and breathing rate were measured, each lasting 4 min. Performance in VR and real archery was determined separately as the sum of the shots. We performed paired sample t-tests to compare individuals' performance, psychological, and psychophysiological responses recorded during VR and real arrow shooting. Afterward, we compared percentage changes between VR and real archery., Results: Results showed that HR and root mean square of successive differences (RMMSD) were significantly higher during real archery compared to virtual archery. In addition, VR archery led to a greater percentage change in RMSSD compared to real archery. Participants reported greater RPE and perceived difficulty after real archery. Performance was also higher during VR archery than real archery., Conclusions: Consequently, the results of the present study illustrated that VR, and real archery might lead to different autonomic response patterns in terms of vagal activity., (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Objectives: This work aimed to evaluate the effect of music-based intervention (MBI) on anxiety and stress-related vital signs (heart rate, respiratory rate and blood pressure) in patients undergoing cardiac catheterization., Design: A systematic review and meta-analysis., Methods: This systematic review and meta-analysis was conducted according to PRISMA guidelines. PubMed, Cochrane Library, Embase and CINAHL were systematically searched from inception to October 31, 2023. Two authors independently searched electronic databases, selected literature, extracted data and assessed the risk of bias according to the eligibility criteria. The Review Manager software (RevMan version 5.4.1) was used to perform meta-analysis., Results: Eleven randomized controlled trials (RCTs) with adult patients (n = 1204) (passive music therapy, 8 studies; passive music listening, 3 studies) were enrolled and brought into qualitative assessment. Nine of these RCTs (n = 868) were taken into quantitative analysis. Meta-analysis using the random-effects model revealed that the difference in the pre-post anxiety level in the music group was significantly greater than that in the control group. However, meta-analysis results for heart rate, respiratory rate, systolic blood pressure and diastolic blood pressure did not show significant differences., Conclusion: The findings suggested that MBI had a significant effect on reducing anxiety in patients undergoing cardiac catheterization. However, the limited quantity and quality of included studies highlight the need for additional research to comprehensively analyze the influence of MBI on anxiety reduction in this patient population., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Moderate-intensity aerobic exercise is safe and beneficial in atrial fibrillation (AF) and coronary heart disease (CHD). Irregular or rapid heart rates (HR) in AF and other heart conditions create a challenge to using HR to monitor exercise intensity. The purpose of this study was to assess the potential of breathing frequency (BF) to monitor exercise intensity in people with AF and CHD without AF., Methods: This observational study included 30 AF participants (19 Male, 70.7 ± 8.7 yrs) and 67 non-AF CHD participants (38 Male, 56.9 ± 11.4 yrs). All performed an incremental maximal exercise test with pulmonary gas exchange., Results: Peak aerobic power in AF ( V ˙ O 2 peak; 17.8 ± 5.0 ml.kg -1 .min -1 ) was lower than in CHD (26.7 ml.kg -1 .min -1 ) (p < .001). BF responses in AF and CHD were similar (BF peak: AF 34.6 ± 5.4 and CHD 36.5 ± 5.0 breaths.min -1 ; p = .106); at the 1st ventilatory threshold (BF@VT-1: AF 23.2 ± 4.6; CHD 22.4 ± 4.6 breaths.min -1 ; p = .240). % V ˙ O 2 peak at VT-1 were similar in AF and CHD (AF: 59%; CHD: 57%; p = .656)., Conclusion: With the use of wearable technologies on the rise, that now include BF, this first study provides an encouraging potential for BF to be used in AF and CHD. As the supporting data are based on incremental ramp protocol results, further research is required to assess BF validity to manage exercise intensity during longer bouts of exercise., (© 2024. The Author(s).)

Background: Respiration is a crucial determinant of autonomic balance and heart rate variability (HRV). The comparative effect of spontaneous versus paced breathing on HRV has been almost exclusively explored in healthy adults and never been investigated in an injured military cohort., Objective: To examine the effect of spontaneous versus paced breathing on HRV in veterans with combat-related traumatic injury (CRTI)., Design: Observational cohort study., Setting: ArmeD serVices trAuma rehabilitatioN outComE (ADVANCE) study, Stanford Hall, UK., Participants: The sample consisted of 100 randomly selected participants who sustained CRTI (eg, amputation) during their deployment (Afghanistan 2003-2014) and were recruited into the ongoing ADVANCE prospective cohort study., Intervention: Not applicable., Main Outcome Measure: HRV was recorded using a single-lead ECG. HRV data were acquired during a sequential protocol of 5-minute spontaneous breathing followed immediately by 5 minutes of paced breathing (six cycles/minute) among fully rested and supine participants. HRV was reported using time domain (root mean square of successive differences), frequency domain (low frequency and high frequency) and nonlinear (sample entropy) measures. The agreement between HRV during spontaneous versus paced breathing was examined using the Bland-Altman analysis., Results: The mean age of participants was 36.5 ± 4.6 years. Resting respiratory rate was significantly higher with spontaneous versus paced breathing (13.4 ± 3.4 vs. 7.6 ± 2.0 breaths/minute; p < .001), respectively. Resting mean heart rate and root mean square of successive differences were significantly higher with paced breathing than spontaneous breathing (p < .001). Paced breathing significantly increased median low frequency power than spontaneous breathing (p < .001). No significant difference was found in the absolute power of high frequency between the two breathing protocols. The Bland-Altman analysis revealed poor agreement between HRV values during spontaneous and paced breathing conditions with wide limits of agreement., Conclusion: Slow-paced breathing leads to higher HRV than spontaneous breathing and could overestimate resting "natural-state" HRV., (© 2024 The Authors. PM&R published by Wiley Periodicals LLC on behalf of American Academy of Physical Medicine and Rehabilitation.)

Introduction: The interaction between the cardiovascular and respiratory systems in healthy subjects is determined by the autonomic nervous system and reflected in respiratory sinus arrhythmia. Recently, another pattern of cardio-respiratory coupling (CRC) has been proposed linking synchronization of heart and respiratory system. However, CRC has not been studied precisely in heart failure (HF) with reduced ejection fraction (EF) (HFrEF) according to the myocardial recovery., Methods: 10-min resting electrocardiography measurements were performed in persistent HFrEF patients (n=40) who had a subsequent left ventricular EF (LVEF) of ≤ 40 %, HF with recovered EF patients (HFrecEF) (n=41) who had a subsequent LVEF of > 40 % and healthy controls (n=40). Respiratory frequency, respiratory rate, CRC index, time-domain, frequency-domain and nonlinear heart rate variability indices were obtained using standardized software-Kubios™. CRC index was defined as respiratory high-frequency peak minus heart rate variability high-frequency peak., Results: Respiratory rate was positively correlated with high-frequency (HF) peak (Hz) in both persistent HFrEF group (p<0.001) and HFrecEF group (p<0.001), while respiratory rate was negatively correlated with HF power (ms 2 ) in the healthy controls (p<0.05). CRC index was lowest in the persistent HFrEF group followed by HFrecEF and was high in healthy controls (0.008 vs 0.012 vs 0.056 Hz, p=0.03)., Conclusion: CRC index was lowest in patients with impaired myocardial recovery, which indicates that cardio-respiratory synchrony is stronger in persistent HFrEF. This may represent a higher HF peak (Hz)/lower HF power (ms 2 ) and abnormal sympathovagal balance in persistent HFrEF group compared to healthy controls. Further work is underway to tests this hypothesis and determine the utility of CRC index in HF phenotypes and its utility as a potential biomarker of response with neuromodulation., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

(1) Background: Exploring the relationship between spontaneous breathing patterns and aerobic and anerobic running exercise performance can greatly improve our understanding of optimizing physical fitness. Spontaneous breathing patterns refer to how the rhythm and depth of breathing affect performance and physical adaptation during exercise. (2) Methods: This study aimed to investigate this relationship by enrolling 240 college students (120 males and 120 females, aged 18-22). We evaluated their resting respiratory rate (RR), the combined total of abdominal and thoracic movements (AM+TM), the proportion of abdominal movement to the overall respiratory movement (AM/(AM+TM)), and the inhalation to exhalation ratio (I/E ratio). Additionally, their performance in a 50 m sprint (measuring anerobic capability), an 800- or 1000 m run (assessing mixed aerobic and anerobic capacity), and a 12 min distance run (evaluating aerobic capacity) was recorded. (3) Results: Our findings, through both correlational and comparative analyses, indicate that a larger AM+TM is predictive of a greater distance covered in the 12 min run, suggesting enhanced aerobic capacity. Interestingly, among female participants, a lower body mass index (BMI) coupled with a higher proportion of abdominal movement (AM/(AM+TM)) was linked to better performance in the 800 m run, indicative of superior mixed aerobic and anerobic capacities. These results imply that women with a larger tidal volume and those with a lower BMI but higher abdominal contribution to breathing at rest may exhibit better aerobic and mixed exercise capacities, respectively. (4) Conclusions: Based on these findings, we recommend that healthcare professionals and physical education instructors incorporate respiratory pattern assessments into their practices to potentially improve the physical health of their clients and students, with a particular emphasis on female populations. This study underscores the importance of understanding the intricate relationship between spontaneous respiratory patterns and exercise capabilities in enhancing overall physical fitness and health.

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.

Objective. The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS). Approach. The analysed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5 min sessions of controlled breathing at three different frequencies: 6, 10 and 15 breaths min -1 . CO 2 concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including phase-rectified signal averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy, approximate entropy, sample entropy, and fuzzy entropy, as well as time and frequency-domain measures (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV). Main results. Higher breathing rates resulted in a significant decrease in end-tidal CO 2 concentration ( p < 0.001), accompanied by increases in both ABP ( p < 0.001) and heart rate (HR, p < 0.001). A strong, linear decline in AC and DC ( p < 0.001 for both) was observed with increasing breathing rate. All entropy metrics increased with breathing frequency ( p < 0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency ( p < 0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased ( p = 0.038 and p = 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies. Significance. Alterations in CO 2 levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations., (Creative Commons Attribution license.)

This study focuses on the integration and validation of a filtering face piece 3 (FFP3) facemask module for monitoring breathing activity in industrial environments. The key objective is to ensure accurate, real-time respiratory rate (RR) monitoring while maintaining workers' comfort. RR monitoring is conducted through temperature variations detected using temperature sensors tested in two configurations: sensor t 1 , integrated inside the exhalation valve and necessitating structural mask modifications, and sensor t 2 , mounted externally in a 3D-printed structure, thus preserving its certification as a piece of personal protective equipment (PPE). Ten healthy volunteers participated in static and dynamic tests, simulating typical daily life and industrial occupational activities while wearing the breathing activity monitoring module and a chest strap as a reference instrument. These tests were carried out in both indoor and outdoor settings. The results demonstrate comparable mean absolute error (MAE) for t 1 and t 2 in both indoor (i.e., 0.31 bpm and 0.34 bpm) and outdoor conditions (i.e., 0.43 bpm and 0.83 bpm). During simulated working activities, both sensors showed consistency with MAE values in static tests and were not influenced by motion artifacts, with more than 97% of RR estimated errors within ±2 bpm. These findings demonstrate the effectiveness of integrating a smart module into protective masks, enhancing occupational health monitoring by providing continuous and precise RR data without requiring additional wearable devices.

Monitoring health status at home has garnered increasing interest. Therefore, this study investigated the potential feasibility of using noncontact sensors in actual home settings. We searched PubMed for relevant studies published until February 19, 2024, using the keywords "home-based," "home," "monitoring," "sensor," and "noncontact." The studies included in this review involved the installation of noncontact sensors in actual home settings and the evaluation of their performance for health status monitoring. Among the 3 included studies, 2 monitored respiratory status during sleep and 1 monitored body weight and cardiopulmonary physiology. Measurements such as heart rate, respiratory rate, and body weight obtained with noncontact sensors were compared with the results obtained from polysomnography, polygraphy, and commercial scales. All included studies demonstrated that noncontact sensors produced results comparable to those of standard measurement tools, confirming their excellent capability for biometric measurements. Overall, noncontact sensors have sufficient potential for monitoring health status at home., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

The noninvasive measurement and sensing of vital bio signs, such as respiration and cardiopulmonary parameters, has become an essential part of the evaluation of a patient's physiological condition. The demand for new technologies that facilitate remote and noninvasive techniques for such measurements continues to grow. While previous research has made strides in the continuous monitoring of vital bio signs using lasers, this paper introduces a novel technique for remote noncontact measurements based on radio frequencies. Unlike laser-based methods, this innovative approach offers the advantage of penetrating through walls and tissues, enabling the measurement of respiration and heart rate. Our method, diverging from traditional radar systems, introduces a unique sensing concept that enables the detection of micro-movements in all directions, including those parallel to the antenna surface. The main goal of this work is to present a novel, simple, and cost-effective measurement tool capable of indicating changes in a subject's condition. By leveraging the unique properties of radio frequencies, this technique allows for the noninvasive monitoring of vital bio signs without the need for physical contact or invasive procedures. Moreover, the ability to penetrate barriers such as walls and tissues opens new possibilities for remote monitoring in various settings, including home healthcare, hospital environments, and even search and rescue operations. In order to validate the effectiveness of this technique, a series of experiments were conducted using a prototype device. The results demonstrated the feasibility of accurately measuring respiration patterns and heart rate remotely, showcasing the potential for real-time monitoring of a patient's physiological parameters. Furthermore, the simplicity and low-cost nature of the proposed measurement tool make it accessible to a wide range of users, including healthcare professionals, caregivers, and individuals seeking to monitor their own health.

Normal respiratory rates (RR) for children under five in the tropics are well-documented, but data for older children are limited. This study tracked RR changes with age and examined associations with nutritional status and environmental factors. We monitored rural Gambian children aged 6 months to 14 years, recording RR during home visits twice weekly over two rainy seasons. Using a generalized additive model, we constructed RR reference curves, and a linear mixed-effect model identified factors influencing RR. A total of 830 children provided 67,512 RR measurements. Their median age was 6.07 years (interquartile range 4.21-8.55) and 400 (48.2%) were female. Age, stunting, ambient temperature, and time of RR measurement were independent predictors of respiratory rate. Strikingly, children showing signs of illness had greater variability in repeat RR measurements. We constructed a RR reference chart for children aged one to 13 years and proposed a cutoff of > 26 breaths/min for raised RR among children aged > 5 years bridging an important gap in this age group. Although the time of data collection, nutritional status, and ambient temperature were predictors of RR, their effect size is not clinically significant enough to warrant a change in the current WHO guidelines owing to the prevailing uncertainty in the measurement of RR. The finding that RRs between repeat measurements were more variable among children with signs of illness suggests that a single RR measurement may be inadequate to reliably assess the status of sick children-a population in which accurate diagnosis is essential to enable targeted interventions with lifesaving treatment., (© 2024. The Author(s).)

Background: The current standard treatment for obstructive sleep apnea (OSA), continuous positive airway pressure (CPAP), is characterized by a low adherence rate due to various factors including circuit-dependent carbon dioxide (CO 2 ) rebreathing, which can exacerbated by disparate factors, such as low PAP, use of auto-titrating PAP or ramps. However, risk factors for rebreathing are often overlooked or poorly understood in clinical practice. Therefore, our objective was to evaluate the extent of rebreathing occurring with commonly used CPAP masks across varying PAPs, tidal volumes, and respiratory rates., Methods: In a bench study, we assessed the rebreathing rate of nine masks interfacing a CPAP with a lung simulator providing different breathing respiratory rates (15 or 20 breaths/min) and tidal volumes (400, 500, 600, 700 and 750 mL). Additionally, a theoretical model was developed to describe the likelihood of CO 2 rebreathing from four different masks at various breathing settings., Results: Overall, all masks performed worse in situations characterized by low PAPs, high tidal volumes, and high respiratory rates. However, Dreamwear, Nuance, Siesta, Vitera, and particularly V2 masks exhibited greater susceptibility to rebreathing compared to F20, P10, Brevida, and Rio masks for the same variations of PAPs or ventilatory parameters. The mathematical model suggested that the risk of rebreathing for Rio, P10 and Nuance mask is negligible for respiratory rates of 10 breaths/min or below., Conclusions: Circuit-dependent CO 2 rebreathing can be a common occurrence and warrants careful mask selection upon CPAP therapy initiation for optimal clinical outcomes., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ryan Diesem reports financial support was provided by SleepRes. William Noah reports a relationship with SleepRes that includes: equity or stocks. Bernard Hete reports a relationship with SleepRes that includes: equity or stocks. William Noah has patent pending to SleepRes. Bernard Hete has patent pending to SleepRes. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Patients with surgical, pulmonary, and cardiac problems, continual monitoring of Oxygen Saturation of a Person (SpO2) and Respiratory Rate (RR) is essential. Similarly, the persons with cardiopulmonary health issues, RR estimation is crucial. The performance of the ventilator assistance and lung medicines are evaluated using SpO2 and RR. For the persons, those who are living alone with respiratory illnesses need a compulsory estimation of RR. In case of serious illness, the RR might face abrupt changes. The immobility of the disturbance and RR makes the RR evaluation from the PhotoPlethysmoGraphic (PPG) signals is a difficult challenge. So, an efficient RR and SpO2 estimation framework from the PPG signal using the deep learning method is developed in this paper. At first, the PPG signal is collected from standard data sources. The collected PPG signals undergo signal pre-processing. The pre-processing procedures include Motion Artifacts (MA) removal and filtering techniques. The pre-processed signals are split into distinct windows. From the split windows of the signals, the spectral features, RR, and Respiratory Peak Variance (RPV) features are extracted. The retrieved features are selected optimally with the help of Advanced Golden Tortoise Beetle Optimizer (AGTBO). The weights are chosen optimally with the same AGTBO. The optimally selected features are fused with the optimal features to get the weighted optimal features. These weighted optimal features are fed into the Ensemble Learning-based RR and SpO2 Estimation Network (ELRR-SpO2EN). The ensemble learning model is developed by combining Multilayer Perceptron (MLP), AdaBoost, and Attention-based Long Short Term Memory (A-LSTM). The performance of the developed RR and SpO2 estimation model is compared with other existing techniques. The experimental analysis results revealed that the proposed AGTBO-ELRR-SpO2EN model attained 96 % accuracy for the second dataset, which is higher than the conventional models such as MLP (90 %), Adaboost (92 %), A-LSTM (92 %), and MLP-ADA-ALSTM (94 %). Thus, it has been confirmed that the designed RR and SpO2 estimation framework from PPG signals is more efficient than the other conventional models., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Unsteady respiratory airflow characteristics play a crucial role in understanding the deposition of toxic particles and inhaled aerosol drugs in the human respiratory tract. Considering the variations in respiratory flow rate and glottis motion under different respiratory frequencies, these respiratory airflow characteristics are studied by large-eddy simulations, including pressure field, power loss, modal spatial patterns, and vortex structures. Firstly, the results reveal that varying respiratory frequencies significantly affect airflow unsteadiness, turbulent evolution, and vortex structure dissipation, as they increase the complexity and butterfly effect introduced by the turbulent disturbance. Secondly, the pressure drops and flow rate at the glottis also conform to a power-law relationship considering the respiratory physiological characteristics, especially under low respiratory frequencies. Glottis motion plays different roles in energy consumption during inspiration and expiration, and its magnitude can be predicted using a polynomial function based on glottis area and respiratory flowrate under different respiratory frequencies. Finally, modal decomposition can be effectively applied to the study of respiratory flow characteristics, but we recommend separately studying the inspiration and expiration. The spatial distribution of the dominant mode characterizes the majority of respiratory flow characteristics and are influenced by respiratory frequency. Spectral entropy results indicate that glottis motion and slow breathing both delay the transitions in the upper respiratory tract during inspiration and expiration. These results confirm that the respiratory physiology characteristics under different respiratory frequencies have a significant impact on the unsteady respiratory airflow characteristics and warrant further study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Physiologically based stress detection systems have proven to be effective in identifying different stress conditions in the body to determine the source of stress and be able to counteract it. However, some stress conditions have not been widely studied, including thermal stress, cognitive stress, and combined (thermal-cognitive) stress conditions, which are frequently encountered in work or school environments. In order to develop systems to detect and differentiate these conditions, it is necessary to identify the physiological indicators that characterize each of them. The present research aims to identify which physiological indicators (heart rate, respiratory rate, galvanic skin response, and local temperature) could differentiate different stress conditions (no-stress, cognitive stress, thermal stress, and combined (thermal-cognitive) stress conditions). Thirty participants were exposed to cognitive, thermal, and combined stress sources while recording their physiological signals. The findings indicate that both mean heart rate and mean galvanic skin response identify moderate thermal and cognitive stress conditions as distinct from a no-stress condition, yet they do not differentiate between the two stress conditions. Additionally, heart rate uniquely identifies the cognitive-thermal stress condition, effectively distinguishing this combined stress condition from the singular stress conditions and the no-stress condition. Mean local temperature specifically signals thermal stress conditions, whereas mean respiratory rate accurately identifies cognitive stress conditions, with both indicators effectively separating these conditions from each other and from the no-stress condition. This is the first basis for differentiating thermal and cognitive stress conditions through physiological indicators., (© 2024 Society for Psychophysiological Research.)

Measuring vital signs (VS) contained in the echoes is crucial to the analyses of breathing and heartbeat signals using medical radar. Although many advanced signal processing algorithms have been developed for radar-based VS measurement and make some improved progress, existing schemes cannot achieve a good estimation of echo phases modulated by the respiratory and cardiac activities with high accuracy or low computation, and thus resulting in serious performance degradation on the subsequent separation of breathing and heartbeat patterns as well as the assessment of breathing rate (BR), heart rate (HR), and heart rate variability (HRV). In this paper, we propose a simple yet effective method to measure VS for medical radar, named 3M method. Specifically, our method firstly introduces the Markov-Gauss model to obtain the recursive expression of the echo phases carrying VS, and secondly derive a simple observation equation (SOE) to reflect the relationship between the observed signal and VS of radar measurement. Thirdly, the aforementioned Markov-Gauss model and SOE are fused by Kalman filter to measure VS with accurate estimation. The 3M method demonstrates an elegant structure, low complexity and excellent features introduced by Kalman filter. Simulation results show the superiority of 3M over other methods. Then, we conduct extensive experiments with insightful visualizations to validate the effectiveness of the 3M method. Comparative results on different scenarios illustrate that the 3M method not only achieves state-of-the-art VS measurement performance but also expresses robust properties to HRV analysis.

Purpose: Seizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA)., Methods: We extracted ECG signals across three time points - five minutes baseline and preictal, ten minutes postictal - and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means., Results: The mean preictal heart rate was 69.95 bpm (95 % CI 65.6 - 74.3), and it increased to 82 bpm, 95 % CI (77.51 - 86.47) and 84.11 bpm, 95 % CI (76.9 - 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 - 17.1), 14.8 (95 % CI 13.4 - 16.2) and 15.1 (95 % CI 14 - 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures., Conclusions: We found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Slow-paced breathing at an individual's resonance frequency (RF) is a common element of heart rate variability (HRV) biofeedback training (Laborde et al. in Psychophysiology 59:e13952, 2022). Although there is strong empirical support for teaching clients to slow their respiration rate (RR) to the adult RF range between 4.5 and 6.5 bpm (Lehrer & Gevirtz, 2014), there have been no definitive findings regarding the best inhalation-to-exhalation (IE) ratio to increase HRV when breathing within this range. Three methodological challenges have frustrated previous studies: ensuring participants breathed at the target RR, IE ratio, and the same RR during each IE ratio. The reviewed studies disagreed regarding the effect of IE ratios. Three studies found no IE ratio effect (Cappo & Holmes in J Psychosom Res 28:265-273, 1984; Edmonds et al. in Biofeedback 37:141-146, 2009; Klintworth et al. in Physiol Meas 33:1717-1731, 2012). One reported an advantage for equal inhalations and exhalations (Lin et al. in Int J Psychophysiol 91:206?211, 2014). Four studies observed an advantage for longer exhalations than inhalations (Bae et al. in Psychophysiology 58:e13905, 2021; Laborde et al. in Sustainability 13:7775, 2021; Strauss-Blasche et al. in Clin Exp Pharmacol Physiol 27:601?60, 2000; Van Diest et al. in Appl Psychophysiol Biofeedback 39:171?180, 2014). One study reported an advantage for longer inhalations than exhalations (Paprika et al. in Acta Physiol Hung 101:273?281, 2014). We conducted original (N = 26) and replication (N = 16) studies to determine whether a 1:2 IE ratio produces different HRV time-domain, frequency-domain, or nonlinear metrics than a 1:1 ratio when breathing at 6 bpm. Our original study found that IE ratio did not affect HRV time- and frequency-domain metrics. The replication study confirmed these results and found no effect on HRV nonlinear measurements., (© 2024. The Author(s).)

Background: Accurate blood pressure measurement necessitates meticulous methodology, as even minor variations like speaking or sitting can impact the readings. Additionally, respiration plays a role in blood pressure, exhibiting a dip during inhalation and a subsequent increase during exhalation. However, the influence of breathing patterns, duration, and rate on blood pressure remains largely unexplored., Methods: In this study, we have attempted to quantify the changes in blood pressure and heart rate (HR) of normotensive individuals while performing predefined respiratory patterns - slow, medium, and fast (respiratory rates of 5, 10, and 20/m, respectively). For each respiratory pattern, age-matched and gender-matched (18-40 years) volunteered normotensive individuals were selected as volunteers. Baseline readings were recorded and each volunteer was randomly allotted a respiratory pattern. The volunteers performed the assigned respiratory pattern while the BP and HR were recorded., Results: A fall in both SBP and DBP was observed in all three patterns of respiration. There was no inter-pattern difference in the blood pressure change. A novel finding in our study was a trend of respiratory rate and HR - the change in HR linearly increased with a rise in respiratory rate difference. We also proposed a linear regression equation for this increase in HR with the increase in respiratory rate which was statistically significant., Conclusion: The findings suggest the decline in blood pressure is independent of the respiratory pattern employed. The study also demonstrates that the HR is a linear function of respiratory rate., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations., Objective: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting., Methods: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA., Results: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r 2 =0.76; P<.001; WSA apnea-hypopnea index: r 2 =0.59; P<.001)., Conclusions: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories., International Registered Report Identifier (irrid): RR2-10.3390/clockssleep6010010., (©Kiran K G Ravindran, Ciro della Monica, Giuseppe Atzori, Damion Lambert, Hana Hassanin, Victoria Revell, Derk-Jan Dijk. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 27.08.2024.)

Rumination behavior in cattle can provide valuable information for monitoring health status and animal welfare, but continuous monitoring is essential to detect changes in rumination behavior. In a previous study validating the use of a respiration rate sensor equipped with a triaxial accelerometer, the regurgitation process was also clearly visible in the pressure and accelerometer data. The aim of the present study, therefore, was to measure the individual lengths of rumination cycles and to validate whether the sensor data showed the same number of regurgitations as those counted visually (video or direct observation). For this purpose, 19 Holstein Friesian cows equipped with a respiration rate sensor were observed for two years, with a focus on rumination behavior. The results showed a mean duration of 59.27 ± 9.01 s (mean ± SD) per rumination cycle and good agreement (sensitivity: 99.1-100%, specificity: 87.8-95%) between the two methods (sensor and visual observations). However, the frequency of data streaming (continuously or every 30 s) from the sensor to the data storage system strongly influenced the classification performance. In the future, an algorithm and a data cache will be integrated into the sensor to provide rumination time as an additional output.

Oxygen uptake (V˙O2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with V˙O2 and is often used for indirect V˙O2 prediction. This study investigates the impact of HR placement on V˙O2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (V˙E) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V˙O2, RESP, V˙E, and HR were collected and used to develop a neural network model for V˙O2 prediction. The influence of HR position on prediction accuracy was assessed via Bland-Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R 2 ), and mean absolute percentage error (MAPE). Our findings indicate that HR combined with RESP and V˙E (V˙O2HR+RESP+V˙E) produces the most accurate V˙O2 predictions (MAE: 165 mL/min, R 2 : 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V˙O2 prediction decreases, particularly within high-intensity exercise. The substitution of HR with different anatomical sites significantly impacts V˙O2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering HR placement in V˙O2 prediction models, with RESP and V˙E serving as effective compensatory factors. These findings contribute to refining indirect V˙O2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements.

Introduction: Respiratory rate (RR) is a crucial vital sign in patient monitoring and is often the best marker of the deterioration of a sick patient. It can be used to help diagnose numerous medical conditions and has been demonstrated to be an independent predictor of patient outcomes in various critical care settings and is incorporated in many clinical early warning scores. Here, we report on the performance of depth-camera-based system for the noncontact monitoring of RR during a ramped RR protocol. The ramped breathing protocol was developed specifically to test the relatively rapid changes in rates, which include clinically important low and high ranges of RRs., Materials and Methods: We performed a series of experimental runs with healthy volunteers who were instructed to breathe over a wide range of RRs, where the rates were ramped up from 4 breaths/min to 50 breaths/min then back down to 4 breaths/min in a series of ramped steps. Depth information was acquired from the scene and used to determine a respiratory rate (RRdepth), and this was compared to capnograph or spirometer respiratory rate reference (RRref). A total of 9,482 contemporaneous data pairs (RRdepth, RRref) were collected during the study for comparison., Results: A Pearson correlation coefficient of 0.995 was achieved and a line of best fit given by RRdepth = 0.99 × RRref + 0.36 breaths/min. The overall root mean squared difference (RMSD) across the runs was 1.29 breaths/min with a corresponding bias of 0.16 breaths/min, respectively. The associated Bland-Altman analysis found limits of agreement of -2.45 and 2.75 breaths/min. When the data were subdivided according to low, medium, and high RRs, corresponding to ≤10, >10 to 20, and >20 breaths/min, the RMSD accuracies were found to be 0.94, 1.34, and 1.55 breaths/min, respectively., Conclusions: The technology performed well, exhibiting an RMSD accuracy well within our target of 3 breaths/min, both across the whole range and across each individual subrange. In summary, our results indicate the potential viability of continuous noncontact monitoring for the determination of RR over a clinically relevant range., (Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2024. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

In the quest to uncover biological cues that help explain organic changes brought on by an external stimulus, like stress, new technologies have become necessary. The Laser Speckle Contrast Analysis (LASCA) approach is one of these technologies that may be used to analyze biological data, including respiratory rate (RR) intervals, and then use the results to determine heart rate variability (HRV Thus, to evaluate the stress brought on by physical activity, this study used the LASCA approach. A stress induction procedure involving physical exertion was employed, and the results were compared to other established techniques (cortisol analysis and ECG signal) to verify the LASCA methodology as a tool for measuring HRV and stress. The study sample comprised 27 willing participants. The technique involving LASCA allowed for the non-invasive (non-contact) acquisition of HRV and the study of stress. Furthermore, it made it possible to gather pertinent data, such as recognizing modifications to the thermoregulation, peripheral vasomotor tonus, and renin-angiotensin-aldosterone systems that were brought on by elevated stress and, as a result, variations in HRV readings., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

Background: Colic, a primary cause of illness and death in horses, necessitates the development of improved prognostic tools., Aim: The aim of this study was to investigate the prognostic significance of physiological and biochemical parameters in horses suffering from colic., Methods: A comprehensive clinical evaluation of 117 horses included assessment of heart rate, mucous membranes, capillary refill time, rectal temperature, respiratory rate, gut motility, reflux, and limb pulse strength., Results: Stomach reflux, absence of gut noises, and increased heart rate (mean increase of 12 bpm) strongly correlate with a poor prognosis. Prolonged capillary refill time (mean increase of 3 seconds), rectal temperature (over 38.5°C), elevated packed cell volume (mean increase of 4%), and blood lactate levels (mean increase of 5 mmol/l) underscore the significance of these markers. Notably, blood lactate ( p < 0.001), gut noises ( p < 0.05), and heart rate ( p < 0.001) demonstrate the highest predictive significance based on statistical analysis., Conclusion: Future research should investigate the prognostic potential of additional parameters and assess the impact of recommended treatments on colic prognosis. This data-driven study emphasizes the critical role of early recognition and thorough assessment in colic cases, offering vital insights into improving equine healthcare and mitigating mortality rates., Competing Interests: The authors declare that there is no conflict of interest.

Objective: The measurement and recording of vital signs may be impacted by biases, including preferences for even and round numbers. However, other biases, such as variation due to defined numerical boundaries (also known as boundary effects), may be present in vital signs data and have not yet been investigated in a medical setting. We aimed to assess vital signs data for such biases. These parameters are clinically significant as they influence care escalation., Methods: Vital signs data (heart rate, respiratory rate, oxygen saturation and systolic blood pressure) were collected from a tertiary hospital electronic medical record over a 2-year period. These data were analysed using polynomial regression with additional terms to assess for underreporting of out-of-range observations and overreporting numbers with terminal digits of 0 (round numbers), 2 (even numbers) and 5., Results: It was found that heart rate, oxygen saturation and systolic blood pressure demonstrated 'boundary effects', with values inside the 'normal' range disproportionately more likely to be recorded. Even number bias was observed in systolic heart rate, respiratory rate and blood pressure. Preference for multiples of 5 was observed for heart rate and blood pressure. Independent overrepresentation of multiples of 10 was demonstrated in heart rate data., Conclusion: Although often considered objective, vital signs data are affected by bias. These biases may impact the care patients receive. Additionally, it may have implications for creating and training machine learning models that utilise vital signs data., (© 2024 The Authors. Emergency Medicine Australasia published by John Wiley & Sons Australia, Ltd on behalf of Australasian College for Emergency Medicine.)

Sleep monitoring plays a vital role in various scenarios such as hospitals and living-assisted homes, contributing to the prevention of sleep accidents as well as the assessment of sleep health. Contactless camera-based sleep monitoring is promising due to its user-friendly nature and rich visual semantics. However, the privacy concern of video cameras limits their applications in sleep monitoring. In this paper, we explored the opportunity of using a defocused camera that does not allow identification of the monitored subject when measuring sleep-related parameters, as face detection and recognition are impossible on optically blurred images. We proposed a novel privacy-protected sleep parameters measurement framework, including a physiological measurement branch and a semantic analysis branch based on ResNet-18. Four important sleep parameters are measured: heart rate (HR), respiration rate (RR), sleep posture, and movement. The results of HR, RR, and movement have strong correlations with the reference (HR: R = 0.9076; RR: R = 0.9734; Movement: R = 0.9946). The overall mean absolute errors (MAE) for HR and RR are 5.2 bpm and 1.5 bpm respectively. The measurement of HR and RR achieve reliable estimation coverage of 72.1% and 93.6%, respectively. The sleep posture detection achieves an overall accuracy of 94.5%. Experimental results show that the defocused camera is promising for sleep monitoring as it fundamentally eliminates the privacy issue while still allowing the measurement of multiple parameters that are essential for sleep health informatics.

It has been indicated that extreme sport activities result in a highly rewarding experience, despite also providing fear, stress and anxiety. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate-HR, and breathing rate-BR) and neural (electroencephalographic-EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m-low-walk- and 45 m-high-walk-from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network-VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network-DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e ., configured as a 'task-constantly-opened-to-subtle-information experience', rather than a 'task-focused experience'., Competing Interests: The authors declare that they have no competing interests., (© 2024 de Sampaio Barros et al.)

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S 3 CNN) for detecting sleep posture. This S 3 CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S 3 CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S 3 CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Background: Long-distance runs with equipment are practised in military training establishments to develop physical stamina and prepare for battles. A study was planned to quantify and compare the physiological and mental workloads of the early and late finishers of a military endurance run through the jungle terrain of north-eastern India in hot and humid conditions., Methods: Twenty-five soldiers of the Indian Army completed a 15 km run through a rural and jungle track with 6.5 kg of equipment. Twelve participants who finished the race before 130 min were categorised as 'early finishers', and 13 were categorised as 'late finishers' who finished the race after 130 min. Physiological parameters, viz., heart rate (HR), breathing rate (BR), estimated core temperature (ECT), peak acceleration (PAC), and physiological intensity (PHYI) and mechanical intensity (MECHI) were recorded using BioHarness V.3 (Zephyr Technologies, USA) and NASA Task Load Index scores were obtained to assess mental workload. Mann-Whitney U test was applied to assess the level of significance at a p value of <0.05., Results: HR, ECT and PAC were significantly higher (167.9 and 156.0 beats/min, 39.0℃ and 38.4℃, and 1.1 and 1.0 g, respectively) for the first group compared with the second group. The second group expressed higher BR and subjective responses ('total scores' were 76.5 and 82.5, respectively) than the first. The PHYI was significantly higher (from 7.2 to 6.3) for the first group, whereas the MECHI was similar in both groups., Conclusion: The successful completion by the first group can be attributed to their motivation to continue heavy work in an adverse environment, and their ability to negotiate with natural obstacles was reflected through controlled subjective responses. Adoption of a possible safe and stable approach to the same task might have led the second group to take a longer duration, exert higher mental effort and demand lower physiological cost., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

The respiratory rate is an important factor for assessing patient status and detecting changes in the severity of illness. Real-time determination of the respiratory rate will enable early responses to changes in the patient condition. Several methods of wearable devices have enabled remote respiratory rate monitoring. However, gaps persist in large-scale validation, patient-specific calibration, standardization and their usefulness in clinical practice has not been fully elucidated. The aim of this study was to evaluate the accuracy of 2 wearable stretch sensors, C-STRECH® which is used in clinical practice and a novel stretchable capacitor in measuring the respiratory rate. The respiratory rate of 20 healthy subjects was measured by a spirometer with the stretch sensor applied to 1 of 5 locations (umbilicus, lateral abdomen, epigastrium, lateral chest, or chest) of their body at rest while they were in a sitting or supine position before or after exercise. The sensors detected the largest amplitudes at the epigastrium and umbilicus compared to other sites of measurement for the sitting and supine positions, respectively. At rest, the respiratory rate of the sensors had an error of 0.06 to 2.39 breaths/minute, whereas after exercise, an error of 1.57 to 3.72 breaths/minute was observed compared to the spirometer. The sensors were able to detect the respiratory rate of healthy volunteers in the sitting and supine positions, but there was a need for improvement in detection after exercise., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet., Objective: The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD., Methods: We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings., Results: In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively., Conclusions: Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep., (©Xiaolan Chen, Han Zhang, Zhiwen Li, Shuang Liu, Yuqi Zhou. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.07.2024.)

A series of eleven public concerts (staging chamber music by Ludwig van Beethoven, Brett Dean, Johannes Brahms) was organized with the goal to analyze physiological synchronies within the audiences and associations of synchrony with psychological variables. We hypothesized that the music would induce synchronized physiology, which would be linked to participants' aesthetic experiences, affect, and personality traits. Physiological measures (cardiac, electrodermal, respiration) of 695 participants were recorded during presentations. Before and after concerts, questionnaires provided self-report scales and standardized measures of participants' affectivity, personality traits, aesthetic experiences and listening modes. Synchrony was computed by a cross-correlational algorithm to obtain, for each participant and physiological variable (heart rate, heart-rate variability, respiration rate, respiration, skin-conductance response), how much each individual participant contributed to overall audience synchrony. In hierarchical models, such synchrony contribution was used as the dependent and the various self-report scales as predictor variables. We found that physiology throughout audiences was significantly synchronized, as expected with the exception of breathing behavior. There were links between synchrony and affectivity. Personality moderated the synchrony levels: Openness was positively associated, Extraversion and Neuroticism negatively. Several factors of experiences and listening modes predicted synchrony. Emotional listening was associated with reduced, whereas both structual and sound-focused listening was associated with increased synchrony. We concluded with an updated, nuanced understanding of synchrony on the timescale of whole concerts, inviting elaboration by synchony studies on shorter timescales of music passages., (© 2024. The Author(s).)

Growing evidence suggests that respiratory frequency ( f R ) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor f R in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring f R during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech ® (CT) vest or a Bioharness TM (BH) 3.0 strap and a Tyme Wear TM (TW) vest. f R was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The f R time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure f R in soccer.