THE JOURNAL OF ALTERNATIVE AND COMPLEMENTARY MEDICINE
Volume 16, Number 10, 2010, pp. 1039–1045
ª Mary Ann Liebert, Inc.
DOI: 10.1089/acm.2009.0577
Effect of Slow Abdominal Breathing Combined
with Biofeedback on Blood Pressure
and Heart Rate Variability in Prehypertension
Shu-Zhen Wang, MS,1 Sha Li, MM,1 Xiao-Yang Xu, PhD,1,2 Gui-Ping Lin, MM,1 Li Shao, MM,1
Yan Zhao, MM,1 and Ting Huai Wang, MD1
Abstract
Objective: Prehypertension is a new category designated by the Seventh Report of the Joint National Committee
on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure ( JNC7) in 2003. Managing prehypertension with nonpharmacological intervention is possibly beneficial to the prevention of hypertension. In
this study, we observed the effect of slow abdominal breathing combined with electromyographic (EMG)
biofeedback training on blood pressure (BP) in prehypertensives and assessed the changes of heart rate variability (HRV) in order to find an optional intervention to prevent hypertension and acquire some experimental
data to clarify the underlying neural mechanism.
Methods: Twenty-two (22) postmenopausal women with prehypertension were randomly assigned to either the
experiment group or the control group. The experiment group performed 10 sessions of slow abdominal
breathing (six cycles/min) combined with frontal electromyographic (EMG) biofeedback training and daily home
practice, while the control group only performed slow abdominal breathing and daily home practice. BP and HRV
(including R–R interval and standard deviation of the normal–normal intervals [SDNN]) were measured.
Results: Participants with prehypertension could lower their systolic blood pressure (SBP) 8.4 mm Hg ( p < 0.001)
and diastolic blood pressure (DBP) 3.9 mm Hg ( p < 0.05) using slow abdominal breathing combined with EMG
biofeedback. The slow abdominal breathing also significantly decreased the SBP 4.3 mm Hg ( p < 0.05), while
it had no effect on the DBP ( p > 0.05). Repeated-measures analyses showed that the biofeedback group þ
abdominal respiratory group (ABþBF) training was more effective in lowering the BP than the slow breathing
( p < 0.05). Compared with the control group, the R–R interval increased significantly during the training in the
ABþBF group ( p < 0.05). The SDNN increased remarkably in both groups during the training ( p < 0.05).
Conclusions: Slow abdominal breathing combined with EMG biofeedback is an effective intervention to manage
prehypertension. The possible mechanism is that slow abdominal breathing combined with EMG biofeedback
could reduce sympathetic activity and meanwhile could enhance vagal activity.
Introduction
P
rehypertension is a new category defined by the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood
Pressure ( JNC7) in 2003.1
Some reports demonstrated that prehypertensives have a
greater risk of cardiovascular events than normotensives.2–4
Early intervention significantly prevents or delays progression to hypertension or to other cardiovascular events.
Lifestyle changes have been recommended for most pre-
hypertensives by the JNC7, but there is limited evidence for
its effectiveness.5 This may be related to the diversity of
factors affecting blood pressure (BP), among which stress
exerts the most important influence.6–8 Therefore, relaxation
trainings, such as autogenic training, progressive muscle
relaxation, visualization and breathing exercises, qigong, and
yoga, can be used for high BP intervention and have
achieved some positive results.9
Clinical trials documents reveal that slow abdominal
breathing reduces BP,10–12 but there are also contradictory
reports.13 This may be partly because abdominal breathing
1
Department of Physiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China.
Department of Physiology, Guangzhou Medical College, Guangzhou, Guangdong, People’s Republic of China.
2
1039
1040
lacks homologous physiologic feedback; therefore, the
trainees cannot make accurate adjustments based on visualized signals of their physiologic feedback. This shortcoming may be corrected through biofeedback-assisted
relaxation training, which is commonly used for relaxation
training. With real-time visualization techniques, trainees
can monitor changes in their physiologic signals instantly,
which improves training skills and effectiveness. At present, biofeedback-assisted relaxation training has achieved
significant efficacy in the treatment of hypertension.14–18 A
meta-analysis carried out by Nakao indicated that biofeedback intervention decreased systolic (SBP) and diastolic
blood pressures (DBP) more than nonintervention controls;
however, only the relaxation-assisted biofeedback and not
the simple biofeedback significantly decreased both SBP and
DBP compared with sham or nonspecific behavioral interventions.14 Kaushik et al.19 found that biofeedback-assisted
diaphragmatic breathing and systematic relaxation could
prevent the starting event of vasoconstriction and counter
the stress-mediated response in migraine by leading to
parasympathetic stimulation and decrease in sympathetic overactivity. However, we find no clinical trials documenting biofeedback combined with slow abdominal
breathing to prevent or delay progress to hypertension in
prehypertensives.
Based on these evidences, we assume that abdominal
breathing combined with electromyography (EMG) biofeedback-assisted relaxation training could exert positive
effects in reducing BP. We approach this problem by
observing the changes of BP during abdominal breathing combined with electromyographic (EMG) biofeedback
training in prehypertensives so as to provide experimental
evidence for early intervention of prehypertension. Meanwhile, we analyzed the influence of abdominal breathing
combined with EMG biofeedback on heart rate variability
(HRV),20,21 a noninvasive indicator used to reflect cardiac
autonomic nervous function, in order to explore the underlying neural mechanisms.
Materials and Methods
Participants
Twenty-six (26) postmenopausal women (right handed,
ages 45–60 years, and averaged 52.55 3.81 years) with
prehypertension from two communities of Guangzhou
enrolled in this study. They were randomly assigned to
two groups: experimental group (n ¼ 13, biofeedback
group þ abdominal respiratory group, AB þ BF) and control
group (n ¼ 13, simple abdominal breathing group). During
the experiment, 4 patients (1 from the AB þ BF group and 3
from the control group) dropped out because of conflicting
schedules. Twenty-two (22) participants completed this
study. All participants stopped using any drugs during the
month prior to initiation of the experiment. Exclusion criteria
were cardiovascular and cerebrovascular diseases, respiratory diseases, autoimmune diseases, diabetes, neuropathy,
and other autonomic neuropathies.
This study was performed in the Laboratory of Biofeedback, Department of Physiology, Zhongshan School of Medicine, Sun Yat-sen University, People’s Republic of China. The
protocol was approved by the local ethics committee. Informed written consents were obtained from all participants.
WANG ET AL.
Procedure
Before the experiment began, all participants received
slow abdominal breathing training to grasp the essential
skills. The participants were asked to lie supine with an inductive belt ( JD/PW-5; Boda Electron Co., Beijing, China)
connecting their abdomens to a personal computer (Lenovo,
M4600 P3.0HT 25640VN), and raised their hands to the abdomen and chest to feel each breathing pattern: abdominal
or thoracic breathing. Then, they were instructed to apply
abdominal breathing, and subsequently, they were instructed to reduce their respiratory rate to six times per
minute gradually while increasing respiratory amplitude.
The amplitude and frequency of abdominal movement could
be instantly viewed from a screen in front of the participants
in order to guide their adjustments in breathing patterns and
frequencies (respiration biofeedback). After participants
mastered the slow abdominal breathing essentials, the experiment could proceed to the next section.
Both groups of participants received 10 sessions of treatment once every 3 days. Each session lasted 25 minutes; the
initial 5 minutes of the session were set aside for baseline
recording and the remaining 20 minutes for treatment. An
electrocardiogram (ECG) was recorded simultaneously. The
participants were asked to refrain from alcohol, caffeine, tea,
and spicy food on the day before each appointment. All
participants were instructed to rest for 20 minutes to adapt to
the laboratory before each session. The treatment was conducted at the same time of day in a quiet laboratory, with
controlled air temperature (248C–268C) and relative humidity (60%–70%).
The subjects in the ABþBF group were treated by coupling the slow abdominal breathing with the frontal EMG
biofeedback-assisted relaxation training. The relaxation
techniques were similar to autogenic training and guided
imagery, including relaxing the frontal muscle, mimetic
muscles, masseter, shoulders, and limbs gradually, imagining the arms and legs limp and heavy, and imagining the
hands and feet getting warmer and warmer.21 During the
ABþBF training, participants were instructed to lie in a
comfortable bed, and three electrodes (one reference electrode and two recording electrodes) were fixed over the
frontal muscle and connected to a biofeedback machine ( JD/
PW-5; Boda Electron Co.). The participants were then instructed to take the abdominal breathing described above
(six cycles/min, with respiration biofeedback) and to attempt
to relax with the relaxation techniques to lower the amplitude of their frontal EMG signal (feedback signal), which was
displayed in the monitor of the biofeedback machine 1 m
above their eyes. The frontal EMG signal was displayed as a
yellow cursor that moved rightward when the amplitude of
the frontal EMG signal increased, and vice versa. The target
level was set 5% below the baseline value of the EMG signal.
When the EMG signal decreased below the target level, the
color of the cursor would change to green. The participants
were encouraged to relax with the relaxation skills and make
the yellow cursor move leftward and change to green. If the
EMG signal decreased below the target level and lasted for
2 minutes, a new target level was reset automatically 2%
below the prior target by the biofeedback machine. During
the interval between each session, the participants in the
ABþBF group were asked to perform the same procedure
BREATHING EFFECT ON BP, HRV IN PREHYPERTENSION
(without biofeedback) twice every day at home, with each
period lasting 20 minutes.
Participants in the control group underwent the same
protocol as the ABþBF group with the exception of the
relaxation-assisted biofeedback treatment. Specifically, participants in the control group were simply instructed to
perform the slow abdominal breathing (six cycles/min, with
respiration biofeedback) during each session, while participants in the biofeedback group were instructed to perform
the slow abdominal breathing and the EMG biofeedback
with relaxation techniques. During the interval between
each session, the participants in the control group were
asked to perform the slow abdominal breathing (six cycles/
min, counted by themselves without respiration biofeedback) twice every day at home, with each period lasting 20
minutes.
BP was measured before and after each session by the
same research assistant using a mercury sphygmomanometer according to the standard guidelines.1 At least three
measurements were made. The mean of the last two stable
readings was recorded. The BP measured at the beginning of
the first session was regarded as the pretreatment value, and
the BP measured at the beginning of the 10th session was
regarded as the post-treatment value. One (1) and 3 months
after the study was completed, the BP of the participants was
measured and designated as the follow-up value.
The ECG was recorded simultaneously during each session to evaluate the HRV. The ECG (lead II) signal, amplified
by a preamplifier (Cl-810232, Gould Inc., OH) and processed
via a 12-bit analog-to-digital converter (BL-420, Taimeng
Technology Corp., Chengdu, China), was recorded by a
computer with a sampling rate of 1 kHz. The ECG recorded
at the initial 5 minutes in the first session was regarded as the
baseline for pretreatment (prebaseline data). The ECG recorded during the initial 5 minutes of the 10th session was
regarded as the baseline for post-treatment (postbaseline
data), and the recordings during the last 5 minutes in the
10th session were collected as the treatment data for posttreatment (training data).
HRV analysis
All ECG data were carefully checked through visual
screening. Artifacts were edited following the user’s manual
for the BL-420. No beats were manually deleted so as to
avoid bias. Since the HRV analysis software could only
process the R-R interval text file, the primary ECG data
were transformed and saved as an R–R interval text file
using software edited with MATLAB software platform,
version 7.1 (The MathWorks Inc., Natick, MA). In the editing process, the peaks of the R-wave were automatically
labeled and each R–R interval was calculated by the software. If the EEG baseline shifted, the R–R interval may not
be accurately calculated as the peaks of the R waves could
not be labeled at the precise position and hence should be
adjusted manually.
The HRV analysis software (HRV Analysis Software,
version 1.1 SP1) designed and donated by Biomedical Signal
Analysis Group (Department of Physics, University of Kuopio, Finland) was utilized to assess several measures of HRV,
including R-R interval and SDNN [standard deviation of the
normal–normal intervals].
1041
Statistical analyses
Data were analyzed using SPSS, version 12.0 (SPSS Inc.,
Chicago, IL). Results are presented as mean standard deviation. A p value <0.05 was considered significant.
Independent samples t test was applied to examine the
baseline demographic characteristics of participants. Paired t
test and repeated-measures analyses of variance were applied to examine the changes of BP. In the repeated-measures
analyses of variance, the within-subjects factor was time (BP
measured at different times), and the between-subjects factor
was treatment (ABþBF and control). The Greenhouse-Geisser
correction was used for the analyses to correct type I error
created by the violation of the sphericity assumption. Paired
t test and independent samples t test was used to assess the
changes of HRV during the experiment.
Results
The characteristics of participants
Table 1 shows the baseline demographic characteristics of
22 participants allocated to two different groups. There was
no significant difference between the two groups.
BP changes after the experiment
Paired t test was applied to assess the BP variance after
10 sessions of training. With the progression of training, the
SBP and DBP of the participants in both groups decreased
gradually. After 10 sessions of training, the ABþBF training
significantly decreased the SBP by 8.4 mm Hg (from
133.58 4.46 to 125.17 5.54 mm Hg, p < 0.001) and the
DBP by 3.9 mm Hg (from 81.92 4.83 to 78.00 3.19 mm
Hg, p < 0.05). At the follow-up of 1 and 3 months after the
experiment, the SBP was also kept at a low level (125.25 4.39 and 125.08 4.85 mm Hg, compared with the baseline,
p < 0.001, respectively), but the DBP changed differently
(78.67 2.87 and 79.17 2.41 mm Hg, compared with the
baseline, p < 0.05 and p > 0.05, respectively). The slow abdominal breathing also significantly decreased the SBP
4.3 mm Hg (from 133.90 4.56 to 129.60 4.28 mm Hg,
p < 0.05) after 10 sessions of training. However, it had no
remarkable effect on the SBP at the follow-up of 1 and
3 months. The slow abdominal breathing had no effect on
the DBP either after 10 sessions of training or during the
follow-up.
Repeated-measures analyses of variance were applied to
assess the different influence of the two trainings (abdominal
breathing plus EMG biofeedback versus abdominal breathing) on the BP. The results showed that there were remarkable main effects of treatment and time on SBP; the
treatmenttime interaction for SBP was also significant
(Table 2). The results of the repeated-measures analyses of
SBP indicated that ABþBF training was more effective in
lowering the SBP than slow breathing alone. Although neither main effect of time on DBP nor treatmenttime interaction for DBP was found, the main effect of treatment on
DBP was significant (Table 2). Under this situation, evaluating the difference between the two groups was based on
the main effect of treatment. Therefore, this result also indicated ABþBF training was also more effective in lowering
the DBP than slow breathing alone.
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WANG ET AL.
Table 1. Baseline Demographic Characteristics
of 22 Participants
Characteristics
ABþBF group Control group
(n ¼ 12)
(n ¼ 10)
t
p
Age (y)
51.75 3.49 53.51 4.14 1.076 0.295
Menopause (y)
4.58 2.91
4.95 3.25 0.279 0.783
Education year
10.83 3.12 10.62 3.68 0.161 0.874
(y)
Height (cm)
158.13 5.06 158.29 4.29 0.143 0.888
Weight (kg)
59.42 6.96 61.13 10.88 0.444 0.662
BMI (kg/m2)
23.90 3.56 24.47 4.93 0.312 0.758
Blood pressure
SBP (mm Hg) 133.58 4.46 133.94 4.56 0.164 0.879
DBP (mm Hg) 81.92 4.83 82.23 4.85 0.137 0.893
TABP score
TH
12.42 2.75 12.74 2.95 0.233 0.818
CH
12.08 2.97 12.31 2.31 0.188 0.853
THþCH
24.42 4.66 25.01 4.37 0.301 0.767
Values are expressed as mean standard deviation; ABþBF,
abdominal breathing combined with biofeedback; BMI, body–mass
index; SBP, systolic blood pressure; DBP, diastolic blood pressure;
TABP, type A behavior pattern scale; TH, time hurry; CH, competitive and hostility.
HRV changes during the training
The R-R interval baseline of the participants in both groups
did not change remarkably after the experiment, but the R-R
interval increased significantly during the training in the
ABþBF group ( p < 0.01, Fig. 1). And, during the training, the
difference of R-R interval between the two groups is remarkable ( p < 0.05, Figure 1). No remarkable change was
found in the SDNN baseline of both groups after the experiment. However, during the training, it increased remarkably
in the ABþBF group ( p < 0.01, Fig. 2); it also increased significantly in the control group ( p < 0.05, Fig. 2). No remarkable difference was found between the control group and the
ABþBF group during the training ( p > 0.05, Fig. 2).
Discussion
According to the present diagnostic criteria of hypertension, prehypertension is regarded as a normal BP that need
not be treated. However, the increasing of BP and the increasing of cardiovascular disease is a slowly evolving process. An increasing body of research has shown that those
with prehypertension are at a higher risk for hypertension22
and cardiovascular disease.2,3 Compared with the normotensive individual (office BP <120/80 mm Hg), the common
carotid artery intima-media thickness and left ventricular
mass of prehypertension are significantly higher, which
shows that prehypertension status is cross-sectionally associated with subclinical atherosclerosis and target-organ damage.4 Therefore, it is essential to find a nonpharmacological
way to treat prehypertension.
In general, biofeedback has been more successful in the
treatment of hypertension when respiratory therapies have
been a component of the biofeedback.23 McGrady24 has established that certain types of patients with hypertension
with a high degree of sympathetic arousal fare better with
biofeedback than others. We found that ABþBF could lower
the SBP by 8.4 mm Hg and DBP by 3.9 mm Hg on prehypertension. This BP-lowering effect could last 3 months at
least. This was similar to the effects on BP by simple
breathing training10–12 and by simple biofeedback training.16,25 However, results of the self-matching comparison
showed that slow abdominal breathing relaxation only had a
relative long-term effect on the systolic pressure, but it had
no remarkable effect on the SBP in the follow-up, and it has
no significant effect on the diastolic pressure, which is pertinent with the results observed by Altena et al.13 but not
consistent with all other studies.10–12
The mechanism by which biofeedback and slow abdominal breathing regulates the BP is still unclear. As is known,
the autonomic nervous system is responsible for the regulation of BP. In this study, we applied HRV analyses that
reflect cardiac autonomic nervous function to explore the
potential mechanism underlying the BP changes.
We found that slow abdominal breathing at six cycles/
min had no effect on R-R interval (Fig. 1) while it increased
SDNN (R-R interval fluctuations, Fig. 2). This is consistent
with the results reported by Joseph et al.26 and Bernardi
et al.27 The increase of R-R interval fluctuations has some
effects of improving arterial baroreflex sensitivity,26,28 reducing sympathetic activity,29,30 and reducing chemoreflex
sensitivity (deriving from the activation of the Hering-Breuer
Table 2. Changes of Blood Pressure Assessed with Repeated-Measures Analyses of Variance
Observation items
SBP (mm Hg)
ABþBF group (n ¼ 12)
Control group (n ¼ 10)
Treatment
Time
Treatmenttime
DBP (mm Hg)
ABþBF group (n ¼ 12)
Control group (n ¼ 10)
Treatment
Time
Treatmenttime
Pretreatment
Post-treatment
Follow-up (1 month)
Follow-up (3 months)
133.58 4.46
133.90 4.56
125.17 5.54
129.60 4.28
125.25 4.39
130.60 3.81
125.08 4.85
131.80 3.36
81.92 4.83
82.20 4.85
78.00 3.19
81.70 3.20
78.67 2.87
81.70 3.65
F
p
4.552
20.862
3.764
0.045
0.000
0.006
6.924
1.926
0.839
0.016
0.117
0.501
79.17 2.41
83.60 2.88
BP values are expressed as mean standard deviation. SBP, systolic blood pressure; ABþBF, abdominal breathing combined with
biofeedback; DBP, diastolic blood pressure.
BREATHING EFFECT ON BP, HRV IN PREHYPERTENSION
FIG. 1. Effect of different training on R–R interval.
*p < 0.01, training versus postbaseline; D, p < 0.05, ABþBF
group versus control group; ABþBF, abdominal breathing
combined with biofeedback; prebaseline, baseline for pretreatment; postbaseline, baseline for post-treatment.
reflex induced by the increased tidal volume), which in turn
enhances the baroreflex sensitivity.27 All of them may contribute to lower BP. It has been demonstrated that slow
breathing has a short-term effect of reducing BP.26 In this
study, we found that slow breathing had a relative long-term
effect of reducing SBP, but it has no significant effect on DBP.
This can be explained by the relatively short practice time.
Much longer practice than this study, such as in qigong and
yoga, might be expected to gain a better result. Another
reason for it may be due to the lack of corresponding feedback signals that reflect the body’s state in slow abdominal
breathing relaxation. Without the feedback signals, participants could not make proper adjustment to get a greater
relaxation state, thus achieving a relatively lower effect in
reducing the BP. To achieve a more effective power, another
intervention that could provide feedback signals, such as
1043
EMG biofeedback, should been coupled with slow breathing
training.
EMG biofeedback-assisted relaxation, as a nonpharmacological therapy, is commonly used for relaxation training.
In this study, slow abdominal breathing combined with
EMG biofeedback had a more powerful effect of reducing BP
(Table 1), and also increased the R-R interval (Fig. 1) and
SDNN remarkably (Fig. 2). It is consistent with the prior
report, which showed that the combination of respiratory
and cardiac biofeedback training biofeedback increased HRV
(SDNN) in patients with coronary artery disease, and the
increasing of SDNN indicated that patients’ status was improving from the ‘‘unhealthy’’ range to the ‘‘compromised
health’’ range.31
The induction and maintenance of hypertension proved to
be closely related to stress6–8 and increased tension of the
sympathetic nervous system.32–34 Biofeedback, as a nonpharmacological intervention, proved to have the effect of
improving vagal tone as it inhibits sympathetic activity.21,35,36 Therefore, the effect of slow breathing on BP will be
enhanced when combined with EMG biofeedback. Meanwhile, with the feedback signals provided by the EMG biofeedback machine, participants feel their psychophysiologic
status, which benefits them to perform the slow breathing
with more relaxation. In view of these, we suggest that slow
abdominal breathing combined with EMG biofeedback, by
reducing sympathetic tone, may improve vagal nerve tension, resulting in inhibiting stress reaction as well as lowering BP.
A limitation of this study is that all participants were
postmenopausal women. Postmenopausal women are inclined to have an increasing incidence of hypertension;
therefore, they are more concerned about their own health,
and their willingness to participate in this study is higher
than in their counterparts. In addition, in order to avoid
possible effects of changes of sex hormone levels on BP, this
study used as subjects women in the older age group. Another limitation is the relatively small sample. In light of this,
we only designed two groups, which led to the result that
only an additive effect of slow breathing combined with
EMG biofeedback on BP was found. To determine the predominant effect, a nonintervention control group and/or a
simple biofeedback group should have been included.
Nevertheless, the results of this study suggest that one
nonpharmacological intervention assisted with another may
improve the therapeutic efficacy.
In summary, consistent with other nonpharmacological
intervention to lower the BP in hypertensive, slow abdominal breathing combined with EMG biofeedback can effectively reduce BP in those suffering from prehypertension.
This occurs possibly by increasing the excitability of the vagus nerve and reducing the excitability of sympathetic
nerves.
Conclusions
FIG. 2. Effect of different training on SDNN. *p < 0.01,
**p < 0.01, training versus postbaseline; SDNN, standard
deviation of the normal-normal intervals. ABþBF, abdominal
breathing combined with biofeedback; prebaseline, baseline
for pretreatment; postbaseline, baseline for post-treatment.
SDNN, standard deviation of normal–normal intervals.
Slow abdominal breathing combined with EMG biofeedback is an effective intervention to manage prehypertension.
This effect lasted at least 3 months. The possible mechanism
is that slow abdominal breathing combined with EMG biofeedback could reduce sympathetic activity, enhance vagal
activity.
1044
Acknowledgments
This study was supported by Guangdong province Science Foundation (no. 2007J1-C0171). We gratefully acknowledge the efforts of all of the volunteers. The help of the
technical staff Weidong Li is highly appreciated.
Disclosure Statement
No competing financial interests exist.
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1045
Address correspondence to:
Ting Huai Wang, MD
Department of Physiology
Zhongshan School of Medicine
Sun Yat-sen University
74 Zhongshan Road II
Guangzhou, Guangdong, 510080
People’s Republic of China
E-mail: wangth@mail.sysu.edu.cn
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