A Method for More Accurate Determination of Resonance Frequency of the Cardiovascular System, and Evaluation of a Program to Perform It

The Effects of Heart Rhythm Meditation on Vagal Tone and Well-being: A Mixed Methods Research Study

Many studies have examined the effects of meditation practice focused on the normal breath on vagal tone with mixed results. Heart Rhythm Meditation (HRM) is a unique meditation form that engages in the deep slow full breath, and puts the focus of attention on the heart. This form of breathing likely stimulates the vagus nerve with greater intensity. The purpose of this study was (a) to examine how the practice of HRM affects vagal activity as measured by heart rate variability (HRV); and (b) to examine how it affects participants' well-being. 74 participants signed consent agreeing to: (a) take a six-week course to learn the practice of HRM; (b) engage in a daily practice for 10 weeks; (c) have their heart rate variability read through ECG technology and to take two validated well-being instruments at the beginning and end of the 10 weeks; and (d) participate in a focus group interview examining their perceptions of how the practice affected their well-being. 48 participants completed the study. Quantitative findings show the effect of the practice of HRM approached significance for multiple measures of HRV and vagal tone. An increase in well-being scores for those who did the meditation more than 10-minutes per day did meet statistical significance. Qualitative data indicate: (a) the positive effects of HRM on stress and well-being; (b) the development of a more expanded sense of self; and (c) an increased awareness of the interconnection of the body-heart-emotions and HRM's role in emotion regulation.

Do Longer Exhalations Increase HRV During Slow-Paced Breathing?

Slow-paced breathing at an individual's resonance frequency (RF) is a common element of heart rate variability (HRV) biofeedback training (Laborde et al. in Psychophysiology 59:e13952, 2022). Although there is strong empirical support for teaching clients to slow their respiration rate (RR) to the adult RF range between 4.5 and 6.5 bpm (Lehrer & Gevirtz, 2014), there have been no definitive findings regarding the best inhalation-to-exhalation (IE) ratio to increase HRV when breathing within this range. Three methodological challenges have frustrated previous studies: ensuring participants breathed at the target RR, IE ratio, and the same RR during each IE ratio. The reviewed studies disagreed regarding the effect of IE ratios. Three studies found no IE ratio effect (Cappo & Holmes in J Psychosom Res 28:265-273, 1984; Edmonds et al. in Biofeedback 37:141-146, 2009; Klintworth et al. in Physiol Meas 33:1717-1731, 2012). One reported an advantage for equal inhalations and exhalations (Lin et al. in Int J Psychophysiol 91:206?211, 2014). Four studies observed an advantage for longer exhalations than inhalations (Bae et al. in Psychophysiology 58:e13905, 2021; Laborde et al. in Sustainability 13:7775, 2021; Strauss-Blasche et al. in Clin Exp Pharmacol Physiol 27:601?60, 2000; Van Diest et al. in Appl Psychophysiol Biofeedback 39:171?180, 2014). One study reported an advantage for longer inhalations than exhalations (Paprika et al. in Acta Physiol Hung 101:273?281, 2014). We conducted original (N = 26) and replication (N = 16) studies to determine whether a 1:2 IE ratio produces different HRV time-domain, frequency-domain, or nonlinear metrics than a 1:1 ratio when breathing at 6 bpm. Our original study found that IE ratio did not affect HRV time- and frequency-domain metrics. The replication study confirmed these results and found no effect on HRV nonlinear measurements.

Influence of Respiratory Frequency of Slow-Paced Breathing on Vagally-Mediated Heart Rate Variability

Breathing techniques, particularly slow-paced breathing (SPB), have gained popularity among athletes due to their potential to enhance performance by increasing cardiac vagal activity (CVA), which in turn can help manage stress and regulate emotions. However, it is still unclear whether the frequency of SPB affects its effectiveness in increasing CVA. Therefore, this study aimed to investigate the effects of a brief SPB intervention (i.e., 5 min) on CVA using heart rate variability (HRV) measurement as an index. A total of 75 athletes (22 female; Mage = 22.32; age range = 19-31) participated in the study, attending one lab session where they performed six breathing exercises, including SPB at different frequencies (5 cycles per minute (cpm), 5.5 cpm, 6 cpm, 6.5 cpm, 7 cpm), and a control condition of spontaneous breathing. The study found that CVA was significantly higher in all SPB conditions compared to the control condition, as indexed by both root mean square of the successive differences (RMSSD) and low-frequency HRV (LF-HRVms2). Interestingly, LF-HRVms2 was more sensitive in differentiating the respiratory frequencies than RMSSD. These results suggest that SPB at a range of 5 cpm to 7 cpm can be an effective method to increase CVA and potentially improve stress management and emotion regulation in athletes. This short SPB exercise can be a simple yet useful tool for athletes to use during competitive scenarios and short breaks in competitions. Overall, these findings highlight the potential benefits of incorporating SPB into athletes' training and competition routines.

Methods for Heart Rate Variability Biofeedback (HRVB): A Systematic Review and Guidelines

Heart Rate Variability Biofeedback (HRVB) has been widely used to improve cardiovascular health and well-being. HRVB is based on breathing at an individual's resonance frequency, which stimulates respiratory sinus arrhythmia (RSA) and the baroreflex. There is, however, no methodological consensus on how to apply HRVB, while details about the protocol used are often not well reported. Thus, the objectives of this systematic review are to describe the different HRVB protocols and detect methodological concerns. PsycINFO, CINALH, Medline and Web of Science were searched between 2000 and April 2021. Data extraction and quality assessment were based on PRISMA guidelines. A total of 143 studies were finally included from any scientific field and any type of sample. Three protocols for HRVB were found: (i) "Optimal RF" (n = 37), each participant breathes at their previously detected RF; (ii) "Individual RF" (n = 48), each participant follows a biofeedback device that shows the optimal breathing rate based on cardiovascular data in real time, and (iii) "Preset-pace RF" (n = 51), all participants breathe at the same rate rate, usually 6 breaths/minute. In addition, we found several methodological differences for applying HRVB in terms of number of weeks, duration of breathing or combination of laboratory and home sessions. Remarkably, almost 2/3 of the studies did not report enough information to replicate the HRVB protocol in terms of breathing duration, inhalation/exhalation ratio, breathing control or body position. Methodological guidelines and a checklist are proposed to enhance the methodological quality of future HRVB studies and increase the information reported.

Dynamic Phase Extraction: Applications in Pulse Rate Variability

Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between inter-beat interval, IBI, and breathing has very rarely been studied. Develop a technique by which the phase relationship between IBI and breathing can be accurately and efficiently extracted from photoplethysmography (PPG) data. A program based on Lock-in Amplifier technology was written in Python to implement a novel technique, Dynamic Phase Extraction. It was tested using a breath pacer and a PPG sensor on 6 subjects who followed a breath pacer at varied breathing rates. The data were then analyzed using both traditional methods and the novel technique (Dynamic Phase Extraction) utilizing a breath pacer. Pulse data was extracted using a PPG sensor. Dynamic Phase Extraction (DPE) gave the magnitudes of the variation in IBI associated with breathing [Formula: see text] measured with photoplethysmography during paced breathing (with premature ventricular contractions, abnormal arrhythmias, and other artifacts edited out). [Formula: see text] correlated well with two standard measures of pulse rate variability: the Standard Deviation of the inter-beat interval (SDNN) (ρ = 0.911) and with the integrated value of the Power Spectral Density between 0.04 and 0.15 Hz (Low Frequency Power or LF Power) (ρ = 0.885). These correlations were comparable to the correlation between the SDNN and the LF Power (ρ = 0.877). In addition to the magnitude [Formula: see text], Dynamic Phase Extraction also gave the phase between the breath pacer and the changes in the inter-beat interval (IBI) due to respiratory sinus arrythmia (RSA), and correlated well with the phase extracted using a Fourier transform (ρ = 0.857). Dynamic Phase Extraction can extract both the phase between the breath pacer and the changes in IBI due to the respiratory sinus arrhythmia component of pulse rate variability ([Formula: see text], but is limited by needing a breath pacer.

An Undergraduate Program with Heart: Thirty Years of Truman HRV Research

This article celebrates the contributors who inspired Truman's heart rate variability (HRV) research program. These seminal influences include Robert Fried, Richard Gevirtz, Paul Lehrer, Erik Peper, and Evgeny Vaschillo. The Truman State University Applied Psychophysiology Laboratory's HRV research has spanned five arcs: interventions to teach diaphragmatic breathing, adjunctive procedures to increase HRV, HRV biofeedback (HRVB) training studies, the concurrent validity of ultra-short-term HRV measurements, and rhythmical skeletal muscle tension strategies to increase HRV. We have conducted randomized controlled trials, primarily using within-subjects and mixed designs. These studies have produced eight findings that could benefit HRVB training. Effortful diaphragmatic breathing can lower end-tidal CO2 through larger tidal volumes. A 1:2 inhalation-to-exhalation (I/E) ratio does not increase HRV compared to a 1:1 I/E ratio. Chanting "om," listening to the Norman Cousins relaxation exercise, and singing a fundamental note are promising exercises to increase HRV. Heartfelt emotion activation does not increase HRV, enhance the effects of resonance frequency breathing, "immunize" HRV against a math stressor, or speed HRV recovery following a math stressor. Resonance frequency assessment achieved moderate (r = 0.73) 2-week test-reliability. Four weeks of HRVB training increased HRV and temperature, and decreased skin conductance level compared with temperature biofeedback training. Concurrent-validity assessment of ultra-short-term HRV measurements should utilize rigorous Pearson r and limits of agreement criteria. Finally, rhythmical skeletal muscle tension can increase HRV at rates of 1-, 3-, and 6-cpm. We describe representative studies, their findings, significance, and limitations in each arc. Finally, we summarize some of the most interesting unanswered questions to enable future investigators to build on our work.