Heart Rate Variability Threshold Research Articles

Myofascial release for adults with chronic neck pain and depression

BackgroundDepression affects a significant portion of the global adult population, with chronic pain patients being particularly susceptible to severe depression. Pain and mental illness contribute to an imbalance in the autonomic nervous system, affecting heart function. Myofascial release promises to improve mental and physical health by addressing fascial dysfunctions. ObjectiveThis study aims to investigate the influence of myofascial release on emotional states and autonomic nervous system functioning in individuals with chronic neck pain and depression. Additionally, it seeks to evaluate the myofascial release effect on fascial properties, pain intensity and sensitivity, and cervical spine range of motion. MethodExperimental Study. ResultThe study revealed significant enhancements in the myofascial release group, such as a substantial reduction in pain perception and stiffness, increased range of motion of the cervical spine, heart rate variability, positive affect, and pressure pain threshold. The effect sizes of these improvements ranged from small to large. No significant differences were observed in elasticity and tone. ConclusionThe findings suggest that myofascial release has a positive impact on individuals with chronic neck pain and depression, particularly in reducing pain intensity. Integrating myofascial release into treatment approaches may be beneficial. However, further research is needed to confirm and expand upon these findings, explore long-term effects, and better understand the clinical significance of certain outcomes. Trial registrationhttp://www.osf.io, doi.org/10.17605/OSF.IO/6F5RS

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.

Heart Rate Variability Thresholds: Agreement with Established Approaches and Reproducibility in Trained Females and Males.

To determine in trained females and males i) the agreement between the gas exchange threshold (GET), lactate threshold 1 (LT1), and heart rate variability threshold 1 (HRVT1), as well as between the respiratory compensation point (RCP), lactate threshold 2 (LT2), and heart rate variability threshold 2 (HRVT2), and ii) the reproducibility of HRVT1 and HRVT2 during 2-min incremental step protocols. Fifty-seven trained participants (24 females) completed a 2-min step incremental test to task failure. Nineteen participants (eight females) completed a second test to evaluate reproducibility. Gas exchange and ventilatory responses, blood lactate concentration, and RR time series were recorded to assess the oxygen consumption (V̇O 2 ) and heart rate (HR) associated with the GET, RCP, LT1, LT2, HRVT1, and HRVT2. V̇O 2 -GET versus V̇O 2 -HRVT1 and HR-GET versus HR-HRVT1 were statistically different for females (29.5 ± 4.0 vs 34.6 ± 6.1 mL·kg -1 ·min -1 ; 154 ± 11 vs 166 ± 12 bpm) and for males (33.9 ± 4.2 vs 42.7 ± 4.6 mL·kg -1 ·min -1 ; 145 ± 11 vs 165 ± 9 bpm; P < 0.001). V̇O 2 and HR at HRVT1 were greater than at LT1 ( P < 0.05). V̇O 2 -RCP versus V̇O 2 -HRVT2 and HR-RCP versus HR-HRVT2 were not statistically different for females (40.1 ± 4.7 vs 39.5 ± 6.7 mL·kg -1 ·min -1 ; 177 ± 9 vs 176 ± 9 bpm) and males (48.4 ± 5.4 vs 47.8 ± 4.8 mL·kg -1 ·min -1 ; 176 ± 8 vs 175 ± 9 bpm; P > 0.05). V̇O 2 and HR responses at LT2 were similar to HRVT2 ( P > 0.05). Intraclass correlation coefficient for V̇O 2 -HRVT1, HR-HRVT1, V̇O 2 -HRVT2, and HR-HRVT2 indicated good reproducibility when comparing the two different time points to standard methods. Whereas HRVT2 is a valid and reproducible estimate of the RCP/LT2, current approaches for HRVT1 estimation did not show good agreement with outcomes at GET and LT1.

Reliability and validity of a non-linear index of heart rate variability to determine intensity thresholds.

Exercise intensity distribution is crucial for exercise individualization, prescription, and monitoring. As traditional methods to determine intensity thresholds present limitations, heart rate variability (HRV) using DFA a1 has been proposed as a biomarker for exercise intensity distribution. This index has been associated with ventilatory and lactate thresholds in previous literature. This study aims to assess DFA a1's reliability and validity in determining intensity thresholds during an incremental cycling test in untrained healthy adults. Sixteen volunteers (13 males and 3 females) performed two identical incremental cycling stage tests at least 1week apart. First and second ventilatory thresholds, lactate thresholds, and HRV thresholds (DFA a1 values of 0.75 and 0.5 for HRVT1 and HRVT2, respectively) were determined in heart rate (HR), relative oxygen uptake (VO2rel), and power output (PO) values for both tests. We used intraclass correlation coefficient (ICC), change in mean, and typical error for the reliability analysis, and paired t-tests, correlation coefficients, ICC, and Bland-Altman analysis to assess the agreement between methods. Regarding reliability, HRV thresholds showed the best ICCs when measured in PO (HRVT1: ICC = .87; HRVT2: ICC = .97), comparable to ventilatory and lactate methods. HRVT1 showed the strongest agreement with LA 2.5 in PO (p = 0.09, r = .93, ICC = .93, bias = 9.9 ± 21.1), while HRVT2 reported it with VT2 in PO (p = 0.367, r = .92, ICC = .92, bias = 5.3 ± 21.9). DFA a1 method using 0.75 and 0.5 values is reliable and valid to determine HRV thresholds in this population, especially in PO values.

Improving the Agreement Between the First Heart-Rate-Variability Threshold and the Gas-Exchange Threshold.

The first heart-rate (HR) -variability (HRV) -derived threshold based on detrended fluctuation analysis alpha 1 (DFA a1) has shown inconsistent agreement with the gas-exchange threshold (GET). This study examined whether a custom method of computing thefirst HRV threshold (HRVT1) based on individual HRV characteristics would improve agreement. Fourteen participants underwent ramp incremental testing measuring gas-exchange variables and RR intervals. Comparisons were made between the oxygen consumption (V˙O2)/HR at the GET versus the V˙O2/HR at the standard DFA a1 = 0.75 (HRVT1s) or a custom value (HRVT1c) based on the DFA a1 midway between the maximum seen during the early ramp incremental and 0.5. Mean values for GET V˙O2 versus HRVT1s V˙O2 and GETHR versus HRVT1sHR were statistically different (25.4 [3.3] vs 29.8 [6.8]mL·kg-1·min-1, P = .01, d = 0.80; 131 [11] vs 146 [22]beats·min-1, P = .005, d = 0.91). There were no statistical differences when using the HRVT1c (25.4 [3.3] vs 25.1 [5.7]mL·kg-1·min-1, P = .77, d = 0.08; 131 [11] vs 132 [17]beats·min-1, P = .65, d = 0.12). Equivalence between GET and HRVT1c V˙O2/HR was also verified. Mean maximal DFA a1 during the early ramp incremental was 1.52 (0.22) with mean HRVT1c of 1.01 (0.11). Pearson r correlation coefficients were between .67 and .70 for all GET to HRVT1 comparisons. The second HRV threshold and respiratory compensation point parameters showed agreement and correlations in line with prior studies. The HRVT1c showed stronger agreement to GET parameters than seen using the HRVT1s. It is recommended that evaluations of the HRVT1 consider this approach in determining the HR and V˙O2 at this threshold.

Real-Time Estimation of Anaerobic Threshold during Exercise Using Electrocardiogram in Heart Failure Patients.

Exercise therapy at the aerobic level is highly recommended to improve clinical outcomes in patients with heart failure, in which cardiopulmonary exercise testing (CPX) is required to determine anaerobic thresholds (ATs) but is not available everywhere. This study aimed to validate a method to estimate the AT using heart rate variability (HRV) analysis from electrocardiography data in patients with heart failure. Between 2014 and 2019, 67 patients with symptomatic heart failure underwent CPXs in a single university hospital. During the CPX, RR intervals was measured continuously and the HRV threshold (HRVT), defined as the inflection point of <5 ms2 of a high-frequency component (HFC) using the power spectrum analysis, was determined. Patients were divided into two groups according to the mean HFC at rest (high-HFC group, n = 34 and low-HFC group, n = 33). The high-HFC group showed good correlation between the VO2 at AT and HRVT (r = 0.63, p < 0.001) and strong agreement (mean difference, -0.38 mL/kg, p = 0.571). The low-HFC group also showed modest correlation (r = 0.41, p = 0.017) but poor agreement (mean differences, 3.75 mL/kg, p < 0.001). In conclusion, the HRVT obtained from electrocardiography may be a useful indicator for estimating AT in patients with heart failure.

Effect of ramp slope on intensity thresholds based on correlation properties of heart rate variability during cycling.

An index of heart rate variability (HRV), detrended fluctuation analysis (DFA a1) has gathered interest as a surrogate marker of exercise intensity boundaries. The aim of this report was to examine heart rate variability threshold (HRVT) behavior across different ramp incremental (RI) slopes. Seventeen participants completed a series of three RI (15, 30, and 45 W · min-1 slopes) with monitoring of gas exchange parameters, heart rate (HR) and HRV. HRVT1 was defined as the V̇O2 or HR at which DFA a1 reached 0.75 and the HRVT2 at which these values reached 0.5. HRVTs were compared by Pearson's r, Bland-Altman analysis, ICC3,1 , ANOVA, and paired t-testing. An excellent degree of reliability was seen across all three ramps, with an ICC3,1 of 0.93 and 0.88 for the HRVT1 V̇O2 and HR, respectively, and 0.90 and 0.92 for the HRVT2 V̇O2 and HR, respectively. Correlations between HRVT1/2 of the individual ramps were high with r values 0.84-0.95 for both HR and V̇O2 . Bland-Altman differences ranged between -1.4 and 1.2 mL · kg-1 · min-1 and -2 and +2 bpm. Paired t-testing showed no mean differences between any HRVT1/2 ramp comparisons. Cycling ramp slope does not appear to affect either HRVT1 or HRVT2 in terms of HR or V̇O2 .

Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency.

Identifying exercise intensity boundaries has been shown to be important during endurance training for performance enhancement and rehabilitation. Unfortunately, even though surrogate markers show promise when assessed on a group level, substantial deviation from gold standards can be present in each individual. The aim of this study was to evaluate whether combining two surrogate intensity markers improved this agreement. Electrocardiogram (ECG) and gas exchange data were obtained from 21 participants who performed an incremental cycling ramp to exhaustion and evaluated for first (VT1) and second (VT2) ventilatory thresholds, heart rate (HR) variability (HRV), and ECG derived respiratory frequency (EDR). HRV thresholds (HRVT) were based on the non-linear index a1 of a Detrended Fluctuation Analysis (DFA a1) and EDR thresholds (EDRT) upon the second derivative of the sixth-order polynomial of EDR over time. The average of HRVT and EDRT HR was set as the combined threshold (Combo). Mean VT1 was reached at a HR of 141 ± 15, HRVT1 at 152 ± 14 (p < 0.001), EDRT1 at 133 ± 12 (p < 0.001), and Combo1 at 140 ± 13 (p = 0.36) bpm with Pearson's r of 0.83, 0.78, and 0.84, respectively, for comparisons to VT1. A Bland-Altman analysis showed mean biases of 8.3 ± 7.9, -8.3 ± 9.5, and -1.7 ± 8.3 bpm, respectively. A mean VT2 was reached at a HR of 165 ± 13, HRVT2 at 167 ± 10 (p = 0.89), EDRT2 at 164 ± 14 (p = 0.36), and Combo2 at 164 ± 13 (p = 0.59) bpm with Pearson's r of 0.58, 0.95, and 0.94, respectively, for comparisons to VT2. A Bland-Altman analysis showed mean biases of -0.3 ± 8.9, -1.0 ± 4.6, and -0.6 ± 4.6 bpm, respectively. Both the DFA a1 and EDR intensity thresholds based on HR taken individually had moderate agreement to targets derived through gas exchange measurements. By combining both non-invasive approaches, there was improved correlation, reduced bias, and limits of agreement to the respective corresponding HRs at VT1 and VT2.

Is There Agreement and Precision between Heart Rate Variability, Ventilatory, and Lactate Thresholds in Healthy Adults?

This study aims to analyze the agreement and precision between heart rate variability thresholds (HRVT1/2) with ventilatory and lactate thresholds 1 and 2 (VT1/2 and LT1/2) on a treadmill. Thirty-four male students were recruited. Day 1 consisted of conducting a health survey, anthropometrics, and Cardiopulmonary Exercise Test (CPx). On Day 2, after 48 h, a second incremental test was performed, the Cardiopulmonary Stepwise Exercise Test consisting of 3 min stages (CPxS), to determine VT1/2, LT1/2, and HRVT1/2. One-way repeated-measures ANOVA and effect size (ηp2) were used, followed by Sidak's post hoc. The Coefficient of Variation (CV) and Typical Error (TE) were applied to verify the precision. Bland Altman and the Intraclass Correlation Coefficient (ICC) were applied to confirm the agreement. HRVT1 showed different values compared to LT1 (lactate, RER, and R-R interval) and VT1 (V̇E, RER, V̇CO2, and HR). No differences were found in threshold 2 (T2) between LT2, VT2, and HRVT2. No difference was found in speed and V̇O2 for T1 and T2. The precision was low to T1 (CV &gt; 12% and TE &gt; 10%) and good to T2 (CV &lt; 12% and TE &lt; 10%). The agreement was good to fair in threshold 1 (VT1, LT1, HRVT1) and excellent to good in T2 (VT1, LT1, HRVT1). HRVT1 is not a valid method (low precision) when using this protocol to estimate LT1 and VT1. However, HRVT2 is a valid and noninvasive method that can estimate LT2 and VT2, showing good agreement and precision in healthy adults.

Reliability of the heart rate variability threshold during treadmill exercise.

The heart rate variability threshold (HRVT) is a clinical parameter used to gain insight into autonomic balance. Prior validation of the HRVT has been with cycle ergometry, with no studies examining the viability of treadmill exercise. The purpose of this study was to examine the reliability of the HRVT during treadmill exercise, and to compare the HRVT to the ventilatory threshold (VT). Ten healthy, college‐aged males completed two maximal graded exercise tests on a treadmill. A Polar RS800CX watch was used for heart rate and HRVT data. The HRVT was determined from three HRV variables including the root mean square of successive differences of continuous R−R intervals (RMSSD), the standard deviation of normal R−R intervals (SDNN) and the standard deviation of instantaneous beat intervals (SD1). A metabolic cart was utilized to determine the VT. Results showed no difference between the HRVT (2.4 ± 0.6 and 2.2 ± 0.3 for RMSSD, 2.8 ± 0.5 and 2.7 ± 0.5 for SDNN and 2.4 ± 0.6 and 2.3 ± 0.6 for SD1) or the VT (3.0 ± 0.3 and 3.1 ± 0.3) between trials. When compared to the VT, averaged HRVT values for RMSSD (2.3 ± 0.3) and SD1 (2.3 ± 0.5) were lower than averaged VT (2.8 ± 0.4, p < 0.05). The averaged HRVT from SDNN (2.8 ± 0.5) did not differ from the VT. These results suggest that treadmill is a viable mode for HRVT determination, and that HRVT determined by SDNN may be a better comparison to the VT.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update.

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

An Index of Non-Linear HRV as a Proxy of the Aerobic Threshold Based on Blood Lactate Concentration in Elite Triathletes.

A non-linear index of heart rate (HR) variability (HRV) known as alpha1 of Detrended Fluctuation Analysis (DFA a1) has been shown to change with increasing exercise intensity, crossing a value of 0.75 at the aerobic threshold (AT) in recreational runners defining a HRV threshold (HRVT). Since large volumes of low-intensity training below the AT is recommended for many elite endurance athletes, confirmation of this relationship in this specific group would be advantageous for the purposes of training intensity distribution monitoring. Nine elite triathletes (7 male, 2 female) attended a training camp for diagnostic purposes. Lactate testing was performed with an incremental cycling ramp test to exhaustion for the determination of the first lactate threshold based on the log–log calculation method (LT1). Concurrent measurements of cardiac beta-to-beat intervals were performed to determine the HRVT. Mean LT1 HR of all 9 participants was 155.8 bpm (±7.0) vs. HRVT HR of 153.7 bpm (±10.1) (p = 0.52). Mean LT1 cycling power was 252.3 W (±48.1) vs. HRVT power of 247.0 W (±53.6) (p = 0.17). Bland–Altman analysis showed mean differences of −1.7 bpm and −5.3 W with limits of agreement (LOA) 13.3 to −16.7 bpm and 15.1 to −25.6 W for HR and cycling power, respectively. The DFA a1-based HRVT closely agreed with the LT1 in a group of elite triathletes. Since large volumes of low-intensity exercise are recommended for successful endurance performance, the fractal correlation properties of HRV show promise as a low-cost, non-invasive option to that of lactate testing for identification of AT-related training boundaries.

Agreement between the Heart Rate Variability Threshold and Ventilatory Threshold in Young Women: Impact of Cardiac Parasympathetic Status and Cardiorespiratory Fitness

ABSTRACT The heart rate variability threshold (HRVT) is a useful and inexpensive alternative to estimate the ventilatory threshold (VT). However, its validity in women remains underexplored. We investigated the agreement between HRVT and VT in young women and the influence of cardiac parasympathetic status and cardiorespiratory fitness (CRF). Sixty-one women [Age: 24.5 ± 4.6 years, BMI: 23.4 ± 2.6 kg/m2, O2max: 36.6 ± 6.1 (mL (kg.min)−1)] underwent a maximal exercise test on a treadmill. We assessed HRVT using four methods: HRVTvisual, HRVTDmax, HRVT3ms, HRVT1ms. Low bias and limits of agreement were observed between VT and HRVTvisual [−.1 (−1.5. 1.3) km/h] and HRVTDmax [−.1 (−1.6, 1.4) km/h] methods. Higher limits of agreement were observed for HRVT3ms [.1 (−2.1, 2.1) km/h] and HRVT1ms [.4 (−1.8, 2.7) km/h]. In conclusion, acceptable agreement was observed between VT and HRVT in women, mainly when visual and Dmax methods were used for HRVT assessment.

Aerobic Threshold Identification in a Cardiac Disease Population Based on Correlation Properties of Heart Rate Variability.

An index of heart rate (HR) variability correlation properties, the short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1) has shown potential to delineate the first ventilatory threshold (VT1). This study aims to extend this concept to a group of participants with cardiac disease. Sixteen volunteers with stable coronary disease or heart failure performed an incremental cycling ramp to exhaustion PRE and POST a 3-week training intervention. Oxygen uptake (VO2) and HR at VT1 were obtained from a metabolic cart. An ECG was processed for DFA a1 and HR. The HR variability threshold (HRVT) was defined as the VO2, HR or power where DFA a1 reached a value of 0.75. Mean VT1 was reached at 16.82 ± 5.72 mL/kg/min, HR of 91.3 ± 11.9 bpm and power of 67.8 ± 17.9 watts compared to HRVT at 18.02 ± 7.74 mL/kg/min, HR of 94.7 ± 14.2 bpm and power of 73.2 ± 25.0 watts. Linear relationships were seen between modalities, with Pearson’s r of 0.95 (VO2), 0.86 (HR) and 0.87 (power). Bland–Altman assessment showed mean differences of 1.20 mL/kg/min, 3.4 bpm and 5.4 watts. Mean peak VO2 and VT1 did not change after training intervention. However, the correlation between PRE to POST change in VO2 at VT1 with the change in VO2 at HRVT was significant (r = 0.84, p < 0.001). Reaching a DFA a1 of 0.75 was associated with the VT1 in a population with cardiac disease. VT1 change after training intervention followed that of the HRVT, confirming the relationship between these parameters.

Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete.

A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) estimation and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, (1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, (2) estimation of the HRVT during real-time monitoring using a cycling 6 min stage test, (3) a simulated 1 h training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.

Obesity does not Impair Ambulatory Cardiovascular and Autonomic Responses Post-exercise.

This study aimed to assess whether obesity and/or maximal exercise can change 24 h cardiac autonomic modulation and blood pressure in young men. Thirty-nine men (n: 20; 21.9±1.8 kg·m-2, and n: 19; 32.9±2.4 kg·m-2) were randomly assigned to perform a control (non-exercise) and an experimental day exercise (after maximal incremental test). Cardiac autonomic modulation was evaluated through frequency domain heart rate variability (HRV). Obesity did not impair the ambulatory HRV (p>0.05), however higher diastolic blood pressure during asleep time (p=0.02; group main effect) was observed. The 24 h and awake heart rate was higher on the experimental day (p<0.05; day main effect), regardless of obesity. Hypotension on the experimental day, compared to control day, was observed (p<0.05). Obesity indicators were significantly correlated with heart rate during asleep time (Rho=0.34 to 0.36) and with ambulatory blood pressure(r/Rho=0.32 to 0.53). Furthermore, the HRV threshold workload was significantly correlated with ambulatory heart rate (r/Rho=- 0.38 to-0.52). Finally, ambulatory HRV in obese young men was preserved; however, diastolic blood pressure was increased during asleep time. Maximal exercise caused heart rate increase and 24h hypotension, with decreased cardiac autonomic modulation in the first hour, regardless of obesity.

Vibration Perception Threshold and Heart Rate Variability as methods to assess chemotherapy-induced neuropathy in women with breast cancer – a pilot study

PurposeChemotherapy-induced peripheral neuropathy (CIPN) is a recognized adverse effect of standard (neo)adjuvant chemotherapy in breast cancer (BC) treatment. However, there is no consensus on a validated method for assessing CIPN. Heart rate variability (HRV) and vibration perception threshold (VPT) could be used as objective measures to describe CIPN. The aim of this pilot study was to investigate whether subjectively reported CIPN was associated with altered HRV and VPT in BC patients. MethodsWe performed a cross-sectional pilot study evaluating 30 BCE patients previously treated with chemotherapy, 26 BCE patients who did not receive chemotherapy, and 22 controls without breast cancer. Self-reported CIPN was registered for the BC patients. All participants were subjected to multi-frequency vibration analyses to determine VPT along with short ECG measurements to determine HRV measures. ResultsSelf-reported CIPN was registered in 14 (46.6%) BC patients treated with chemotherapy. The VPT at 64 Hz (P = 0.022) and mean HR (P = 0.022) were significantly higher and the HRV measures SDNN (P = 0.023), RMSSD (P = 0.007), LF (P = 0.050) and HF (P = 0.045) were significantly lower in BC patients reporting CIPN compared to controls when adjusted for age. VPT at 64 Hz and 125 Hz were significantly higher in BC patients not reporting CIPN compared to controls when adjusted for age. ConclusionWe found elevated VPT and mean HR along with decreased HRV parameters in 14 BCE patients reporting CIPN. These findings support the need for further investigation into whether HRV and vibration analysis could contribute to an objective assessment of CIPN.

Intrarater reliability of different methods of heart rate variability threshold analysis and postexercise parasympathetic reactivation in young women

Abstract Objective: To evaluate the reliability of HRVT and postexercise parasympathetic reactivation analysis after a submaximal exercise test in young women. Methods: Twenty-four young women [21.1 (20.1, 24.7) years; 21.4 (20.1, 23.1) kg/m2] underwent three incremental exercise tests on a treadmill on occasions separated by 48 h. R-R intervals were continuously recorded during the incremental tests and throughout 5 min of post-exercise active recovery for HRVT and parasympathetic reactivation analysis, respectively. HRVT was identified using two methods: a) the intensity where no significant reduction of SD1 HRV index was identified by visual inspection of the graphic (HRVTvisual), b) the first stage to present SD1 value < 3ms (HRVT3ms). Postexercise parasympathetic reactivation was assessed at each minute during five minutes of recovery using SD1 and r-MSSD indexes. Absolute and relative reliability were assessed using the coefficient of variation (CV) and the intraclass correlation coefficient (ICC), respectively. Results: Good (ICC = 0.81, CV = 17.3) and excellent (ICC = 0.90, CV = 4.6) reliability were observed for HRVT3ms and HRVTvisual, respectively. On the postexercise period, good reliability was observed for both SD1 (ICC = 0.82-89, CV = 22.1-28.9) and r-MSSD (ICC = 0.82-89, CV = 21.1-28.6), with a high correlation between indexes in all-time points of analysis (r = 0.96-0.99). Conclusions: HRVT may be reproducibly assessed in women, mainly when HRVTvisual is used for analysis. In addition, SD1 and r-MSSD provide reliable and redundant measures of postexercise parasympathetic reactivation.

Influence of aerobic fitness on the correspondence between heart rate variability and ventilatory threshold

The aims of this study were to verify the correspondence between heart rate variability (HRV) and ventilatory thresholds during a progressive exercise test and the relationship with low and high aerobic fitness levels. Twenty male volunteers (29.5±6.2 years; 75.9±13.0 kg; 175.0±7.4 cm) were recruited. The subjects were allocated to two groups according to their VO2max &lt;48.8 ml•kg−1•min−1 (low cardiorespiratory fitness group) (n=10) and &gt;48.81 ml•kg−1•min−1 (high cardiorespiratory fitness group) (n=10). A progressive test was performed, consisting of 3-min stages beginning at 25 watts and increasing by 25 watts every 3-min. The HRV threshold (HRVT) and ventilatory threshold (VT) analyses were performed through visual inspection. The comparisons with RMSSD values in percentage of maximum workload resulted in a higher effect size (ES) than the SDNN values. The VO2 in the high cardiorespiratory fitness group at VT (+32%), HRVTRMSSD (+27%), and HRVTSDNN (+31%) was signifi cantly higher compared to the group with low cardiorespiratory fitness. Higher values were observed for relative load (W•kg-1) at VT and HRVTSDNN in the high cardiorespiratory fitness group in comparison with the low cardiorespiratory fitness group (P&lt;0.05), but no difference for VT and HRVTRMSSD. Signifi cant correlations between at VT and HRVTSDNN (r=0.77) were found only in the low cardiorespiratory fitness group. Cardiorespiratory fitness should be regarded as a factor for HRVT evaluation. The HRVTSDNN was closer to the VT in the low cardiorespiratory fitness group than the HRVTRMSSD, however, the use of vagal modulation assessed using the HRV parameter was more sensitive to observe possible differences regarding cardiorespiratory fitness.

Influence of aerobic fitness on the correspondence between heart rate variability and ventilatory threshold

The aims of this study were to verify the correspondence between heart rate variability (HRV) and ventilatory thresholds during a progressive exercise test and the relationship with low and high aerobic fitness levels. Twenty male volunteers (29.5±6.2 years; 75.9±13.0 kg; 175.0±7.4 cm) were recruited. The subjects were allocated to two groups according to their VO2max &lt;48.8 ml•kg−1•min−1 (low cardiorespiratory fitness group) (n=10) and &gt;48.81 ml•kg−1•min−1 (high cardiorespiratory fitness group) (n=10). A progressive test was performed, consisting of 3-min stages beginning at 25 watts and increasing by 25 watts every 3-min. The HRV threshold (HRVT) and ventilatory threshold (VT) analyses were performed through visual inspection. The comparisons with RMSSD values in percentage of maximum workload resulted in a higher effect size (ES) than the SDNN values. The VO2 in the high cardiorespiratory fitness group at VT (+32%), HRVTRMSSD (+27%), and HRVTSDNN (+31%) was signifi cantly higher compared to the group with low cardiorespiratory fitness. Higher values were observed for relative load (W•kg-1) at VT and HRVTSDNN in the high cardiorespiratory fitness group in comparison with the low cardiorespiratory fitness group (P&lt;0.05), but no difference for VT and HRVTRMSSD. Signifi cant correlations between at VT and HRVTSDNN (r=0.77) were found only in the low cardiorespiratory fitness group. Cardiorespiratory fitness should be regarded as a factor for HRVT evaluation. The HRVTSDNN was closer to the VT in the low cardiorespiratory fitness group than the HRVTRMSSD, however, the use of vagal modulation assessed using the HRV parameter was more sensitive to observe possible differences regarding cardiorespiratory fitness.