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The Effects of Breath-Counting Meditation and Deep Breathing on Heart Rate Variability

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Objectives: This study aimed to evaluate the effects of breath-counting meditation (BCM) and deep breathing (DB) on heart rate variability (HRV). These breathing techniques have the characteristics of non-paced and self-controlled breathings, resulting in less increase of HRV. We also compared BCM and DB with usual breathing (UB) or relaxing breathing (RB) which can reveal the characteristics of those. Methods: 83 healthy volunteers sitting in chairs performed non-paced breathing; UB, RB, BCM, and DB each for 5 minutes. One minute of relaxation was permitted between breathings. Participants surfed the internet sitting in front of a computer during UB, while for RB, they remained steady with eyes closed. For BCM, they breathed inwardly counting from 1 to 10 repetitively, while they took a deep breath during DB. Physiological indices were simultaneously recorded with a biofeedback system. Results: Respiration rate, thoracic amplitude, and mean heart rate decreased in RB compared with UB, but there was no change in HRV. Respiration rate in BCM and DB was lower than that in UB or RB, and the amplitude of thorax or abdomen, and HRV all increased (p<0.05). However, mean heart rate and skin conductance decreased in BCM compared with UB (p<0.05), whereas those were no different between DB and UB. Conclusion: BCM, just concentrating mentally on breathing with counting each breath, can increase HRV with less sympathetic activation, while DB, actively moving thorax and abdomen for achieving the deepest respiration rate, can greatly raise HRV with the maintenance of mean vagal or sympathetic tone.
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http://dx.doi.org/10.13048/jkm.16019
36
The Effects of Breath-Counting Meditation and Deep
Breathing on Heart Rate Variability
Ji-Hwan Kim, Hyo-Sang Bae, Seong-Sik Park
Department of Sasang Constitutional Medicine, College of Korean Medicine, Dongguk University,
Republic of Korea
Original Article
Received
12 May 2016
Revised
16 June 2016
Accepted
16 June 2016
Correspondence to
Seong-Sik Park
Dept. of Sasang Constitutional Medicine, Dongguk university Bundang hospital of Korean Medicine
268, Buljeong-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Rep. of Korea
Tel
+82-10-3359-8421, E-mail
parkss@dongguk.ac.kr
Obje ct iv e s: This study aimed to evaluate the effects of breath-counting meditation (BCM) and deep breathing (DB)
on heart rate variability (HRV). These breathing techniques have the characteristics of non-paced and self-controlled
breathings, resulting in less increase of HRV. We also compared BCM and DB with usual breathing (UB) or relaxing
breathing (RB) which can reveal the characteristics of those.
Methods: 83 healthy volunteers sitting in chairs performed non-paced breathing; UB, RB, BCM, and DB each for
5 minutes. One minute of relaxation was permitted between breathings. Participants surfed the internet sitting in front
of a computer during UB, while for RB, they remained steady with eyes closed. For BCM, they breathed inwardly
counting from 1 to 10 repetitively, while they took a deep breath during DB. Physiological indices were
simultaneously recorded with a biofeedback system.
Results: Respiration rate, thoracic amplitude, and mean heart rate decreased in RB compared with UB, but there was
no change in HRV. Respiration rate in BCM and DB was lower than that in UB or RB, and the amplitude of thorax
or abdomen, and HRV all increased (p<0.05). However, mean heart rate and skin conductance decreased in BCM
compared with UB (p<0.05), whereas those were no different between DB and UB.
Conclusion: BCM, just concentrating mentally on breathing with counting each breath, can increase HRV with less
sympathetic activation, while DB, actively moving thorax and abdomen for achieving the deepest respiration rate, can
greatly raise HRV with the maintenance of mean vagal or sympathetic tone.
Key
W
ords
: Usual breathing, Relaxing breathing, Breath-counting meditation, Deep breathing, Heart rate
variability, Biofeedback
Introduction
Respiration is naturally controlled by a pacemaker
in the brain stem that receives signals from the
autonomic nervous system, chemoreflexes, and
baroreflexes generated by exercise, stress, and
temperature changes. However, it can also be
controlled consciously; individuals can control
respiration rate, volume of each breath, ratio of
inspiration and expiration, etc. [1]. Consciously
controlled breathing affects mind and body, and acts
as an important component of meditations such as
breathing awareness meditation [2], yoga [3],
Dantian breathing [4], and mindfulness meditation
[5].
Some previous studies used the direction of
listening [6] or sight [7] for controlling participants’
respiration rate. This method has an advantage in
J Korean Med. 2016;37(2):36-44
http://dx.doi.org/10.13048/jkm.16019
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ISSN 1010-0695 eISSN 2288-3339
The Effects of Breath-Counting Meditation and Deep Breathing on Heart Rate Variability
http://dx.doi.org/10.13048/jkm.16019 37
Fig. 1. Participants flow
Forty five men and 38 women were recruited. One woman did not
complete measurements, and dropped out of the study. Therefore,
total 82 participants (45 men and 37 women) were statistically
analyzed.
(
205
)
that the respiration rates of participants can be
matched as fixed pace. However, paced deep
breathing caused stress in 60% of study participants
[8], and autonomic nervous input heightened by this
stress could lower heart rate variability (HRV) [9].
The occurrence of physical problems such as
functional digestive disorders [10], inflammation
[11], and hypertension [12] may correspond with a
lower HRV. It is worthy to find how HRV can be
increased effectively by breathing technique.
Therefore, we chose breath-counting meditation
(BCM) and deep breathing (DB) among non-paced
and self-controlled breathing techniques. BCM is a
type of Zen meditation called Su-soku; it is
performed by concentrating mentally on breathing
while counting each breath [13]. On the other hand,
in DB, participants concentrate physically on
breathing while participants consciously maximize
inspiration and expiration [14]. In order to observe
the effect of these on HRV, we evaluated various
physiological indices including respiration rate, the
amplitude of thorax and abdomen, mean heart rate,
skin conductance (SC) and body temperature with
HRV while participants performed BCM and DB.
We also compared usual breathing (UB) and relaxing
breathing (RB) with those in order to reveal the
characteristics of BCM and DB.
Materials and Methods
1. Participants
83 young men and women attending Dongguk
University were enrolled in this study (45 males, 38
females, average age 26.07±4.06). All participants
were given a full explanation of the study and
completed an informed consent form confirming
participation of their own free will. Participants
responded to questions regarding their health status,
past disease history, and medication use. Participants
had no previous experience with yoga or meditation,
and did not have any respiratory, cardiovascular or
autonomic diseases. Participants were allowed to eat
breakfast 2 hours before the beginning of the study.
However, all substances that may cause physiological
changes, including coffee, were prohibited 12 hours
prior to the study. One woman did not complete
measurements of HRV, and dropped out of the
study. Therefore, a total of 82 participants completed
the study (Figure 1).
2. Outcome Measures
All measurements were digitized and recorded
with the ProComp biofeedback system (Korea
medical technology, http://www.kmtec.com, Republic
of Korea). Respiration rate, the amplitude of thorax
and abdomen were measured using respiratory
inductive plethysmography(RIP) belts strapped to the
thorax and abdomen. Mean heart rate, standard
deviation of heart rate, standard deviation of normal
intervals (SDNN), low frequency(LF; 0.04~0.15Hz),
and high frequency (HF; 0.15~0.4Hz) were measured
using an infrared photo plethysmography machine
attached to the left middle finger. SC and body
temperature were measured with sensors on the left
index and ring finger.
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3. Procedures
Testing was performed in a quiet room between 9
a.m. and 11 a.m. Before the study, the participants
were educated on the characteristics of each
breathing method and practiced the methods with an
investigator trained in advance. Afterwards, when the
participants were resting while sitting in chairs, the
investigator attached the measuring tools to
participants’ bodies. The participants then performed
UB, RB, BCM, and DB for 5 minutes each at the
direction of the investigator. 1 minute of relaxation
was permitted between each method. For UB, the
participant was told to surf the internet using a
mouse with right hand while sitting in front of a
computer monitor. For RB, the participant with eyes
closed breathed naturally while trying not to think of
anything in particular. For BCM, the participant with
eyes closed counted from 1 to 10 repetitively during
expiration. For DB, the participant took a deep
breath and concentrate just on physical act consciously
for controlling respiration. All procedures were
performed under the approval of the Institutional
Review Board (IRB) of Dongguk University.
4. Statistical Analyses
Data was analyzed with SPSS (IBM SPSS Inc.
version 20). The results of LF and HF were skewed;
therefore, they were analyzed after natural
log-transformation (lnLF and lnHF, respectively).
Repeated measures- analysis of variance
(RM-ANOVA) was performed in order to compare
breathing methods. When the sphericity assumption
was not satisfied, significance was tested after
correcting for the number of degrees of freedom
using Greenhouse-Geisser’s
ε
-corrected degrees of
freedom. Post-hoc analysis was performed using
Fisher's Least Significant Difference (LSD) method.
Results were judged to be statistically significant if
the
α
value was less than 0.05 (p<0.05), and are
shown as mean±S.D.
Results
We performed RM-ANOVA for the measured
physiological indices. Post-hoc analyses of the
statistically significant indices are presented in this
section and summarized in Table 1.
Respiration rate significantly decreased in the
following order: UB, RB, BCM, and DB (p<0.05).
Respiration rate was lowest in DB (Figure 1).
Thoracic amplitude was significantly lower during
RB compared with UB and BCM (p<0.05), and was
the highest during DB (p<0.05). However, there was
no statistically significant difference between UB and
BCM. Abdominal amplitude was significantly greater
in BCM compared with RB (p<0.05), and was the
highest in DB (p<0.05). However, abdominal
amplitude was not significantly different between UB
and either RB or BCM. Mean heart rate significantly
decreased in the following order: UB, RB, and BCM
(p<0.05). The mean heart rate in DB was higher
than that of RB or BCM (p<0.05), and showed no
difference compared with UB (Figure 2). The
standard deviation of heart rate was significantly
higher for DB compared with all other conditions
(p<0.05). SDNN, lnLF, and lnHF were all
significantly increased in BCM and DB compared
with UB or RB (p<0.05). Those in DB was
especially increased compared with BCM (p<0.05).
However, there was no statistically significant
difference between UB and RB (Figure 3). SC was
higher in UB and DB compared with RB or BCM
(p<0.05). However there was no significant
difference in SC between UB and DB, or between
RB and BCM (Figure 4). Body temperature
significantly increased in the following order: UB,
RB, and BCM (p<0.05). However, there was no
difference between DB and UB.
Discussion
The purpose of this study was to evaluate the
The Effects of Breath-Counting Meditation and Deep Breathing on Heart Rate Variability
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Table 1. Physiological Indices Obtained from the Results of Four Breathing States
Measure
(Mean±S.D.)
Usual breath
(UB)
Relaxing
Breath
(RB)
Breath-counting
meditation
(BCM)
Deep
breathing
(DB)
RM-ANOVA Post hoc
(p<0.05)
Respiration Rate
(breaths/min)
15.72
±1.59d
14.34
±2.47c
13.53
±3.00b
8.51
±2.61a
F(3,808.98)=166.95
p=0.000
UB-RB
UB-BCM
UB-DB
RB-BCM
RB-DB
BCM-DB
Thoracic amplitude
(cm)
1.81
±0.79b
1.56
±0.79a
1.86
±1.27b
7.07
±3.78c
F(1.20,1454.51)=171.30
p=0.000
ε
=0.400
UB-RB
UB-DB
RB-BCM
RB-DB
BCM-DB
Abdominal amplitude
(cm)
2.88
±1.27ab
2.68
±1.23a
3.23
±2.06b
10.18
±5.41c
F(1.27,2557.11)=159.04
p=0.000
ε
=0.423
UB-DB
RB-BCM
RB-DB
BCM-DB
Mean Heart rate
(beats/min)
75.06
±9.64c
73.31
±9.62b
71.98
±9.39a
75.41
±8.58c
F(1.89,332.41)=18.49
p=0.000
ε
=0.628
UB-RB
UB-BCM
RB-BCM
RB-DB
BCM-DB
Standard deviation
of heart rate
(beats/min)
5.54
±3.45a
5.04
±1.84a
5.24
±2.38a
9.10
±4.86b
F(2.11,432.49)=44.60
p=0.000
ε
=0.702
UB-DB
RB-DB
BCM-DB
Standard Deviation of
N-N Interval (ms)
55.07
±26.53a
56.15
±23.16a
60.13
±27.97b
96.44
±50.60c
F(1.52,63342.53)=56.22
p=0.000
ε
=0.507 UB-BCM
UB-DB
RB-BCM
RB-DB
BCM-DB
ln LF (ms2)5.03
±0.84a
4.92
±0.89a
5.20
±0.97b
6.34
±1.20c
F(2.21,47.66)=75.71
p=0.000
ε
=0.736
ln HF (ms2)4.74
±1.06a
4.76
±0.94a
4.98
±1.02b
5.58
±1.29c
F(2.37,15.79)=22.78
p=0.000
ε
=0.789
Skin conductance
(
μ
V)
0.95
±1.06b
0.76
±0.80a
0.73
±0.95a
1.09
±1.54b
F(1.65,4.22)=6.61
p=0.003
ε
=0.548
UB-RB
UB-BCM
RB-DB
BCM-DB
Body temperature
(
)
32.96
±1.89a
33.34
±1.39b
33.50
±1.16c
33.03
±1.31a
F(1.46,10.77)=8.56
p=0.001
ε
=0.487
UB-RB
UB-BCM
RB-BCM
RB-DB
BCM-DB
ln : Natural logarithm
LF: Low-frequency component (0.04Hz to 0.15Hz)
HF: High-frequency component (0.15Hz to 0.40 Hz)
a, b, c, d
: A st atistical significance is not present between the figures sharing the same alphabet subscript in the line of each index.
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)
effect of BCM and DB on HRV using a non-paced
and self-controlled breathing method. UB and RB
could reveal the characteristics of those by comparison.
In our study, in RB compared with UB, mean
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Fig. 2. Changes of mean heart rate and respiration rate according to four breathing states
The mean heart rate became statistically and significantly lower as UB progressed toward RB and BCM (
p
<0.05). It was statistically and
significantly higher for DB compared with RB and BCM (p<0.05), but was not significantly different regarding UB. Contrarily, the respiration rate
became statistically, significantly, and gradually lower as UB progressed toward RB, BCM, and DB (
p
<0.05).
UB: Usual breathing; RB: Relaxing breathing; BCM: Breath-counting meditation; DB: Deep breathing
*
p
<0.05
Fig. 3. Changes of standard devia tio n o f N-N int er va l (SDNN) and l ow frequency (LF) component of Heart rate variability
(HRV) according to four breathing states
a) In the SDNN, there was no statistically significant difference for both UB and RB, but it showed a statistically significant increase pattern
for BCM (p<0.05), and these indices tended to increase more for DB compared with BCM (
p
<0.05).
b) The changes of lnLFalsoshowed the same tendency as SDNN.This tendency was the same for lnHF(Figure not shown)
SDNN: Standard deviation of N-N interval
ln: Natural logarithm
LF: Low frequency(0.04~0.15Hz)component of Heart rate variability (HRV)
HF: High frequency(0.15~0.4Hz)component of Heart rate variability (HRV)
UB: Usual breathing; RB: Relaxing breathing; BCM: Breath-counting meditation; DB: Deep breathing
*
p
<0.05
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208
)
The Effects of Breath-Counting Meditation and Deep Breathing on Heart Rate Variability
http://dx.doi.org/10.13048/jkm.16019 41
Fig. 4. Changes of skin conductance according to four breathing states
In skin conductance, UB was statistically significant compared with RB and BCM (p<0.05). DB was also statistically significant compared with
RB and BCM (
p
<0.05). However, there was no statistically significant difference between UB and DB and between RB and BCM.
UB: Usual breathing; RB: Relaxing breathing; BCM: Breath-counting meditation; DB: Deep breathing
*
p
<0.05
(
209
)
heart rate, and SC decreased, and body temperature
increased according to the decrease of respiration
rate and thoracic amplitude. Sympathetic activity in
body can be indirectly observed via SC. In a
relaxed, stress-free state, sympathetic activity is
reduced, which reduces perspiration [15]. The
decrease of SC reflects that participants were less
sympathetically activated when performing RB than
UB. In conclusion, mean heart rate decreased, and
body temperature increased with less sympathetic
activity. However, SDNN, LF, and HF were similar
in RB and UB, so this change seems to be
insufficient to change HRV.
In BCM compared with RB, SDNN, LF, and HF
all increased with both the decrease of respiration
rate and the increase of thoracic and abdominal
amplitude. SC was not statistically different between
RB and BCM. It looked like participants similarly
relaxed in both BCM and RB, but unlike RB, BCM
could increase HRV although participants just
concentrated mentally on breathing with counting
each breath. The amplitude of RSA is inversely
proportional to respiration rate and is tidal volume
dependent under certain conditions [16]. Respiration
rate was slower during BCM than during RB. The
enough higher thoracic and abdominal amplitude in
BCM than RB could result in the increase of tidal
volume. LF is primarily under vagal control, although
some research suggests a component of sympathetic
mediation [17]. LF also is influenced by the
baroreflex, by which heart rate is affected by
changes in arterial blood pressure [18]. HF reflects
respiratory sinus arrhythmia (RSA) by parasympathetic
nervous reaction to changes in respiration [19].
SDNN is related to the R-R interval [20]. SDNN
and total power are measures of overall change in
HRV [21]. Therefore, active changes of thorax and
abdomen may influence baroreflex, resulting in the
increase of LF, HF, and SDNN. BCM and RB are
active relaxation procedures that might decrease
sympathetic arousal, while only BCM may increase
HRV.
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)
In DB, standard deviation of the heart rate, LF,
HF, and SDNN all were greatly increased. Participants
took in the fewest breaths during DB. If the
respiration rate decreases and approaches the LF
range, RSA due to respiration and LF related to the
baroreflex can overlap, causing great variability in
heart rate [22]. In states of slow respiration rate such
as DB (8.51±2.61 breaths/min), the increased RSA
can also influence LF [23]. The respiration rate of
DB among the tested breathing states was closest to
0.1Hz (6 breaths/min), ‘the resonance frequency of
the cardiovascular system’, at which the maximal
oscillation of heart rate can be produced by paced
breathing [24]. Therefore, HRV significantly increased
in DB actively changing the amplitude of thorax and
abdomen with the slowest respiration rate. However,
In DB, SC and body temperature were maintained at
levels seen in UB, and the mean heart rate in DB
was not statistically different from that in UB. This
means that the degree of sympathetic activation in
DB is similar to UB. It is not necessarily changed in
autonomic nerves’ traffic in order to increase the
amplitude of RSA because the slower respiration
rates help acetylcholine from vagal nerve ending
more fully hydrolyzed [25] or because vagal efferent
traffic can be affected on heart with the phasic
pattern due to the longer expiration period [26].
Therefore, HRV can increase without the change of
‘mean’ autonomic pattern and ‘mean’ heart rate.
Our study has some limitations. First, the breathing
methods were not performed in a random order.
Instead, they were performed in the order of
increasing intervention such as BCM followed by
DB. One-minute resting time between each method
may also reduce influences by the preceding
breathing. Second, we had no choice but to rely
entirely on the participants’ compliance with the
investigators’ instructions because our study incorporated
non-paced breathing in order to rule out the
possibility of stress induced by paced breathing
methods. Well-designed, additional studies are needed
in the future.
Conclusion
Compared with UB in the daily living condition,
both BCM and DB could make the decrease in
respiration rate. Mean heart rate and SC decreased in
BCM compared with UB, while in DB, those were
not statistically different from UB. SDNN, LF, and
HF more increased in both BCM and DB than UB.
BCM could increase HRV unlike RB, although those
were both active relaxation procedures. Therefore,
our study showed that BCM, just concentrating
mentally on breathing with counting each breath, can
increase HRV with less sympathetic activation than
UB, while DB, actively moving thorax and abdomen
for achieving the deepest respiration rate, can greatly
raise HRV with the maintenance of mean vagal or
sympathetic tone at the level of UB.
Conflicts of interest
The authors declare no conflicts of interest.
Acknowledgement
This work was supported by a National Research
Foundation of Korea Grant funded by the Korean
Government (NRF-2014S1A5B6A02049047).
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vagal tone, evolution and biobehavioral functions.
Biol Psychol. 2007;74(2):263-85.
... In terms of the time domain variables, the increase in the SDNN but not the mean HR and RMSSD is consistent with previous reported literature (Kim et al. 2016;Lin et al. 2014;Prinsloo et al. 2013a;Wang et al. 2010). From Fig. 2b, it can be seen that the RMSSD value for the DB groups was larger than that of Con although they were not statistically different. ...
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