Exploring the efficacy of sense of Okayness (SOK) as an antidote for stress in older adults: the role of SOK elevation intervention, heart rate variability (HRV), and cognitive performance in stressful and relaxing situations.

Sense of Okayness (SOK) is an emerging concept that describes a person's ability to remain stable and unshaken in the face of life transitions and hardships. This quality enables effective stress regulation and heightened tolerance to uncertainty. To investigate the possible role of the parasympathetic nervous system (PNS) in mediating the relationship between SOK and stress regulation among older individuals, an analytical sample of N = 69 participants (74% women) with a mean age of 78.75 years (SD age = 6.78) was recruited for a standardized cognitive assessment and stress induction. Baseline heart rate variability (HRV), measured via electrocardiogram (ECG), and SOK assessments were conducted prior to stress induction, along with a baseline cognitive evaluation. Subsequently, participants were subjected to a psychosocial stress paradigm, followed by either a 30-minute SOK elevation intervention (n = 40) or a control condition with nature sounds (n = 29). A second cognitive assessment was administered post-intervention, with continuous HRV measurement through ECG. The results revealed significant HRV changes due to the experimental intervention, though no significant differences were observed between the SOK intervention and control groups. Interestingly, individuals with high trait SOK displayed more stable HRV trajectories, exhibiting a smaller decline during the stress intervention and a milder increase during both the stressor and SOK intervention phases. Overall, these findings do suggest a significant association between SOK, parasympathetic activity, and stress reactivity. These results prompt further investigation into whether personality patterns, such as a strong SOK, may be linked to reduced vagal reactivity and better coping in old age.

Association between obstructive sleep apnea and arrhythmia and heart rate variability among hypertensive patients.

BACKGROUND: The relationship between obstructive sleep apnea (OSA) and the occurrence of arrhythmias and heart rate variability (HRV) in hypertensive patients is not elucidated. Our study investigates the association between OSA, arrhythmias, and HRV in hypertensive patients. METHODS: We conducted a cross-sectional analysis involving hypertensive patients divided based on their apnea-hypopnea index (AHI) into two groups: the AHI ≤ 15 and the AHI > 15. All participants underwent polysomnography (PSG), 24-hour dynamic electrocardiography (DCG), cardiac Doppler ultrasound, and other relevant evaluations. RESULTS: The AHI > 15 group showed a significantly higher prevalence of frequent atrial premature beats and atrial tachycardia (P = 0.030 and P = 0.035, respectively) than the AHI ≤ 15 group. Time-domain analysis indicated that the standard deviation of normal-to-normal R-R intervals (SDNN) and the standard deviation of every 5-minute normal-to-normal R-R intervals (SDANN) were significantly higher in the AHI > 15 group (P = 0.020 and P = 0.033, respectively). Frequency domain analysis revealed that the low-frequency (LF), high-frequency (HF) components, and the LF/HF ratio were also significantly elevated in the AHI > 15 group (P < 0.001, P = 0.031, and P = 0.028, respectively). Furthermore, left atrial diameter (LAD) was significantly larger in the AHI > 15 group (P < 0.001). Both univariate and multivariable linear regression analyses confirmed a significant association between PSG-derived independent variables and the dependent HRV parameters SDNN, LF, and LF/HF ratio (F = 8.929, P < 0.001; F = 14.832, P < 0.001; F = 5.917, P = 0.016, respectively). CONCLUSIONS: Hypertensive patients with AHI > 15 are at an increased risk for atrial arrhythmias and left atrial dilation, with HRV significantly correlating with OSA severity.

Developing a Five-Minute Normative Database of Heart Rate Variability for Diagnosing Cardiac Autonomic Dysregulation for Patients with Major Depressive Disorder.

Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This study aimed to develop the Taiwan HRV normative database covering individuals aged 20 to 70 years and to assess its diagnosing validity in patients with major depressive disorder (MDD). A total of 311 healthy participants were in the HRV normative database and divided into five groups in 10-year age groups, and then the means and standard deviations of the HRV indices were calculated. We recruited 272 patients with MDD for cross-validation, compared their HRV indices with the normative database, and then converted them to Z-scores to explore the deviation of HRV in MDD patients from healthy groups. The results found a gradual decline in HRV indices with advancing age in the HC group, and females in the HC group exhibit higher cardiac vagal control and parasympathetic activity than males. Conversely, patients in the MDD group demonstrate lower HRV indices than those in the HC group, with their symptoms of depression and anxiety showing a negative correlation with HRV indices. The Taiwan HRV normative database has good psychometric characteristics of cross-validation.

Surge of Sympathetic Activity during Hyperventilation at the End of Apnea for Patients with Obstructive Sleep Apnea.

Background and Objectives: The mechanisms connecting obstructive sleep apnea (OSA) and cardiovascular disease are multifactorial, involving intermittent hypoxia, hypercapnia, and sympathetic activation. The aim of this study was to explore the oscillations of sympathetic activity during the sleep apnea episodes throughout the entire night in patients with OSA. Materials and Methods: The participants received whole-night polysomnography (PSG), and electrocardiogram (EKG) data from the PSG were collected for heart rate variability (HRV) analysis. HRV measurements were conducted in the time and frequency domains. The root mean square of successive differences between normal heartbeats (RMSSD), which reflects parasympathetic activity, and the ratio of the absolute power of the low-frequency band (0.04-0.15 Hz) to the absolute power of the high-frequency band (0.015-0.4 Hz) (LF/HF ratio), which indicates sympathetic activity, were computed. Results: A total of 43 participants (35 men and 8 women) were included in the analysis. The mean age of the participants was 44.1 ± 11.3 years old, and the mean BMI was 28.6 ± 5.4 kg/m2. The sleep apnea episodes throughout the entire night in patients with OSA were selected randomly and occurred most frequently during the non-REM stages (39, 90.7%). The selected sleep apnea episodes typically exhibited multiple apneas, often interrupted by snoring respiration and followed by hyperventilation at the end of the episode (HE). Our findings indicate that the centers of the 5 min HRV window for the lowest and highest LF/HF ratios, at 111.8 ± 88.2 and 117.4 ± 88.6 min after sleep onset, respectively, showed a statistically significant difference (p < 0.001). Similarly, the ratios of the lowest and highest LF/HF, at 0.82 ± 0.56 and 3.53 ± 2.94, respectively, exhibited a statistically significant difference (p < 0.001). Conclusions: In the current study, the selected sleep apnea episodes throughout the entire night in patients with OSA occurred primarily during the non-REM stages. Additionally, we observed that sympathetic activity reached its peak in the window that includes hyperventilation at the end stage of apnea, potentially posing a cardiovascular risk. However, additional studies are needed to validate these results.

The short term adaptation of the autonomic nervous systems (ANS) by type of urban environment and ethnicity.

BACKGROUND: Previous studies examined the effects of urban environments on the Autonomic Nervous System (ANS). These studies measured the effects of environments on Heart Rate Variability (HRV) averaging different time intervals to one value. Yet, the dynamics of change, reflecting the functions and their derivatives that describe the adaptation to the new environments remain unknown. In addition, ethnic differences in the ANS adaptation were not investigated. METHOD: Forty-eight Arab and 24 Jewish women ages 20-35 years, all healthy, non-smokers were recruited by a snowball sample. Both groups were of a similar socioeconomic status and BMI distributions. Using a portable monitor, the HRV response was continuously analyzed for 35 min of sedentary sitting in each of the three environments: a park, a city center and a residential area. LF/HF polynomial function was adapted to describe the dynamic change in each environment for each ethnic group. RESULTS: Green area exposure was associated with 90% immediate change while in built-up areas, the change in HRV is about 40% adaptive (changing gradually). The adaptive process of HRV may stabilize after 15 min in the city center yet not even after 35 min in the residential environment. The total change (immediate + adaptive) reached 24% in city centers and 10% in residential areas. Changes in HRV rates in the park and the city center environments were higher among Arab women as compared to Jewish women but similar between the two groups in the residential area. The distributions of LF/HF in each time cohort were normal, meaning that shifting the focus to analyze functions of change in HRV, opens the possibility to employ analytic methods that assume the normal distribution. CONCLUSIONS: Changing the focus from average levels of HRV to functions of change and their derivatives brings new insight into the understanding of the ANS response to environmental challenges. ANS short term adaptation to different environments is gradual and spans differently both in magnitude of response and latencies between different environments. Importantly, in green areas, the response is immediate unlike the adaptation to urban environments that is significantly more gradual. The ethnic differences in ANS adaptation is also noteworthy. In addition, adaptation proceeesses are normaly distributed in each time cohort suggesting a possible novel ANS index.

A systematic review of heart rate variability (HRV) biofeedback treatment following traumatic brain injury (TBI).

OBJECTIVE: Autonomic nervous system dysregulation is a common consequence of traumatic brain injury (TBI). Heart rate variability (HRV) is a cost-effective measure of autonomic nervous system functioning, with studies suggesting decreased HRV following moderate-to-severe TBI. HRV biofeedback treatment may improve post-TBI autonomic nervous system functioning and post-injury emotional and cognitive functioning. We provide a systematic evidence-based review of the state of the literature and effectiveness of HRV biofeedback following TBI. METHOD: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two coders coded each article and provided quality ratings. Seven papers met inclusion criteria. All studies included a measure of emotional functioning and 5 studies (63%) included neuropsychological outcomes. RESULTS: Participants completed 11 sessions of HRV biofeedback on average (range = 1 to 40). HRV biofeedback was associated with improved HRV following TBI. There was a positive relationship between increased HRV and TBI recovery following biofeedback, including improvements in cognitive and emotional functioning, and physical symptoms such as headaches, dizziness, and sleep problems. CONCLUSION: The literature on HRV biofeedback for TBI is promising, but in its infancy; effectiveness is unclear due to poor-to-fair study quality, and potential publication bias (all studies reported positive results).

Statistical heart rate variability analysis for healthy person: Influence of gender and body posture.

Short-term ECG-derived heart rate variability can assess autonomic function non-invasively. The purpose of this study is to investigate the influence of body posture and gender on parasympathetic-sympathetic balance by utilising electrocardiogram (ECG). A total of sixty participants including thirty males (95% CI: 23.34-26.32 years old) and thirty females (95% CI: 23.33-26.07 years old) voluntarily executed three sets of 5-min ECG recordings in supine, sitting and standing posture. A nonparametric Friedman test followed by Bonferroni post-hoc test was carried out to find the statistical differences between the group. A significant difference was observed for RR mean, low frequency (LF), high frequency (HF), ratio LF/HF and the ratio long term variability to short term variability (SD2/SD1) for p < 0.01 while respiration rate (Resp Rate), standard deviation of heart rate (STD_HR), long term variability (SD2), approximate entropy (ApEn), correlation dimension (CD) are non-significant (p > 0.01) for supine, sitting and standing. HRV indices such as standard deviation of NN (SDNN), HRV triangular index (HRVi), and triangular interpolation of NN interval (TINN) are statistically not significant for males but there are significant differences for females at a significance level 1%. Relative reliability and relatedness were evaluated through the interclass coefficient (ICC) and spearman correlation coefficient. The experimental results advocate that there is a posture-specific difference in HRV indices while the correlational studies suggest no such significant differences.

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications.

BACKGROUND: Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, existing algorithmic methods of HRV analysis do not check such eligibility and require manual identification of eligible window (portion of ECG record) to ensure reliability. OBJECTIVE: We aimed to propose a robust algorithm with a sliding window feature to automate the identification of an eligible window, if available, which compute HRV parameters within that window obviating manual input. METHODS: The proposed algorithm classifies each window as either eligible or ineligible. With a window classified eligible, we stop sliding through the record, otherwise we move to the next window and repeat the eligibility identification process, until either an eligible window is found, or all windows are exhausted. RESULTS: When evaluated on random subset of 100 records from MIMIC-III waveform database, the proposed algorithm excluded every ineligible record, and missed only 1.25% of eligible ones. The HRV parameters computed using proposed method closely approximated the standard HRV analysis with Pearson correlation coefficients (ideally one) and fractions of variance unexplained (ideally zero) ranging from 96.3% to 99.8% and 0.34% to 7.43%, respectively. CONCLUSIONS: When translated into practice, proposed algorithm will reduce clinicians'' burden without compromising the accuracy of HRV analysis, potentially leading to its wider adoption.

Study on the Psychological States of Olfactory Stimuli Using Electroencephalography and Heart Rate Variability.

In the modern information society, people are constantly exposed to stress due to complex work environments and various interpersonal relationships. Aromatherapy is attracting attention as one of the methods for relieving stress using aroma. A method to quantitatively evaluate such an effect is necessary to clarify the effect of aroma on the human psychological state. In this study, we propose a method of using two biological indexes, electroencephalogram (EEG) and heart rate variability (HRV), to evaluate human psychological states during the inhalation of aroma. The purpose is to investigate the relationship between biological indexes and the psychological effect of aromas. First, we conducted an aroma presentation experiment using seven different olfactory stimuli while collecting data from EEG and pulse sensors. Next, we extracted the EEG and HRV indexes from the experimental data and analyzed them with respect to the olfactory stimuli. Our study found that olfactory stimuli have a strong effect on psychological states during aroma stimuli and that the human response to olfactory stimuli is immediate but gradually adapts to a more neutral state. The EEG and HRV indexes showed significant differences between aromas and unpleasant odors especially for male participants in their 20-30s, while the delta wave and RMSSD indexes showed potential for generalizing the method to evaluate psychological states influenced by olfactory stimuli across genders and generations. The results suggest the possibility of using EEG and HRV indexes to evaluate psychological states toward olfactory stimuli such as aroma. In addition, we visualized the psychological states affected by the olfactory stimuli on an emotion map, suggesting an appropriate range of EEG frequency bands for evaluating psychological states applied to the olfactory stimuli. The novelty of this research lies in our proposed method to provide a more detailed picture of the psychological responses to olfactory stimuli using the integration of biological indexes and emotion map, which contributes to the areas such as marketing and product design by providing insights into the emotional responses of consumers to different olfactory products.

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models.

Signal Quality Analysis of Single-Arm Electrocardiography.

The number of people experiencing mental stress or emotional dysfunction has increased since the onset of the COVID-19 pandemic, as many individuals have had to adapt their daily lives. Numerous studies have demonstrated that mental health disorders can pose a risk for certain diseases, and they are also closely associated with the problem of mental workload. Now, wearable devices and mobile health applications are being utilized to monitor and assess individuals' mental health conditions on a daily basis using heart rate variability (HRV), typically measured by the R-to-R wave interval (RRI) of an electrocardiogram (ECG). However, portable or wearable ECG devices generally require two electrodes to perform bipolar limb leads, such as the Einthoven triangle. This study aims to develop a single-arm ECG measurement method, with lead I ECG serving as the gold standard. We conducted static and dynamic experiments to analyze the morphological performance and signal-to-noise ratio (SNR) of the single-arm ECG. Three morphological features were defined, RRI, the duration of the QRS complex wave, and the amplitude of the R wave. Thirty subjects participated in this study. The results indicated that RRI exhibited the highest cross-correlation (R = 0.9942) between the single-arm ECG and lead I ECG, while the duration of the QRS complex wave showed the weakest cross-correlation (R = 0.2201). The best SNR obtained was 26.1 ± 5.9 dB during the resting experiment, whereas the worst SNR was 12.5 ± 5.1 dB during the raising and lowering of the arm along the z-axis. This single-arm ECG measurement method offers easier operation compared to traditional ECG measurement techniques, making it applicable for HRV measurement and the detection of an irregular RRI.

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Harnessing Wearable Devices for Emotional Intelligence: Therapeutic Applications in Digital Health.

Emotional intelligence strives to bridge the gap between human and machine interactions. The application of such systems varies and is becoming more prominent as healthcare services seek to provide more efficient care by utilizing smart digital health apps. One application in digital health is the incorporation of emotion recognition systems as a tool for therapeutic interventions. To this end, a system is designed to collect and analyze physiological signal data, such as electrodermal activity (EDA) and electrocardiogram (ECG), from smart wearable devices. The data are collected from different subjects of varying ages taking part in a study on emotion induction methods. The obtained signals are processed to identify stimulus trigger instances and classify the different reaction stages, as well as arousal strength, using signal processing and machine learning techniques. The reaction stages are identified using a support vector machine algorithm, while the arousal strength is classified using the ResNet50 network architecture. The findings indicate that the EDA signal effectively identifies the emotional trigger, registering a root mean squared error (RMSE) of 0.9871. The features collected from the ECG signal show efficient emotion detection with 94.19% accuracy. However, arousal strength classification is only able to reach 60.37% accuracy on the given dataset. The proposed system effectively detects emotional reactions and can categorize their arousal strength in response to specific stimuli. Such a system could be integrated into therapeutic settings to monitor patients' emotional responses during therapy sessions. This real-time feedback can guide therapists in adjusting their strategies or interventions.

Effects of Post-awakening Light Exposure on Heart Rate Variability in Healthy Male Individuals.

Light-induced effects on the autonomic nervous system (ANS) are assumed to be mediated by retinal projections to the hypothalamic suprachiasmatic nucleus (SCN) via different routes. Light information for the circadian system is detected by a subset of intrinsically photosensitive retinal ganglion cells (ipRGCs), however, inconsistency exists in research concerning the effects of light exposure on heart rate variability (HRV). Two within-subject experiments were conducted in a standardized sleep laboratory to investigate effects of light intensity (study I, n = 29: 2 days dim vs. bright light) and spectral composition (study II, n = 24: 3 days using red vs. blue vs. green light) on HRV parameters (RMSSD, LF, HF-HRV, LF/HF ratio). Light exposure was conducted for one-hour in the post-awakening phase at 5:00 AM. Results revealed no significant light intensity effect comparing dim light versus bright white light on HRV parameters. Light color of different wavelengths significantly influenced all HRV parameters except the low frequency, with moderate to large effect sizes. RMSSD values were elevated for all three colors compared to norm values, indicating stronger parasympathetic activation. LED light of different spectral compositions demonstrated bidirectional effects on spectral components of the HRV. Red light decreased the LF/HF ratio within 30 min, whereas with blue light, LF/HF ratio consistently increased across 40 min of light exposure.

Expiratory-gated Transcutaneous Auricular Vagus Nerve Stimulation (taVNS) does not Further Augment Heart Rate Variability During Slow Breathing at 0.1 Hz.

As cardiac vagal control is a hallmark of good health and self-regulatory capacity, researchers are seeking ways to increase vagally mediated heart rate variability (vmHRV) in an accessible and non-invasive way. Findings with transcutaneous auricular vagus nerve stimulation (taVNS) have been disappointing in this respect, as its effects on vmHRV are inconsistent at best. It has been speculated that combining taVNS with other established ways to increase vmHRV may produce synergistic effects. To test this idea, the present study combined taVNS with slow breathing in a cross-over design. A total of 22 participants took part in two sessions of breathing at 6 breaths/min: once combined with taVNS, and once combined with sham stimulation. Electrical stimulation (100 Hz, 400 µs) was applied during expiration, either to the tragus and cavum conchae (taVNS) or to the earlobe (sham). ECG was recorded during baseline, 20-minutes of stimulation, and the recovery period. Frequentist and Bayesian analyses showed no effect of taVNS (in comparison to sham stimulation) on the root mean square of successive differences between normal heartbeats, mean inter-beat interval, or spectral power of heart rate variability at a breathing frequency of 0.1 Hz. These findings suggest that expiratory-gated taVNS combined with the stimulation parameters examined here does not produce acute effects on vmHRV during slow breathing.

Stress of emergency physicians during helicopter operations: impact of patients' diagnoses, severity of diagnoses, and physicians' work experience.

PURPOSE: Emergency physicians are in danger of developing illnesses due to stress in their demanding work environment. Until today, scholars have not identified stressors or resilience factors that qualify to promote the preservation of emergency physicians' well-being. Therefore, potential influencing variables such as patients' diagnoses, the severity of diagnoses, as well as physicians' work experience have to be considered. The present study aims at investigating emergency physicians in the Helicopter Emergency Medical Service (HEMS)' autonomic nervous system activity during emergency operations in one shift with respect to patients' diagnoses, severity of diagnoses, and physicians' work experience. METHODS: Measurement of HRV (employing the parameters RMSSD and LF/HF) for 59 EPs (age: M = 39.69, SD = 6.19) was performed during two complete air-rescue-days, the alarm and landing phase being investigated in particular. Besides patients' diagnoses, the National Advisory Committee for Aeronautics Score (NACA) was included as an indicator for severity. Diagnoses' and NACA's effect on HRV were examined using a linear mixed model. RESULTS: Both HRV parameters indicate a significant decrease of the parasympathetic nervous system as a function of the diagnoses. Furthermore, high NACA scores (≥ V) predicted a significantly lower HRV. In addition, a lower HRV/RMSSD with increasing work experience was observed as well as a positive association between physicians' work experience and sympathetic activation (LF/HF). CONCLUSION: The present study showed that pediatric diagnoses as well as time-critical diagnoses are most stressful and have the highest impact on the physicians' ANS. This knowledge allows the development of specific training to reduce stress.

Parent Stress and Trauma, Autonomic Responses, and Negative Child Behaviors.

Cumulative stress and trauma in parents may alter autonomic function. Both may negatively impact child behaviors, however these links have not been well established. We tested hypotheses that parent stress and trauma are associated with and interact with altered autonomic function during the toy wait task, an acute parent-child interaction challenge, to predict greater negative child behaviors. Sixty-eight parents and their 2-5 year old children were enrolled. More parent major and traumatic life events, and more parent recent life events coupled with increased heart rate and decreased heart rate variability (HRV), each related to more child disruptive/aggressive behavior. More major life and traumatic life events coupled with greater HRV predicted more child attention seeking behavior. Our novel approach to assessing parental life stress offers a unique perspective. Interventions mitigating parent stress and regulating physiological coping during parent-child interactions may both promote better parent health and improve child behavioral outcomes.

Effects of different extreme cold exposure on heart rate variability.

Frequent extreme cold events in recent years have brought serious threats to outdoor workers and rescuers. Changes in ambient temperature are associated with altered cardiac autonomic function. The study aims to investigate heart rate variability (HRV) and its relationship to other physiological parameters under extreme cold exposures. Twelve males underwent a 30-min preconditioning phase in a neutral environment followed by a 30-min cold exposure (-5, -10, -15, and -20 °C). Time-domain indexes(meanRR, SDNN, RMSSD, and pNN50), frequency domain indexes [Log(HF), Log(LF), and low frequency/high frequency (LF/HF)], parasympathetic nervous system (PNS), and sympathetic nervous system (SNS) were analysed. Results showed all HRV indexes of four cold exposures were significant. The decrease in temperature was accompanied by progressive PNS activation with SNS retraction. SDNN was the most sensitive HRV index and had good linear relationships with blood pressure, pulse, and hand temperature. The results are significant for formulating safety protection strategies for workers in extremely cold environments.Practitioner Summary: This study investigated heart rate variability (HRV) in 12 males during a 30-min cold exposure (-5, -10, -15, and -20 °C). Results showed all HRV indexes of four cold exposures were significant. The decrease in temperature was accompanied by progressive PNS activation with SNS retraction. SDNN was the most sensitive HRV index and had good linear relationships with blood pressure, pulse, and hand temperature.

The impact of a violent community on mental health and the benefits of a sport program for social development.

The aim of the present study was to evaluate the impact of a violent environment on mental health and the impact of a sport for social development (SSD) program on quality of life, mental distress symptoms, and heart rate variability (HRV). HRV and psychometric data were measured from 20 men professional athletes assisted by the SSD and 20 men living in the same violent community. The comparison of groups revealed greater sympathetic parameters of HRV, positive affect, and quality of life in the SSD group. Multiple regression analysis showed that the quality of life in the SSD group was positively predicted by positive affect, while in the control group the quality of life was negatively predicted by their history of traumatic events. Both groups reported high levels of exposure to traumatic events and posttraumatic stress symptoms. However, this study demonstrates the benefits of SSD programs in mental health.

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.

BACKGROUND: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. METHODS: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190-220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). RESULTS: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1-5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. CONCLUSION: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.