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Heart rate variability biofeedback (HRVB) significantly improves heart rate variability (HRV). Breathing at resonance frequency (RF, approximately 6 breaths/min) constitutes a key part of HRVB training and is hypothesized to be a pathway through which biofeedback improves HRV. No studies to date, however, have experimentally examined whether RF breathing impacts measures of HRV. The present study addressed this question by comparing three groups: the RF group breathed at their determined RF for 15 min; the RF + 1 group breathed at 1 breath/min higher than their determined RF for 15 min; and the third group sat quietly for 15 min. After this 15-min period, all groups participated in the Paced Auditory Serial Addition Task (PASAT) for 8 min, and then sat quietly during a 10-min recovery period. HRV, blood pressure, and mood were measured throughout the experiment. Groups were not significantly different on any of the measures at baseline. After the breathing exercise, the RF group reported higher positive mood than the other two groups and a significantly higher LF/HF HRV ratio relative to the control group, a key goal in HRVB training (p < 0.05). Additionally, the RF group showed lower systolic blood pressure during the PASAT and during the recovery period relative to the control group, with the RF + 1 group not being significantly different from either group (p < 0.05). Overall, RF breathing appears to play an important role in the positive effect HRVB has on measures of HRV.
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August 2017 | Volume 5 | Article 2221
ORIGINAL RESEARCH
published: 25 August 2017
doi: 10.3389/fpubh.2017.00222
Frontiers in Public Health | www.frontiersin.org
Edited by:
J. P. Ginsberg,
Dorn VA Medical Center,
United States
Reviewed by:
Richard Gevirtz,
Alliant International University,
United States
Inna Khazan,
Harvard Medical School,
United States
*Correspondence:
Patrick R. Steffen
steffen@byu.edu
Specialty section:
This article was submitted to Family
Medicine and Primary Care,
a section of the journal
Frontiers in Public Health
Received: 16June2017
Accepted: 08August2017
Published: 25August2017
Citation:
SteffenPR, AustinT, DeBarrosA and
BrownT (2017) The Impact of
Resonance Frequency Breathing on
Measures of Heart Rate Variability,
Blood Pressure, and Mood.
Front. Public Health 5:222.
doi: 10.3389/fpubh.2017.00222
The Impact of Resonance Frequency
Breathing on Measures of Heart
Rate Variability, Blood Pressure,
and Mood
Patrick R. Steffen*, Tara Austin, Andrea DeBarros and Tracy Brown
Brigham Young University, Provo, UT, United States
Heart rate variability biofeedback (HRVB) signicantly improves heart rate variability (HRV).
Breathing at resonance frequency (RF, approximately 6 breaths/min) constitutes a key
part of HRVB training and is hypothesized to be a pathway through which biofeedback
improves HRV. No studies to date, however, have experimentally examined whether
RF breathing impacts measures of HRV. The present study addressed this question by
comparing three groups: the RF group breathed at their determined RF for 15min; the
RF+1 group breathed at 1 breath/min higher than their determined RF for 15min; and
the third group sat quietly for 15min. After this 15-min period, all groups participated in
the Paced Auditory Serial Addition Task (PASAT) for 8min, and then sat quietly during a
10-min recovery period. HRV, blood pressure, and mood were measured throughout the
experiment. Groups were not signicantly different on any of the measures at baseline.
After the breathing exercise, the RF group reported higher positive mood than the other
two groups and a signicantly higher LF/HF HRV ratio relative to the control group, a
key goal in HRVB training (p<0.05). Additionally, the RF group showed lower systolic
blood pressure during the PASAT and during the recovery period relative to the control
group, with the RF+1 group not being signicantly different from either group (p<0.05).
Overall, RF breathing appears to play an important role in the positive effect HRVB has
on measures of HRV.
Keywords: resonance frequency breathing, heart rate variability, blood pressure, mood, biofeedback
Heart rate variability (HRV) is a key marker of health, mood, and adaptation, and hence improve-
ments in HRV improves health, mood, and adaptation to stress (1, 2). Heart rate variability biofeed-
back (HRVB) reliably increases HRV, mood, and adaptability (3, 4). A key aspect of HRVB involves
identifying each persons unique resonance frequency (RF) breathing rate and then teaching them
how to breathe at this rate in the clinic and through home practice. No studies to date, however,
have examined if breathing at RF is an important aspect of HRVB training. e purpose of this
study was to examine whether dierent breathing rates would dierentially aect measures of HRV,
blood pressure, and mood. Specically, does RF breathing result in more positive outcomes relative
to other breathing rates?
Heart rate variability is the variation in time intervals between heart beats (5). Multiple physi-
ological systems inuence heart rhythm and greater uctuations in heart rhythm over time indi-
cate healthy systemic balance and ability to respond to physiological needs (3, 6). Consequently,
higher levels of HRV are indicative of a healthy heart and a marker of overall healthy physiological
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functioning (5). Low HRV, or less responsiveness to physiologi-
cal needs, predicts mortality and morbidity and also occurs in
depression, anxiety, and chronic stress (7). As an example, the
central nervous system uses negative feedback loops (such as
the baroreex) to maintain homeostatic balance in heart rate
and blood pressure while interacting with the environment (1).
However, these feedback loops become less sensitive with chronic
stress and sympathetic nervous system arousal found in physi-
cal and psychiatric disorders (3). High heart rates are inversely
correlated with the levels of HRV, as there is less opportunity
for variability between heart beats when they are closer (5). Low
HRV predicts negative outcomes in cardiovascular disease and in
all-cause mortality (8), with depression playing a signicant role
in cardiovascular disease as well (1).
Heart rate variability biofeedback reliably improves HRV.
Biofeedback is an interactive process where individuals directly
learn how to change their physiological activity (9). Sensors
placed on their skin measure physiological functions such as
heart rate, respiration, muscle tension, etc., and this informa-
tion is displayed on a computer screen in real time allowing
participants to become directly aware of and then change their
physiological functioning (5). HRVB is particularly benecial in
learning to regulate the physiological stress response. HRVB is a
relatively quick and inexpensive treatment, with protocols typi-
cally including ten 30-min sessions, although positive eects on
anxiety, depressive symptoms, and cognitive performance have
been found aer the rst session (1012). HRVB interventions
are ecacious for both physical and mental disorders, including
treating depression, anxiety, panic disorder, as well as improv-
ing outcomes in patients with cardiac problems and asthma
(4,14,15).
A key aspect of HRVB is RF breathing. HRV is directly
inuenced by breathing and respiratory sinus arrhythmia (RSA).
RSA is the uctuation in heart rate corresponding to breathing,
with heart rate increasing with inhalation and decreasing with
exhalation (15). RSA typically occurs in the high frequency (HF)
range of HRV (0.15–0.4Hz) and is a measure of parasympathetic
activity (16). At resting breathing rates (between 9 and 24 breaths/
min), heart rate increases with inhalation at about the mid breath
point and heart rate decreases with exhalation at about the mid
breath point (5). RSA impacts gas exchange such that heart rate
tends to be higher when the air in the lungs is richest in oxygen
and exhalation occurs when carbon dioxide in the lungs is high-
est. Hayano etal. (17), however, found that the most ecient gas
exchange occurs when heart rate starts increasing at the beginning
of inhalation and decreasing as exhalation begins rather than at
the mid breath points. Synchrony of heart rate and breathing also
increases the amplitude of heart rate oscillations leading to high
levels of HRV.
Heart rate and breathing synchronize, or become resonance,
at about 6 breaths/min (0.1Hz). Each person has a unique RF
breathing rate, ranging typically between 4.5 and 7.0 breaths/min.
In studies of HRV biofeedback, the most common RF breathing
rate is 5.5 breaths/min (18). RF breathing rate is identied by
having the person breathe at 4.5, 5.0, 5.5, 6.0, 6.5 and 7.0 breaths/
min during EKG recording. HRV frequency and time domain
measures are then evaluated to nd which breathing rate results
in the largest changes in HRV. A common approach is to examine
the low frequency (LF) range (0.05–0.14) of the HRV spectrum
to nd the largest frequency spike, which usually occurs at about
0.1Hz. As people slow their breathing down and approach RF,
HRV amplitude increases signicantly. When a person breathes
at their identied RF breathing rate, heart rate and breathing
become synchronized and the highest levels of HRV are typically
obtained.
Although breathing at RF is a key part of HRVB training,
there are no published studies to date that have examined if RF
breathing is essential. We therefore tested the hypotheses that
breathing at RF would lead to improved HRV, blood pressure,
and mood using a randomized controlled design. Specically, we
examined three hypotheses. First, we hypothesized that the RF
group would show higher positive mood and decreased negative
mood and anxious arousal following breathing practice relative
to the RF+1 and control groups. Second, we hypothesized that
the RF group would score higher on the HRV measures of LF and
LF/HF ratio, and lower on HF relative to the RF+1 and control
groups. And third, we hypothesized that the RF group would
show decreased blood pressure reactivity during the PASAT
stressor relative to the RF+1 and control groups, and that LF/
HF ratio following breathing practice would predict decreased
blood pressure reactivity during the PASAT stressor and that this
eect would be strongest in the RF group.
METHOD
Participants
A convenience sample of 95 participants (60% female, average
age of 20) was recruited from undergraduate psychology classes
using an online recruitment site and randomized into three
experimental groups: RF breathing, breathing at 1breath/min
above established RF, and a control group that sat quietly. Class
credit was given for research participation. Participants were
excluded if they reported a history of heart disease or taking
medications that aect blood pressure or heart rate. is study
was approved by a university institutional review board and all
participants read and provided informed consent before starting
the study.
Measures
Heart Rate Variability
Heart rate variability was measured using both frequency domain
measures (LF, HF, and LF/HF ratio) and time domain measures
[standard deviation of normal to normal R-R intervals (SDNN)
and Root Mean Square of the Successive Dierences (RMSSD)].
HRV was measured using the Nexus 10 and Biotrace soware
(Mind media soware). Data were corrected for artifact and heart
variability measures were calculated using the Kubios program
(University of Finland). Power spectral analyses (LF and HF)
measures examine dierent frequencies: 0.15–0.4 Hz for HF,
and 0.04–0.15 for LF (3, 19). e SDNN measures the standard
deviation of the R spike to R spike. Larger SDNN values show
more variation in heart rate (3, 19). RMSSD is a time domain
measure of HRV, and measures the dierences between adjacent
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heart rates. RMSSD correlated with vagus-mediated components
of HRV (3, 19).
Blood Pressure
Data on heart rate, diastolic, and systolic blood pressure were
collected using a Dinamp Model 8100 automated blood pressure
monitor (Critikon Corporation, Tampa, FL, USA) that uses an
oscillometric method. Readings were taken placing a cu on the
upper non-dominant arm of the participant following manufac-
turer specications. Two blood pressure readings were averaged
for each time period.
Mood and Anxious Arousal
e Scale of Positive and Negative Emotions (SPANE) was used
to measure current mood. e SPANE consists of 12 items, 6
positive and 6 negative, to assess positive and negative emotions
over a specic time frame. ese are rated on a Likert scale from 1
(“very rarely or never”) to 5 (“very oen or always”). e SPANE
has demonstrated good internal consistency with Cronbachs
alphas between 0.81 and 0.89. It also correlates from 0.57 to 0.70
with other mood scales (20). e anxious arousal subscale was
taken from the Mini Mood and Anxiety Questionnaire (21). e
anxious arousal subscale contains 10 items and is designed to
assess physiological symptoms associated with anxiety such as
feeling short of breath, cold hands, or trembling muscles. Items
are measured on a 5-point Likert scale, ranging from 0 (“Not at
all”) to 5 (“Extremely”) and the subscale has been shown to have
good internal consistency (α=0.85).
Procedure
Aer obtaining consent, participants were randomly assigned
to the RF, RF +1, or the control conditions. e experimental
conditions consisted of breathing at RF, breathing at one breath
above the RF (RF+1), and a control group who are sitting quietly.
Participants lled out an initial battery of measures, including
the anxious arousal subscale from the Mini Mood and Anxiety
Questionnaire, and SPANE. Researchers attached EKG sensors
to participants’ wrists and forearm. e participant also wore a
respiration belt and a blood pressure cu on their non-dominant
arm. RF was determined for all participants, used Rosenthal’s
HRV determination protocol. e pacer was set at ve dierent
frequencies (7, 5, 6.5, 5.5, and 6 breaths/min) for 2min, resting
1min between each pace. Because previous studies failed to nd
students breathing at 4.5 breaths/min, this rate was not included
in the present study. Participants then lled out self-report meas-
ures of typical stress responses. Participants in the biofeedback
conditions then continued to breathe for 15min, with those in t he
RF condition breathing at RF, and those in the RF +1 group at
one above their RF. Participants in the control condition sat qui-
etly with their eyes open for 15min. Aer this time, participants
lled out self-report measures of stress and anxiety symptoms. All
participants then took the Paced Auditory Serial Addition Task
[PASAT (22)] as a brief laboratory stressor, followed by measures
of mood and anxious arousal. Participants then sat quietly for
10min, followed by measures of mood and anxious arousal, and
were then thanked for their time.
Data Analysis
e purpose of this study was to examine the impact of dierent
breathing rates on physiological response using a randomized
controlled design. We therefore used repeated measures analysis
of variance to test the rst two hypotheses, analyzing the impact
of group membership on measures of heart variability, blood
pressure, and mood during breathing exercises (or sitting qui-
etly), during the PASAT stressor, and during a recovery period.
We also used hierarchical linear regression to test the last hypoth-
esis, examining if a measure of HRV (LF/HF) measured during
breathing practice predicted blood pressure response to the
PASAT stressor. All analyses were conducted using SPSS (IBM
SPSS version 23). ree hypotheses were examined.
RESULTS
Mood and Physiological Distress by
Experimental Group
e experimental groups did not dier on gender composition or
age, and there were no signicant dierences between groups on
reported positive mood, negative mood, or physiological distress
at baseline. e 3-Group×4-Time ANOVAs on positive mood,
negative mood, and physiological distress yielded signicant
main eects of time: F(3, 276)= 30.38, p< 0.001, η=0.25 for
positive mood; F(3, 276)=30.55, p<0.001, η=0.25 for negative
mood; and F(3, 276)=16.19, p<0.001, η=0.15 for physiological
distress, providing evidence that the experimental manipulations
impacted the participants. Compared to baseline, positive mood
fell signicantly during the PASAT stressor and then recovered
somewhat during the recovery period, whereas both negative
mood and physiological distress increased from baseline to the
PASAT stressor and then decreased during recovery.
e Group × Time interaction was signicant for positive
mood only, F(6, 276)=3.44, p<0.01, η=07. Follow-up contrasts
examining the changes from baseline to the end of the breathing
practice showed that individuals in the RF group had increased
positive mood whereas the other groups did not, F(2, 92)=10.73,
p< 0.001, η=0.19. ere were no signicant Group × Time
interactions for the PASAT and recovery periods.
Measures of HRV by Experimental Group
e main hypothesis of this study was that dierent rates of breath-
ing would dierentially impact measure of HRV, with RF breath-
ing leading to more benecial results. e 3-Group × 5-Time
ANOVAs on frequency domain measures of HRV (LF, HF, and
LF/HF) yielded signicant main eects of time: F(4, 372)=15.78,
p<0.001, η=0.15 for LF; F(4, 372)=4.13, p<0.01, η=0.04 for
HF; and F(4, 372)=12.07, p<0.001, η=0.12 for the LF/HF ratio.
e control showed little change over time in measures of HRV,
whereas the RF and RF+1 groups increased LF and decreased
HF during breathing practice and decreased LF and increased
HF during the PASAT stressor. Time domain measures of HRV
(SDNN and RMSSD) did not show main eect dierences over
time nor were there Group×Time interactions.
e Group× Time interactions were signicant for LF, HF,
and the LF/HF ratio, F(8, 372)=3.32, p<0.001, η=0.07 for LF,
FIGURE 2 | The RF and RF+1 groups showed decreased blood pressure
during breathing training compared to the control group. The RF group
showed the smallest increase (least reactivity) in blood pressure in response
to the stressor compared to the RF+1 and control groups.
FIGURE 1 | The RF group showed the largest LF/HF response to the
breathing practice, with the RF+1 showing minimal LF/HF changes and the
control group decreasing during this time period.
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Frontiers in Public Health | www.frontiersin.org August 2017 | Volume 5 | Article 222
F(8, 372)=2.18, p<0.05, η=0.05 for HF, and F(8, 372)=3.96,
p<0.001, η=0.08 for LF/HF. e RF group showed the largest
changes during the breathing exercise, particularly for the LF/
HF ratio which is a key variable tracked in HRVB. Follow-up
contrasts examining changes from baseline to the end of the
breathing practice revealed that the RF group increased in LF/
HF ratio following breathing practice, whereas the RF + 1
group remained about the same and the control decreased, F(2,
93)=4.64, p=0.01, η=0.09. Only the RF group was signicantly
higher than the control group, with the RF+1 not being dier-
ent from either group, indicating that a key therapeutic goal of
HRV-B was most clearly achieved in the RF group (Figure1).
Impact of Experimental Group on Blood
Pressure and Impact of LF/HF Ratio on
SBP and DBP
We also hypothesized that RF breathing would result in lower
blood pressure during breathing practice and reduced physiologi-
cal responsiveness during a lab stressor. e 3 Group×5-Time
ANOVAs for SBP, DBP, HR revealed signicant main eects with
physiology decreasing during breathing practice and increasing
the PASAT stressor, F(4, 292)= 36.71, p< 0.001, η= 0.34, for
SBP; F(4, 292) = 13.86, p< 0.001, η=0.16, for DBP; and F(4,
292)= 10.25, p< 0.001, η= 0.12, for HR, indicating that the
experimental manipulations impacted physiology as expected.
e Group×Time interactions were signicant for SBP and
DBP but not HR, F(8, 292)=3.53, p<0.001, η=0.09, for SBP, and
F(8, 292)=3.69, p<0.001, η=0.09, for DBP. During breathing
practice, both the RF and the RF+1 groups showed lower SBP
and DBP compared to the control group, F(2, 73)=3.56, p<0.05,
η=0.09 for SBP, and F(2, 73)=4.94, p<0.01, η=0.12 for DBP.
In response to the PASAT stressor, however, the RF group showed
lower SBP compared to both the RF+1 and control groups, F(2,
73)=3.29, p<0.05, η=0.04 for SBP, suggesting that RF breathing
buered the stress response to the PASAT (Figure2).
e nal hypothesis was that LF/HF ratio would predict blood
pressure response to the PASAT stressor. Using hierarchical linear
regression, it was found that higher LF/HF at the end of breath-
ing practice (controlling for baseline levels) predicted lower SBP
during the PASAT (β=0.21, p<0.01) and during the recovery
period (β=0.28, p<0.01). When examining eects by group, it
was found that the eect was strongest in the RF group, β=0.43,
p<0.02 for recovery period. Similar results were found for DBP
with lower DBP found during recovery only (r=0.26, p<0.05),
with the eect strongest for the RF group (r=0.64, p<0.05).
ese analyses indicate that the higher the LF/HF ratio, the less
physiologically reactive people are to stress, with this eect being
driven primarily by the RF group.
DISCUSSION
We examined whether breathing at RF would improve HRV,
blood pressure, and mood, compared to breathing 1 breath/
min above RF and sitting quietly using a randomized controlled
design. Following the breathing exercise, the RF group reported
the highest positive mood and showed a higher LF/HF ratio.
Whereas the RF group increased signicantly in LF/HF ratio, the
RF+1 group did not change signicantly and the control group
decreased. e LF/HF ratio is a key variable in HRVB and only the
RF group displayed the increase that is sought for. e RF group
also showed larger reductions in BP, particularly in response to
the PASAT stressor. We also examined whether LF/HF ratio at the
end of breathing practice would predict less BP reactivity during
the subsequent PASAT stressor. Higher LF/HF did predict lower
BP reactivity and this eect was seen most strongly in the RF
group. Overall, results indicate that RF breathing contributes to
healthier physiological response and mood, supporting its use in
HRVB specically and stress reduction generally.
Resonance frequency breathing is a key part of HRVB pro-
tocols (3). A number of research studies, however, have found
positive results only emphasizing slow breathing or breathing at
6 breaths/min (2325). Lin etal. (23) had healthy college students
breathe at 6.0 and 5.5 breaths/min and found that the 5.5 rate
resulted in higher HRV. RF breathing rate was not determined,
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rather everyone breathed at the same rate. Mason etal. (24) found
that breathing at 6 breaths/min as part of yoga exercise improved
oxygen saturation and baroreex sensitivity. Zautra et al. (25)
examined bromyalgia pain patients when they were breathing
normally and when breathing at half their normal rate and found
slower breathing was related to decreased pain and depressive
symptoms. A key nding of this study is that breathing at RF does
matter. It is not clear if this would be the case in all situations, but
there is evidence that HRVB and RF breathing results in stronger
eects (4). Future studies could continue this line of research
by examining slow breathing versus RF breathing with dierent
populations and diagnoses to examine these possibilities.
Vaschillo et al. (18, 26, 27) documented that in addition to
being an RF for breathing, there is also an RF for vascular tone,
and that these resonance frequencies interact. erefore, vascular
tone may be impacted by RF breathing in terms of BP response to
stress, with RF breathing decreasing BP and reducing BP response
during stress. at is what was found in this study. e RF group
increased their LF and HF/LF ratio following breathing practice,
and this predicted decreased BP during the PASAT stressor. is
conrms earlier research that those with higher HRV are more
physically and emotionally resilient (5), but goes one step further
to show that this can occur aer one 15-min session RF breathing
session.
Heart rate variability biofeedback potentially creates favora-
ble outcomes in physical and mental health disorders through
several mechanisms of action. Recent research has explored
how strengthening and activating the baroreex and vagal
nerve increase physical and emotional resilience (3). Increases
in LF and the HF/LF ratio indicate that these systems are being
activated and strengthened. Breathing at RF, versus breathing
near or slightly above RF, produced the highest LF/HF ratio,
which can be interpreted as higher levels of baroreex and vagal
nerve activity. As these are proposed mechanisms of action
for the reduction of symptoms in physical and psychological
disorders, breathing at RF will produce greater eects in HRV
interventions.
A limitation of this study is the cross-sectional design. While
positive changes were seen with only one session, it is unknown
what would happen over the course of weeks as HRVB is typi-
cally administered. Additionally, the sample consisted of college
students, so it is unknown if these results would be found in dif-
ferent age groups. We did not assess baroreex in this study which
would have been helpful in understanding the pathways through
which HRV changes occurred. A strength of this study was the
use of a randomized controlled design with participants not
being signicantly dierent on the variables assessed at baseline.
erefore, there is condence that the changes observed aer
exposure to the experimental conditions was not due to random
eects.
CONCLUSION AND FUTURE DIRECTIONS
Overall, evidence supports RF breathing as a key factor in HRVB.
RF breathing, compared to breathing at 1 breath above RF and
sitting quietly control group, leads to more positive outcomes,
resulted in a more positive mood, a higher LF/HF ratio (a key
variable in HRVB), and a decreased BP response to stress. is
study contributes to research on HRV showing that breathing
at RF, as opposed to breathing near RF, promotes more adaptive
physiological and emotional response. Future directions in this
line of research include examining these relationships longi-
tudinally over the course of HRVB, to study older age groups,
and to explore if these relationships hold in people with clinical
diagnoses such as depression or anxiety.
ETHICS STATEMENT
is study was carried out in accordance with the recommenda-
tions of Brigham Young University Institutional Review Board
with written informed consent from all subjects. All subjects gave
written informed consent in accordance with the Declaration of
Helsinki. e protocol was approved by the BYU Institutional
Review Board.
AUTHOR NOTES
Patrick R. Steen, Ph.D., Department of Psychology, Brigham
Young University.
AUTHOR CONTRIBUTIONS
PS was involved with study conceptualization, design, statistical
analysis, and writing up the manuscript. TA was involved with
study design, analysis, data collection, and writing. AD and TB
were involved with design, analysis, and data collection.
REFERENCES
1. Gorman JM, Sloan RP. Heart rate variability in depressive and anxiety disor-
ders. Am Heart J (2000) 140:S77–83. doi:10.1067/mhj.2000.109981
2. ayer JF, Yamamoto SS, Brosschot JF. e relationship of autonomic imbal-
ance, heart rate variability and cardiovascular disease risk factors. Int J Cardiol
(2010) 141:122–31. doi:10.1016/j.ijcard.2009.09.543
3. Lehrer PM. How does heart variability biofeedback work? Resonance, the
baroreex, and other mechanism. Biofeedback (2013) 41:26–31. doi:10.5298/
1081-5937-41.1.02
4. Gevirtz R. e promise of heart rate variability biofeedback: evidence-based
applications. Biofeedback (2013) 41(3):110–20. doi:10.5298/1081-5937-41.3.01
5. Lehrer PM, Gevirtz R. Heart rate variability biofeedback: how and why does it
work? Front Psychol (2014) 5:756. doi:10.3389/fpsyg.2014.00756
6. Shaer F, McCraty R, Zerr CL. A healthy heart is not a metronome: an inte-
grative review of the heart’s anatomy and heart rate variability. Front Psychol
(2014) 5:1040. doi:10.3389/fpsyg.2014.01040
7. Kemp AH, Quintana DS, Felmingham KL, Matthews S, Jelinek HF. Depression,
comorbid anxiety disorders, and heart rate variability in physically healthy,
unmedicated patients: implications for cardiovascular risk. PLoS One (2012)
7(2):e30777. doi:10.1371/journal.pone.0030777
8. Del Pozo JM, Gevirtz RN, Scher B, Guarneri E. Biofeedback treatment
increases heart rate variability in patients with known coronary artery disease.
Am Heart J (2004) 147:G1–6. doi:10.1016/j.ahj.2003.08.013
9. Lehrer PM, Woolfolk RL, Sime WE. Principles and Practice of Stress
Management. New York: Guilford Press (2007).
10. Karavidas MK, Lehrer PM, Vaschillo E, Vaschillo B, Marin H, Buyske S, etal.
Preliminary results of an open label study of heart rate variability biofeedback
6
Steffen et al. Resonance Frequency Breathing
Frontiers in Public Health | www.frontiersin.org August 2017 | Volume 5 | Article 222
for the treatment of major depression. Appl Psychophysiol Biofeedback (2007)
32:19–30. doi:10.1007/s10484-006-9029-z
11. Lehrer PM, Vaschillo E, Vaschillo B, Lu SE, Eckberg DL, Edelberg R, etal. Heart
rate variability biofeedback increases baroreex gain and peak expiratory
ow. Psychosom Med (2003) 65:796–805. doi:10.1097/01.PSY.0000089200.
81962.19
12. Wells R, Outhrad T, Heathers JAJ, Quintana DS, Kemo AH. Matter over
mind: a randomised-controlled trial of single-session biofeedback training on
performance anxiety and heart rate variability in musicians. PLOS ONE (2012)
7(10):e46597. doi:10.1371/journal.pone.0046597
13. Schwartz M, Andrasik F. Biofeedback: A Practitioner’s Guide. 3rd ed. New York,
NY: Guilford Press (2003).
14. Paul M, Garg K. e eect of heart rate variability biofeedback on perfor-
mance psychology of basketball players. Appl Psychophysiol Biofeedback
(2012) 37:131–44. doi:10.1007/s10484-012-9185-2
15. Yasuma F, Hayano J. Respiratory sinus arrhythmia: why does the heartbeat
synchronize with respiratory rhythm? Chest (2004) 125:638–90. doi:10.1378/
chest.125.2.683
16. Berntson GG, Bigger JT, Eckberg DL, Grossman P, Kaufmann PG, Malik
M, et al. Heart rate variability: origins, methods, and interpretive cave-
ats. Psychophysiology (1997) 34:623–48. doi:10.1111/j.1469-8986.1997.
tb02140.x
17. Hayano J, Yasuma F, Okada A, Mukai S, Fujinami T. Respiratory sinus arrhyth-
mia: a phenomenon improving pulmonary gas exchange and circulatory
eciency. Circulation (1996) 94:842–7. doi:10.1161/01.CIR.94.4.842
18. Vaschillo E, Lehrer P, Rishe N, Konstantinov M. Heart rate variability bio-
feedback as a method for assessing baroreex function: a preliminary study of
resonance in the cardiovascular system. Appl Psychophysiol Biofeedback (2002)
27:1–27. doi:10.1023/A:1014587304314
19. Draghici AE, Taylor JA. e physiological basis and measurement of heart
rate variability in humans. J Physiol Anthropol (2016) 35:22. doi:10.1186/
s40101-016-0113-7
20. Diener E, Wirtz D, Tov W, Kim-Prieto C, Choi D, Oishi S, etal. New measures
of well-being: short scales to assess ourishing and positive and negative
feelings. Soc Indic Res (2010) 97:143–56. doi:10.1007/s11205-009-9493-y
21. Wardenaar KJ, van Veen T, Giltay EJ, de Beurs E, Penninx BW, Zitman FG.
Development and validation of a 30-item short adaptation of the Mood
and Anxiety Symptoms Questionnaire (MASQ). Psychiatry Res (2010)
179:101–6.
22. Tombaugh TN. A comprehensive review of the Paced Auditory Serial Addition
Test (PASAT). Arch Clin Neuropsychol (2006) 21:53–76. doi:10.1016/j.
acn.2005.07.006
23. Lin IM, Tai LY, Fan SY. Breathing at a rate of 5.5 breaths per minute with
equal inhalation-to-exhalation ratio increases heart rate variability. Int
J Psychophysiol (2014) 91:206–11. doi:10.1016/j.ijpsycho.2013.12.006
24. Mason H, Vandoni M, deBarbieri G, Codrons E, Ugargol V, Bernardi L.
Cardiovascular and respiratory eect of yogic slow breathing in the yoga
beginner: what is the best approach? Evid Based Complement Alternat Med
(2013) 2013:743504. doi:10.1155/2013/743504
25. Zautra AJ, Fasman R, Davis MC, Craig AD. e eects of slow breathing
on aective responses to pain stimuli: an experimental study. Pain (2010)
149:12–8. doi:10.1016/j.pain.2009.10.001
26. Vaschillo EG, Bates ME, Vaschillo B, Lehrer P, Udo T, Mun EY, etal. Heart rate
variability response to alcohol, placebo, and emotional picture cue challenges:
eects of 0.1-Hz stimulation. Psychophysiology (2008) 45:847–58. doi:10.1007/
s10484-006-9009-3
27. Vaschillo EG, Vaschillo B, Buckman JF, Pandina RJ, Bates ME. Measurement
of vascular tone and stroke volume baroreex gain. Psychophysiology (2012)
49:193–7. doi:10.1111/j.1469-8986.2011.01305.x
Conict of Interest Statement: e authors declare that the research was
conducted in the absence of any commercial or nancial relationships that could
be construed as a potential conict of interest.
Copyright © 2017 Steen, Austin, DeBarros and Brown. is is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
e use, distribution or reproduction in other forums is permitted, provided the
original author(s) or licensor are credited and that the original publication in this
journal is cited, in accordance with accepted academic practice. No use, distribution
or reproduction is permitted which does not comply with these terms.
... There is evidence that a single 5-min session of slow-paced breathing can improve state mood (Magnon et al., 2022;Mikosch et al., 2010). Evidence also suggests that benefits to HRV are co-occurring with state mood changes (Wells, 1995), and that breathing specifically at resonance frequency may also maximise the positive influence of slow-paced breathing on HRV (Steffen et al., 2017). By comparison, effects of slow-paced breathing/RFB on cognitive performance are less consistent, and warrant further exploration, particularly in the context of single-session breathing training protocols. ...
... Furthermore, we asked participants to breathe at their own individual resonance frequency as opposed to a generic slow-paced breathing rate, which may maximally increase HRV (cf. Steffen et al., 2017). We also sought to employ an active control breathing condition which was generally comparable with the RFB procedures aside from the breathing pace. ...
... This confirms our chosen manipulation of HRV was effective in inducing in-the-moment changes to HRV. Increases in RMSSD and both LF and HF frequencies during 2-5 min of slow-paced and/or RFB periods have been observed consistently in the literature (Bonomini et al., 2020;Laborde et al., 2022a;MacKinnon et al., 2013;Steffen et al., 2017;Strausse-Blasche et al., 2001;You et al., 2022You et al., , 2024. However, a lack of significant increases in RMSSD or HF-HRV between executive function task administrations in the present study suggests that change in HRV was not sustained when participants subsequently engaged in a cognitively demanding task. ...
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Generalised anxiety disorder (GAD) is associated with cognitive and physiological symptoms including uncontrollable worry, inhibitory control deficits, and low heart rate variability (HRV). Literature linking HRV and inhibition in GAD is predominantly correlational. The present experiment investigated whether HRV has a causal role in maintaining inhibitory control. Participants (N = 135, 111 female) aged 18–37 reporting high levels of symptoms associated with GAD (GAD-7 scores ≥ 10; Penn State Worry Questionnaire scores ≥ 56) were assigned to an experimental or active control condition and completed baseline measures of HRV, respiration rate, and inhibitory control. The experimental condition completed resonance frequency breathing (RFB) training, and the control condition practiced breathing at their mean breathing rate before repeating the inhibitory control assessment. Participants also completed the breathing training before a behavioural worry task. The experimental condition was predicted to show increased HRV, alongside improved inhibitory control and better ability to stop worrying as compared to the control condition. HRV increased during the experimental condition, as compared to the control condition. However, there were no significant effects of RFB on inhibitory control or worry, or on HRV during the inhibitory control tasks. In conclusion, RFB can increase HRV in high GAD scorers, but further research is required to determine whether there is a relationship between increased HRV and symptoms of GAD in a single session experiment.
... Resonance frequency (RF) is typically lower than the spontaneous breathing rate. Early research correlated RF with the Mayer wave, leading to the assumption that 0.1 Hz represented the RF (Leganes-Fonteneau et al., 2021;Lehrer & Vaschillo, 2004;Steffen et al., 2017;Vaschillo et al., 2008). However, recent studies have shown that each individual possesses a unique RF, determined by the intrinsic characteristics of their cardiovascular system . ...
... Traditional studies suggested that RF was related to the Mayer wave so that 6 bpm was confirmed as RF (Leganes-Fonteneau et al., 2021;Lehrer & Vaschillo, 2004;Steffen et al., 2017;Vaschillo et al., 2008). However, recent study demonstrated that every individual has personalized RF, which was determined by inherent features of the cardiovascular system. ...
... Prior work documented the effects of slow-paced breathing on the cardiovascular system, such as increasing the oscillatory amplitude of HR and BP and reducing BP by cardiovascular reflex. Consistent with previous researches (Pagaduan et al., 2019;Steffen et al., 2017;Tanzmeister et al., 2022), our study showed that a significant decrease in systolic Fig. 7 Brain-cardiovascular information matrix for the SB stage and three breathing task stages. (a) The global average matrix of information transfer between the brain and cardiopulmonary system. ...
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Resonance frequency (RF) is characterized as the specific frequency at which a system, equipped with delayed self-correction or negative feedback mechanisms, exhibits maximal amplitude oscillations in response to an external stimulus of a particular frequency. Emerging evidence suggests that the cardiovascular system has an inherent RF, and that breathing at this frequency can markedly enhance health and cardiovascular function. However, the efficacy of resonance frequency breathing (RFB) and the specific responses of the cardiovascular, respiratory, and central nervous systems during RFB remain unclear. In this study, we recruited 27 healthy young male subjects (aged 20–30 years) and used the corrected sliding method to accurately determine each subject’s RF. We then investigated cardiovascular activity, cardiorespiratory coupling, and the brain-cardiovascular network to clarify the effects and mechanisms associated with RFB. Our results indicate that: (a) the corrected sliding method can precisely evaluate RF; (b) the reduction in blood pressure is unique to RFB and not observed in other slow-paced breathing patterns (RF + 1 and 6 breaths per minute), which we attribute to the α-wave and parasympathetic-BRS pathway; (c) during slow-paced breathing, cardiorespiratory coupling predominantly favors the respiration-to-heart direction, with the RF stage eliciting the most significant response, while brain-cardiopulmonary information transfer increases across all tasks. These findings offer valuable insights into the impact of RFB on the cardiovascular, respiratory, and central nervous systems, potentially laying the groundwork for future research to optimize respiratory training protocols and improve health outcomes.
... Biofeedback therapists recorded average respirations per minute. Although optimal rates of relaxed diaphragmatic breathing for adults are often considered to be approximately six breaths per minute [17], recommended breathing rates for physiologic relaxation in children are not well established [18] and it can be difficult for adolescents to achieve rates below 8 bpm [19]. Therefore, a goal of 6-8 breaths per minute was established. ...
... Responses range from 0 (no trouble) to 4 (impossible). Total scores range from 0-60, with four levels of disability: no-minimal (0-12), mild (13)(14)(15)(16)(17)(18)(19)(20), moderate (21)(22)(23)(24)(25)(26)(27)(28)(29), and severe (>29). Multiple studies have found the FDI to have good reliability and validity [21,22]. ...
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Background/Objectives: Adolescents with autonomic disorders who attend Intensive Interdisciplinary Pain Treatment (IIPT) programs report improvements in functioning. However, it is unclear whether they experience corresponding improvements in physiological measures. As such, the aim of this pilot study was to examine changes in physiological measures in youth attending an IIPT program who demonstrated excessive postural tachycardia on an active stand test. The secondary goal was to examine associations between physiological measurements and self-reported measures of chronic orthostatic intolerance (cOI) and functioning. Methods: At admission and discharge, eighteen adolescents and young adults (AYAs) attending IIPT (M age = 17.39 years; SD = 2.15 years) completed an active stand test, measures of breathing rate and muscle tension, as well as self-reported measures of cOI symptoms and functioning. Results: AYAs showed significant reduction in active stand test heart rate increase (p < 0.001; d = 1.07) and maximum heart rate (p = 0.002, d = 0.76) from admission to discharge. Improvements were also observed in resting respiration rate (p = 0.001, d = 89) and resting trapezoid tension (p = 0.03, d = 0.49). Although patients showed significant improvements on self-report measures of functioning (p < 0.001, d = 1.78), changes on subjective report of cOI symptoms did not reach significance. Exploratory analyses that only included patients with a POTS diagnosis were consistent with the overall results. Conclusions: Youth who demonstrated excessive postural tachycardia on active stand test at admission to an IIPT showed significant improvements from admission to discharge in their active stand maximum heart rate and heart rate increase, as well as respiration rate, muscle tension, and reports of their functioning. Future research is necessary to examine the mechanisms of change that contribute to symptom improvement.
... Teaching effortless slow diaphragmatic breathing is a precursor of Heart rate variability (HRV) biofeedback and is based on slow paced breathing (Lehrer & Gevirtz, 2014;Steffen et al., 2017;Shaffer and Meehan, 2020). Mastering effortless diaphragmatic breathing is a powerful tool in the treatment of a variety of physical, behavioral, and cognitive conditions; however, to integrate this method into clinical or educational practice is easier said than done. ...
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... Hz, que es de 4.5 a 6.5 veces por minuto. El ritmo del barorreflejo es un ritmo bien establecido que no se puede cambiar; sin embargo, se ha demostrado que la respiración a una velocidad cercana a 6 rpm (0.1 Hz) mejora la sensibilidad del barorreflejo y, junto con la propiedad de resonancia de la respiración, provoca mayores oscilaciones en la HRV en una banda de frecuencia específica (Vaschillo et al., 2002;Steffen et al., 2017;Sevoz-Couche y Laborde, 2022). ...
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Esperamos que este libro despierte el interés de los lectores y se convierta en un valioso recurso para estudiantes, investigadores y profesionales interesados en ampliar su comprensión de este apasionante campo del conocimiento. También, resaltamos y agradecemos, el gran trabajo que realizaron todos los que contribuyeron a la obra y que hicieron posible ofrecer este producto final. Rosa María Hidalgo y Maryed Rojas
... giorni) la variabilità che si riscontra nei vari parametri del test HRV. Come abbiamo sopra evidenziato, la scienza PNEI ha chiaramente messo in luce la stretta relazione (positiva e negativa) tra mente e corpo, la patogenicità dell'iperattivazione dell'asse dello stress nell'insorgenza e nell'evoluzione di malattie cronico-degenerative a tutti i livelli [11,12]. ...
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... The MOPR program also demonstrated significant effects on arousal modulation, as evidenced by increases in the Optimal Arousal Zone (OAZ) and reductions in maladaptive stress responses, including hyperarousal (Fight/Flight, Freeze) and hypoarousal (Feigned Death). This outcome suggests that the mindfulness practices and vagal stimulation exercises included in the MOPR program, such as resonance frequency breathing, provided participants with effective tools for autonomic regulation [65,66]. Regression analysis findings further substantiated that arousal regulation, represented by the breadth of the OAZ, serves as a critical factor in enhancing professional quality of life. ...
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... The use of breathing rhythm in biofeedback technologies is based on the concept that breathing significantly influences cortical brain activity through multiple sensory pathways [21,22], as well as the concept of cardiorespiratory synchrony, which suggests a close relationship between the cardiovascular and respiratory systems in regulating physiological status [23]. In respiratory biofeedback procedures, target parameters often include resonance or slow breathing [24,25]. Studies have shown that developing a stable skill in calm diaphragmatic breathing with slow exhalation helps normalise autonomic nervous system balance, improve respiratory function, and enhance overall well-being. ...
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Heart rate variability biofeedback (HRV BF) training aids adaptation to new climatic, geographical, and social environments. Neurophysiological changes during the HRV BF in individuals from tropical regions studying in the Arctic are not well understood. The aim of this study was to research electroencephalographic (EEG) changes during a single short-term HRV BF session in Indian and Russian students studying in the Russian Arctic. The Indian (n = 40) and Russian (n = 40) healthy students (age 19–21 years) at a medical university in Arkhangelsk (64°33′ N 40°32′ E) were studied. HRV and EEG parameters were measured at baseline (5 min) and during a short-term HRV BF session (5 min) to increase the total power (TP, ms2) of the HRV spectrum. The baseline heart rate and stress index levels were significantly higher in the Indian students. During the HRV BF sessions, the sympathetic activity decreased more significantly in Russian students, while the alpha EEG activity significantly increased across all brain regions in both groups. In Indian students, there was a notable increase in theta and beta1 EEG spectral power in the frontal, central, and temporal regions. HRV BF training in Indian students was associated with a more pronounced activation of brain systems compared with Russian students.
... Breathing predominantly in the chest may increase the risk of anxiety, neck, back and shoulder pain as well as increase abdominal discomfort, acid reflux, irritable bowel, dysmenorrhea and pelvic floor pain (Banushi et al., 2023;Salah et al., 2023;Peper and Cohen, 2017;Peper et al., 2020. Learning slow, diaphragmatic or effortless breathing at about 6 breaths per minute (resonant frequency ) is also an 'active ingredient' in heart rate variability (HRV) training (Steffen et al., 2017;Shaffer and Meehan, 2020). ...
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Breathing techniques are commonly employed with complimentary treatments, biofeedback, neurofeedback or adjunctive therapeutic strategies to reduce stress and symptoms associated with excessive sympathetic arousal such as anxiety, high blood pressure, insomnia, or gastrointestinal discomfort. Even though it seems so simple, some participants experience difficulty in mastering effortless breathing and/or transferring slow breathing skills into daily life. The purpose of this article is to describe: 1) factors that may interfere with learning slow diaphragmatic breathing (also called cadence or paced breathing, HRV or resonant frequency breathing along with other names), 2) challenges that may occur when learning diaphragmatic breathing, and 3) strategies to generalize the effortless breathing into daily life.
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In recent years there has been substantial support for heart rate variability biofeedback (HRVB) as a treatment for a variety of disorders and for performance enhancement (Gevirtz, 2013). Since conditions as widely varied as asthma and depression seem to respond to this form of cardiorespiratory feedback training, the issue of possible mechanisms becomes more salient. The most supported possible mechanism is the strengthening of homeostasis in the baroreceptor (Vaschillo et al., 2002; Lehrer et al., 2003). Recently, the effect on the vagal afferent pathway to the frontal cortical areas has been proposed. In this article, we review these and other possible mechanisms that might explain the positive effects of HRVB.
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Objectives Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation-to-exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns. Methods Forty-seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6). The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns. Results The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline. Conclusion The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.
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Slow breathing increases cardiac-vagal baroreflex sensitivity (BRS), improves oxygen saturation, lowers blood pressure, and reduces anxiety. Within the yoga tradition slow breathing is often paired with a contraction of the glottis muscles. This resistance breath "ujjayi" is performed at various rates and ratios of inspiration/expiration. To test whether ujjayi had additional positive effects to slow breathing, we compared BRS and ventilatory control under different breathing patterns (equal/unequal inspiration/expiration at 6 breath/min, with/without ujjayi), in 17 yoga-naive young healthy participants. BRS increased with slow breathing techniques with or without expiratory ujjayi (P < 0.05 or higher) except with inspiratory + expiratory ujjayi. The maximal increase in BRS and decrease in blood pressure were found in slow breathing with equal inspiration and expiration. This corresponded with a significant improvement in oxygen saturation without increase in heart rate and ventilation. Ujjayi showed similar increase in oxygen saturation but slightly lesser improvement in baroreflex sensitivity with no change in blood pressure. The slow breathing with equal inspiration and expiration seems the best technique for improving baroreflex sensitivity in yoga-naive subjects. The effects of ujjayi seems dependent on increased intrathoracic pressure that requires greater effort than normal slow breathing.
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Background: Musical performance is a skilled activity performed under intense pressure, thus is often a profound source of anxiety. In other contexts, anxiety and its concomitant symptoms of sympathetic nervous system arousal have been successfully ameliorated with HRV biofeedback (HRV BF), a technique involving slow breathing which augments autonomic and emotional regulatory capacity. Objective: This randomised-controlled study explored the impact of a single 30-minute session of HRV BF on anxiety in response to a highly stressful music performance. Methods: A total of 46 trained musicians participated in this study and were randomly allocated to a slow breathing with or without biofeedback or no-treatment control group. A 3 Group×2 Time mixed experimental design was employed to compare the effect of group before and after intervention on performance anxiety (STAI-S) and frequency domain measures of HRV. Results: Slow breathing groups (n=30) showed significantly greater improvements in high frequency (HF) and LF/HF ratio measures of HRV relative to control (n=15) during 5 minute recordings of performance anticipation following the intervention (effect size: η(2) =0.122 and η(2) =0.116, respectively). The addition of biofeedback to a slow breathing protocol did not produce differential results. While intervention groups did not exhibit an overall reduction in self-reported anxiety, participants with high baseline anxiety who received the intervention (n=15) displayed greater reductions in self-reported state anxiety relative to those in the control condition (n=7) (r=0.379). Conclusions: These findings indicate that a single session of slow breathing, regardless of biofeedback, is sufficient for controlling physiological arousal in anticipation of psychosocial stress associated with music performance and that slow breathing is particularly helpful for musicians with high levels of anxiety. Future research is needed to further examine the effects of HRV BF as a low-cost, non-pharmacological treatment for music performance anxiety.
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