Heart rate variability biofeedback: how and why does it work?

Heart rate variability (HRV) is a known risk factor for mortality in both healthy and patient populations. There are currently no normative data for short-term measures of HRV. A thorough review of short-term HRV data published since 1996 was therefore performed. Data from studies published after the 1996 Task Force report (i.e., between January 1997 and September 2008) and reporting short-term measures of HRV obtained in normally healthy individuals were collated and factors underlying discrepant values were identified. Forty-four studies met the pre-set inclusion criteria involving 21,438 participants. Values for short-term HRV measures from the literature were lower than Task Force norms. A degree of homogeneity for common measures of HRV in healthy adults was shown across studies. A number of studies demonstrate large interindividual variations (up to 260,000%), particularly for spectral measures. A number of methodological discrepancies underlined disparate values. These include a systematic failure within the literature (a) to recognize the importance of RR data recognition/editing procedures and (b) to question disparate HRV values observed in normally healthy individuals. A need for large-scale population studies and a review of the Task Force recommendations for short-term HRV that covers the full-age spectrum were identified. Data presented should be used to quantify reference ranges for short-term measures of HRV in healthy adult populations but should be undertaken with reference to methodological factors underlying disparate values. Recommendations for the measurement of HRV require updating to include current technologies. ©2010, The Authors. Journal compilation ©2010 Wiley Periodicals, Inc.

Mind-body interventions are beneficial in stress-related mental and physical disorders. Current research is finding associations between emotional disorders and vagal tone as indicated by heart rate variability. A neurophysiologic model of yogic breathing proposes to integrate research on yoga with polyvagal theory, vagal stimulation, hyperventilation, and clinical observations. Yogic breathing is a unique method for balancing the autonomic nervous system and influencing psychologic and stress-related disorders. Many studies demonstrate effects of yogic breathing on brain function and physiologic parameters, but the mechanisms have not been clarified. Sudarshan Kriya yoga (SKY), a sequence of specific breathing techniques (ujjayi, bhastrika, and Sudarshan Kriya) can alleviate anxiety, depression, everyday stress, post-traumatic stress, and stress-related medical illnesses. Mechanisms contributing to a state of calm alertness include increased parasympathetic drive, calming of stress response systems, neuroendocrine release of hormones, and thalamic generators. This model has heuristic value, research implications, and clinical applications.

Frequency-domain analyses of R-R intervals are used widely to estimate levels of autonomic neural traffic to the human heart. Because respiration modulates autonomic activity, we determined for nine healthy subjects the influence of breathing frequency and tidal volume on R-R interval power spectra (fast-Fourier transform method). We also surveyed published literature to determine current practices in this burgeoning field of scientific inquiry. Supine subjects breathed at rates of 6, 7.5, 10, 15, 17.1, 20, and 24 breaths/min and with nominal tidal volumes of 1,000 and 1,500 ml. R-R interval power at respiratory and low (0.06-0.14 Hz) frequencies declined significantly as breathing frequency increased. R-R interval power at respiratory frequencies was significantly greater at a tidal volume of 1,500 than 1,000 ml. Neither breathing frequency nor tidal volume influenced average R-R intervals significantly. Our review of studies reporting human R-R interval power spectra showed that 51% of the studies controlled respiratory rate, 11% controlled tidal volume, and 11% controlled both respiratory rate and tidal volume. The major implications of our analyses are that breathing parameters strongly influence low-frequency as well as respiratory frequency R-R interval power spectra and that this influence is largely ignored in published research.