Top PDF Heart rate variability frequency domain alterations among healthy nurses exposed to prolonged work stress

Heart rate variability frequency domain alterations among healthy nurses exposed to prolonged work stress

Heart rate variability frequency domain alterations among healthy nurses exposed to prolonged work stress

Int. J. Environ. Res. Public Health 2018, 15, 113 8 of 11 4. Discussion With the purpose of assessing the effects of chronic work stress on cardiac autonomic control, we investigated twice, in a one-year interval, a sample of 36 healthy adult nurses, predominantly women. The spectral analysis of HRV of two-hour ECG recordings showed in the working periods a statistically significant difference of HF among the work stress groups, with lower mean values in the PHS and RHS groups, when compared to the SLS. These differences decrease in non-working and resting periods. Based on the same two-hour ECG recordings, we also reported lower mean values of LF in stressed nurses during working time. This reduced HRV in the low frequency power tended to recover in nurses under recent stress conditions over a time period covering the non-working period of the same working day and the following resting period. Instead, in nurses under prolonged working stress conditions, the recovery did not take place, and the decreased LF power stabilized even in non-working and resting periods. This may explain the detrimental effects on the cardiovascular system, previously repeatedly documented [ 1 , 2 ]. Finally, a decrease over time of LF/HF ratio was observed in all the three groups, driven only by the increase of HF power during the resting periods. A plausible pathophysiological mechanism of this relationship advocates for disturbances of the autonomic nervous system based on a disequilibrium of the sympathetic and the vagal branch [ 4 – 6 ]. Many authors have found an inverse association between high levels of work stress and HF values [ 20 , 21 , 23 , 26 , 29 , 32 ]. Chandola et al. found low job control was associated with lower HF means [ 20 , 21 ]. Similarly, Collins et al. reported that low job control was associated with lower HF in a sample of 36 healthy men, healthcare employees [ 23 ]. Hintsanen et al. in a sample of 863 healthy Finnish workers found that high perceived work stress, assessed by the ERI questionnaire, was associated with a lower HF in woman, but not in men [ 29 ]. In a Dutch study on 70 healthy workers, Hanson et al. found significant decrements of HF in the subjects with a high ERI ratio [ 32 ]. Hernandez-Gaytan et al. in a sample of 54 healthy men and women, resident physicians in a Mexico City hospital, showed a lower HF in passive subjects, based on the JCQ questionnaire [ 26 ].
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The role of physical activity and heart rate variability for the control of work related stress

The role of physical activity and heart rate variability for the control of work related stress

the detection of cardiac autonomic responses (Hynynen et al., 2011). Aspects such as variations in HRV measures examined (e.g., linear, non-linear, etc.), data analysis (e.g., supine, seated, Fast Fourier Transform, Autoregression, etc.) as well as the quality of data (e.g., degree of ectopy/artifact, sampling rate, record- ing length, etc.) make comparisons between studies challeng- ing (Jarczok et al., 2013). For instance, Hynynen et al. (2011) reported that HRV during an orthostatic test upon wakening may be more useful for the analysis of stress in real life compared to night time recordings. In contrast, HRV measures recorded during both work and at night were reported to be also sen- sitive markers of mental stress alterations at work (Hjortskov et al., 2004; Yarnell, 2008). Further studies should determine the optimal HRV methods for detecting cardiac autonomic stress related adaptations. Factors such as the HRV measure (i.e., time or frequency domain, nonlinear, etc.), time, and dura- tion of recordings and body posture (Young and Leicht, 2011; Boullosa et al., 2012) may play an important role that requires further clarification. Given the possible influence of factors exter- nal to work related stressors during long recordings, the use of laboratory controlled recordings should be included in fur- ther studies for a better evaluation of autonomic control of HR (Lombardi and Stein, 2011). Additionally, other cardiac auto- nomic indices (i.e., heart rate recovery; HRR, post-exercise) could also be employed as a delayed HRR was reported in individuals with high levels of stress and depression (Gordon et al., 2012). Consequently, the simple assessment of HRV and HRR may provide a comprehensive evaluation of the cardiac autonomic health of workers and prognosis for work-related stress and responsiveness to exercise training (Huovinen et al., 2011).
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Validation of the apple watch for heart rate variability measurements during relax and mental stress in healthy subjects

Validation of the apple watch for heart rate variability measurements during relax and mental stress in healthy subjects

* Correspondence: dhernand@unizar.es; Tel.: +34-876-555462 Received: 4 July 2018; Accepted: 8 August 2018; Published: 10 August 2018    Abstract: Heart rate variability (HRV) analysis is a noninvasive tool widely used to assess autonomic nervous system state. The market for wearable devices that measure the heart rate has grown exponentially, as well as their potential use for healthcare and wellbeing applications. Still, there is a lack of validation of these devices. In particular, this work aims to validate the Apple Watch in terms of HRV derived from the RR interval series provided by the device, both in temporal (HRM (mean heart rate), SDNN, RMSSD and pNN50) and frequency (low and high frequency powers, LF and HF) domain. For this purpose, a database of 20 healthy volunteers subjected to relax and a mild cognitive stress was used. First, RR interval series provided by Apple Watch were validated using as reference the RR interval series provided by a Polar H7 using Bland-Altman plots and reliability and agreement coefficients. Then, HRV parameters derived from both RR interval series were compared and their ability to identify autonomic nervous system (ANS) response to mild cognitive stress was studied. Apple Watch measurements presented very good reliability and agreement (>0.9). RR interval series provided by Apple Watch contain gaps due to missing RR interval values (on average, 5 gaps per recording, lasting 6.5 s per gap). Temporal HRV indices were not significantly affected by the gaps. However, they produced a significant decrease in the LF and HF power. Despite these differences, HRV indices derived from the Apple Watch RR interval series were able to reflect changes induced by a mild mental stress, showing a significant decrease of HF power as well as RMSSD in stress with respect to relax, suggesting the potential use of HRV measurements derived from Apple Watch for stress monitoring.
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Normal values of heart rate variability at rest in a young, healthy and active Mexican population

Normal values of heart rate variability at rest in a young, healthy and active Mexican population

This study analyzed Heart Rate Variability in a large sample of active young subjects within a narrow age range (18 to 25), using time and frequency domain methods and a Poincaré plot. Heart rate was recorded (beat to beat) for 30 minutes at rest in 200 healthy subjects divided into 4 groups: 50 sportsmen (20.54 ± 1.52 years); 50 active men (21.22 ± 1.31 years); 50 sports- women (20.10 ± 1.87 years) and 50 active women (20.92 ± 1.87 years). Significant differences were found for most parameters between athletes and active subjects (male and female) but not be- tween genders. Percentile distributions were provided for all parameters (according to gender and physical activity level) to be used as refer- ences in future researches.
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Recovery time and heart rate variability following extreme endurance exercise in healthy women

Recovery time and heart rate variability following extreme endurance exercise in healthy women

following extreme exercise was a key premise of this study. In what was probably the first study to examine heart rate and autonomic recovery following arduous exercise, Hautala et al. (2001) examined HRV in nine healthy men (age 36.2 10.6 years), before and after a 75 km cross-country skiing race. R-R intervals were recorded 24 h pre-exercise and 48 h postexercise. There was no difference in heart rate or autonomic function at 24 h postexercise, however by 48 h postexercise, there was a significant decrease in heart rate, LF and the LF:HF power, and increased standard deviation of NNIs and HF power. More recently, Murrell et al. (2007) examined seven healthy adult men and women (average age 32 10 years) 2 weeks before and at 1–4 h after and 48 h after a mountain marathon (42.2 km; cumulative gain 1000 m; ambient temperature 15°C (59°F); comple- tion time, 261 27 min). Compared with pre-exercise, heart rate, LF and LF:HF power were notably higher in the early postexercise period (1–4 h), however there were no differences between pre-exercise and 48 h postexercise. It is worth noting that both the method of breathing (controlled vs. spontaneous) and posture (whether supine, seated or upright) were significant confounders, underlin- ing the importance of consistent HRV protocols. In another study, 25 men (36.7 1.53 years) were investi- gated 1 day before, 1 h, and 1, 3 and 7 days after an Ironman ultra-endurance event (3.9 km swim, 180.2 km cycle, 42.2 km run) (Gratze et al. 2005). The sympathetic dominance and higher heart rate observed 1 h postexer- cise had resolved within 3 days; heart rate and LF power decreased and HF power increased to pre-event levels.
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Recovery time and heart rate variability following extreme endurance exercise in healthy women.

Recovery time and heart rate variability following extreme endurance exercise in healthy women.

following extreme exercise was a key premise of this study. In what was probably the first study to examine heart rate and autonomic recovery following arduous exercise, Hautala et al. (2001) examined HRV in nine healthy men (age 36.2  10.6 years), before and after a 75 km cross-country skiing race. R-R intervals were recorded 24 h pre-exercise and 48 h postexercise. There was no difference in heart rate or autonomic function at 24 h postexercise, however by 48 h postexercise, there was a significant decrease in heart rate, LF and the LF:HF power, and increased standard deviation of NNIs and HF power. More recently, Murrell et al. (2007) examined seven healthy adult men and women (average age 32  10 years) 2 weeks before and at 1–4 h after and 48 h after a mountain marathon (42.2 km; cumulative gain 1000 m; ambient temperature 15°C (59°F); comple- tion time, 261  27 min). Compared with pre-exercise, heart rate, LF and LF:HF power were notably higher in the early postexercise period (1–4 h), however there were no differences between pre-exercise and 48 h postexercise. It is worth noting that both the method of breathing (controlled vs. spontaneous) and posture (whether supine, seated or upright) were significant confounders, underlin- ing the importance of consistent HRV protocols. In another study, 25 men (36.7  1.53 years) were investi- gated 1 day before, 1 h, and 1, 3 and 7 days after an Ironman ultra-endurance event (3.9 km swim, 180.2 km cycle, 42.2 km run) (Gratze et al. 2005). The sympathetic dominance and higher heart rate observed 1 h postexer- cise had resolved within 3 days; heart rate and LF power decreased and HF power increased to pre-event levels.
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Effects of the Call with the Mobile Phone on Heart Rate Variability Parameters of healthy young people

Effects of the Call with the Mobile Phone on Heart Rate Variability Parameters of healthy young people

In our study, VLF increased during the telephone call. The increase in very low frequency in the exposed subjects could be related to parasympathetic activation as VLF is very much dependent on parasympathetic tone [29]. Taylor et al. suggested that parasympathelic nervous system is the dominant determinant of VLF [29]. In studies on atrial fibrillation (AF) the increase in VLF component together with other parasympathetic markers predicted the early recurrence of AF after cardioversion [30]. However, the physiologic interpretation of VLF oscillations is still a subject of debate. Reduction in VLF is associated with increased risk for sudden cardiac death [31]. Different physiological mechanisms for VLF have been proposed: physical activity, thermoregulation, renin-angiotensin-aldosterone system, slow respiratory patterns and parasympathetic mechanisms [29].
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Heart Rate and Heart Rate Variability Estimation using Spectral Analysis

Heart Rate and Heart Rate Variability Estimation using Spectral Analysis

ABSTRACT:Electrocardiogram records the different electrical activity of the heart. This record can be used to extract the different features of heart such as heart rate, respiratory rate etc. Heart rate plays an important role to determine the abnormalities in the heart. Hence in this work calculation of heart rate and heart rate variability by analysing the ECG signal is carried out. Heart rate is calculated using Spectral Domain Analysis. The results obtained from these methods are compared. Spectral Domain Analysis was found to give accurate results for calculation of heart rate.
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Effects of Autogenic Training on Stress Response and Heart Rate Variability in Nursing Students

Effects of Autogenic Training on Stress Response and Heart Rate Variability in Nursing Students

Sixth, this study suggests the need to manage stresses experi- enced by nursing students, and provides methods for coping with stress. Previous studies have shown nursing students experience various stresses in clinical practice ( Kim & Lee, 2011 ) that affect self-ef ficacy, self-respect, autonomous neuro-systems, and clinical competence ( Kim, 2002; Kim & Lee, 2011; Whang, 2006 ). However, it is regarded as the students' responsibility to take care of their own stress in nursing school. Therefore, we recommend that more speci fic, concrete approaches by instructor in the nursing school be taken to address the issue of nursing students' stress. Furthermore, nursing students should be given training guidelines, which cite active stress coping strategies that allow students to adapt to col- lege life, accept stressful situations positively, and manage them in advance. In particular, we recommend that a relaxation program, such as, AT, designed to enable nursing students to manage stresses arising during clinical training, be incorporated into all university nursing training programs.
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Effect Of The PPG Sampling Frequency Of An IIR Filter On Heart Rate Variability Parameters

Effect Of The PPG Sampling Frequency Of An IIR Filter On Heart Rate Variability Parameters

We investigated the effect of changes in the sampling frequency of an IIR Butterworth BPF on HRV parameters in the frequency, time, and nonlinear domains. In this study, it was assumed that one of many factors that determines the performance of the HRV analyzer used in clinical applications is the sampling frequency, the frequency with which a PPG analog signal is sampled and captured by a microcontroller. The results demonstrated that there were no significant differences among all HRV parameters obtained from two HRV datasets at sampling frequencies of 1000 Hz and 500 Hz and that a sampling frequency lower than 500 Hz might lead to inaccurate HRV parameters for the HRV analysis. However, the advantages of low sampling frequency include characteristics of signals that are not vulnerable to noise, low calculation time, and low power consumption, when various digital filters and advanced signal processing were embedded into the microcontroller. A sampling frequency higher than 500 Hz is recommended for evaluating all HRV parameters for the HRV analysis, resulting in obtaining complete discrete time signals for an analog PPG waveform. In the future, various sampling frequencies will be investigated to select an optimal sampling frequency that is robust to motion artifacts, which is appropriate for the environmental conditions in which a PPG measurement is made.
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An Exploration of Stress Reactivity, Stress Recovery, Mindfulness Meditation and Prayer with the use of Heart Rate Variability

An Exploration of Stress Reactivity, Stress Recovery, Mindfulness Meditation and Prayer with the use of Heart Rate Variability

While MM involves conscious and deliberate regulations of the breath, the participated assigned to the PExp group did not receive specific instructions on how to sustain attention during the practice. A domain of HRV called respiratory sinus arrhythmia (RSA) involves HRV being synchronized with respiration, where there is a decrease and an increase in HRV during inspiration and expiration, respectively (Allen et al., 2007; Force, 1996; Yasuma & Hayano, 2004). This synchronization can help understand how deliberately elongating the out-breath during MM practice (e.g., “Emphasize longer, slower and more complete breathes; focus on breathing out thoroughly, emptying the air in your lungs; feel your breath moving outwards as you breath out and see if you can feel the outward breath linked to other flows of sensation in your body; when you breath out, you are stimulating a nerve called the vagus nerve that naturally relaxes your heart and the rest of your body; allow the exhaling breath and the action of the vagus nerve to take place”), can in turn, increase HRV. In contrast, while listening to the prayer passaged (presented to the PExp group), individuals were not told to attend to their in- and out-breathe nor reminded to focus attention on the present moment. Due to the lack of continuous on-going instructions, it is possible that the participants assigned to the PExp group and both control groups (MCon and PCon) allowed distractions to draw attention back to the past or future concerns.
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The Efficacy of a Modified Trier Social Stress Test as Indicated by Heart Rate Variability

The Efficacy of a Modified Trier Social Stress Test as Indicated by Heart Rate Variability

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License This material may be protected by copyright law (Title 17 U.S. Code). This Open Access is brought to you for free and open access by the Theses, Dissertations, and Honors Projects at InSPIRe @ Redlands. It has been accepted for inclusion in Undergraduate Honors Theses by an authorized administrator of InSPIRe @ Redlands. For more information, please contact

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Twenty-four Hour Ambulatory Holter Monitoring and Heart Rate Variability in Healthy Individuals

Twenty-four Hour Ambulatory Holter Monitoring and Heart Rate Variability in Healthy Individuals

observations reflect the increased parasympathetic and decreased sympathetic tone during NREM sleep. The occurrence of heart rates more than 100/minute during sleep probably were associated with sympathetic surges of REM sleep. The occurrence of bradycardia episodes during sleep suggests that episodes of bradycardia occur during sleep even in normal persons and should not be regarded as essentially indicating sinus node disease. The observation that no subject had maximum sinus RR more than 1800 ms indicates that sinus pauses (value > 2 seconds) are not noted in healthy adult population. A similar finding was reported by Molgaard et al (1990) 7 .
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Fetal heart rate and fetal heart rate variability in Lipizzaner broodmares

Fetal heart rate and fetal heart rate variability in Lipizzaner broodmares

facts. Clinically, this analysis has proved to be the most useful in horses. The two most important parameters belonging here are the standard deviation of normal- normal intervals (SDNN) and the root mean square of successive differences (RMSSD). The former depends on the regulatory effect of the sympathetic and the parasympathetic nervous system, while the latter reflects the long-term vari- ability of heart function and its value primarily depends on the parasympathetic tone (Bowen, 2010; Kovács et al., 2012, 2013). Frequency-domain analysis aims at discriminating the effects of different elements of the neuroendocrine system. It studies how many per cent of the signals falls within a specific frequency do- main. Within this system, three factors influence cardiac function: the renin- angiotensin-aldosterone (RAAS) system that acts on the heart rate for a longer period of time (seconds or minutes), the sympathetic nervous system (seconds) and the parasympathetic nervous system (during the RR interval). This method of analysis (also called spectral analysis) differentiates three frequency domains: a high-frequency, a low-frequency and a very low frequency range. It should be mentioned that, before running the analysis, the frequency domains established for horses must be set in the computer programme, as these differ from those used in human medicine (von Borell et al., 2007; Bowen, 2010). Within the fre- quency-domain analysis, the so-called LF/HF ratio, which is an indicator of sympathetic-parasympathetic balance and sympathetic activity, is very important. If the animal is exposed to stress of shorter or longer duration, the HF part de- creases and thus the LF/HF ratio increases (Kovács et al., 2012, 2013).
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Heart rate variability changes during high frequency yoga breathingand breath awareness

Heart rate variability changes during high frequency yoga breathingand breath awareness

increased attention is associated with higher sympathetic modulation. While the sympathetic modulation was not increased during HFYB, the vagal withdrawal during HFYB could result in a shift in autonomic balance towards sympathetic dominance. The exact mechanism by which both practices may be influencing the auto- nomic nervous system remains a speculation. The respiratory and cardiovascular centers are closely asso- ciated in the brainstem. It is possible that conscious corti- cal regulation of brainstem respiratory centers may influence cardiovascular centers and hence bring about the changes in heart rate variability seen. However at this stage this is merely a speculation.
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Quantitative Comparison of Time Frequency Distribution for Heart Rate Variability Using Performance Measure

Quantitative Comparison of Time Frequency Distribution for Heart Rate Variability Using Performance Measure

Figure 3 presents the time-frequency plot of optimised TFDs of HRV signal. The left plot shows the time-domain of the HRV, the bottom shows the spectral component and the middle one is the TF representation of the HRV. In Figure 3(a) which represents the WVD of the HRV, the dominant frequency content was observed in both LF and HF. However, this distribution is difficult to interpret due to the presence of interference terms also known as cross-terms, which is the result of the quadratic nature of the transformation. The improvised TFD of WVD which is SPWVD is displayed in Figure 3(b). The figure shows that the crossterm was eliminated whilst retaining the desirable properties of WVD with the hamming window of length 333. In Figure 3(c), CWD with its kernel parameter σ = 0.7848 has been found to represent the HRV with no cross-terms. However, both LF and HF components were smeared which was caused by the synchronization effects prevailing, due to the trade-off between the suppression of cross-terms and auto-component terms. The result shown in Figure 3(d) is almost similar where the SP, known to lack in time resolution smears the TF components. The quadratic SP smoothes away all interference terms and therefore causing the side effect of reducing signal component’s resolution. Figure 3(e) represents the time-frequency plot of MBD with its kernel parameter β = 0.001. From the figure, we can see that there are no cross-terms observed and the plot shows good quality of time-frequency resolution. Among the studied TFDs, MBD and SPWVD demonstrated better time-frequency resolution compared to WVD, CWD and SP. This effect was expected since the value of performance
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A Longitudinal Examination of Heart-Rate and Heart Rate Variability as Risk Markers for Child Posttraumatic Stress Symptoms in an Acute Injury Sample

A Longitudinal Examination of Heart-Rate and Heart Rate Variability as Risk Markers for Child Posttraumatic Stress Symptoms in an Acute Injury Sample

Data Analytic Strategy Data were analysed using SPSS Version 22. A square root transformation was applied to UCLA-RI scores to address skewness. Correlational analyses were performed to deter- mine basic associations between variables. Subsequently, linear regression analyses were used to investigate wheth- er HR indices (mean HR, LFBP and HFBP) predicted child PTSD symptoms at T1, T2 and T3. Residualized change scores from baseline HR to task HR were calcu- lated for both narratives to control for pre-existing base- line HR differences. Shapiro-Wilk tests indicated these change scores were normally distributed. Since HR indi- ces may be associated with each other, each HR predictor was entered into a separate regression model. Where T1 HR indices were found to predict symptoms at T2 or T3, analyses were re-run controlling for T1 symptom severity, to determine whether HR has predictive power above and beyond initial symptoms. Age, sex and triage were con- sidered as potential control variables for all analyses. The Benjamini-Hochberg procedure (Benjamini and Hochberg 1995 ) with a false discovery rate set to 5% was used to control to control for multiple testing. All findings remained significant.
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Method of estimation of synchronization strength between low-frequency oscillations in heart rate variability and photoplethysmographic waveform variability

Method of estimation of synchronization strength between low-frequency oscillations in heart rate variability and photoplethysmographic waveform variability

in experimental signals of studied systems, it is not trivial to detect gently sloping regions of instantaneous phase difference. The method based on Δφ(t) linear approximation in sliding windows was proposed to lower the effects of fluctuations caused by noise. For that purpose the line equation was matched to Δφ(t) time series in b(с) width sliding window via least square method [86]. The procedure consists in choosing of initially unknown coefficients α and β for abstract line equation: z(t) = αt + β to fit this line between the points of region with width b of noisy experimental time series Δφ(t) (thin line in Figure 7). Coefficient α = tgγ corresponds to the slope angle γ of z(t) line in relation to horizontal axis (horizontal dashed line in Figure 7). α is the time derivative of instantaneous phase, i.e. instantaneous frequency mismatch at a time (3). If z line is horizontal, as it supposed to be in synchronous sections, then α = γ = 0. Because of the noises and unavoidable error, caused by finite calculations accuracy, approximating line z commonly would be slightly not horizontal during processing of experimental data. It was taken into account during in the proposed method for synchronization detection.
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Heart Rate Variability in Cirrhosis Liver.

Heart Rate Variability in Cirrhosis Liver.

Laffi et al (37), performed a study on the effects of passive head tilting on the cardiovascular autonomic responses, in 15 cirrhotic patients with ascites and in 13 healthy subjects, using heart rate variability analysis and measurement of plasma norepinephrine levels. In the supine position, no significant differences in the Power Spectral Analysis data (LF, HF) were observed between the control subjects and cirrhotic patients, who had higher plasma norepinephrine levels. In healthy subjects, tilting was associated with an increase in the LF and arterial pressure and a decrease in the HF. In contrast, patients with cirrhosis showed a decrease of both LF and HF. Consequently, the LF/HF ratio significantly increased in healthy subjects, whereas it was unchanged in cirrhotic patients. The LF component of the diastolic pressure also decreased during tilting in cirrhotic patients. Plasma norepinephrine increased after tilting in both groups. They concluded that the autonomic responnse to passive tilting is impaired in cirrhotic patients with ascites at both the cardiac and vascular levels, as a result of an altered sympatho-vagal balance, with reduced sympathetic predominance. They also postulated that since these alterations occurred despite an appropriate response to the tilting of plasma norepinephrine, it pointed to a receptorial or postreceptorial site of the autonomic impairment.
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The Association between Work-Related Rumination and Heart Rate Variability: A Field Study

The Association between Work-Related Rumination and Heart Rate Variability: A Field Study

Heart Rate Variability Assessment The Microsoft Band v2 was selected to capture interbeat intervals because it exposes peak-to-peak (PP) intervals (the duration of every detected heartbeat) through its developer SDK, while most wearables only provide average heart rate, and from which we can derive HRV. Whilst deriving HRV from PPG data is quite novel, there is a precedent for ECG/PPG correlation when looking at HRV data ( Selvaraj et al., 2008 ). Moreover, the device can be programmed to turn on and off the sensors without any explicit interaction with the user, i.e., accelerometer and heart rate sensors can be turned on and off while the user is sleeping, which makes it possible to acquire and analyze data without the potential bias introduced by interaction, which can in itself be a form of intervention. We created an app for iOS, that pairs with the Microsoft Band v2, and periodically collects the data from the sensors, and uploads the collected data to our cloud, for analysis. We implemented an algorithmic policy to balance the amount of captured data and battery consumption to better preserve battery life. The heart rate was captured for three consecutive minutes (in order to acquire a continuous stream of PP data long enough to ensure a minimum level of validity in HRV analysis), then turned off for 3 min (to avoid depleting the battery unnecessarily). Accelerometry data was measured along HRV to assess movement. The accelerometer was periodically turned on for 15 s, then turned off for 45 s. The accelerometer captures data from a triaxial accelerometer and a triaxial gyroscope, at a sampling frequency of 60 Hz. To keep the communication from the smartphone to the cloud to a minimum, we uploaded a small cluster of statistical features of the accelerometer data, instead of all the raw data. The uploaded features provided sufficient
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