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Applied Psychophysiology and Biofeedback
https://doi.org/10.1007/s10484-020-09466-z
Heart Rate Variability Biofeedback Improves Emotional andPhysical
Health andPerformance: ASystematic Review andMeta Analysis
PaulLehrer1 · KarenjotKaur2· AgrattaSharma3· KhushbuShah4· RobertHuseby1· JayBhavsar5· YingtingZhang1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
We performed a systematic and meta analytic review of heart rate variability biofeedback (HRVB) for various symptoms
and human functioning. We analyzed all problems addressed by HRVB and all outcome measures in all studies, whether
or not relevant to the studied population, among randomly controlled studies. Targets included various biological and psy-
chological problems and issues with athletic, cognitive, and artistic performance. Our initial review yielded 1868 papers,
from which 58 met inclusion criteria. A significant small to moderate effect size was found favoring HRVB, which does not
differ from that of other effective treatments. With a small number of studies for each, HRVB has the largest effect sizes for
anxiety, depression, anger and athletic/artistic performance and the smallest effect sizes on PTSD, sleep and quality of life.
We found no significant differences for number of treatment sessions or weeks between pretest and post-test, whether the
outcome measure was targeted to the population, or year of publication. Effect sizes are larger in comparison to inactive than
active control conditions although significant for both. HRVB improves symptoms and functioning in many areas, both in
the normal and pathological ranges. It appears useful as a complementary treatment. Further research is needed to confirm
its efficacy for particular applications.
Keywords Applied physiology· Rehabilitation· Emotional dysregulation· Disease· Performance
This review focuses on heart rate variability biofeedback
(HRVB), a method that has become increasingly popular in
recent years among psychophysiologically-minded psycho-
therapists(Kaur etal. 2016; Lehrer 2016, 2018). A grow-
ing body of literature has consistently shown that organized
variability in heart rate (HR) may be a reasonable index of
general health, both physical and emotional (Joyce and Bar-
rett 2019; Kristal-Boneh etal. 1995; McCraty and Shaffer
2015; Perna etal. 2019; Sessa etal. 2018; Young and Benton
2018), and that biofeedback as a method to increase heart
rate variability has widespread beneficial effects.
The pattern of heart rate variability is complex, but, in
the healthy heart, heart rate variability can be decomposed
to a small set of overlapping oscillations. The complexity is
organized in that it can be described using a set of nonlinear
formulas that generally track control of heart rate by the cen-
tral nervous system. The autonomic nervous system is the
primary controller of these oscillations, which reflect a set of
reflexes that help control various body functions. Heart rate
variability biofeedback directly affects two of these reflexes:
respiratory sinus arrhythmia (RSA) and the baroreflex (BR).
Respiratory Sinus Arrhythmia
RSA is the variation in HR that accompanies breathing,
such that HR increases during inhalation and decreases
during exhalation. It has an important function in
Electronic supplementary material The online version of this
article (https ://doi.org/10.1007/s1048 4-020-09466 -z) contains
supplementary material, which is available to authorized users.
* Paul Lehrer
lehrer@rwjms.rutgers.edu
1 Rutgers Robert Wood Johnson Medical School, Piscataway,
NJ, USA
2 Ferkauf Graduate School ofPsychology, Yeshiva University,
NewYork, NY, USA
3 St. George’s University School ofMedicine, TrueBlue, W.I.,
Grenada
4 Richmond University Medical Center, StatenIsland, NY,
USA
5 Reid Health, Richmond, IN, USA
Applied Psychophysiology and Biofeedback
1 3
controlling ventilation, such that the amount of blood
flowing to the lung can be maximized when the greatest
amount of oxygen is in the lung. This relationship can
be important for respiratory disease as well as for ath-
letic and mental performance requiring additional oxygen
to the muscles and the brain. When breathing and heart
rate oscillations are entirely in phase with each other gas
exchange efficiency is maximized (Yasuma and Hayano
2004). Moving the phase angle more out of phase can
similarly be helpful to avoid hyperventilation. In everyday
activity heart rate and breathing usually oscillate at a 90°
phase angle, such that the peak heart rate tends to occur
in the middle of a breath, and gas exchange efficiency is at
an intermediate level. When heart rate and breathing are
completely out of phase, such that the peak of inhalation
occurs during the lowest point in heart rate, gas exchange
efficiency is at its lowest. In HRVB, our studies of young
healthy people have shown that breathing and heart rate
usually oscillate in phase with each other (Vaschillo etal.
2002).
Another important aspect of RSA for psychology is its
neural control. It is mediated by the vagus nerve, a major
parasympathetic nerve, such that it is stimulated in periods
of calmness and relaxation and depressed during periods
of stress. The amount of RSA can be quantified by the
amplitude of peak-to-trough excursions in heart rate that
occur with each breath. The amplitude of changes (in beats
per minute) tends to be greater in healthy people than in
sick people, in younger people than in older people, and
in people who are aerobically more fit. It is smaller in
states and traits of anxiety, anger, and depression, and in
a host of physical diseases ranging from heart disease to
febrile infection. It is quantified in many ways. In addition
to average peak-to-trough amplitude, it can be measured
by the root mean square of successive interbeat differences
in adjacent heart periods (the time between adjacent heart
beats), the percent of adjacent interbeat intervals (IBI’s)
differing by 50ms or more, and by the spectral amplitude
in the range of normal respiration rate, between 0.15 and
0.4Hz, or nine to 24 breaths per minute, a range con-
ventionally described as the “high frequency” range in
heart rate variability. When doing HRVB, RSA amplitude
increases dramatically (Lehrer etal. 2003, 2004).
A third aspect of RSA of interest to psychologists is
the relationship between RSA and sociability. Porges has
pointed out that social animals, like people, dogs, horses,
and certain monkeys, have large amounts of RSA. Less
sociable animals, like cats and certain nonsocial rodents,
have little RSA. It also is related to sociability among peo-
ple (Doussard-Roosevelt etal. 2003; Porges etal. 1996;
Porges and Furman 2011). Among people, those with
major social deficits, particularly autism, have very low
levels of RSA (Patriquin etal. 2019; Porges etal. 2013).
Among married couples, those with good marriages tend
to have high levels of RSA while interacting, while those
with bad marriages tend to have less. Although little
research has been done yet on effects of HRVB on socia-
bility, this is a potential application.
The Baroreex andtheBrain
Another modulatory reflex that is greatly stimulated by
HRVB is the BR (Lehrer etal. 2003). The BR sets the con-
ditions for resonant effects of breathing between 4.5 and 6.5
breaths/min, which produce the large changes in RSA. It is
a reflex that modulates changes in blood pressure. It is medi-
ated through stretch receptors in the carotid artery and aorta,
called baroreceptors. When blood pressure (BP) rises the
walls of these arteries stretch. When the baroreceptors sense
an increase in BP, the BR’s cause an immediate decrease
in HR, leading to a subsequent mechanical decrease in BP
caused by less blood flowing through the vasculature, with a
constant delay of close to five seconds (Eckberg and Sleight
1992), the length of which is apparently caused by the
amount of blood in the system, with a longer delay among
taller and more muscular people (Vaschillo etal. 2006). The
BR helps control changes in BP, and is a modulating force
for promoting blood pressure homeostasis. It is controlled
though centers in the brain stem, chiefly the nucleus tractus
solitarius, which communicates directly with structures in
the limbic system and prefrontal cortex that both generate
and modulate emotion (Henderson etal. 2004; Mather and
Thayer 2018;Rogers etal. 2000; Sakaki etal. 2016; Shoe-
maker and Goswami 2015; Yoo etal. 2018). As with RSA,
the heart rate component of the BR is also under parasym-
pathetic control. BR gain can be quantified as the amount of
change in HR that is triggered by each millimeter of mercury
change in BP. As with RSA, BR gain also is smaller in vari-
ous illnesses than in healthy people (Davydov etal. 2018;
Haji-Michael etal. 2000; Suzuki etal. 2017; Peckerman
etal. 2003) and depressed in various states and traits of
negative emotion (Dawood etal. 2008; Vasudev etal. 2011).
Recent findings on effects of HRVB on the brain also
show large increases in blood flow oscillations during
HRVB throughout areas involved in emotional generation
and modulation, particularly the limbic system and the cin-
gulate and prefrontal cortices (Mather 2019; Vaschillo etal.
2019), and there is some evidence for greater connectivity
between limbic and prefrontal structures, including evidence
for increases in brain tissues in these connectivity pathways,
after people have practiced the technique for several weeks.
This may be a mechanism whereby HRVB can help modu-
late emotional swings.
Because of the time delay in the BR system, it tends to
produce a rhythm in everyone’s heart rate with a period of
Applied Psychophysiology and Biofeedback
1 3
about ten seconds. This rhythm is found in everyone, and has
long been identified as the ‘Meyer wave’. The rhythm varies
among people in the range of 4.5 to 6.5 cycles per minute
(Fuller etal. 2011; Vaschillo etal. 2002, 2006), and corre-
lates highly with what has been termed the “low frequency”
spectral range in heart period, between 0.05 and 0.15Hz
(three to nine cycles per minute). The BR system can be
considered a “closed loop” system because it is character-
ized by an internal feedback loop that helps control cardio-
vascular stability when stimulated from the outside. Any
closed loop negative feedback loop with a constant delay has
the characteristics of resonance (Grodins 1963; Ringwood
and Malpas 2001), and any resonant system stimulated at its
resonance frequency produces very large amplitude oscil-
lations at that frequency, recruiting most other oscillations
at other frequencies (Ogata 2004). Thus, when breathing at
resonance frequency, RSA stimulates the BR. Other forms
of stimulation, such as rhythmical muscle tension (Lehrer
etal. 2009) and rhythmical exposure to emotional pictures
(Vaschillo etal. 2008) can produce a similar effect, although
usually with a smaller amplitude of heart rate oscillations.
HRVB thus creates tremendous increases in vagus nerve
activity, with increases in RSA amplitude regularly increas-
ing two to fivefold while people are breathing at their indi-
vidual resonance frequency, which varies between 4.5 and
6.5 breaths/min (Lehrer etal. 2003, 2004; Vaschillo etal.
2006). This increase is entirely mediated by the vagus nerve,
a major parasympathetic nerve. Vagus nerve activation, and
parasympathetic activity in general, are characteristics of
relaxation and lower levels of stress. Importantly, many of
the vagus nerve fibers are afferent, meaning that activity
in the vagus nerve affects the brain as well as vice versa,
through the pathways described above.
Why HRV Biofeedback?
HRVB directly stimulates various homeostatic ‘negative
feedback loops’ (Lehrer and Eddie 2013). Particularly,
because the baroreflex and RSA are both stimulated by
HRVB and parasympathetic activity is increased, there
is therefore reason to believe that HRVB should improve
emotional regulation (Mather and Thayer 2018; Thayer etal.
2012). Because of the in-phase relationship between heart
rate and breathing during HRVB, there is reason to believe
that it improves gas exchange efficiency and helps respira-
tory disease and other breathing disorders. Because it stimu-
lates the BR, there is reason to believe it helps control blood
pressure. Because it stimulates the vagus nerve it might be
expected to produce a sense of relaxation and well-being.
Also, in addition to stimulating parasympathetic activity,
HRVB directly stimulates a variety of homeostatic reflexes,
perhaps a unique characteristic of this intervention, while
other accepted methods of stress management directly target
other mechanisms. HRVB stimulates an interaction between
RSA and the BR, two reflexes with regulatory functions
(Lehrer 2013).
Other methods work through other pathways. For exam-
ple, Jacobson’s method of progressive muscle relaxation,
which, when done according to his method, teaches relaxa-
tion of the muscles down to the level of underlying muscle
tone (Jacobson 1938). Because the muscles are part of the
sympathetic nervous system (Di Bona etal. 2019; Mitch-
ell and Victor 1996; Notarius etal. 2015) the direct effect
of progressive muscle relaxation is to decrease the level
of sympathetic arousal (Cottier etal. 1984; Larkin etal.
1990), with more indirect effects on homeostatic functions,
as reflected in stress recovery (English and Baker 1983).
Similarly, methods such as hypnosis, cognitive therapy, and
meditation focus on thought processes than on direct physi-
ological control, with a less direct pathway to physiology. It
is the unique pathway of effects that prompts this evaluation
of the usefulness of HRVB.
A particular symptom targeted by HRVB is hyperventi-
lation. HRVB appears to inoculate people against this ten-
dency in the face of various respiratory stimulants, including
altitude, exposure to high levels of ambient carbon dioxide,
and stress. Patients with panic disorder, many of whose
symptoms are those of hyperventilation, are particularly
trained to use the method to abort panic attacks when symp-
toms first start, and to avoid hyperventilation symptoms
when exposed to various panic triggers. Having a reliable
method of controlling panic symptoms then becomes a use-
ful tool for decreasing fear of hyperventilatory body symp-
toms. The mechanism by which hyperventilation is targeted
by HRVB has not been proven, although it is reasonable to
hypothesize that it involves a combination of slow breathing,
decreased emotional and autonomic reactivity, and attention
to breathing mechanisms for controlling it. The increased
gas exchange efficiency, described above, may also have an
effect on modulating respiratory drive.
Perhaps because of these unique effects of HRVB, interest
in the method has grown. The number of studies of HRVB
published each year has grown exponentially between the
early 1990’s and 2016 (Kaur etal. 2016).
Although not covered in this review, it is important to
mention possible side effects of HRVB. These are quite
minor in most cases. It is common for people to hyper-
ventilate slightly when first doing slow breathing, where
increased depth of breathing overcompensates for the slow
pace. In the standard protocol, the trainee is specifically
instructed to breathe shallowly, particularly in response
to feelings of lightheaded ness, which usually is the first
hyperventilation symptom to occur. Another possible side
effect, of unknown risk, occurs among people with frequent
cardiac arrhythmias. In rare cases, individuals with frequent
Applied Psychophysiology and Biofeedback
1 3
preventricular contractions may show an increase in these
events, particularly toward the end of exhalation when doing
slow breathing. These events may be caused by a buildup
of carbon dioxide during a long exhalation. They are easy
to detect from a biofeedback heart rate tracing. The cardiac
risk of these biofeedback-induced arrhythmic episodes is
unknown so the method should be used with caution among
people with this condition, although some people who have
continued practicing the method despite this pattern of
arrhythmias actually have shown a decrease in the sponta-
neous occurrence of them.
The Method ofHRVB
In HRVB, people are taught to breathe slowly, at the par-
ticular rate of the baroreflex rhythm. Because of resonance
characteristics of the BR system (Hammer and Saul 2005;
Lehrer etal. 2009; van de Vooren etal. 2007; Vaschillo etal.
2002) and the particular phase relationships among HR, BP
and breathing when people breathe at the BR frequency
(Vaschillo etal. 2002, 2006), a very large increase in the
amplitude of HR oscillations occurs when people breathe
at the BR frequency, caused by an interaction between RSA
and the BR.
In HRVB, people learn through biofeedback to detect the
particular frequency at which HRV is maximized for each
individual when they breathe at that rate. This can easily be
detected by following a simple heart rate monitor. These
are increasingly free or low cost, and easy to use (Hunkin
etal. 2019). There are various methods for HRVB training,
which usually include a combination of paced breathing at
various rates in order to determine the rate producing the
biggest swings in HR from inhalation to exhalation, and sim-
ply following a HR tracing on a computer screen or playing
various computer games where displays are proportional to
the change in HR with each breath. People are instructed to
follow the tracing in order to maximize swings in HR with
each breath, which only can be achieved by breathing at their
individual resonance frequencies. When people practice
HRVB daily over a period of time, amplitudes of HR excur-
sions at both RSA and BR frequencies are increased even
when people are not practicing the technique (Lehrer etal.
2003). Thus, these two important regulatory reflexes, RSA
and the BR, appear to be strengthened by exercise during
biofeedback, with expected effects of improved immunity to
and recovery from stress and adaptability to various mental
and athletic demands on the system.
As will be reviewed below, a number of studies have
found that HRVB does, in fact, produce improvement in
a variety of physical and emotional conditions including
anxiety, depression, hypertension, asthma, and pain, as well
as improvement in various kinds of human performance
including mental concentration and agility, athletics,
dance, and music. The technique is easily learned and can
be trained using inexpensive equipment including several
free smart phone applications. HRVB has been proposed
as a psychotherapy component that specifically targets the
neurovegetative components of emotional problems and
may improve treatment effectiveness (Caldwell and Steffen
2018; Lehrer 2018; Wheeler 2018). Most people can achieve
high-amplitude oscillations in HR after just a few minutes
of training, and almost everyone can master the technique
within one to four sessions of coaching. After initial training
some people still achieve better results by following a heart
monitor, while others do just as well doing paced breathing
at their resonance frequency, once this frequency has been
determined by biofeedback, following the second hand on a
clock or counting seconds silently. The exceptions are peo-
ple with frequent cardiac arrhythmias, such as preatrial or
preventricular contractions, which make it difficult for them
to determine their resonance frequency.
To test the hypothesis that HRVB promotes general health
and performance, we conducted a systematic review and
meta-analysis of all randomly controlled trials of HRVB,
including all outcome measures used in all studies, regard-
less of the target problem or population, and whether the
particular outcome measure was closely related to the tar-
get problem, e.g., measures of anxiety for a study on treat-
ment of asthma even where baseline levels of anxiety are
in the normal range at pre-test. We consider this to be a
conservative test because it maximizes the possibility of
floor effects on some variables, where little improvement
is possible. We excluded HRV variables because the effect
sizes would be very high for acute changes during biofeed-
back (Lehrer etal. 2003, 2004), and because it would be
circular to impute higher levels of resilience from higher
HRV. Although baseline changes in HRV after treatment
vary widely among studies, they are mostly related to age
and are unrelated to symptom changes (Lehrer etal. 2006;
Wheat 2014). Older people have smaller HRV amplitudes
and smaller changes in HRV after HRVB and, in some cases,
greater symptom improvement (Alayan etal. 2019; Lehrer
etal. 2006). It is possible that the symptom effects reported
here may be due to frequent and cumulative application of
HRVB to ameliorate acute symptom changes associated with
the large changes in HRV, and that neural mechanisms for
these effects may differ from peripheral effects on HRVB,
perhaps due to age-related effects on the cardiovascular
system.
Because resonant effects on heart rate variability tend
to occur when the system is stimulated close to the reso-
nance frequency but not at it exactly (Vaschillo etal. 2004),
it is possible that simply doing paced breathing at about
six breaths per minute would have the same salutary effects
as breathing more exactly at resonance frequency. This can
Applied Psychophysiology and Biofeedback
1 3
easily be taught by following a computer-generated pac-
ing signal or a clock. For greater comfort, some respiratory
biofeedback devices provide signals to gradually decrease
respiration rate to the desired frequency. It has not yet been
definitively established whether HRVB has better clinical
effects than simple paced breathing at six breaths per minute,
although one small study on borderline hypertension found
that both methods produced significant effects on decreas-
ing blood pressure, although, as would be expected from the
description of mechanisms described above, the effect of
HRVB was slightly, although nonsignificantly, greater (Lin
etal. 2012). In this review we decided to include studies of
breathing at approximately six breaths per minute as well
as HRVB studies because the effects are so similar, and to
compare the effect sizes for the two methods.
Methods
Identication ofStudies forInclusion
A literature search was performed to generate articles for
the meta-analysis, with specific search criteria, using the
databases CINAHL, Cochrane, PsychINFO, PubMed, Sco-
pus, and Web of Science. The search terms included com-
mon HRVB maneuvers and the equipment used to conduct
HRVB as well as various descriptors of voluntary control
and various outcomes. The complete search criteria are in
the supplement to this paper, TableS1.
A search of all published papers and grey literature
(unpublished convention papers, dissertations, etc.) through
November 15th 2018 generated 1868 papers, of which 1514
were unduplicated. Studies with a 2019 publication date
were reviewed from prior convention presentations. At least
two of five independent reviewers (KK, AS, KS, RH, and
JB) performed a preliminary review of each abstract search-
ing for inclusion criteria. The inclusion criteria were the use
of HRVB or paced breathing (PB) at a rate of approximately
six breaths/min (bpm, the approximate rate of breathing dur-
ing HRVB), use of this maneuver for any condition, and
consisting of a randomized controlled trial. The reviewers
reconciled differences after their independent reviews and
eliminated 1272 papers. Where reviewers disagreed, final
decisions were made by PL.
The remaining 242 papers were analyzed in a secondary
review by the same combination of reviewers, where papers
were read in their entirety, additionally screening for the
inclusion criteria and for the following exclusion criteria:
lack of a treatment goal other than increasing HRV, use of
PB at a rate other than six bpm, biofeedback for average
heart rate but not HRV, a small sample sizes (n < 10), con-
founding effects of HRVB and other methods (e.g. HRVB
along with another intervention, compared with a control
group), or insufficient usable data for the Comprehensive
Meta-Analysis (CMA) program, version 3.3.070 (Borenstein
etal. 2009), which we used for all calculations. All of these
papers were additionally reviewed by PL. Reasonable efforts
were taken to contact the authors of studies with insufficient
data so the studies could be included. Studies combining
HRVB or PB with other interventions were included where
the same additional interventions were given to control
groups.
After the secondary review, 185 additional papers were
excluded, and a total of 58 studies from 57 papers were
entered into the CMA program. Coding of these papers for
various mediators was done by three independent reviewers
per study, who reconciled differences after coding.
Data Extraction
We coded all outcome measures reported for each study
other than heart rate variability measures and process meas-
ures (e.g., home practice time, treatment believability, etc.)
The components extracted from each study included: study
name and year, comparison used (HRVB vs control, PB vs
control, HRVB vs PB, etc.), outcome measures, time points
at which data were collected, data format (e.g., pre- and
post-treatment, follow-up, midpoint, etc.), outcome meas-
ures (e.g., pre- and post-treatment means and standard devia-
tions, effect sizes for therapeutic effects of treatment vs. con-
trol conditions, or values of F or chi square), sample size in
the treatment and control groups, year of publication, type
of treatment received (HRVB or PB), the number of weeks
spanning the beginning and end points, number of treat-
ment sessions, type of control used, e.g., active or inactive,
control description (e.g., standard care, relaxation, cognitive
therapy), disorder or target problem studied, description of
each outcome measure, type of measure, e.g., self-reported,
physiological, whether each particular outcome measure was
specifically targeted to the study and the population stud-
ied, and measure direction (improvement indicated either
by low or high scores). The outcome measures we analyzed
are summarized in Table1 along with ways in which we
categorized each, and the types of control groups are in
Table2. The standardization method usually was pre-post
standard deviation. Where various outcome statistics were
reported, we favored using pretest and posttest means and
standard deviations. Where only pre-post difference scores
were reported, we calculated g based on these and standard-
ized using the standard deviation of the difference score.
Each outcome measure within each study was given a
separate entry in the CMA program, such that multiple
entries existed for many studies. Follow-up and post-test
analyses were given separate entries, but the results of these
were averaged in the analyses. Fifty-eight studies generated
360 entries of usable data in the CMA analysis, with 2485
Applied Psychophysiology and Biofeedback
1 3
Table 1 List and categorization
of outcome measures Measure Category
# ER visits cardiac disease Cardiovascular
# readmissions cardiac disease Cardiovascular
% symptom free days asthma Respiratory
6min walk distance test cardiac disease Cardiovascular
Activation deactivation adjective checklist—calmness Emotional regulation
Activation deactivation adjective checklist—energy Activation
Activation deactivation adjective checklist—tension Emotional regulation
Activation deactivation adjective checklist—tiredness Activation
Activity reduction Behavioral function
Anxiety Anxiety, emotional regulation
Artistry Performance
Asthma control test Respiratory
Asthma quality of life Respiratory, quality of life
Attention control Cognitive performance
Batting performance Performance
Beck depression inventory Depression, emotional regulation
Blood pressure Cardiovascular
Borg dyspnea scales Respiratory
Bother (menopausal symptoms) Emotional regulation
Blood pressure medication Cardiovascular
BSI anxiety Anxiety, emotional regulation
BSI depression Depression, emotional regulation
BSI Global Severity Index Emotional regulation
BSI hostility Anxiety, emotional regulation
BSI interpersonal sensitivity Emotional regulation
BSI obsessive compulsive Emotional regulation
BSI paranoid ideation Emotional regulation
BSI phobic anxiety Anxiety, emotional regulation
BSI psychoticism Emotional regulation
BSI somatization Emotional regulation
Caps (clinician administered ptsd scale) Emotional regulation
Centre for Epidemiologic Studies of Depression scale Depression, emotional regulation
Clinical Anxiety Scale Anxiety, emotional regulation
Cognitive anxiety Anxiety, emotional regulation
Cognitive flexibility Cognitive performance
Cognitive somatic anxiety: cognitive Anxiety, emotional regulation
Cognitive somatic anxiety: somatic Anxiety, emotional regulation
Concentration performance attention test Performance
Craving for substances Substance problem, emotional regulation
Dance score Performance
DASS—anxiety Anxiety, emotional regulation
DASS—depression Depression, emotional regulation
DASS-stress Emotional regulation
Domestic environment Functioning, quality of life
Dribbling Performance
Drinking capacity Gastrointestinal
Driven behavior Functioning, quality of life
Eating disorder measures Eating disorder, emotional regulation
Employment Functioning, quality of life
EPDS depression scale Depression, emotional regulation
Executive function Emotional regulation
Applied Psychophysiology and Biofeedback
1 3
Table 1 (continued) Measure Category
Exhaled nitric oxide Respiratory
Fatigue Activation
Food craving Substance problem, emotional regulation
Gastric emptying % Gastrointestinal
Global symptom severity Emotional regulation
HAD-anxiety Anxiety, emotional regulation
HAD-depression Depression, emotional regulation
HAD-SMSS Emotional regulation
HAM-D Depression, emotional regulation
Health environment Quality of life
Peak flow variability Respiratory
Hostility Anger, emotional regulation
Hot flash bother (0–10 scale) Quality of life
Hunger Gastrointestinal
Inhibition Functioning, quality of life
Interference score of Stroop test Cognitive performance
Intragastric volume Gastrointestinal
Irritable bowel symptoms Gastrointestinal,
ISI insomnia Sleep
LHFQ living with heart failure Cardiovascular, functioning, quality of life
Log oscillation resistance at 6hz Respiratory
Math error ratio Cognitive performance
Medication Cardiovascular or respiratory
Meta cognition questionnaire Emotional regulation
Mindfulness Emotional regulation
MPA music performance anxiety Anxiety, emotional regulation
Neck Disability Index Functioning, quality of life
Negative emotion Emotional regulation
NRS Pain Pain
Number of awakenings Sleep
Obsessive Compulsive Drinking Scale Substance problem, emotional regulation
PAI performance anxiety Anxiety, emotional regulation
Pain measures Pain
PANAS negative emotion Emotional regulation
PANAS positive emotion Emotional regulation
Passing Performance
pc20fev1 Respiratory
PTSD measures Emotional regulation
Peak flow variability Respiratory
Penn Alcohol Craving Scale Substance problem, emotional regulation
Penn state worry Anxiety, emotional regulation
Perceived self-regulation, dietary success Eating disorder, emotional regulation
Pittsburgh sleep questionnaire Sleep
POMS depression Emotional regulation
POMS mood Emotional regulation
Positive feelings Emotional regulation
Quality of life fatigue Activation, quality of life
Quality of life: hypertension Cardiovascular, quality of life
Quality of life, dyspepsia Gastrointestinal, quality of life
Relaxation Emotional regulation
Relaxation potential Emotional regulation
Applied Psychophysiology and Biofeedback
1 3
participants across studies. Figure1 shows the flow of pro-
cedures for this study, using PRISMA guidelines (Moher
etal. 2009).
Statistical Analysis
Hedge’s g, a corrected version of Cohen’s d, was used as an
effect size measure, using a random effects model (Boren-
stein etal. 2010). An average Hedge’s g was calculated
across studies, averaging within-study outcome measures
and time points, and weighting studies by sample size. When
a study had two control groups the n in the treatment group
divided by two. Follow-up and post-test data were not sepa-
rated because some time points labeled as ‘follow-up’ were
shorter than some intervals labeled as ‘post-test’. Hedges’
g for each study comprised the average of all measures and
time points within the study. Where post tests and follow-
up measures were reported in separate papers, a single g
was calculated for each study across papers. Cohen (1988)
suggests that effect sizes of 0.2 be considered small, 0.5
medium, and 0.8 large. A funnel plot was used to detect
outlying studies. In this plot the Y axis represents the size of
the sample, with smaller variation among studies expected
among studies having a larger n. Although sometimes inter-
preted as having experimental bias, outlying studies also
could represent unique characteristics of procedures or study
participants, so we examined the outlying studies for unusual
characteristics (Borenstein etal. 2009). Because it may be
unclear whether the outliers represent bias or unique study
characteristics, data are presented both with and without
the outliers. We also report the significance of heteroge-
neity among studies using the Q statistic despite the fact
Table 1 (continued) Measure Category
Respiratory impedance Respiratory
Response times (in ms) of Sternberg test Performance, cognitive performance
Role adjustment Functioning, quality of life
School burnout Emotional regulation
Self-compassion Emotional regulation
Self confidence Emotional regulation
SF36 emotional function Emotional regulation
SF36 general health Quality of life
SF36 physical functioning Functioning, quality of life
SF-36 bodily pain Pain
SF-36 mental health Emotional regulation
SF-36 role functioning emotional Emotional regulation. functioning, quality of life
SF-36 role functioning physical Functioning, quality of life
SF-36 social functioning Functioning, quality of life
SF-36 vitality Activation, quality of life
SF-NDI dyspepsia Gastrointestinal
Shooting Performance
Sleep efficiency Sleep
Sleep onset latency Sleep
Sleep overall quality Sleep
Somatic anxiety Anxiety, emotional regulation
State trait anxiety inventory Anxiety, emotional regulation
State anger Anger, emotional regulation
Stress inventories Emotional regulation
Symptoms of asthma Respiratory
Time awake after sleep onset Sleep
Time pressure Emotional regulation
Sleep quality Sleep
Total sleep time Sleep
Vocational environment Functioning, quality of life
Working memory Cognitive performance
Wor r y Anxiety, emotional regulation
Yale food addiction Substance problem, emotional regulation
Applied Psychophysiology and Biofeedback
1 3
that an analysis with many studies and large sample sizes,
as the current one, may yield a significant Q statistic with
small amounts of heterogeneity, rendering the statistic less
meaningful. We also assessed the percentage of heteroge-
neity among studies due to real heterogeneity vs. chance
(within-study) variance using the I2statistic (Higgins and
Thompson 2002), and calculated the prediction interval as
the average g ± 2 × tau, the standard deviation of real effect
sizes, to estimate the range of values within which there is
95% confidence that another study would find g (Higgins
2008). We additionally calculated separate effect sizes com-
parisons for HRVB/PB compared with control conditions
from studies with active and inactive control groups, and
for various individual target problems and types of outcome
measures in order to examine the effect sizes of treatment
vs. control conditions on specific problems. We used meta-
regression analysis to examine the effect of treatment length
and intensity (number of sessions), whether particular meas-
ures were targeted, whether controls were active or inac-
tive, whether treatment was by HRVB or PB, and year of
publication. We included the intercept in the model for these
analyses. Data were coded such that more negative values,
yielding negative g’s, represented a therapeutic effect, with
positive values indicating a deterioration in the participant’s
condition on that measure. We used a mixed effects analysis
for examining dichotomous mediators (e.g., whether or not
Table 2 Control conditions
17 studies had more than one type of control group
EEG electroencephalographic, EMG electromyographic, HRV heart
rate variability
Active n = 23 Inactive n = 41
EEG biofeedback Waiting list
Physical exercise No treatment
Medication Treatment as usual
Cognitive behavior therapy Quiet study
Skill training Sitting quietly
Monitor HRV Watching a video
Mindfulness meditation
Progressive muscle relaxation
Patient education
Breathing controls n = 8 Placebo n = 3
Fast paced breathing Placebo treatment
Deep breathing (not paced) False biofeedback
Fig. 1 PRISMA flow of study
procedures
Records excluded
(n = 1272)
Records idenfied through database search
(n = 1868, 142 from grey literature).
CINHAL=68PsycInfo = 313
Pubmed = 708 Scopus = 294
Web of Science = 341
Screening
Full-text arcles excluded
(n = 218)
Included
EligibilitynoitacifitnedI
Records aer duplicates removed
(n = 1514 )
Full-text arcles assessed
for eligibility
(n = 276)
Studies included in
synthesis (meta-analysis)
(n = 58 )
Applied Psychophysiology and Biofeedback
1 3
a particular measure was targeted to the population stud-
ied, whether the control group was active or inactive), and
computed g for each alternative and computed differences
between them using the Q statistic.
Results
Figure2 shows a forest plot of all studies, with individual
study statistics shown in Table3 and summary statistics
summarized in Table4. The average effect size for HRVB /
PB vs. control conditions was found to be small to medium
(g = 0.37) with significant heterogeneity, considerable het-
erogeneity and error variance, and a 95% prediction interval
between a large effect favoring HRVB and a small effect
favoring a control group (g = − 1.03 and + 0.29). A funnel
plot (Fig.3) shows three outlying studies with greater thera-
peutic effect than others, studies by Lehrer etal. (2004),
Munafo etal. 2016, and Paul and Garg (2012). Although
the possibility of bias cannot be ruled out for these outlying
studies, each had some unique characteristics that may have
contributed to the very high effect sizes (g = 1.9–2.7). Lehrer
etal. (2004) used an unusual design, including biweekly
adjustment of medication (an outcome variable) and an unu-
sually sensitive measure of pulmonary function, forced oscil-
lation pneumography. Munafo etal. (2016) also used only
physiological measures as outcomes, with systolic blood
pressure closely related to baroreflex function, which was
directly targeted by HRVB. Paul and Garg (2012) used acute
measures of basketball performance. Although three addi-
tional studies also were slightly beyond the expected limits,
one showing a slightly higher effect size than expected and
two showing a slightly lower size, we decided not to treat
these studies as outliers because they were not influential
and did not create significant heterogeneity. When we rec-
omputed meta-analytic statistics without the three outliers,
we found a small but still significant effect size without sig-
nificant heterogeneity (Table4) and a funnel plot showing
no influential outliers (Fig.4).
When we compared HRVB with inactive control con-
ditions (treatment as usual, sham procedures, etc.) we still
found a significant small to medium effect size, both with
and without the outliers (Table5). When compared with
paced breathing at a relaxed respiratory rate of about 15
breaths per minute, HRVB/PB (at 6 breaths per minute)
was nonsignificantly superior, with a small effect size,
g = − 0.26, p < 0.06 (Botha et al. 2015; Breach 2013; Car-
penter etal. 2013; Lehrer etal. 2017; Tsai etal. 2015). When
compared with all breathing interventions other than reso-
nance frequency breathing, including attention to breathing,
counting breaths, and deep breathing, a small but signifi-
cant effect size was found (Table5). Two studies using PB,
both with inactive controls, found a nonsignificant small
to medium effect size, g = 0.38, p = 0.32, and, with greater
power, 41 studies using HRVB with inactive controls yielded
a similar effect size without outliers, g = 0.33, p < 0.0005,
and g = 0.45, p < 0.0005 with outliers. When HRVB was
contrasted with effects of all other effective interventions
(active interventions), we found little difference (Table5).
Effect sizes are similar for behavioral, physiological, and
self-report outcome measures. We found small to medium
effect sizes for physiological and self-report measures and
a medium but nonsignificant effect size for behavioral per-
formance measures, but no significant differences among
these three types of outcome measures (Table5). When we
compared the effects of HRVB/PB on measures that were
specific to the target population, the effects do not differ
from those on nontargeted measures (Table5).
For individual problems, effect sizes varied widely
between small and medium to large across most disorders
or targets. The number of studies for each symptom is low,
so some of the results may be unreliable and nonsignificant
due to lack of power (Table6). Irrespective of statistical
significance, the highest effect sizes were found for athletic/
artistic performance, depression, gastrointestinal problems,
anger, anxiety, respiratory disorders (including an outlying
study), systolic blood pressure, substance craving, and pain.
The lowest effect sizes were for self-reported stress, physi-
cal functioning/quality of life, diastolic blood pressure, post
traumatic stress, general activation/energy, and sleep. Some
effect sizes were slightly higher for measures related to the
problems targeted in individual studies than for nontargeted
measures. A particularly wide dispersion was found among
studies of anxiety and artistic/athletic performance, where a
significant effect was found only where outlying studies were
included in the analysis. Larger effect sizes were found for
anger and gastrointestinal problems, but the effects are not
statistically significant due to lack of power.
Regression analyses for linear moderators are shown in
Table7. Very small and nonsignificant regression coeffi-
cients both for number of treatment sessions (median = 6,
range = 1–40), and number of weeks between pre-treatment
and post-treatment assessments (median = 5, range = 0–40).
In all cases participants had been encouraged to practice the
HRVB/PB techniques between sessions. The meta regression
on year of publication also yielded nonsignificant regression.
Discussion
The results of this review provide evidence that HRVB and
PB at approximately six breaths per minute have positive
effects on a variety of physical, behavioral, and cognitive
conditions. The overall effect sizes are modest but highly
significant, suggesting that these methods may not be suf-
ficient for treating any one problem but may be useful as
Applied Psychophysiology and Biofeedback
1 3
Fig. 2 Forest plot of Hedges’s g.
Studies with more than one con-
trol group are entered separately
for each control
[S t ud y N ame ] He d g es ' s g and 95 % CI
Paul. 2012a
Paul. 2012
Lee, 2015
Lehrer , 2004
Munafo, 2016
Lehrer , 2004a
Browne. 2002
Lin, 2012
Berry, 2014
Kudo. 2014
Caldwell 2018
Prinsloo, 2013
Lehrer , 1997a
Botha, 2015
Wells, 2012
Wells, 2012b
Windhorst 2017
Lehrer , 1997
Sutarto, 2013
Rene, 2008
Raymond, 2005
Tan 2011
Hjelland, 2007
Thurber 2007
Yu 2018
Lehrer 2017
Sutarto 2012
Zucker 2009
Alabdulgader, 2012
Tsai, 2015
Dziembowska, 2016
Schuman, 2019
Hallman, 2011
Eddie, 2014
Hallman, 2011a
Siepmann ,2014
Meule 2017
Yetwin, 2012
Meier, 2016
Lin, 2015
May, 2018
Patron, 2013
Meule, 2012
Siepmann, 2014
May, 2018a
Carpenter, 2013
Soer, 2014
Reineke, 2008
Strack, 2004
Raymond, 2005a
Climov, 2014
wheat 2014
Meier, 2016a
White 2008
Penzlin 20115-7
Carpenter, 2013a
Kenien, 2015
Thompson, 2010
Murphy 2009
Debruin 2016a
Swanson 2009 Study 1
Debruin 2016b
Breach, 2013
Gruz elier, 2014
Gruz elier, 2014a
Swanson 2009 Study 2
Van Der Zwan, 2015
Cullins, 2013
Browne. 2002a
- 4.5 0 - 2.2 5 0.0 0 2. 25 4. 50
Fa v ou r s HR VB Fa v ou r s Con tr o l
Applied Psychophysiology and Biofeedback
1 3
Table 3 Individual statistics for
all studies (in order of Hedges’s
g as in Fig.2)
Study g95% confidence limit Z p
Upper limit Lower limit
Paul and Garg (2012)a− 2.713 − 1.460 − 3.965 − 4.244 < 0.0005
Lee etal. (2015)− 2.017 − 0.590 − 3.444 − 2.770 0.006
Munafo etal. (2016)a− 1.915 − 1.080 − 2.750 − 4.493 0.0005
Lehrer etal. (2004)a− 1.357 2.953 − 5.293 − 5.293 < 0.0005
Yetwin etal. (2012)− 1.232 − 0.286 − 2.177 − 2.554 0.011
Prinsloo etal. (2013)− 1.085 − 0.137 − 2.034 − 2.242 0.025
Windhorst etal. (2017)− 1.083 − 0.249 − 1.917 − 2.545 0.011
Lin etal. (2012)− 1.039 − 0.226 − 1.852 − 2.505 0.012
Patron etal. (2013)− 0.958 − 0.169 − 1.747 − 2.381 0.017
Kudo etal. (2014)− 0.921 − 0.370 − 1.472 − 3.277 0.001
Berry etal. (2014)− 0.875 0.168 − 1.917 − 1.645 0.100
Caldwell and Steffen (2018)− 0.834 0.044 − 1.713 − 1.861 0.063
Rene (2008)− 0.748 − 0.008 − 1.487 − 1.982 0.048
Zucker etal. (2009)− 0.739 − 0.095 − 1.384 − 2.248 0.025
Thurber (2007)− 0.686 0.327 − 1.700 − 1.328 0.184
Wheat (2014)− 0.682 0.072 − 1.436 − 1.772 0.076
Botha etal. (2015)− 0.671 − 0.091 − 1.251 − 2.266 0.023
Sutarto etal. (2013)− 0.559 0.093 − 1.212 − 1.679 0.093
Strack etal. (2004)− 0.536 0.055 − 1.127 − 1.778 0.075
Lehrer etal. (1997)− 0.487 0.597 − 1.571 − 0.880 0.379
Yu etal. (2018)− 0.478 − 0.135 − 0.822 − 2.727 0.006
Swanson etal. (2009b) − 0.432 0.628 − 1.493 − 0.799 0.424
Sutarto etal. (2012)− 0.432 0.215 − 1.080 − 1.308 0.191
Wells etal. (2012)− 0.389 0.326 − 1.104 − 1.065 0.287
Penzlin etal. (2015)− 0.371 0.463 − 1.204 − 0.871 0.384
Raymond etal. (2005)− 0.340 0.797 − 1.478 − 0.587 0.557
Hjelland etal. (2007)− 0.331 0.288 − 0.951 − 1.049 0.294
Thompson (2010)− 0.315 0.387 − 1.016 − 0.879 0.379
Dziembowska etal. (2016)− 0.299 0.305 − 0.903 − 0.970 0.332
Tan etal. (2011)− 0.296 0.548 − 1.141 − 0.687 0.492
Meier and Welch (2016)− 0.287 0.200 − 0.774 − 1.155 0.248
Schuman and Killian (2019)− 0.281 0.811 − 1.372 − 0.504 0.614
Eddie etal. (2014)− 0.250 0.353 − 0.853 − 0.813 0.416
Hallman etal. (2011)− 0.248 0.528 − 1.024 − 0.627 0.531
Meule and Kubler (2017)− 0.237 0.246 − 0.719 − 0.962 0.336
Siepmann etal. (2014)− 0.210 0.357 − 0.777 − 0.726 0.468
Climov etal. (2014)− 0.213 0.565 − 0.991 − 0.537 0.591
Soer etal. (2014)− 0.186 0.415 − 0.787 − 0.608 0.543
Kenien (2015)− 0.177 0.312 − 0.666 − 0.708 0.479
May etal. (2018)− 0.168 0.333 − 0.669 − 0.656 0.512
Tsai etal. (2015)− 0.166 0.579 − 0.912 − 0.437 0.662
Carpenter etal. (2013)− 0.161 0.159 − 0.481 − 0.988 0.323
Lin etal. (2015)− 0.143 0.171 − 0.458 − 0.892 0.372
Yetwin etal. (2012)− 0.105 0.755 − 0.966 − 0.240 0.810
Lehrer etal. (2017)− 0.100 0.370 − 0.570 − 0.417 0.677
White (2008)− 0.064 0.579 − 0.707 − 0.194 0.846
Murphy (2009) 0.002 0.412 − 0.408 0.009 0.992
de Bruin etal. (2016) 0.149 0.946 − 0.648 0.366 0.715
Swanson etal. (2009a) 0.173 1.080 − 0.733 0.375 0.708
Applied Psychophysiology and Biofeedback
1 3
Table 3 (continued) Study g95% confidence limit Z p
Upper limit Lower limit
Reineke (2008) 0.196 0.829 − 0.436 0.609 0.543
Meule etal. (2012) 0.221 0.942 − 0.501 0.600 0.549
Gruzelier etal. (2014) 0.226 0.867 − 0.416 0.689 0.491
Alabdulgader (2012) 0.315 0.875 − 0.245 1.102 0.271
Cullins etal. (2013) 0.318 0.915 − 0.278 1.045 0.296
Breach (2013) 0.329 1.472 − 0.814 0.564 0.573
Van Der Zwan etal. (2015) 0.419 0.982 − 0.145 1.457 0.145
Browne (2002) 0.420 1.372 − 0.533 0.864 0.388
In this table each study was entered only once, averaging across post-test and follow-up, outcome meas-
ures, and multiple control groups. The paper by Swanson etal. (2009) contained two studies, labeled 2009a
and b in this table
a This study was an outlier
Table 4 Mixed effects met
analyses on all studies
a One study (Swanson et al. 2009) contained experiments on two different patient populations, so was
included in this analysis as two separate studies. Eleven studies included two control groups (Browne
2002; Carpenter etal. 2013; de Bruin et al. 2016; Lehrer et al. 2004, 2017; May etal. 2018; Meier and
Welch 2016; Paul and Garg 2012; Raymond etal. 2005; Wells etal. 2012). Each comparison was entered
as a separate study, but, for calculating probabilities, the n in the HRVB group was divided by two because
the same group appeared in two calculations of g. One study (Gruzilier etal. 2014) had three comparison
groups, so n for HRVB was divided by three for the three entries
b I2 is the percentage of heterogeneity among studies that represents real heterogeneity rather than error
c Prediction interval is the 95% confidence interval within which results of a new study might be expected
to fall. It was calculated by g ± 2 × tau (the standard deviation of real heterogeneity in g)
Analysis Nag p for g Q p for Q I2b Tau 95% prediction intervalc
Including outliers 72 − 0.37 < 0.0005 135.6 < 0.0005 47.6 0.33 − 1.03 to + 0.29
Excluding outliers 67 − 0.26 < 0.0005 67.8 0.414 2.7 0.06 − 0.38 to − 0.14
Fig. 3 Funnel plot of all studies
-4 -3 -2 -1 0123
4
0.0
0.2
0.4
0.6
0.8
rorrEdradnatS
Hedges's g
Funnel Plot of Standard Error by Hedges's g
Munafo, 2016
Paul, 2012
Lehrer, 2004
Applied Psychophysiology and Biofeedback
1 3
Fig. 4 Funnel plot deleting
three outlying studies
-3 -2 -1 0
123
0.0
0.2
0.4
0.6
0.8
Standard Error
Hedges's g
Funnel Plot of Standard Error by Hedges's g
Table 5 Effects of HRVB and
PB on particular symptoms
Items in this table are not mutually exclusive. Some items appear in more than one category (See Table1).
Only categories with n > 2 are reported. Effect sizes are for targeted and untargeted measures except where
indicated. Measures targeted at the primary outcome condition for studies are reported separately where
n > 2. Values are combined for active and inactive controls
Symptoms With outliers Without outliers
n g p n g p
Emotional regulation 42 − 0.34 < 0.0005 40 − 0.26 < 0.0005
Anger/hostility (all nontargeted) 5 − 0.54 < 0.07 5− 0.54 < 0.07
Anxiety 20 − 0.19 0.11 19 − 0.09 n.s
Targeted anxiety 9 − 0.56 < 0.05 8− 0.24 n.s
Depression 21 − 0.25 < 0.02 21 − 0.25 < 0.02
Targeted depression 5 − 0.72 < 0.0005 5− 0.72 < 0.0005
Stress (perceived) 7 − 0.16 n.s 7 − 0.16 n.s
Targeted stress 2 − 0.02 n.s 2 − 0.02 n.s
Activation/energy/fatigue 6 − 0.29 n.s 6 − 0.29 n.s
Craving/substance problem 4 − 0.45 < 0.007 4− 0.45 < 0.007
PTSD 4 − 0.29 n.s 3 − 0.14 n.s
Physical symptoms, signs 22 − 0.40 < 0.0005 20 − 0.27 < 0.0005
Pain (targeted) 6 − 0.43 < 0.02 6− 0.43 < 0.02
Cardiovascular (targeted) 9 − 0.24 < 0.009 9− 0.24 < 0.009
Diastolic blood pressure 6 − 0.12 n.s 6 − 0.12 n.s
Systolic blood pressure 7 − 0.48 n.s 6 − 0.29 n.s
Respiratory (targeted) 3 − 0.60 < 0.002 2− 0.30 n.s
Gastrointestinal (targeted) 2 − 0.71 < 0.07 2− 0.71 < 0.07
Quality of Life 9 − 0.14 n.s 9 − 0.14 n.s
Physical functioning (targeted) 8 − 0.16 n.s 8 − 0.16 n.s
Athletic/artistic/performance 6 − 0.90 < 0.03 4− 0.25 n.s
Executive/cognitive function 5 − 0.30 < 0.02 5− 0.30 < 0.02
Sleep 6 − 0.17 n.s 6 − 0.17 n.s
Applied Psychophysiology and Biofeedback
1 3
Table 6 Mixed effects contrasts
a Where a study contained more than one contrast, the two values were treated as separate entries, hence the larger n than for number of studies in the meta-analysis and a different number of
studies for each comparison. Similarly, some studies had more than one control group, and each was treated as a separate entry
b Placebos include false biofeedback, paced breathing at baseline rates
Mixed effects contrast Group Including outliers Excluding outliers
ng s.e Z or Q(df) p ng s.e Z or Q(df) p
Control group type Active 19 − 0.18 0.1 Z = 1.78 < 0.08 18 − 0.09 0.08 Z = 1.23 0.22
Inactive 41 − 0.52 0.09 Z = 5.68 < 0.0005 38 − 0.4 0.08 Z = 5.26 < 0.0005
Placebob4− 0.82 0.66 Z = 1.24 0.214 3 − 0.17 28 Z = 0.58 0.56
Breathing 8 − 0.25 0.1 Z = 2.64 < 0.008 8− 0.25 0.1 Z = 2.64 < 0.008
Comparison Placebo vs. inactive Q(1) = 0.19 0.661 Q(1) = 0.84 0.36
Breathing vs. inactive Q(1) = 4.18 < 0.05 Q(1) = 2.04 0.15
Active vs. Inactive Q(1) = 6.12 < 0.02 Q(1) = 3.58 < 0.06
Targeted vs. nontargeted
All studies Targeted 57 − 0.41 0.09 Z = 5.75 < 0.0005 54 − 0.3 0.05 Z = 5.93 < 0.0005
Nontargeted 28 − 0.27 0.07 Z = 3.08 < 0.002 27 − 0.24 0.08 Z = 2.87 < 0.004
Comparison Q(1) = 0.1.55 0.21 Q(1) = 0.40 0.53
Type of outcome measure Behavioral 7 − 0.52 0.19 Z = 2.74 < 0.006 7− 0.52 0.19 Z = 2.74 < 0.006
Physiological 16 − 0.49 0.15 Z = 3.26 < 0.001 14 − 0.25 0.09 Z = 2.72 < 0.007
Self-report 47 − 0.33 0.07 Z = 4.94 < 0.0005 46 − 0.3 0.003 Z = 5.15 < 0.0005
Comparison Q(2) = 1.57 0.5 Q(2) = 1.57 0.46
Applied Psychophysiology and Biofeedback
1 3
a complementary intervention. The effect sizes appear to
be equivalent to those of other established psychological
treatment modalities, although there are not enough studies
to evaluate relative superiority to any particular other treat-
ment. These results suggest that HRVB might be a useful
addition to the skill sets of clinicians working in a variety
of settings, including mental health, behavioral medicine,
sports psychology, and education. The method is easy to
learn and can easily be used along with other forms of inter-
vention, with rare side effects.
Although the effect sizes are magnified by three outlying
studies, they remain significant and small to medium with
these studies removed. When this is done, the heterogeneity
among studies is small and nonsignificant with a narrow
prediction interval, suggesting that the effect size estimate is
stable. Effect sizes tend to be similar for targeted and nontar-
geted measures, suggesting that the method may be as useful
for helping various problems in the normal range as well as
those that generally require special treatment. Between small
and medium-to-large effect sizes were found for a variety
of individual problems, although here too there are insuf-
ficient data for evaluating effects for most specific applica-
tions, due to lack of power. The largest number of studies are
for anxiety and cardiovascular disorders, where the evidence
for a significant although small to medium effect is strong.
Across problems, the effect size appears greater than that of
various placebo interventions or breathing exercises that do
not affect the baroreflex system, which is the pathway that
appears to mediate modulation of emotion. The differences
in effect sizes between HRVB and placebo interventions are
not significant, probably due to lack of power, but it seems
probable that some specific ingredient in HRVB/PB con-
tributes to effectiveness. It is also probable that suggestion
is a component in the overall effect of HRVB, as it is in all
pharmacological as well as nonpharmacological interven-
tions (Petrie and Rief 2019).
It is surprising that response to direct questions about
stress tended to have a low effect size, despite the wide use
of HRVB for treating stress reactions. This may be due to
focus of stress questionnaires on sources rather than symp-
toms of stress. Measures of stress, quality of life, PTSD, and
sleep may reflect impairments that are less directly related to
the RSA-BR systems than are depression, anxiety, and some
symptoms of physical disease.
Limitations andQuestions Raised forFurther
Research
The possible pathways for mediating mechanisms for HRVB
were not covered in this review. In addition to possible
effects of suggestion, attention to breathing has a medita-
tive component and may foster acceptance of various body
sensations and processes, a purported mechanism for effects
of mindfulness training and ‘acceptance and commitment
therapy’ (Gaudiano 2017). Also, explanations to the client
for how biofeedback can help may have a cognitive effect in
decatastrophizing various problems by conveying a notion
that various physiological, behavioral, and emotional events
can be brought under voluntary control (Mizener etal. 1988;
Nanke and Rief 2000; Wilson 2018). Nevertheless, what-
ever the complex mechanisms are for the placebo response
and for other interventions (Brook and Fauver 2014; Jensen
etal. 2015; Levine etal. 2013; Tu etal. 2019), the equiva-
lence to other methods of known effectiveness suggests that
the method has an active effect. Only one study compared
HRVB with mindfulness meditation and found a minimally
greater effect size for meditation g = + 0.137 for treating
stress (de Bruin etal. 2016), and one study compared HRVB
to cognitive behavior therapy and found a nonsignificantly
greater effect size for HRVB g = − 0.038 for treating irritable
bowel syndrome (Thompson 2010). Further research com-
paring HRVB to various specific treatments is warranted,
as well a research on mechanisms by which HRVB has its
effects.
It is possible that mechanisms for HRVB effects may dif-
fer for various applications. Fit may be found to have spe-
cific applications to emotional, cardiovascular, and perhaps
gastrointestinal effects, where autonomic and baroreflex
effects may be involved. More research on blood pressure
effects are needed, particularly since HRVB directly impacts
a blood pressure modulator, the baroreflex. Greater effects
on systolic than diastolic blood pressure would be consistent
with baroreflex action, which more directly affects systolic
blood pressure. Similarly, the zero degree phase relation-
ship between breathing and heart rate oscillations may have
specific effects on respiratory disease, athletic performance,
and perhaps cognitive performance, where gas exchange in
the lung and oxygen perfusion in the muscles and brain may
play a role. Mediator analyses of these effects remain to be
done.
Additionally, this review did not consider the effect size
needed for clinically relevant results. HRVB and PB appear
to have at least a moderate beneficial effect on almost all of
the problems studied, and a small but significant incremental
Table 7 Meta regression analyses
Contrast Standard error 95% 95% Z-value p
Lower Upper
Year of publication 0.02 − 0.01 0.05 1.06 0.29
Weeks between pre
and post test − 0.01 − 0.06 0.03 − 0.65 0.52
Number of treat-
ment
Sessions 0.00 − 0.04 0.04 − 0.10 0.92
Applied Psychophysiology and Biofeedback
1 3
effect, on average, over other established treatments, but the
clinical utility of this effect remains to be evaluated. Even
where benefits or incremental benefits are small, the mini-
mal risk involved in these methods may make them worthy
of use. We have found no studies of effects on mortality or
very severe exacerbations, where even a small effect would
be worthwhile. One study (Lehrer etal. 2004), an outlier, did
find a significant effect in preventing asthma exacerbations
requiring additional medical intervention, and another study
(Reineke 2008) found similar results for hypertension. At
present these interventions appear to be useful as minimal-
risk complementary methods for these and various other
applications.
An interesting implication of our findings is that length
of treatment and home practice does not influence the effect
size. It is possible that very short training periods may
suffice. Perhaps learning how to breathe at resonance fre-
quency provides a sufficient method for most of the benefi-
cial effects, such that it is mostly used when needed. Study
of acute effects and their influence on chronic effects could
clarify this question.
Although the data look very consistent without the three
outliers and the effect sizes did not diminish over the years,
it still is possible that subtle biases may have contributed to
assessments of HRVB effects. Few studies mentioned blind-
ing of data analysts, and double blinding is never possible in
behavioral intervention research, although comparisons with
other credible treatments may have reduced the potential for
experimenter bias, where biases of experimenters may have
gone in either direction. Also, the large amount of effect size
heterogeneity among studies does not provide confidence
that positive effects will be obtained in particular cases or
particular studies.
Additionally, our procedure of averaging across various
outcome measures may obscure some effects because of the
irrelevance or unreliability of some measures, a probable
cause for the heterogeneity of effects between and within
studies. Combining various disparate measures within and
between studies, a hallmark of meta-analysis, may make it
difficult to determine highly pinpointed effects. Neverthe-
less, because financial support for behavioral research does
not reach the level necessary to test thousands of people to
evaluate modest but important effects, meta-analysis, with
all of its flaws, may be the best alternative for evaluating
these effects.
References
Alabdulgader, A. A. (2012). Coherence: A novel nonpharmacological
modality for lowering blood pressure in hypertensive patients.
Global Advances in Health and Medicine, 1(2), 56–64. https ://
doi.org/10.7453/gahmj .2012.1.2.011.
Alayan, N., Eddie, D., Eller, L., Bates, M. E., & Carmody, D. P. (2019).
Substance craving changes in university students receiving heart
rate variability biofeedback: A longitudinal multilevel modeling
approach. Addictive Behaviors, 97(2019), 35–41.
Berry, M. E., Chapple, I. T., Ginsberg, J. P., Gleichauf, K. J., Meyer, J.
A., & Nagpal, M. L. (2014). Non-pharmacological intervention
for chronic pain in veterans: A pilot study of heart rate vari-
ability biofeedback. Global Advances In Health and Medicine,
3(2), 28–33.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009).
Introduction to meta-analysis. West Sussex: Wiley.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2010).
A basic introduction to fixed-effect and random-effects models
for meta-analysis. Research Synthesis Methods, 1(2), 97–111.
https ://doi.org/10.1002/jrsm.12.
Botha, C., Farmer, A. D., Nilsson, M., Brock, C., Gavrila, A. D.,
Drewes, A. M., . . . Aziz, Q. (2015). Preliminary report: modula-
tion of parasympathetic nervous system tone influences oesoph-
ageal pain hypersensitivity. Gut, 64(4), 611–617. https://doi.
org/10.1136/gutjnl-2013-306698
Breach, N. B. (2013). Heart rate variability biofeedback in the treat-
ment of major depression. Dissertation Abstracts International:
Section B: The Sciences and Engineering Dissertation Abstracts
International, 74(4-b(e))
Brook, M. G., & Fauver, R. (2014). A possible mechanism of action for
the placebo response: Human biofield activation via therapeutic
ritual. International Journal of Transpersonal Studies, 33(1),
131–147.
Browne, T. G. (2002). EEG theta enhancement and heart rate variabil-
ity biofeedback and interactional stress in a clinical population.
Applied Psychophysiology and Biofeedback, 27(4), 312.
Caldwell, Y. T., & Steffen, P. R. (2018). Adding HRV biofeedback to
psychotherapy increases heart rate variability and improves the
treatment of major depressive disorder. International Journal of
Psychophysiology, 131, 96–101.
Carpenter, J. S., Burns, D. S., Wu, J., Otte, J. L., Schneider, B., Ryker,
K., … Yu, M. (2013). Paced respiration for vasomotor and other
menopausal symptoms: a randomized, controlled trial. Journal
of General Internal Medicine, 28(2) 193–200. Retrieved from
https ://onlin elibr ary.wiley .com/o/cochr ane/clcen tral/artic les/078/
CN-00859 078/frame .html doi:10.1007/s11606–012–2202–6
Climov, D., Lysy, C., Berteau, S., Dutrannois, J., Dereppe, H., Bro-
het, C., etal. (2014). Biofeedback on heart rate variability in
cardiac rehabilitation: Practical feasibility and psycho-physio-
logical effects. Acta Cardiologica, 69(3), 299–307. https ://doi.
org/10.2143/ac.69.3.30278 33.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences.
Cambridge: Routledge.
Cottier, C., Shapiro, K., & Julius, S. (1984). Treatment of mild hyper-
tension with progressive muscle relaxation. Predictive value
of indexes of sympathetic tone. Archives of Internal Medicine,
144(10), 1954–1958.
Cullins, S. W., Gevirtz, R. N., Poeltler, D. M., Cousins, L. M., Harpin,
R., & Muench, F. (2013). An exploratory analysis of the util-
ity of adding cardiorespiratory biofeedback in the standard care
of pregnancy-induced hypertension. Applied Psychophysiology
and Biofeedback, 38(3), 161–170. https ://doi.org/10.1007/s1048
4-013-9219-4.
Dawood, T., Lambert, E. A., Barton, D. A., & Lambert, G. W. (2008).
Depressive illness: Biological mechanisms of cardiac risk. Stress
and Health: Journal of the International Society for the Investi-
gation of Stress, 24(3), 213–222.
Davydov, D. M., Naliboff, B., Shahabi, L., & Shapiro, D. (2018).
Asymmetries in reciprocal baroreflex mechanisms and chronic
Applied Psychophysiology and Biofeedback
1 3
pain severity: Focusing on irritable bowel syndrome. Neurogas-
troenterology & Motility, 30(2), 02.
Doussard-Roosevelt, J. A., Montgomery, L. A., & Porges, S. W. (2003).
Short-term stability of physiological measures in kindergarten
children: Respiratory sinus arrhythmia, heart period, and cortisol.
Developmental Psychobiology, 43(3), 231–242.
Dziembowska, I., Izdebski, P., Rasmus, A., Brudny, J., Grzelczak,
M., & Cysewski, P. (2016). Effects of heart rate variability
biofeedback on EEG alpha asymmetry and anxiety symptoms
in male athletes: A pilot study. Applied Psychophysiology and
Biofeedback, 41(2), 141–150. https ://doi.org/10.1007/s1048
4-015-9319-4.
Eckberg, D. L., & Sleight, P. (1992). Human baroreflexes in health and
disease. Oxford: Oxford Medical Publications.
Eddie, D., Kim, C., Lehrer, P., Deneke, E., & Bates, M. (2014). A
pilot study of brief heart rate variability biofeedback to reduce
craving in young adult men receiving inpatient treatment for sub-
stance use disorders. Applied Psychophysiology and Biofeedback,
39(3–4), 181–192. https ://doi.org/10.1007/s1048 4-014-9251-z.
de Bruin, E. I., van der Zwan, J. E., & Bögels, S. M. (2016). A RCT
comparing daily mindfulness meditations, biofeedback exercises,
and daily physical exercise on attention control, executive func-
tioning, mindful awareness, self-compassion, and worrying in
stressed young adults. Mindfulness, 7, 1182–1192. https ://doi.
org/10.1007/s1267 1-016-0561-5.
Di Bona, A., Vita, V., Costantini, I., & Zaglia, T. (2019). Towards a
clearer view of sympathetic innervation of cardiac and skeletal
muscles.Progress in Biophysics and Molecular Biology. https ://
doi.org/10.1016/j.pbiom olbio .2019.07.003.
English, E. H., & Baker, T. B. (1983). Relaxation training and cardio-
vascular response to experimental stressors. PsycINFOHealth
Psychology, 2(3), 239–259.
Fuller, J., Wally, C., Westermann-Long, A., Korenfeld, D., & Car-
rell, D. (2011). Resonance frequency measurements are reliable
[Abstract]. Applied Psychophysiology and Biofeedback, 36(3),
219.
Gaudiano, B. A. (2017). Mindfulness: Clinical applications of mind-
fulness and acceptance: Specific interventions for psychiatric,
behavioural, and physical health conditions (Vol. III). New York:
Routledge/Taylor & Francis Group.
Grodins, F. S. (1963). Control theory and biological systems. New
York: Columbia University Press.
Gruzelier, J., Thompson, T., Redding, E., Brandt, R., & Steffert, T.
(2014). Application of alpha/theta neurofeedback and heart rate
variability training to young contemporary dancers: State anxi-
ety and creativity. International Journal of Psychophysiology,
93(1), 105–111. https ://doi.org/10.1016/j.ijpsy cho.2013.05.004.
Haji-Michael, P. G., Vincent, J. L., Degaute, J. P., & van de Borne,
P. (2000). Power spectral analysis of cardiovascular variability
in critically ill neurosurgical patients. Critical Care Medicine,
28(7), 2578–2583.
Hallman, D. M., Olsson, E. M., Scheele, B., Melin, L., & Lyskov, E.
(2011). Effects of heart rate variability biofeedback in subjects
with stress-related chronic neck pain: A pilot study. Applied Psy-
chophysiology and Biofeedback, 36(2), 71–80.
Hammer, P. E., & Saul, J. P. (2005). Resonance in a mathematical
model of baroreflex control: Arterial blood pressure waves
accompanying postural stress. American Journal of Physiol-
ogy Regulatory Integrative & Comparative Physiology, 288(6),
R1637–1648.
Henderson, L. A., Richard, C. A., Macey, P. M., Runquist, M. L., Yu,
P. L., Galons, J. P., etal. (2004). Functional magnetic resonance
signal changes in neural structures to baroreceptor reflex activa-
tion. Journal of Applied Physiology, 96(2), 693–703.
Higgins, J. P. T. (2008). Commentary: Heterogeneity in meta-anal-
ysis should be expected and appropriately quantified. Interna-
tional Journal of Epidemiology, 37(5), 1156–1160. https ://doi.
org/10.1093/ije/dyn20 4.
Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heteroge-
neity in a meta-analysis. Statistics in Medicine. https ://doi.
org/10.1002/sim.1186.
Hunkin, H., King, D. L., & Zajac, I. T. (2019). Wearable devices as
adjuncts in the treatment of anxiety-related symptoms: A narra-
tive review of five device modalities and implications for clinical
practice. Clinical Psychology: Science and Practice, 26, e12296.
Hjelland, I. E., Svebak, S., Berstad, A., Flatabø, G., & Hausken, T.
(2007). Breathing exercises with vagal biofeedback may benefit
patients with functional dyspepsia. Scandinavian Journal of Gas-
troenterology, 42(9), 1054–1062. https ://doi.org/10.1080/00365
52070 12592 08.
Jacobson, E. (1938). Progressive relaxation. Chicago: University of
Chicago press.
Jensen, K. B., Kaptchuk, T. J., Chen, X., Kirsch, I., Ingvar, M., Gollub,
R. L., etal. (2015). A neural mechanism for nonconscious acti-
vation of conditioned placebo and nocebo responses. Cerebral
Cortex, 25(10), 3903–3910.
Joyce, D., & Barrett, M. (2019). State of the science: Heart rate vari-
ability in health and disease. BMJ Supportive & Palliative Care,
9(3), 274–276.
Kaur, K., Sharma, A., Huseby, R., Zhang, Y., Lehrer, P. (2016). Num-
ber of studies on heart rate variability biofeedback and 6 min
breathing. Paper presented at the 23rd Annual Meeting of the
International Society for the Advancement of Respiratory Psy-
chophysiology, Seattle, WA., October 7–9
Kenien, N. (2015). The impact of cardiac coherence on executive func-
tioning in children with emotional disturbances. Global Advances
in Health and Medicine, 4(2), 25–29. https ://doi.org/10.7453/
gahmj .2014.060.
Kristal-Boneh, E., Raifel, M., Froom, P., & Ribak, J. (1995). Heart rate
variability in health and disease. Scandinavian Journal of Work,
Environment & Health, 21(2), 85–95.
Kudo, N., Shinohara, H., & Kodama, H. (2014). Heart rate variability
biofeedback intervention for reduction of psychological stress
during the early postpartum period. Applied Psychophysiology
and Biofeedback, 39(3–4), 203–211. https ://doi.org/10.1007/
s1048 4-014-9259-4.
Larkin, K. T., Knowlton, G. E., & D’Alessandri, R. (1990). Predict-
ing treatment outcome to progressive relaxation training in
essential hypertensive patients. Journal of Behavioral Medi-
cine, 13(6), 605–618.
Lee, J., Kim, J. K., & Wachholtz, A. (2015). The benefit of heart rate
variability biofeedback and relaxation training in reducing trait
anxiety. Hanguk Simni Hakhoe Chi Kongang, 20(2), 391–408.
Lehrer, P. (2013). How does heart rate variability biofeedback work?
Resonance, the baroreflex, and other mechanisms. Biofeed-
back, 41, 26–31.
Lehrer, P. M. (2016). Biofeedback: An important but often-ignored
ingredient in psychotherapy. Policy Insights from the Behav-
ioral and Brain Sciences, 4, 57–63.
Lehrer, P. M. (2018). Heart rate variability biofeedback and other
psychophysiological procedures as important elements in psy-
chotherapy. International Journal of Psychophysiology, 131,
89–95.
Lehrer, P., Carr, R. E., Smetankine, A., Vaschillo, E., Peper, E., Porges,
S., … Hochron, S. (1997). Respiratory sinus arrhythmia versus
neck/trapezius EMG and incentive inspirometry biofeedback for
asthma: a pilot study. Applied Psychophysiology and Biofeed-
back, 22(2), 95–109
Applied Psychophysiology and Biofeedback
1 3
Lehrer, P., & Eddie, D. (2013). Dynamic processes in regulation and
some implications for biofeedback and biobehavioral interven-
tions. Applied Psychophysiology and Biofeedback, 38, 143–155.
Lehrer, P. M., Irvin, C. G., Lu, S.-E., Scardella, A., Roehmheld-Hamm,
B., Aviles-Velez, M., … Wamboldt, F. S. (2017). Heart rate vari-
ability biofeedback does not substitute for asthma steroid con-
troller medication. Applied Psychophysiology and Biofeedback,
43(1), 57–71
Lehrer, P., Vaschillo, E., Lu, S.-E., Eckberg, D., Vaschillo, B., Scard-
ella, A., etal. (2006). Heart rate variability biofeedback: Effects
of age on heart rate variability, baroreflex gain, and asthma.
Chest, 129, 278–284.
Lehrer, P., Vaschillo, E., Trost, Z., & France, C. R. (2009). Effects of
rhythmical muscle tension at 0.1 Hz on cardiovascular resonance
and the baroreflex. Biological Psychology, 81(1), 24–30.
Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S. E., Scardella, A.,
Siddique, M., etal. (2004). Biofeedback treatment for asthma.
Chest, 126(2), 352–361. https ://doi.org/10.1378/chest .126.2.352.
Lehrer, P. M., Vaschillo, E. G., Vaschillo, B., Lu, S.-E., Eckberg, D.
L., Edelberg, R., … Hamer, R. M. (2003). Heart rate variability
biofeedback increases baroreflex gain and peak expiratory flow.
Psychosomatic Medicine, 65(5), 796–805
Levine, J. D., Gordon, N. C., & Fields, H. L. (2013). The mechanism
of placebo analgesia The placebo: A reader (pp. 106–109). Bal-
timore, MD: Johns Hopkins University Press.
Lin, G., Xiang, Q., Fu, X., Wang, S., Wang, S., Chen, S.,… Wang, T.
(2012). Heart rate variability biofeedback decreases blood pres-
sure in prehypertensive subjects by improving autonomic func-
tion and baroreflex. Journal of Alternative and Complementary
Medicine18(2), 143–152
Lin, I. M., Fan, S. Y., Lu, H. C., Lin, T. H., Chu, C. S., Kuo, H. F.,…
Lu, Y. H. (2015). Randomized controlled trial of heart rate vari-
ability biofeedback in cardiac autonomic and hostility among
patients with coronary artery disease. Behaviour Research and
Therapy,70, 38–46
Mather, M. (2019). Can we improve brain mechanisms of emotion regu-
lation by increasing heart rate variability? Paper presented at the
annual meeting of the Society for Psychophysiological Research,
washington, D.C., September 27–29
Mather, M., & Thayer, J. (2018). How heart rate variability affects emo-
tion regulation brain networks. Current Opinion in Behavioral
Sciences, 19, 98–104.
May, R. W., Seibert, G. S., Sanchez-Gonzalez, M. A., & Fincham, F. D.
(2018). Self-regulatory biofeedback training: An intervention to
reduce school burnout and improve cardiac functioning in college
students. Stress. https ://doi.org/10.1080/10253 890.2018.15010
21.
McCraty, R., & Shaffer, F. (2015). Heart rate variability: New perspec-
tives on physiological mechanisms, assessment of self-regulatory
capacity, and health risk. Global Advances in Health & Medicine,
4(1), 46–61.
Meier, N. F., & Welch, A. S. (2016). Walking versus biofeedback:
A comparison of acute interventions for stressed students.
Anxiety, Stress, and Coping, 29(5), 463–478. https ://doi.
org/10.1080/10615 806.2015.10855 14.
Meule, A., Freund, R., Skirde, A. K., Vögele, C., & Kübler, A. (2012).
Heart rate variability biofeedback reduces food cravings in high
food cravers. Applied Psychophysiology and Biofeedback, 37(4),
241–251. https ://doi.org/10.1007/s1048 4-012-9197-y.
Meule, A., & Kubler, A. (2017). A pilot study on the effects of slow
paced breathing on current food craving. Applied Psychophysiol-
ogy and Biofeedback, 42(1), 59–68.
Mitchell, H. J., & Victor, G. R. (1996). Neural control of the cardiovas-
cular system: insights from muscle sympathetic nerve recordings
in humans. Medicine & Science in Sports & Exercise, 28(10),
60–69.
Mizener, D., Thomas, M., & Billings, R. F. (1988). Cognitive changes
of migraineurs receiving biofeedback training. Headache: The
Journal of Head and Face Pain, 28(5), 339–343.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA
Group. (2009). Preferred reporting items for systematic review
and meta-analyses: The PRISMA statement. Journal of Clinical
Epidemiology, 62(10), 1006–1012.
Munafò, M., Patron, E., & Palomba, D. (2016). Improving managers’
psychophysical well-being: Effectiveness of respiratory sinus
arrhythmia biofeedback. Applied Psychophysiology Biofeedback,
41(2), 129–139. https ://doi.org/10.1007/s1048 4-015-9320-y.
Murphy, J. A. W. (2009). Comparison of relaxation techniques for
group cognitive behavioral therapy for generalized anxiety dis-
order. Dissertation Abstracts International: Section B: The Sci-
ences and Engineering, 70(3-B):1952
Nanke, A., & Rief, W. (2000). Biofeedback in the treatment of somato-
form disorders. Verhaltenstherapie, 10(4), 238–248.
Notarius, C. F., Millar, P. J., & Floras, J. S. (2015). Muscle sympathetic
activity in resting and exercising humans with and without heart
failure. Applied Physiology, Nutrition, and Metabolism, 40(11),
1107–1115.
Ogata, K. (2004). System dynamics. London: Pearson.
Patriquin, M. A., Hartwig, E. M., Friedman, B. H., Porges, S. W., &
Scarpa, A. (2019). Autonomic response in autism spectrum dis-
order: Relationship to social and cognitive functioning. Biologi-
cal Psychology, 145, 185–197.
Patron, E., Benvenuti, S. M., Favretto, G., Valfre, C., Bonfa, C., Gasp-
arotto, R., etal. (2013). Biofeedback assisted control of respira-
tory sinus arrhythmia as a biobehavioral intervention for depres-
sive symptoms in patients after cardiac surgery: A preliminary
study. Applied Psychophysiology and Biofeedback, 38(1), 1–9.
Paul, M., & Garg, K. (2012). The effect of heart rate variability bio-
feedback on performance psychology of basketball players.
Applied Psychophysiology and Biofeedback, 37(2), 131–144.
https ://doi.org/10.1007/s1048 4-012-9185-2.
Peckerman, A., LaManca, J. J., Qureishi, B., Dahl, K. A., Golfetti, R.,
Yamamoto, Y., etal. (2003). Baroreceptor reflex and integrative
stress responses in chronic fatigue syndrome. Psychosomatic
Medicine., 65(5), 889–895.
Penzlin, A. I., Siepmann, T., Illigens, B. M. W., Weidner, K., & Siep-
mann, M. (2015). Heart rate variability biofeedback in patients
with alcohol dependence: A randomized controlled study. Neu-
ropsychiatric Disease and Treatment, 11, 2619–2627. https ://doi.
org/10.2147/ndt.s8479 8.
Perna, G., Riva, A., Defillo, A., Sangiorgio, E., Nobile, M., & Caldi-
rola, D. (2019). Heart rate variability: Can it serve as a marker
of mental health resilience? In Nobile, M. (ed.) Special section
on translational and neuroscience studies in affective disorders.
Journal of Affective Disorders, 249, 553.
Petrie, K. J., & Rief, W. (2019). Psychobiological mechanisms of pla-
cebo and nocebo effects: Pathways to improve treatments and
reduce side effects. Annual Review of Psychology, 70, 599–625.
https ://doi.org/10.1146/annur e.
Porges, S. W., Doussard-Roosevelt, J. A., Portales, A. L., & Greenspan,
S. I. (1996). Infant regulation of the vagal "brake" predicts child
behavior problems: A psychobiological model of social behavior.
Developmental Psychobiology., 29(8), 697–712.
Porges, S. W., & Furman, S. A. (2011). The early development of the
autonomic nervous system provides a neural platform for social
behavior: A polyvagal perspective. Infant & Child Development,
20(1), 106–118.
Porges, S. W., Macellaio, M., Stanfill, S. D., McCue, K., Lewis, G.
F., Harden, E. R., etal. (2013). Respiratory sinus arrhythmia
and auditory processing in autism: Modifiable deficits of an
integrated social engagement system? International Journal of
Psychophysiology., 88(3), 261–270.
Applied Psychophysiology and Biofeedback
1 3
Prinsloo, G. E., Derman, W. E., Lambert, M. I., & Rauch, H. G. L.
(2013). The effect of a single episode of short duration heart
rate variability biofeedback on measures of anxiety and relaxa-
tion states. International Journal of Stress Management, 20(4),
391–411.
Raymond, J., Sajid, I., Parkinson, L. A., & Gruzelier, J. H. (2005).
Biofeedback and dance performance: a preliminary investiga-
tion. Applied Psychophysiology and Biofeedback, 30(1), 64–73.
Reineke, A. (2008). The effects of heart rate variability biofeedback in
reducing blood pressure for the treatment of essential hyperten-
sion. Dissertation Abstracts International: Section B: The Sci-
ences and Engineering, 68(7-B), 4880
Rene, R. (2008). The efficacy of a portable HRV feedback device in
conjunction with mental health treatment of clients with major
depressive disorder enrolled in a county welfare-to-work pro-
gram. Dissertation Abstracts International: Section B: The Sci-
ences and Engineering, 69(3-B) 2000
Ringwood, J. V., & Malpas, S. C. (2001). Slow oscillations in blood
pressure via a nonlinear feedback model. American Journal of
Physiology: Regulatory, Integrative and Comparative Physiol-
ogy, 280(4), R1105–R1115.
Rogers, R. F., Rybak, I. A., & Schwaber, J. S. (2000). Computational
modeling of the baroreflex arc: Nucleus tractus solitarius. Brain
Research Bulletin, 51(2), 139–150.
Sakaki, M., Yoo, H. J., Nga, L., Lee, T.-H., Thayer, J. F., & Mather,
M. (2016). Heart rate variability is associated with amygdala
functional connectivity with MPFC across younger and older
adults. Neuroimage, 139, 11–52. https ://doi.org/10.1016/j.neuro
image .2016.05.076.
Schuman, D., & Killian, M. O. (2019). Pilot study of a single session
heart rate variability biofeedback intervention on veterans’ post-
traumatic stress sympto. Applied Psychophysiology and Biofeed-
back, 44, 9–20.
Sessa, F., Anna, V., Messina, G., Cibelli, G., Monda, V., Marsala, G.,
etal. (2018). Heart rate variability as predictive factor for sudden
cardiac death. Aging, 10(2), 166–177.
Shoemaker, J. K., & Goswami, R. (2015). Forebrain neurocircuitry
associated with human reflex cardiovascular control. Frontiers
in Physiology, 6, 240.
Siepmann, M., Hennig, U.-D., Siepmann, T., Nitzsche, K., Muck-Wey-
mann, M., Petrowski, K., etal. (2014). The effects of heart rate
variability biofeedback in patients with preterm labour. Applied
Psychophysiology and Biofeedback, 39(1), 27–35. https ://doi.
org/10.1007/s1048 4-013-9238-1.
Soer, R., Vos, D., Hofstra, B., & Reneman, M. F. (2014). Heart
coherence training combined with back school in patients with
chronic non-specific low back pain: First pragmatic clinical
results. Applied Psychophysiology and Biofeedback, 39(3–4),
259–267. https ://doi.org/10.1007/s1048 4-014-9260-y.
Strack, B., Gevirtz, R., & Sime, W. (2004). Effect of heart rate vari-
ability (HRV) biofeedback on batting performance in baseball.
Applied Psychophysiology and Biofeedback, 29(4), 299–299.
Sutarto, A. P., Wahab, M. N., & Zin, N. M. (2012). Resonant breath-
ing biofeedback training for stress reduction among manufac-
turing operators. International Journal of Occupational and
Safety Ergonomics, 18(4), 549–561.
Sutarto, A. P., Wahab, M. N., & Zin, N. M. (2013). Effect of bio-
feedback training on operator’s cognitive performance. Work,
44(2), 231–243. https ://doi.org/10.3233/wor-12149 9.
Suzuki, M., Nakamura, T., Hirayama, M., Ueda, M., Katsuno, M.,
& Sobue, G. (2017). Cardiac parasympathetic dysfunction in
the early phase of Parkinson’s disease. Journal of Neurology,
264(2), 333–340.
Swanson, K. S., Gevirtz, R. N., Brown, M., Spira, J., Guarneri, E.,
& Stoletniy, L. (2009). The effect of biofeedback on function
in patients with heart failure. Applied Psychophysiology and
Biofeedback, 34(2), 71–91. https ://doi.org/10.1007/s1048
4-009-9077-2.
Tan, G., Dao, T. K., Farmer, L., Sutherland, R. J., & Gevirtz, R.
(2011). Heart rate variability (HRV) and posttraumatic stress
disorder (PTSD): A pilot study. Applied Psychophysiology
and Biofeedback, 36(1), 27–35. https ://doi.org/10.1007/s1048
4-010-9141-y.
Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D.
(2012). A meta-analysis of heart rate variability and neuroimag-
ing studies: Implications for heart rate variability as a marker
of stress and health. Neurosciences and Biobehavioral Reviews,
36(2), 747–756.
Thompson, M. (2010). Heart rate variability biofeedback therapy ver-
sus cognitive therapy for irritable bowel syndrome: A study of
attendance, compliance, and symptom improvement. Disserta-
tion Abstracts International: Section B: The Sciences and Engi-
neering, 71(3-B),2087.
Thurber, M. R. (2007). Heart-rate variability biofeedback for music
performance anxiety. Applied Psychophysiology and Biofeed-
back, 32(2), 127–128.
Tsai, H. J., Kuo, T. B., Lee, G. S., & Yang, C. C. (2015). Efficacy
of paced breathing for insomnia: Enhances vagal activity and
improves sleep quality. Psychophysiology, 52(3), 388–396. https
://doi.org/10.1111/psyp.12333 .
Tu, Y., Park, J., Ahlfors, S. P., Khan, S., Egorova, N., Lang, C.,…
Kong, J. (2019). A neural mechanism of direct and observational
conditioning for placebo and nocebo responses. NeuroImage,
184, 954–963
van de Vooren, H., Gademan, M. G., Swenne, C. A., TenVoorde, B.
J., Schalij, M. J., & Van der Wall, E. E. (2007). Baroreflex sen-
sitivity, blood pressure buffering, and resonance: What are the
links? Computer simulation of healthy subjects and heart failure
patients. Journal of Applied Physiology, 102(4), 1348–1356.
van der Zwan, J. E., de Vente, W., Huizink, A. C., Bogels, S. M., &
de Bruin, E. I. (2015). Physical activity, mindfulness medita-
tion, or heart rate variability biofeedback for stress reduction:
A randomized controlled trial. Applied Psychophysiology and
Biofeedback, 40(4), 257–268. https ://doi.org/10.1007/s1048
4-015-9293-x.
Vaschillo, E. G., Bates, M. E., Vaschillo, B., Lehrer, P., Udo, T., Mun,
E. Y., etal. (2008). Heart rate variability response to alcohol,
placebo, and emotional picture cue challenges: Effects of 0.1 Hz
stimulation. Psychophysiology, 45, 847–858.
Vaschillo, E., Lehrer, P., Rishe, N., & Konstantinov, M. (2002). Heart
rate variability biofeedback as a method for assessing baroreflex
function: A preliminary study of resonance in the cardiovascular
system. Applied Psychophysiology & Biofeedback, 27(1), 1–27.
Vaschillo, E. G., Vaschillo, B., Buckman, J., & Bates, M. (2019). A
new approach to brain stimulation. Paper presented at the Third
International Brain Stimulation Conference, Vancouver, B.C.,
February 24–27
Vaschillo, E. G., Vaschillo, B., & Lehrer, P. M. (2004). Heartbeat syn-
chronizes with respiratory rhythm only under specific circum-
stances. Chest, 126(4), 1385–1386.
Vaschillo, E. G., Vaschillo, B., & Lehrer, P. M. (2006). Characteristics
of resonance in heart rate variability stimulated by biofeedback.
Applied Psychophysiology & Biofeedback, 31(2), 129–142.
Vasudev, A., O’Brien, J. T., Tan, M. P., Parry, S. W., & Thomas, A. J.
(2011). A study of orthostatic hypotension, heart rate variability
and baroreflex sensitivity in late-life depression. Journal of Affec-
tive Disorders, 131(1–3), 374–378.
Wells, R., Outhred, T., Heathers, J. A., Quintana, D. S., & Kemp, A.
H. (2012). Matter over mind: A randomised-controlled trial of
single-session biofeedback training on performance anxiety and
heart rate variability in musicians. PLoS ONE, 7(10), e46597.
https ://doi.org/10.1371/journ al.pone.00465 97.
Applied Psychophysiology and Biofeedback
1 3
Wheat, A. L. (2014). Effectiveness of biofeedback in improving heart
rate variability recovery following stress. Dissertation Abstracts
International: Section B: The Sciences and Engineering, 74(7-
B(E)), No Pagination Specified
Wheeler, L. F. (2018). Stress and psychotherapy outcome: Imple-
mentation of a heart rate variability biofeedback intervention to
improve psychotherapy outcome. Dissertation Abstracts Inter-
national: Section B: The Sciences and Engineering, 79 (3-B(E))
White, B. (2008). The effects of heart rate variability biofeedback as
an adjunct to therapy on trauma symptoms. (3316785 Ph.D.),
Alliant International University, San Diego, Ann Arbor.
Retrieved February 1, 2020, from https ://searc h.proqu est.com/
docvi ew/30483 4915?accou ntid=13626 https ://links ource .ebsco
.com/linki ng.aspx?sid=Proqu est+Disse rtati ons+%26+These
s+A%26I&fmt=disse rtati on&genre =disse rtati ons+%26+these
s&issn=&volum e=&issue =&date=2008-01-01&spage
=&title =The+effec ts+of+heart +rate+varia bilit y+biofe edbac
k+as+an+adjun ct+to+thera py+on+traum a+sympt oms&atitl
e=&au=White %2C+Brook e&isbn=97805 49651 642&jtitl
e=&btitl e=&id
Wilson, A. M. (2018). Heart rate variability biofeedback training as
an intervention for chronic pain. Dissertation Abstracts Inter-
national: Section B: The Sciences and Engineering, 79, 1-B(E)
Windthorst, P., Mazurak, N., Kuske, M., Hipp, A., Giel, K. E., Enck,
P.,… Teufel, M. (2017). Heart rate variability biofeedback
therapy and graded exercise training in management of chronic
fatigue syndrome: An exploratory pilot study. Journal of Psycho-
somatic Research, 93, 6+13
Yasuma, F., & Hayano, J.-I. (2004). Respiratory sinus arrhythmia: Why
does the heartbeat synchronize with respiratory rhythm? Chest,
125(2), 683–690.
Yetwin, A., Marks, K., Bell, T., & Gold, J. (2012). Heart rate variability
biofeedback therapy for children and adolescents with chronic
pain [abstract]. Journal of pain [abstracts of the 31st annual
scientific meeting of the american pain society. 2012 may 16–19;
honolulu, HI united states]. Retrieved February 1, 2020, from
https ://onlin elibr ary.wiley .com/o/cochr ane/clcen tral/artic les/809/
CN-00851 809/frame .html
Yoo, H. J., Thayer, J. F., Greening, S., Lee4, T.-H., Ponzio, A., Min,
L.,… Koenig, J. (2018). Brain structural concomitants of rest-
ing state heart rate variability in the young and old: Evidence
from two independent samples. Brain Structure and Function,
223, 727-737
Young, H. A., & Benton, D. (2018). Heart-rate variability: A biomarker
to study the influence of nutrition on physiological and psycho-
logical health? Behavioral Pharmacology, 29, 140–151. https ://
doi.org/10.1097/FBP.00000 00000 00038 3.
Yu, L.-C., Lin, M., Fan, S.-Y., Chien, C.-L., & Lin, T.-H. (2018). One-
year cardiovascular prognosis of the randomized, controlled,
short-term heart rate variability biofeedback among patients
with coronary artery disease. International Journal of Behavio-
ral Medicine, 25, 271–282.
Zucker, T. L., Samuelson, K. W., Muench, F., Greenberg, M. A., &
Gevirtz, R. N. (2009). The effects of respiratory sinus arrhyth-
mia biofeedback on heart rate variability and posttraumatic stress
disorder symptoms: A pilot study. Applied Psychophysiology and
Biofeedback, 34(2), 135–143. https ://doi.org/10.1007/s1048
4-009-9085-2.
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