Rate Variability Metrics Research Articles

Heart Rate Variability Metrics during Sleep in Children with Asperger’s Syndrome

Heart Rate Variability Metrics during Sleep in Children with Asperger’s Syndrome

Heart Rate Variability and Cerebral Autoregulation in Patients with Traumatic Brain Injury with Paroxysmal Sympathetic Hyperactivity Syndrome.

Severe traumatic brain injury (TBI) can lead to transient changes in autonomic nervous system (ANS) functioning and development of paroxysmal sympathetic hyperactivity (PSH) syndrome. Clinical manifestation of ANS disorders may be obscured by therapeutic interventions in TBI. This study aims to analyze ANS metrics and cerebral autoregulation in patients with PSH syndrome to determine their significance in early prognostication. This single-center retrospective study investigated the relationship between changes in ANS metrics, cerebral autoregulation, and PSH syndrome. Arterial blood pressure and intracranial pressure signals were monitored for 5 days post TBI. ANS metrics included time and frequency domain heart rate variability (HRV) metrics. Cerebral autoregulation was assessed using the pressure reactivity index. Sixty-six patients with severe TBI (median age 33 [interquartile range 26-50] years) were analyzed, and PSH was confirmed in nine cases. Impairment of cerebral autoregulation was observed in 67% of patients with PSH and 72% without the syndrome. Patients with PSH had higher HRV in the low-frequency range (LF; 253 ± 178 vs. 176 ± 227 ms2; p = 0.035) and lower heart rates (HRs; 70 ± 7 vs. 78 ± 19 bpm; p = 0.027) compared to those without PSH. A receiver operating characteristic curve analysis indicated that HR (area under the curve(AUC) = 0.73, p = 0.006) and HRV in the LF (AUC = 0.70, p = 0.009) are moderate predictors of PSH. In the multiple logistic regression model for PSH, diffuse axonal trauma (odds ratio(OR) = 10.82, 95% confidence interval(CI) = 1.70-68.98, p = 0.012) and HR (OR =0.91, 95% CI 0.84-0.98, p = 0.021) were significant factors. Elevated HRV in the LF and decreased HR may serve as early predictors of PSH syndrome development, particularly in patients with diffuse axonal trauma. Further research is needed to investigate the utility of the cerebral autoregulation-ANS relationship in PSH prognostication.

The consequences of cardiac autonomic nervous system modulation during pulmonary vein isolation: A review.

Pulmonary vein isolation (PVI) is a well-established treatment modality for atrial fibrillation (AF). Apart from the desired effect regarding the arrhythmic substrate within the left atrium, PVI commonly leads to modulation of the intrinsic cardiac autonomic nervous system (ICANS). Using the available literature, this article presents the anatomy of ICANS and describes methods of assessing its function, mainly focusing on heart rate (HR) variability metrics. Then, we summarize the modern pathophysiological outlooks on the onset and recurrence of AF and explain how the arrhythmia and the activation of ICANS are intertwined. Further, the article discusses the extent, dynamics and persistence of ICANS modulation during PVI, accounting for various modalities and procedural strategies. Both the potential benefits and pitfalls of such modulation are explored, considering AF recurrence, HR and HR variability changes, as well as the unclear effect on ventricular arrhythmias and nerve remodeling. Finally, the article aims to outline further directions of research necessary to improve our understanding of ICANS and its modulation.

Hardware-accelerated neural network model for early prediction of sudden cardiac arrest based on heart rate variability metrics

AbstractSudden Cardiac Arrest (SCA) constitutes a dire medical condition, marked by the abrupt cessation of effective blood circulation due to the heart's failure to contract properly. This leads to acute circulatory collapse, often culminating in loss of consciousness within an hour and potentially resulting in fatality within minutes if left unattended. Heart rate variability (HRV) serves as a critical biometric, derived from electrocardiogram (ECG) signals with ventricular depolarization waves to further calculate the R-R Intervals (RRIs). These intervals provide the basis for extracting various characteristics of cardiac rhythm, encompassing time-domain, frequency-domain, and nonlinear features. This study presents a neural network-based classification algorithm that leverages HRV metrics to categorize patients into SCA and Normal Sinus Rhythm (NSR) cohorts. The proposed neural network (NN) model showcased impressive results, achieving an accuracy of 96.23%, a sensitivity of 94.35%, and a specificity of 98.12% in detecting SCA, as evaluated through leave-one-subject-out analysis. In order to harness the benefits of hardware acceleration, the algorithm is implemented on a Field-Programmable Gate Array (FPGA). Its computational efficiency is subsequently benchmarked against traditional software-based methodologies. The hardware-level implementation is made possible in Verilog hardware description language (HDL) and was verified successfully with expected performance by register-transfer level (RTL) simulation via Vivado 2020.2.

Heart rate variability metrics and myocardial recovery in heart failure with reduced ejection fraction

Heart rate variability metrics and myocardial recovery in heart failure with reduced ejection fraction

Network analysis of the relationship between different heart rate variability metrics during sleep

AbstractHeart rate variability (HRV) refers to the physiological phenomenon of variation in heartbeat duration, which can be characterized using various metrics. Considering a complex systems approach, in this study we used network modeling to quantitatively evaluate the relationship between different HRV metrics during sleep. Polysomnography recordings were performed on 24 healthy participants and their cardiac activity was sampled from the N2, N3, and REM sleep stages. Fifty-eight HRV metrics were calculated, and the relationship between each was assessed using mutual information (MI). One network was created for each sleep stage; HRV metrics constituted its nodes, and MI values were used to establish its edges. Repeated measures ANOVA was applied to each metric to assess variation between sleep stages. It was found that all three networks had characteristics of complex networks. Several communities of shared similar metrics were found across the three sleep stages. Of these, one community had the same metrics in stages N2 and N3, but in REM sleep was divided into three communities. REM sleep exhibited significant differences compared to the other sleep stages in several metrics. These preliminary findings allow us to suggest the application of this method in other HRV research contexts, which will determine its scope and limitations.

Heart rate variability as a dynamic marker of surgeons' stress during vascular surgery.

A surgeon experiences elevated stress levels when operating. Acute stress is linked to cognitive overload, worsening surgical performance. Chronic stress poses a significant risk to a surgeon's health. Identifying intraoperative stress may allow for preventative strategies that reduce surgeons' stress and subsequently improve patient outcomes. The aim of this study was to assess the feasibility of using heart rate variability as a marker of stress during vascular surgery. A total of 11 senior surgeons were evaluated performing three different vascular surgery procedures. Heart rate variability metrics (low-frequency to high-frequency ratio and standard deviation of the normal-normal interval) were determined from single-lead ECG traces at predetermined procedural performance points. State-Trait Anxiety Inventory-6, a validated stress tool, was used to assess surgeon-reported stress. Subjective reports of procedural difficulty were also collected. One-way ANOVA compared heart rate variability at key performance points with baseline. Pearson's coefficient assessed correlation between heart rate variability and subjective stress. Data were collected for six carotid endarterectomies, six open abdominal aortic aneurysm repairs, and five lower limb bypasses. Heart rate variability metrics indicating markedly greater stress were observed at key performance points across all procedures. Peaks in stress were consistent across different surgeons performing the same procedure. A significant correlation was observed between heart rate variability metrics and subjective State-Trait Anxiety Inventory-6 stress reports (r = 0.768, P =<0.001). The most difficult procedural steps reported corresponded with heart rate variability metrics displaying the greatest stress. Heart rate variability may be a viable approach to assess intraoperative stress and cognitive load during vascular surgery and could be used to evaluate whether a theatre intervention (for example timeout) could reduce stress in areas of surgical difficulty.

Neuromodulation of Cardiovascular Risks Associated With Cardiotoxic Chemotherapy: A First-in-Human Randomized Pilot Study. Neuromodulation in Cancer Study (NCAN).

Cardiotoxic chemotherapy is used to treat malignancies such as breast cancer and lymphoma. These treatments predispose patients to cardiotoxicity that can lead to cancer treatment-related cardiac dysfunction (CTRCD). The use of high doses of anthracyclines or in combination with human epidermal growth factor receptor 2 antagonists is associated with a progressively higher risk of CTRCD. CTRCD is preceded by increased activation of the sympathetic nervous system and abnormal left ventricular mechanical deformation as measured by abnormal global longitudinal strain (GLS). Low-level tragus stimulation (LLTS) is a new, safe, noninvasive technique that offers great potential to reduce increased sympathetic activation and improve GLS. Here, we describe a study method to examine the effects of LLTS on autonomic balance and cardiac function in breast cancer or lymphoma patients treated with anthracyclines. A first-in-human pilot, randomized, double-blind feasibility study will evaluate 104 patients (age >50y) with breast cancer or lymphoma who receive anthracyclines with one additional CTRCD risk factor. Patients undergo 2 weeks of LLTS daily (1 h/d). Autonomic balance will be measured using heart rate variability metrics. Strain imaging using GLS will be performed pre and post-LLTS. Endothelial inflammation and oxidative stress measures will be performed using in vitro assays at baseline and after 2 weeks. We hypothesize that LLTS stabilizes sympathovagal imbalance and improves cardiac performance in anthracycline-treated patients with breast cancer or lymphoma.

Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.

Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information. In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19. A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies. The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score. We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

The Effect of Time Pressure on Surgical Skill Retention in Novices: A Randomized Controlled Trial.

There are limited opportunities to practice surgical skills and techniques in residency. Therefore, it is important to explore strategies which optimize surgical simulation experiences to enhance learning outcomes and skill retention. Novice medical students (n = 29) were recruited to participate in a Fundamentals of Laparoscopic Surgery (FLS) peg transfer task training. Participants were randomly assigned to a control group, practicing the peg transfer task independently, or an experimental group, practicing with time pressure. Participant skill assessments were completed before the training, after the training, and 8-weeks after the training. Subjective and objective stress measurements were taken in the form of self-report surveys and heart rate variability data, respectively. For all the skill assessment measurements, there was no difference between groups in performance on the FLS task. Both groups showed improvement in performance after the training compared to before. The experimental group reported higher stress during and after the training period compared to the control group; however, there was no difference between groups on heart rate variability metrics. Time pressure while practicing an FLS task did not significantly impact learning acquisition or retention. However, the experimental group reported higher levels of stress. This preliminary study suggests time pressure does not confer an enhanced surgical skill learning experience for novices.

The application value of 24 h Holter monitoring indices in predicting MACEs outside the hospital within three years after PCI in patients with STEMI.

Evaluating cardiovascular risk in patients experiencing acute ST-elevation myocardial infarction (STEMI) and undergoing percutaneous coronary intervention (PCI) is crucial for early intervention and improving long-term outcomes. 24 h Holter monitoring provides continuous cardiac electrophysiological data, enabling the detection of arrhythmias and autonomic dysfunction that are not captured during routine examinations. This study aimed to examine the relationship between Holter monitoring metrics and the occurrence of out-of-hospital major adverse cardiovascular events (MACEs) following PCI in patients with STEMI, offering insights into cardiovascular risk evaluation. This prospective cohort study included STEMI patients undergoing PCI. 24 h Holter monitoring data were recorded, including heart rate, heart rate variability (HRV) metrics such as SDNN and SDANN index, heart rate deceleration capacity (DC) at different time scales (DC2, DC4, DC8), and the frequency of premature ventricular contractions (PVCs). Independent correlations between these indices and MACEs, as well as cardiovascular deaths, were investigated using multifactorial logistic regression. Predictive capacities were assessed through receiver operating characteristic (ROC) curves. A total of 172 participants were enrolled in this study. Over the 3-year follow-up period, MACEs were observed in 57 patients, including 20 cases of cardiac death. In logistic regression models adjusted for confounding variables, SDNN [OR: 0.980; 95% CI: (0.967, 0.994); p = 0.005] and SDANN index [OR: 0.982; 95% CI: (0.969, 0.996); p = 0.009] were negatively associated with the incidence of MACEs. Conversely, the slowest heart rate [OR: 1.075; 95% CI: (1.022, 1.131); p = 0.005] and frequent PVCs [OR: 2.685; 95% CI: (1.204, 5.987); p = 0.016] demonstrated a positive association with MACEs. Furthermore, SDNN [OR: 0.957; 95% CI: (0.933, 0.981); p = 0.001], DC [OR: 0. 702; 95% CI: (0.526, 0.938); p = 0.017]) and DC4 [OR: 0.020; 95% CI: (0.001, 0.664); p = 0.029] were negatively associated with cardiac death. The ROC analysis results indicated that SDNN was an effective predictor of both MACEs [AUC: 0.688 (95% CI: 0.601-0.776)] and cardiac death [AUC: 0.752 (95% CI: 0.625-0.879)]. HRV, DC metrics, and frequent PVCs obtained by 24 h Holter monitoring were associated with the risk of MACEs in STEMI patients. These metrics can help clinicians identify at-risk patients early so that timely interventions.

Exploring Ultra-short Heart Rate Variability Metrics in Patients with Diabetes Mellitus: A Reliability Analysis.

Ultra-short heart rate variability (HRV) metrics represent autonomic tone parameters derived using small epochs of interbeat interval data. These measures have risen in popularity with the advent of wearable devices that can capture interbeat interval data using electrocardiography (ECG) or photoplethysmography. Autonomic neuropathy in diabetes mellitus (DM) is well established, wherein 5-min HRV is conventionally used. Ultra-short measures have the potential to serve as markers of reduced autonomic tone in this patient population. Data of patients with Type I and Type II DM who had presented to our laboratory for autonomic neuropathy assessment were chosen for analysis. One-minute and 2-min epochs were chosen from 5 min of ECG data using standard software. Time domain, frequency domain, and nonlinear measures were computed from 1 to 2 min epochs, and reliability was compared with measures derived from 5-min HRV using intraclass correlation coefficients (ICCs). Data of 131 subjects (79 males, 52 females; mean age = 53.3 ± 12.16 years) were analyzed. All ultra-short HRV measures derived from 1 min to 2 min data showed good to excellent reliability (median ICC values ranging from 0.83 to 0.94) when compared with 5-min metrics. The notable exception was very low frequency (VLF) power, which showed poor reliability (median ICC = 0.43). Ultra-short HRV metrics derived from 1 to 2 min epochs of ECG data can be reliably used as predictors of autonomic tone in patients with DM. VLF power is poorly reproducible in these small epochs, probably due to variability in respiratory rates. Our findings have implications for ultra-short HRV estimation using short epochs of ECG data.

Association between heart rate variability metrics from a smartwatch and self-reported depression and anxiety symptoms: a four-week longitudinal study.

Elucidating the association between heart rate variability (HRV) metrics obtained through non-invasive methods and mental health symptoms could provide an accessible approach to mental health monitoring. This study explores the correlation between HRV, estimated using photoplethysmography (PPG) signals, and self-reported symptoms of depression and anxiety. A 4-week longitudinal study was conducted among 47 participants. Time-domain and frequency-domain HRV metrics were derived from PPG signals collected via smartwatches. Mental health symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) at baseline, week 2, and week 4. Among the investigated HRV metrics, RMSSD, SDNN, SDSD, LF, and the LF/HF ratio were significantly associated with the PHQ-9 score, although the number of significant correlations was relatively small. Furthermore, only SDNN, SDSD and LF showed significant correlations with the GAD-7 score. All HRV metrics showed negative correlations with self-reported clinical symptoms. Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients.

Assembly complexity and physiological response in human-robot collaboration: Insights from a preliminary experimental analysis

Industry 5.0 paradigm has renewed interest in the human sphere, emphasizing the importance of workers’ well-being in manufacturing activities. In such context, collaborative robotics originated as a technology to support humans in tiring and repetitive tasks. This study investigates the effects of assembly complexity in Human-Robot collaboration using physiological indicators of cognitive effort. In a series of experiments, participants performed assembly processes of different products with varying complexity, in two modalities: manually and with cobot assistance. Physiological measures, including skin conductance, heart rate variability and eye-tracking metrics were collected. The analysis of physiological signals showed trends suggesting the impact of assembly complexity and cobot support. One key finding of the study is that a single physiological signal usually may not provide a complete understanding of cognitive load. Therefore, a holistic approach should be followed. This approach highlighted the importance of considering multiple measures simultaneously to accurately assess workers’ well-being in industrial environments.

Autonomic Effects of Pulsed Field vs Thermal Ablation for Treating Atrial Fibrillation: Subanalysis of ADVENT

BackgroundAutonomic denervation is an ancillary phenomenon during thermal ablation of atrial fibrillation (AF), that may have synergistic effects on symptomatic improvement and long-term freedom from AF. Pulsed field ablation (PFA), a nonthermal ablation modality, was noninferior to thermal ablation in treating AF; however, PFA’s relative myocardial selectivity may minimize autonomic effects. ObjectivesThis study sought to compare heart rate (HR) and heart rate variability (HRV) metrics as markers of autonomic function after ablation using PFA vs thermal ablation. MethodsADVENT (FARAPULSE ADVENT PIVOTAL Trial PFA System vs SOC Ablation for Paroxysmal Atrial Fibrillation) was a randomized pivotal study comparing PFA (pentaspline catheter) with thermal ablation (radiofrequency [RF] or cryoballoon [CB]) for treating paroxysmal AF. Baseline HR was acquired from a pre-ablation 12-lead electrocardiogram, whereas follow-up HRs, as well as HRV (standard deviation of all normal to normal RR intervals, standard deviation of 5-minute average RR intervals) metrics, were derived from 72-hour Holter monitors at 6 and 12 months. ResultsThis study included 379 paroxysmal AF patients undergoing PFA (n = 194) or thermal ablation (n = 185; n = 102 RF, n = 83 CB) completing 6- and 12-month Holter monitoring. Compared with PFA, thermal patients had significantly greater increases in HR from baseline to 6 months (ΔHR; 10.1 vs 5.9 beats/min; P = 0.02) and 12 months (ΔHR; 8.8 vs 5.2 beats/min; P = 0.03). This increase in HR at 6 and 12 months was similar between CB and RF (P = 0.94 and 0.83, respectively). HRV, both standard deviation of all normal to normal RR intervals and standard deviation of 5-minute average RR intervals, were significantly lower at both 6 and 12 months after thermal ablation compared with PFA (P < 0.01). ConclusionsPFA’s effect on the autonomic nervous system was attenuated compared with thermal ablation. Whether this affects long-term freedom from AF or symptomatic bradycardia/pauses after AF ablation requires further study.

Heart rate variability as an indicator of fatigue: A structural equation model approach

Driver fatigue significantly impairs performance and is a major risk factor for road crashes. However, fatigue is difficult to objectively measure and quantify. This study aimed to elucidate associations between heart rate variability (HRV) metrics and multidimensional fatigue construct encompassing objective and subjective measures in young drivers with self-reported short sleep. Eighty-four young adults underwent assessments during simulated driving. HRV indices RMSSD and LF/HF ratio were derived from an electrocardiogram, while relative Theta power, oculography drowsiness, lane position variability, and Karolinska Sleepiness Score measured fatigue. RMSSD negatively correlated with Theta power, and LF/HF ratio positively correlated with position variability. Exploratory factor analysis extracted three factors, with RMSSD and LF/HF loading onto one. Structural equation modelling tested the prediction of a latent fatigue construct from HRV. Two models demonstrated an acceptable fit. RMSSD negatively predicted fatigue, explaining 6.4 % of the variance, while the LF/HF ratio positively predicted fatigue, accounting for 3.7 % of the variance. In summary, HRV, particularly RMSSD, showed significant relationships with multidimensional fatigue. Lower RMSSD and higher LF/HF ratio are associated with greater fatigue levels. This study demonstrates that HRV indices exhibit significant relationships within a multidimensional model of fatigue incorporating objective performance, ocular, and subjective measures. The findings provide initial evidence towards using HRV for monitoring complex manifestations of fatigue, rather than single dimensions. Further applied research should investigate translating these findings to naturalistic settings and validating HRV thresholds predictive of on-road impairment.

Virtual Reality Adaptive Training for Personalized Stress Inoculation.

To evaluate a personalized adaptive training program designed for stress prevention using graduated stress exposure. Astronauts in the high-risk space mission environment are prone to performance-impairing stress responses, making preemptive stress inoculation essential for their training. This work developed an adaptive virtual reality-based system that adjusts environmental stressors based on real-time stress indicators to optimize training stress levels. Sixty-five healthy subjects underwent task training in one of three groups: skill-only (no stressors), fixed-graduated (prescheduled stressor changes), and adaptive. Psychological (subjective stress, task engagement, distress, worry, anxiety, and workload) and physiological (heart rate, heart rate variability, blood pressure, and electrodermal activity) responses were measured. The adaptive condition showed a significant decrease in heart rate and a decreasing trend in heart rate variability ratio, with no changes in the other training conditions. Distress showed a decreasing trend for the graduated and adaptive conditions. Task engagement showed a significant increase for adaptive and a significant decrease for the graduated condition. All training conditions showed a significant decrease in worry and anxiety and a significant increase in the other heart rate variability metrics. Although all training conditions mitigated some stress, the preponderance of trial effects for the adaptive condition supports that it is more successful at decreasing stress. The integration of real-time personalized stress exposure within a VR-based training program not only prepares individuals for high-stress situations by preemptively mitigating stress but also customizes stressor levels to the crew member's current state, potentially enhancing resilience to future stressors.

From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings.

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.

Correlation of fetal heart rate dynamics to inflammatory markers and brain-derived neurotrophic factor during pregnancy.

This study aims to show the relation between biomarkers in maternal and cord-blood samples and fetal heart rate variability (fHRV) metrics through a non-invasive fetal magnetocardiography (fMCG) technique. Twenty-three women were enrolled for collection of maternal serum and fMCG tracings immediately prior to their scheduled cesarean delivery. The umbilical cord blood was collected for measurement of biomarker levels. The fMCG metrics were then correlated to the biomarker levels from the maternal serum and cord blood. Brain-derived neurotrophic factor (BDNF) had a moderate correlation with fetal parasympathetic activity (0.416) and fetal sympathovagal ratios (-0.309;-0.356). Interleukin (IL)-6 also had moderate-sized correlations but with an inverse relationship as compared to BDNF. These correlations were primarily in cord-blood samples and not in the maternal blood. In this small sample-sized exploratory study, we observed a moderate correlation between fHRV and cord-blood BDNF and IL-6 immediately preceding scheduled cesarean delivery at term. These findings need to be validated in a larger population.

Effects of three-week intake of Matcha green tea powder on heart rate variability metrics

Introduction: Compared to other green teas, higher intake of multiple phytochemicals is achieved with Matcha green tea consumption. Green tea consumption is known to have metabolic effects but is also consumed for supposed calming effects. The aim of the present study was to examine the effects of encapsulated Matcha green tea on heart rate variability metrics during supine rest, as well as on physiological and metabolic responses during both supine rest and moderate-intensity exercise. Methods: Healthy females (n=8, age: 22±3 yrs, body mass: 68±11 kg, height: 162±5 cm) volunteered. The study employed a placebo-controlled, randomised cross-over design. Time-domain heart rate variability metrics during supine rest (n=5) and physiological and metabolic responses using indirect calorimetry techniques during supine rest and 60-min of moderate-intensity (~ 4-METs) treadmill walking (speed: 4.4±0.5 km·h-1) were measured following 3 weeks of 3 g·day-1 of Matcha green tea or placebo. Results: During supine rest with Matcha green tea, all participants had lower heart rates by 13±7% (P=0.01, d= -1.45), higher mean beat-to-beat RR intervals by 16±9% (P=0.03, d=1.25), higher SDNN by 44±32% (P=0.01, d=0.76) and higher pNN50 by 139±139% (P&lt;0.01, d=1.28). Matcha green tea had no effects on the physiological and metabolic responses during supine rest and moderate-intensity treadmill walking (e.g. respiratory exchange ratio, placebo: 0.78±0.04; Matcha: 0.78±0.03, P=0.87). Fat oxidation during supine rest was correlated (r=0.75, P&lt;0.01) with the moderate-intensity walking induced fat oxidation. Conclusions: In young adult healthy females, Matcha green tea beneficially effects heart rate variability metrics during supine rest indicating an alteration in parasympathetic nervous activity and therefore suggestive of a relaxing effect. Matcha green tea did not change the metabolic responses during supine rest and exercise possibly due to the low respiratory exchange ratio in the female cohort. Future work should address the effectiveness of Matcha green tea during conditions of psychological stress.Keywords: Matcha green tea, heart rate variability, rest, walking, substrate oxidation