Heart Rate Variability Data Research Articles

Methods for studying heart rate variability (review)

Currently, one of the most popular methods worldwide for studying and evaluating the work of the heart, the functional state of the body as a whole and the state of various departments of the autonomic nervous system is the analysis of heart rate variability (HRV). To understand the mechanisms of analysis, it is necessary to review modern HRV research methods and analyze modern scientific data on existing research methods to assess heart rate variability. In order to obtain a complete picture of modern methods of studying heart rate variability, to study the long-term experience of scientists, physicians and engineers. To analyze a number of domestic and foreign publications in order to identify promising areas of research methodologies for an important diagnostic parameter – heart rate variability. Also, to show criteria that can increase the informative characteristics of the studied signal due to new methods of processing the electrocardiosignal. The analysis of a number of publications from domestic and foreign literature has been carried out and modern methods of analyzing heart rate variability have been studied. In order to increase the informative characteristics of the electrocardiosignal and the reliability of the analysis data, it is proposed to use new methods for processing heart rate variability in conjunction with the respiratory signal. The study and analysis of modern methods for studying heart rate variability allowed us to conclude about promising methods for analyzing the electrocardiosignal in real time, which is very important in the realities of time. Identifying the features of HRV analysis data processing leads to errors. The use of new electrocardiosignal processing techniques will increase the reliability of the analysis of heart rate variability data and informative characteristics of the analysis.

Positive affect and heart rate variability: a dynamic analysis

Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.

Autonomic Nervous System Activity and Dental Anxiety in the Northern Finland Birth Cohort (NFBC1966) Population.

Autonomic nervous system (ANS) activity may be associated with dental anxiety; however, no studies exist on the topic. The aim of this study was to assess if ANS activity and dental anxiety are associated. Data were collected as part of a Northern Finland Birth Cohort Study, NFBC1966, at the age of 46 years with eligible data on 1905 participants. Dental anxiety was measured using the Modified Dental Anxiety Scale (MDAS) categorized as follows: 19-25 = high, 10-18 = moderate, 5-9 = no to low dental anxiety. Heart rate variability (HRV) data were collected using an HR monitor and a standard lead-II electrocardiogram (ECG). Baroreflex sensitivity (BRS) was calculated from continuous ECG and blood pressure signals. Participants were categorized into three groups as follows: Low = the lowest 20th percentile, Mid = medium 21-79th percentile, and High = the highest 20th percentile according to their ANS variables. The associations between the MDAS and ANS activity parameters were evaluated using unordered multinomial logit models adjusted for comorbidities, β-blockers, BMI, smoking, and education. High heart rate, reduced HRV, low cardiac vagal modulation, and poor BRS were associated with moderate dental anxiety, and high cardiac vagal modulation and good BRS were associated with no to low dental anxiety. Poorer ANS activity might predispose some to dental anxiety, and better ANS activity might protect from dental anxiety.

An association between heart rate variability and pediatric obstructive sleep apnea

BackgroundThere are different findings on heart rate variability (HRV) and pediatric obstructive sleep apnea (pOSA) by an overnight HRV or a 1-hr HRV. However, there is limited data of HRV and pOSA diagnosis by using a 24-h HRV test. This study aimed to evaluate if HRV had potential for OSA diagnosis by using a 24-h HRV test.MethodsThis was a prospective study included children age between 5 and 15 years old, presenting with snoring, underwent polysomnography and a 24-h Holter monitoring. Predictors for pOSA diagnosis were analyzed using logistic regression analysis.ResultsDuring the study period, there were 81 pediatric patients met the study criteria. Of those, 65 patients (80.25%) were diagnosed as OSA. There were three factors were independently associated with OSA: standard deviation of all normal interval (SDNN), high frequency (HF), and low frequency (LF). The adjusted odds ratios of these factors were 0.949 (95% confidence interval 0.913, 0.985), 0.786 (95% confidence interval 0.624, 0.989), and 1.356 (95% confidence interval 1.075, 1.709).ConclusionsHRV parameters including SDNN, HF, and LF were associated with pOSA diagnosis in children by using the 24-h Holter monitoring.

Automated extraction of heart rate variability from magnetoencephalography signals

Magnetoencephalography (MEG) measures magnetic fluctuations in the brain generated by neural processes, some of which, such as cardiac signals, are generally removed as artifacts and discarded. However, heart rate variability (HRV) has long been regarded as a biomarker related to autonomic function, suggesting the cardiac signal in MEG contains valuable information that can provide supplemental health information about a patient. To enable access to these ancillary HRV data, we created an automated extraction tool capable of capturing HRV directly from raw MEG data with artificial intelligence. Five scans were conducted with simultaneous MEG and electrocardiogram (ECG) acquisition, which provides a ground truth metric for assessing our algorithms and data processing pipeline. In addition to directly comparing R-peaks between the MEG and ECG signals, this work explores the variation of the corresponding HRV output in time, frequency, and non-linear domains. After removing outlier intervals and aligning the ECG and derived cardiac MEG signals, the RMSE between the RR-intervals of each was RMSE1 = 2 ms, RMSE2 = 2 ms, RMSE3 = 8 ms, RMSE4 = 4 ms, RMSE5 = 13 ms. The findings indicate that cardiac artifacts from MEG data carry sufficient signal to approximate an individual's HRV metrics.

Acceptability and Perceived Utility of Virtual Reality Among People Who Are Incarcerated Who Use Drugs.

People who are incarcerated are at heightened risk of overdose upon community reentry. Virtual reality (VR) may provide an innovative tool for overdose prevention intervention in corrections facilities. This mixed methods study sought to understand incarcerated individuals' perspectives on VR for overdose prevention and explore physiological arousal associated with use of VR equipment. Study participants were 20 individuals, stratified by gender, with an opioid use disorder at a county jail. Qualitative interviews assessed acceptability and perceived utility of VR in the jail setting. Thematic analysis indicated high levels of acceptability and potential utility in the following areas: (a) mental health and substance use interventions, (b) community reentry skills training, and (c) communication and conflict resolution skills. Heart rate variability (HRV) data were collected continuously during the interview and during VR exposure to explore whether exposure to the VR environment provoked arousal. Physiological data analyses showed a significant decrease in heart rate (HR) [b = -3.14, t(18) = -3.85, p < .01] and no arousal as measured by root mean square of successive RR interval differences (RMSSD) [b = -0.06, t(18) = -1.06, p = .30] and high frequency-HRV (HF-HRV) [b = -0.21, t(18) = -1.71, p = .10]. This study demonstrated high acceptability and decreased HR response of VR among incarcerated people who use drugs.

Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study.

Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.

Central Autonomic Network and Heart Rate Variability in Premature Neonates

Introduction: The Central Autonomic Network (CAN) is a hierarchy of brain structures that collectively influence cardiac autonomic input, mediating the majority of brain-heart interactions, but has never been studied in premature neonates. In this study, we use heart rate variability (HRV), which has been described as the “primary output” of the CAN, and resting-state functional MRI (rsfMRI) to characterize brain-heart relationships in premature neonates. Methods: We studied premature neonates who underwent rsfMRI at term (37-week postmenstrual age or above) and had HRV data recorded during the same week of their MRI. HRV was derived from continuous electrocardiogram data during the week of the rsfMRI scan. For rsfMRI, a seed-based approach was used to define regions of interest (ROIs) pertinent to the CAN, and blood oxygen level-dependent signal was correlated between each ROI as a measure of functional connectivity. HRV was correlated with CAN connectivity (CANconn) for each region, and subgroup analysis was performed based on sex and clinical comorbidities. Results: Forty-seven premature neonates were included in this study, with a mean gestational age at birth of 28.1 +/− 2.6 weeks. Term CANconn was found to be significantly correlated with HRV in approximately one-fifth of CAN connections. Two distinct patterns emerged among these HRV-CANconn relationships. In the first, increased HRV was associated with stronger CANconn of limbic regions. In the second pattern, stronger CANconn at the precuneus was associated with impaired HRV maturation. These patterns were especially pronounced in male premature neonates. Conclusion: We report for the first time evidence of brain-heart relationships in premature neonates and an emerging CAN, most striking in male neonates, suggesting that the brain-heart axis may be more vulnerable in male premature neonates. Signatures in the heart rate may eventually become an important noninvasive tool to identify premature males at highest risk for neurodevelopmental impairment.

Is reduced heart rate variability associated with functional somatic disorders? A cross-sectional population-based study; DanFunD

ObjectivesIt has been hypothesised that functional somatic disorders (FSD) could be initiated by sympathetic predominance in the autonomic nervous system as measured by low heart rate variability (HRV). Earlier studies...

Psychological, cognitive, and physiological impact of hazards casualties' trainings on first responders: the example of a chemical and radiological training. An exploratory study.

First responders are among the first to respond to hazards casualties. They might operate in volatile, uncertain, complex, and ambiguous (VUCA) environments. While they have underlined the need to improve their knowledge and training to face these environments, there are few data regarding the stress induced by these trainings. Chemical, biological, radiological, and nuclear (CBRN) hazards casualties' trainings seem to be a good model of "in vivo" stress. First responders must operate in a hostile and encountered environment with a CBRN protective equipment that places demand on their psychological, cognitive, and physiological capacities. Current research recognizes that the activity of the parasympathetic system (PSS) can be used as an objective marker of stress adaptation, measured as heart rate variability (HRV). To compare between baseline and simulation the evolution of the parasympathetic activity (primary outcome), anxiety, emotions, cognitive load, and body posture awareness (secondary outcomes). A total of 28 first responders attended to three simulated scenarios requiring CBRN management of casualties. One day before simulation, we collected HRV data (baseline). The simulations' day (pre-, post-simulation) we collected anxiety score (STAI-Y B), emotions (SPANE), cognitive load (NASA TLX), body posture awareness (PAS) and HRV. The morning after we collected the PAS score (recovery). We compare data' evolution between different times of the simulation. (i) A high level of anxiety at baseline [Median 51 (46; 56)] which decreased between pre- and post-simulation (p = 0.04; F = 2.93); (ii) a post-simulation decrease in negative feelings (p = 0.03); (iii) a decrease in body awareness after simulation which returned to the initial level at recovery (p = 0.03; F = 3.48); (iv) a decrease in mean RR between baseline, pre- and post-simulation (p = 0.009; F = 5.11). There were no significant difference between times on others analysis of HRV. Prior to simulation, participants experienced anticipatory anxiety. Simulations training practiced regularly could be one way to combat anticipatory anxiety.

Mild Cognitive Impairment detection based on EEG and HRV data

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals.

A Comparison of the Immediate Effects of Chiropractic Thoracic High-Velocity Low-Amplitude Manipulation Applied Supine and Prone on the Autonomic Nervous System

Thoracic manipulation is one of the spinal manipulative treatment methods frequently used by clinicians. However, when carrying out this task, several methods could be preferred. This study aimed to compare the immediate effects of supine and prone thoracic high-velocity-low-amplitude chiropractic manipulations on the autonomic nervous system. The study included 62 healthy and volunteer participants aged 18-45 years. Participants were randomly assigned to supine and prone manipulation groups. Both groups, heart rate variability data were obtained with the Elite HRV CorSense device for 1 minute before the application, systolic and diastolic blood pressures were measured, pulse and saturation values were recorded. After the measurement, supine chiropractic manipulation was applied to the mid-thoracic region and the same measurements were repeated. Heart rate variability data were analyzed with Elite HRV application, pulse rate, saturation, systolic and diastolic blood pressure values were recorded, and the results were statistically evaluated. In the intra-group comparison, LF/HF ratio increased in the prone group (p=0.025). When the difference between the groups was analyzed, the change in LF/HF ratio was found to be statistically significant between the two groups (p=0.008). The effect of prone application on the autonomic nervous system was found to be higher than supine application. This study revealed that prone and supine thoracic chiropractic HVLA application was effective on OSS in healthy individuals and that the efficiency of prone manipulation was significantly higher.

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models.

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

Integrative neuro-cardiovascular dynamics in response to test anxiety: A brain-heart axis study

Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neuro-cardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.

Heart Rate Variability Measured from Wearable Devices as a Marker of Disease Severity in Tetanus.

Tetanus is a disease associated with significant morbidity and mortality. Heart rate variability (HRV) is an objective clinical marker with potential value in tetanus. This study aimed to investigate the use of wearable devices to collect HRV data and the relationship between HRV and tetanus severity. Data were collected from 110 patients admitted to the intensive care unit in a tertiary hospital in Vietnam. HRV indices were calculated from 5-minute segments of 24-hour electrocardiogram recordings collected using wearable devices. HRV was found to be inversely related to disease severity. The standard deviation of NN intervals and interquartile range of RR intervals (IRRR) were significantly associated with the presence of muscle spasms; low frequency (LF) and high frequency (HF) indices were significantly associated with severe respiratory compromise; and the standard deviation of differences between adjacent NN intervals, root mean square of successive differences between normal heartbeats, LF to HF ratio, total frequency power, and IRRR, were significantly associated with autonomic nervous system dysfunction. The findings support the potential value of HRV as a marker for tetanus severity, identifying specific indices associated with clinical severity thresholds. Data were recorded using wearable devices, demonstrating this approach in resource-limited settings where most tetanus occurs.

INTEGRATING HEART RATE VARIABILITY AND PSYCHOMETRIC ASSESSMENTS TO EVALUATE STRESS AND BURNOUT IN ICU NURSING STAFF: A PILOT STUDY

Background: Intensive Care Units (ICUs) are recognized as highly demanding environments that significantly contribute to stress and burnout among nursing staff. Despite increasing concern over burnout in healthcare, the relationship between physiological stress indicators, such as Heart Rate Variability (HRV), and psychometric assessments has not been thoroughly explored in this setting. Objective: This pilot study aimed to evaluate the relationship between HRV metrics and psychometric assessments of stress and anxiety in ICU nursing staff. Additionally, it explored the influence of shift type, shift duration, demographic factors, and lifestyle habits on these stress indicators. Methods: An observational cross-sectional pilot study was conducted with 24 ICU healthcare professionals at a University Hospital in Madrid, Spain. HRV data were collected under controlled conditions, with measurements taken at the beginning and end of shifts. Psychometric assessments were conducted using the State-Trait Anxiety Inventory (STAI), Perceived Stress Scale (PSS-14), Nursing Stress Scale (NSS) and a Visual Analogue Scale for Stress (VASS). Non-parametric statistical tests were used to analyze correlations between HRV metrics, psychometric scores, and demographic/lifestyle variables. Results: Significant negative correlations were observed between HRV metrics and perceived stress levels, particularly between the Low-Frequency to High-Frequency (LF/HF) ratio and stress measures. Night and extended shifts were associated with elevated stress, as indicated by lower HRV and higher psychometric stress scores. These findings suggest that shift type and duration significantly influence stress levels in ICU nursing staff. Conclusion: This pilot study highlights the potential of HRV as an objective measure of stress in ICU nursing staff, with significant correlations observed between HRV metrics and psychometric assessments. The findings suggest that HRV could be a valuable tool for monitoring stress in real time and identifying individuals at risk of burnout. However, further research with larger samples and a longitudinal approach is needed to validate these results and explore their implications for occupational health practices in ICU settings.

Influence of the method of manual-physical correction on autonomic regulation in patients with essential arterial hypertension

One of the methods of non-drug treatment of essential arterial hypertension, is manual, osteopathic effects on the spine, primarily the cervical spine. Despite the absence of these methods in clinical guidelines for essential arterial hypertension, they continue to develop and improve. One of the promising areas is manual-physical correction with an effect on the cervical spine and subsequent strengthening of the muscle corset with the help of physical exercises (method of A.Yu. Shishonin). One of the supposed mechanisms for reducing blood pressure (BP) in this case is the effect on the autonomic nervous system (ANS). In the course of the study, which was based on the study of heart rate variability (HRV), the presence of pronounced signs of a decrease in sympathetic influences and activation of the parasympathetic nervous system was shown. The parameters of statistical linear analysis (standard deviation of NN intervals; stress index, etc.) and the wave spectrum shifted towards sympathicotonia during therapy using the method of A.Yu. Shishonin. The HRV data were obtained against the background of a decrease in blood pressure and a decrease in heart rate (HR), which suggests the impact of manual physical correction on the ANS as one of the key mechanisms mediating its effect.

Exploring factors associated with heart rate variability in newly injured traumatic cervical spinal cord injury patients

Background: Assessment of heart rate variability (HRV) in traumatic cervical spinal cord injury (SCI) patients is widely reported, but few studies have examined the HRV in the newly injured. Further, there is paucity of data relating HRV to factors that might have a bearing on the extent of variability in them. Aim and Objectives: The current study explored the factors associated with the HRV of newly injured cervical SCI American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade A/B patients. Materials and Methods: A secondary analysis of HRV data acquired from 24 newly injured tetraplegic patients in a previously published study compared the HRV between AIS grade A and B patients, and across cervical levels of lesion. In addition, the correlation of HRV indices with duration since injury and the patients’ age was examined. Results: The ratio of low to high frequency power was significantly lower in AIS B compared to AIS A patients, and significantly negatively correlated with duration since injury. Conclusion: The ratio of modulations of cardiac sympatho-vagal supply was lower in AIS B patients and reduced with increasing duration since injury. AIS grade and duration since injury need to be considered when interpreting the HRV data of newly injured tetraplegic patients.

Features of students’ cardiovascular system functional state according to the electrocardiogram’s dispersion mapping and heart rate variability data

Features of students’ cardiovascular system functional state according to the electrocardiogram’s dispersion mapping and heart rate variability data

Visually assessing work performance using a smartwatch via day-to-day fluctuations in heart rate variability.

To optimize workplace health promotion, a simple method for quantifying allostatic load response is needed. This study examines the feasibility of optimizing objective anxiety and presenteeism monitoring using daily smartwatch-measured ultra-short heart rate variability (HRV). Office workers without diagnosed disease prospectively performed 30 s HRV self-measurement each morning for two months and responded to the State-Trait Anxiety Inventory (STAI) and Work Limitation Questionnaire (WLQ). Logistic regression analysis examined daily HRV parameters in the high-trait anxiety group (HTA, STAI ≥ 40) using mean and variance HRV, age, self-reported gender, and body mass index (BMI). The ideal cutoff value enabled comparison of WLQ using the Mann-Whitney U test. Heart rate variability data were collected for 279 participants (male ratio, 83.9%; age, 42 ± 10 years) who completed questionnaires and monitored HRV for 30+ days. Compared to the low-trait anxiety group, HTA exhibited higher variance of the log-transformed coefficient of component variance of high-frequency component (LnccvHF) and low-frequency per HF (Lnccv L/H), in addition to differences in the means of these HRV parameters. In addition to BMI (odds ratio [OR] = 0.92, p = 0.02) and mean LnccvL/H (OR = 10.75, p < 0.01), the variance of Lnccv L/H was an independent predictor of HTA (OR = 2.39E + 8, p = 0.011). The daily Lnccv L/H dispersion group had a lower WLQ productivity loss score (p = 0.02, r = 0.17). By focusing on HRV dispersion status, this simple and instantly applicable daily HRV monitoring system enables optimized quantitative monitoring of anxiety and productivity.