Heart Rate Variability Metrics Research Articles

SPECIAL ISSUE: Slow-Paced Contraction: An Evidence-Based Method for Increasing Heart Rate Variability

Slow-paced muscle contraction (SPC) is an easily learned method for increasing heart rate variability (HRV). SPC provides an alternative to slow-paced breathing (SPB) in clinical and optimal performance applications. SPB can be challenging (e.g., chronic pain), anatomically impossible (e.g., phrenic nerve damage), or medically contraindicated (e.g., diabetes and kidney disease). SPC may be especially helpful for clients who breathe dysfunctionally or who cannot slow their breathing to the resonance frequency (RF) range. The RF is an individual’s unique stimulation rate that maximally increases HRV. Wrist-core-ankle SPC increases several HRV metrics more than only contracting the wrists and ankles. Preliminary evidence suggests that combining wrist-ankle SPC with SPB may increase HRV more than either method alone.

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.

Design of Training Load Monitoring and Adjustment Algorithm for Athletes: Based on Heart Rate Variability and Body Index Data

The training load monitoring and adjustment algorithm for athletes, based on heart rate variability (HRV) and body index data, offers a comprehensive approach to optimizing athletic performance and minimizing the risk of injury. By leveraging HRV data, which reflects the autonomic nervous system's response to training stress, and body index measurements such as body mass index (BMI) or body fat percentage, the algorithm provides insights into athletes' physiological readiness and recovery status. The design of an effective training load monitoring and adjustment algorithm is critical for optimizing athletic performance while minimizing the risk of injury and overtraining. This paper proposes a novel approach that integrates heart rate variability (HRV) and body index data to tailor training programs to individual athlete needs. This paper presents an innovative approach to training load monitoring and adjustment for athletes, utilizing heart rate variability (HRV) and body index data. Through continuous monitoring and analysis of HRV metrics such as RMSSD and LF/HF Ratio, in conjunction with body mass index (BMI) and body fat percentage, personalized load management strategies are developed to optimize athletic performance while mitigating the risk of injury and overtraining. The optimization algorithm outlined in this study allows for real-time adjustments to training loads based on individual physiological responses, ensuring that athletes receive tailored training programs that maximize performance gains and promote long-term health and well-being. By leveraging HRV and body index data, coaches and sports scientists can enhance athletic performance outcomes and support the overall development and longevity of athletes' careers.

Association between heart rate variability and cardiorespiratory fitness in individuals with type 2 diabetes mellitus

Background: Type 2 Diabetes Mellitus (T2DM) is associated with cardiovascular risk, which is partly due to autonomic dysfunction and decreased cardiorespiratory fitness. This study examines the relationship between heart rate variability (HRV) and maximal oxygen uptake (VO2max) in T2DM patients to understand their interconnected impacts on autonomic and aerobic functions. Methods: In a cross-sectional study, 77 T2DM patients underwent HRV and VO2max assessments using standard protocols. HRV metrics were analysed in conjunction with VO2max, measured through direct breath-by-breath analysis. Pearson's correlation coefficient was used to investigate the relationships between HRV indices and VO2max. Results: VO2max showed strong positive correlations with RMSSD (r = 0.89, p < .001), HF (r = 0.54, p < .001), and pNN50% (r = 0.52, p < .001), indicating higher parasympathetic activity with improved cardiorespiratory fitness. Negative correlations with LF (r = -0.60, p < .001) and the LF/HF ratio (r = -0.39, p < .001) suggested that better fitness levels lead to sympathetic withdrawal and a more favourable autonomic balance. Moderate positive correlations with SDNN (r = 0.46, p < .001) and TP (r = 0.58, p < .001) further suggested that overall autonomic modulation is enhanced with increased cardiorespiratory fitness. Conclusion: This study substantiates a significant correlation between HRV and VO2max in individuals with T2DM, highlighting the intricate relationship between autonomic function and aerobic capacity. These findings suggest that enhancing cardiorespiratory fitness may improve autonomic balance, offering potential avenues for mitigating cardiovascular risk in the T2DM population.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

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.

Unveiling the age-related dynamics in Sjögren's syndrome: Insights from heart rate variability and autonomic function.

Sjögren's Syndrome (SS), mainly affecting women in their midlife, is characterized by persistent inflammation in glands producing tears and saliva, often leading to significant complications. This study investigates the differences in autonomic system functioning between individuals with SS and healthy controls. From April 2019 to December 2022, 329 diagnosed primary SS (pSS) patients and 30 healthy controls were enrolled at Taipei Veterans General Hospital, Taipei, Taiwan. The study assessed autonomic nervous system functioning using various HRV metrics. Participants were divided based on age and AECG criteria, including salivary gland biopsy and autoantibody status. Significant differences in Heart Rate Variability (HRV) were observed between pSS patients and healthy controls. The total power index was notably lower in pSS patients (4.98 ± 1.29) than in controls (5.54 ± 1.21, p = .022). Additionally, Vagal (VAG) activity was significantly reduced in the pSS group (4.95 ± 1.33) compared to the healthy control group (5.47 ± 1.19, p = .041). Age-stratified analysis highlighted that the ≤50 years pSS group had a higher heart rate (77.74 ± 10.42) compared to the >50 years group (73.86 ± 10.35, p = .005). This group also showed a higher total power index (5.78 ± 1.30) versus the >50 years group (4.68 ± 1.19, p < .001), and significantly lower VAG activity (4.70 ± 1.26, p = .007) compared to healthy controls. Furthermore, the Standard Deviation of Normal-to-Normal Intervals (SDNN) was greater in the ≤50 years SS group (44.45 ± 37.12) than in the >50 years group (33.51 ± 26.18, p = .007). In pSS patients, those positive for both salivary gland biopsy and autoantibodies demonstrated a lower Total Power (4.25 ± 1.32) and R-wave validity (93.50 ± 4.79, p < .05) than other groups, suggesting more severe autonomic imbalance. The R-R interval variation (RRIV) was also significantly higher in this dual-positive group (696.10 ± 975.41, p < .05). Additionally, the ESSPRI for dryness was markedly higher in the dual-positive group (8.10 ± 1.45, p < .05), indicating more severe symptoms. These findings reveal significant variations in autonomic function in SS patients, especially in those with dual-positive biopsy and autoantibody status. This study demonstrates significant autonomic dysfunction in pSS patients compared to healthy controls, particularly in those positive for both salivary gland biopsy and autoantibodies. The age-stratified analysis further emphasizes the impact of aging on autonomic system functioning in pSS, suggesting a need for age-specific management approaches in pSS patient care.

Monitoring autonomic responses in Parkinson’s disease individuals: non-linear and chaotic global metrics of heart rate variability

Aim To examine and compare the autonomic responses, as assessed through the non-linear and chaotic global metrics of heart rate variability in two groups: the Parkinson’s Disease Group (PDG) and the Control Group (CG), both at rest and during an active tilt test. Methods The study encompassed 46 participants (PDG: n = 23; 73.73 ± 7.28 years old; CG: n = 23; 70.17 ± 8.20 years old). Initial data collection involved the acquisition of participant’s characteristics. The autonomic modulation was estimated both at rest and during the active tilt test. For this assessment, we computed non-linear indices derived from five entropies (Approximate, Sample, Shannon, Renyi, Tsallis), Detrended Fluctuation Analysis and the seven chaotic global metrics (hsCFP1-hsCFP7). Results At rest, the PDG exhibited lower values of hsCFP3 (0.818 ± 0.116 vs. 0.904 ± 0.065; p < 0.05) and Sample Entropy (0.720 ± 0.149 vs. 0.799 ± 0.171; p < 0.05). During the test, the PDG demonstrated lower values of ApEn, while the CG presented lower values of SampEn, hsCFP1, hsCFP3, hsCFP7, and higher values of hsCFP5. An interaction was observed, indicating that hsCFP1 and hsCFP3 exhibit differential behavior for the CG and PDG in response to the test. Conclusion subjects with PD exhibited reduced complexity of the RR interval series at rest, and a diminished autonomic response to the active tilt test when compared with the CG. The test, together with non-linear indices, may serve for assessing the Autonomic Nervous System in individuals with PD in a clinical setting. The interpretation of these data should be approached with caution, given the possible influences of pharmacotherapies and the inclusion of diabetic participants.

Tracking Cancer: Exploring Heart Rate Variability Patterns by Cancer Location and Progression.

Reduced heart rate variability (HRV) is an autonomic nervous system (ANS) response that may indicate dysfunction in the human body. Consistent evidence shows cancer patients elicit lower HRV; however, only select cancer locations were previously evaluated. Thus, the aim of the current study was to explore HRV patterns in patients diagnosed with and in varying stages of the most prevalent cancers. At a single tertiary academic medical center, 798 patients were recruited. HRV was measured via an armband monitor (Warfighter MonitorTM, Tiger Tech Solutions, Inc., Miami, FL, USA) equipped with electrocardiographic capabilities and was recorded for 5 to 7 min with patients seated in an upright position. Three time-domain metrics were calculated: SDNN (standard deviation of the NN interval), rMSSD (the root mean square of successive differences of NN intervals), and the percentage of time in which the change in successive NN intervals exceeds 50ms within a measurement (pNN50). Of the 798 patients, 399 were diagnosed with cancer. Cancer diagnoses were obtained via medical records one week following the measurement. Analysis of variance models were performed comparing the HRV patterns between different cancers, cancer stages (I-IV), and demographic strata. A total of 85% of the cancer patients had breast, gastrointestinal, genitourinary, or respiratory cancer. The cancer patients were compared to a control non-cancer patient population with similar patient size and distributions for sex, age, body mass index, and co-morbidities. For all HRV metrics, non-cancer patients exhibited significantly higher rMSSDs (11.1 to 13.9 ms, p < 0.0001), SDNNs (22.8 to 27.7 ms, p < 0.0001), and pNN50s (6.2 to 8.1%, p < 0.0001) compared to stage I or II cancer patients. This significant trend was consistently observed across each cancer location. Similarly, compared to patients with stage III or IV cancer, non-cancer patients possessed lower HRs (-11.8 to -14.0 bpm, p < 0.0001) and higher rMSSDs (+31.7 to +32.8 ms, p < 0.0001), SDNNs (+45.2 to +45.8 ms), p < 0.0001, and pNN50s (19.2 to 21.6%, p < 0.0001). The HR and HRV patterns observed did not significantly differ between cancer locations (p = 0.96 to 1.00). The depressed HRVs observed uniformly across the most prevalent cancer locations and stages appeared to occur independent of patients' co-morbidities. This finding highlights the potentially effective use of HRV as a non-invasive tool for determining common cancer locations and their respective stages. More studies are needed to delineate the HRV patterns across different ages, between sexes and race/ethnic groups.

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.

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.

Effects of Acute Sleep Deprivation on the Physiological Response to Woodsmoke and Exercise.

Evaluate sleep deprivation effects on the acute physiological response to a combined stressor of woodsmoke and exercise. Ten participants completed two exercise trials (8 hr of sleep vs. 4 hr) with woodsmoke. Trials were conducted in a crossover design. Key measures examined before and after each trial included heart rate variability (HRV), pulse wave velocity (PWV), blood pressure (BP), pulmonary function testing (PFT), and oxidative stress. Acute sleep deprivation experienced before exercise and woodsmoke exposure did not impact metrics of HRV, PWV, PFT, BP, or oxidative stress. Acute sleep deprivation didn't amplify physiologic metrics in response to moderate-intensity aerobic exercise with inhaled woodsmoke. Although findings do not eliminate the negative impacts of inhaling wood smoke, more research is needed to understand the acute effects of woodsmoke exposure on the cardiovascular system [1].

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 

Assessment of Autonomic Nervous System Activity Using Spectral Analysis of Heart Rate Variability After Continuous Positive Airway Pressure (CPAP) Therapy in Patients With Sleep Apnea.

Heart rate variability (HRV) measurements have emerged as a valuable tool for understanding the functioning of the autonomic nervous system (ANS) and assessing the health outcomes of obstructive sleep apnea (OSA) in patients. Sleep and the ANS exert a mutual influence on each other. Sleep promotes relaxation and recovery of the ANS. Conversely, ANS activity plays a role in regulating the onset and maintenance of sleep. The impact of continuous positive airway pressure (CPAP) therapy on patient recovery levels was investigated by assessing the restoration of ANS activity using HRV indicators. The study included patients with OSA who had been on CPAP for at least eight weeks. The patients were divided into two groups, namely the experimental group (CPAP-compliant) and the control group (CPAP-non-compliant). The study included a total of 38 patients, with 20 in the CPAP-compliant group and 18 in the CPAP-non-compliant group. The HRV analysis included time- and frequency-domain measures. Data was collected in various resting conditions, including lying down, standing, regular breathing, and under physiological stress induced by deep breathing and the Valsalva maneuver. After CPAP treatment, there was an increase in the average values for SDNN for deep breathing and Valsalva maneuvers. The mean changes in SDNN for CPAP-non-compliant versus CPAP-compliant groups for normal breathing increased from 32.50±5.33 to 42.40±8.03, while the values for Valsalva increased from 20.16±2.47 to 25.45±3.03. Despite the observed variations in SDNN, there was no significant change in the average change in heart rate (∆ HR), except during the Valsalva maneuver. Post-CPAP values for the Valsalva ratio were significantly decreased in deep breathing. The E:I ratio for the CPAP-compliant group during normal breathing was 1.08±.16 compared to 1.55±.09; t (36) =-11.15, p <0.001 in the CPAP-non-compliant group. During deep breathing, the ratio was 1.36±.15 versus 1.59±.24; t (36) =-3.578, p <0.001. The high frequency (HF)nu mean values for deep breathing were 34.06±5.546 compared to 35.00±6.358; t (36) = -.485, p=.630. For the Valsalva maneuver, the values were 29.94±4.721 versus 26.95±6.621; t (36) =1.589, p=.060. The HF/low frequency (LF) ratio was found to be significant only in supine, standing, and normal breathing. The utilization of CPAP therapy was found to be effective in achieving and sustaining autonomic balance during tasks like standing and engaging in regular breathing patterns. During activities that involve intense physical effort, like the Valsalva maneuver, the HRV metrics did not indicate any significant balance between sympathetic and parasympathetic activity. However, using CPAP therapy for a prolonged period can be beneficial in consistently improving the sympathovagal balance in these patients.

Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22–67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver’s state.

Classifying engagement in E-learning through GRU-TCN model using photoplethysmography signals

Learning engagement is regarded as an important metric for assessing E-learning since it is related to the quality of online education and student performance. Previous studies have identified a correlation between heart rate variability (HRV) and learning engagement. However, a comprehensive and systematic investigation of the underlying mechanisms remains lacking. In this study, a GRU-TCN model was developed to classify learner engagement levels in E-learning based on photoplethysmography (PPG). PPG signals were collected from 40 participants as they learned the video course under task conditions of no-interference, music & bullet chat stimuli, and mental arithmetic. Twenty-eight HR/HRV metrics (including time-domain, frequency-domain, and Non-linear metrics) were examined, among which 10 metrics were sensitive to changes in learner engagement. To adjust for individual differences in heart response in learning tasks and to enhance the data, a three-step processing procedure was conducted to evaluate the PPG signals of each participant: (1) defined three levels of learning engagement: full attention, external, and internal divided attention, (2) normalized HRV metrics for different engagement levels by using the average metric of participants' baseline states, (3) Enhanced data with sliding time windows. The GRU-TCN classification model achieved F1-scores of 95% in the training set and 85% in the testing set, respectively, enabling effective automatic real-time tracking of learner engagement in E-learning.

Autonomic nervous system responses in the intermediate band to cranial cutaneous stimulation.

Cardiovascular rhythms representing functional states of the autonomic nervous system (ANS) are insufficiently reflected by the current physiological model based on low and high frequency bands (LF, HF, resp.). An intermediate (IM) frequency band generated by a brainstem pacemaker was included in systemic physiological ANS analyses of forehead skin perfusion (SP), ECG, and respiration. Data of 38 healthy participants at T0 and T1 (+1 week) before, during, and following osteopathic cranial vault hold (CVH) stimulation were analyzed including momentary frequencies of highest amplitude, amplitudes in low (0.05-0.12 Hz), IM (0.12-0.18 Hz), and high (0.18-0.4 Hz) frequency bands, and established heart rate variability (HRV) metrics. During CVH, LF interval durations increased, whereas IM/HF band durations decreased significantly. Amplitudes increased significantly in all frequency bands. A cluster analysis found one response pattern dominated by IM activity (47% of participants) with highly stable0.08 Hz oscillation to CVH, and one dominated by LF activity (0.10 Hz) at T0, increasing to IM activity at T1. Showing frequency ratios at ≈3:1, respiration was not responsible for oscillations in PPG during CVH. HRV revealed no significant responses. Rhythmic patterns in SP and respiration matched previous findings on a reticular "0.15 Hz rhythm". Involvement of baroreflex pathways is discussed as alternative explanation.

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads.

Evaluating the Impact of an App-Delivered Mindfulness Meditation Program to Reduce Stress and Anxiety During Pregnancy: Pilot Longitudinal Study.

Stress and anxiety during pregnancy are extremely prevalent and are associated with numerous poor outcomes, among the most serious of which are increased rates of preterm birth and low birth weight infants. Research supports that while in-person mindfulness training is effective in reducing pregnancy stress and anxiety, there are barriers limiting accessibility. The aim of this paper is to determine if mindfulness meditation training with the Headspace app is effective for stress and anxiety reduction during pregnancy. A longitudinal, single-arm trial was implemented with 20 pregnant women who were instructed to practice meditation via the Headspace app twice per day during the month-long trial. Validated scales were used to measure participant's levels of stress and anxiety pre- and postintervention. Physiological measures reflective of stress (heart rate variability and sleep) were collected via the Oura Ring. Statistically significant reductions were found in self-reported levels of stress (P=.005), anxiety (P=.01), and pregnancy anxiety (P<.0001). Hierarchical linear modeling revealed a statistically significant reduction in the physiological data reflective of stress in 1 of 6 heart rate variability metrics, the low-frequency power band, which decreased by 13% (P=.006). A total of 65% of study participants (n=13) reported their sleep improved during the trial, and 95% (n=19) stated that learning mindfulness helped with other aspects of their lives. Participant retention was 100%, with 65% of participants (n=13) completing about two-thirds of the intervention, and 50% of participants (n=10) completing ≥95%. This study found evidence to support the Headspace app as an effective intervention to aid in stress and anxiety reduction during pregnancy.