Mindfulness meditation, well-being, and heart rate variability: A preliminary investigation into the impact of intensive Vipassana meditation

One previous study indicated the significance of trait self-compassion in psychological well-being and adjustment in people with chronic pain. Higher-frequency heart rate variability (HF-HRV) was found to be closely associated with self-compassion and pain coping. The current study was therefore designed to investigate the relationship between self-compassion and experimental pain as well as the impact of HF-HRV. Sixty healthy participants provided self-reported self-compassion and underwent a cold pain protocol during which HF-HRV was evaluated. Results demonstrated a dual relationship between self-compassion and pain, dependent on the level of HF-HRV during pain exposure. Specifically, self-compassion was associated with lower pain in the condition of higher HF-HRV, while there was an inverse relationship between self-compassion and pain when HF-HRV was lower. Our data indicate the significance of HF-HRV in moderating the association between self-compassion and experimental pain.

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The impact of heart rate variability on subjective well-being is mediated by emotion regulation

Personality and Individual Differences ◽

10.1016/j.paid.2010.06.015 ◽

2010 ◽

Vol 49 (7) ◽

pp. 723-728 ◽

Cited By ~ 109

Author(s):

Fay C.M. Geisler ◽

Nadja Vennewald ◽

Thomas Kubiak ◽

Hannelore Weber

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Emotion Regulation ◽

Well Being ◽

Subjective Well Being ◽

The Impact

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Heart Rate Variability, Health and Well-being: A Systems Perspective

10.3389/978-2-88963-297-8 ◽

2020 ◽

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Well Being ◽

Systems Perspective ◽

Health And Well Being

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The interactive effects of heart rate variability and mindfulness on indicators of well-being in healthcare professionals' daily working life

International Journal of Psychophysiology ◽

10.1016/j.ijpsycho.2021.01.012 ◽

2021 ◽

Author(s):

Regina Franziska Schmid ◽

Joachim Thomas

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Healthcare Professionals ◽

Working Life ◽

Well Being ◽

Interactive Effects

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THE IMPACT OF STRESS ON HEART RATE VARIABILITY OF ON-CALL PHYSICIANS

CHEST Journal ◽

10.1378/chest.128.4_meetingabstracts.277s-a ◽

2005 ◽

Vol 128 (4) ◽

pp. 277S

Author(s):

Stavros E. Mountantonakis ◽

Dimitrios A. Moutzouris ◽

Craig McPherson

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

The Impact

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Abstract P315: Increase in Stress May Contribute to Lower Heart Rate Variability in Healthy Young Subjects

Circulation ◽

10.1161/circ.125.suppl_10.ap315 ◽

2012 ◽

Vol 125 (suppl_10) ◽

Author(s):

Amanda C Costa ◽

Ana Gabriela C Silva ◽

Cibele T Ribeiro ◽

Guilherme A Fregonezi ◽

Fernando A Dias

Keyword(s):

Cardiovascular Disease ◽

Heart Rate ◽

Heart Rate Variability ◽

Healthy Volunteers ◽

Perceived Stress ◽

Low Frequency ◽

Statistical Correlation ◽

Total Power ◽

The Impact ◽

Nonparametric Correlation

Background: Stress is one of the risk factors for cardiovascular disease and decreased heart rate variability is associated to increased mortality in some cardiac diseases. The aim of the study was to assess the impact of perceived stress on cardiac autonomic regulation in young healthy volunteers. Methods: 35 young healthy volunteers (19 to 29 years old, 6 men) from a Brazilian population were assessed for perceived stress by the translated and validated Perceived Stress Scale (PSS, 14 questions) and had the R-R intervals recorded at rest on supine position (POLAR RS800CX) and analyzed (5 minutes, Kubius HRV software) by Fast-Fourier Transform for quantification of Heart Rate Variability (HRV). Results: Average data (±SD) for age, heart rate, BMI, waist circumference and percentage of body fat (%BF) were: 21.3±2.7 years; 65.5±7.9 bpm; 22.3±1.9 Kg/m 2 ; 76.0±6.1 cm and 32.1±6.6%; respectively. The mean score for the PSS-14 was 23.5±7.2 and for the HRV parameter as follow: SSDN=54.8±21.2ms; rMSSD=55.9±32.2ms; low-frequency (LF)= 794.8±579.7ms 2 ; High-frequency (HF)= 1508.0±1783.0 ms 2 ; LF(n.u.)= 41.1±16.2; HF(n.u.)= 58.9±16.2; LF/HF=0.89±0.80 and Total power (TP)= 3151±2570ms 2 . Spearman nonparametric correlation was calculated and there was a significant correlation of PSS-14 scores and LF (ms 2 ) (r=−0.343; p= 0.044). Other HRV variables did not shown significant correlation but also had negative values for Spearman r (TP r=−0.265, p=0.124; HF r=−0.158; SSDN r=−0.207; rMSSD r=−0.243, p=0.160). LF/HF and LF(n.u.) did not correlate to PSS-14 having Spearman r very close to zero (LF/HF r=−0.007, p=0.969; LF(n.u.) r=−0.005, p=0.976). No correlation was found for HRV parameters and BMI and there was a trend for statistical correlation of %BF and LF (ms 2 ) (r=−0.309, p=0.071). Conclusions: These data demonstrate a possible association of perceived stress level and HRV at rest. Changes in LF can be a consequence of both sympathetic and parasympathetic activity, however, analyzing the other variables HF, TP, SSDN and rMSSD (all negative Spearman r) and due to the lack of changes in LF/HF ratio and LF(n.u.) we interpret that increased stress may be associated to decrease in overall heart rate variability. These changes were seen in healthy individuals and may point out an important mechanism in cardiovascular disease development.

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Trends in Heart-Rate Variability Signal Analysis

Frontiers in Digital Health ◽

10.3389/fdgth.2021.639444 ◽

2021 ◽

Vol 3 ◽

Author(s):

Syem Ishaque ◽

Naimul Khan ◽

Sri Krishnan

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Electrodermal Activity ◽

Well Being ◽

Good Health ◽

Machine Learning Techniques ◽

Video Gaming ◽

Clinical Events ◽

Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

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