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Measurements were performed in the same In order to compare variables between spontaneous
environment in which they received training and also by breathing and RSA we applied the paired Student t test for
the same teacher. To measure physiological variables we parametric distributions and Wilcoxon test for
used emWave Pro from Heartmath devices, which monitors non-parametric distributions. To compare variables between
the heart rate using a pulse sensor in the ear lobe. The groups (Experimental Group vs. Control Group) we applied
system records tachograms heart rate (time-series data) and the unpaired Student t test for parametric distribution and
the ratio of cardiorespiratory consistency over a given time the Mann-Whitney test for non-parametric distributions.
interval. Level of significance was documented at (P < .05, 5%).
To quantify the magnitude of difference betweem groups
HRV Analysis and between moments, the effect size was calculated using
We followed instructions from the Task Force guidelines. Cohen’s d for significant differences. Large effect sized was
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As previously mentioned, RR intervals were collected considered for values > 0.9, medium for values between
through an ear lobe. Data were filtered using a software 0.9 and 0.5 and small for values betweem 0.5 and 0.25.
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designed by Santos et al. Statistical analysis was done by using Stats Direct
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All indices were assessed using RR intervals taken from Statistical Software (version 1, 9, 15) of Stats Direct Limited.
stationary five minutes. Series with sinus rhythm exceeding
95% were included in the study. RESULTS
Linear analysis of HRV encompassed time (SDNN, We observed no adverse effects in the intervention
standard deviation of normal-to-normal R-R intervals; process, reinforcing the safety of slow breathing protocol in
RMSSD, root-mean square of differences between adjacent children.
normal RR intervals in a time interval; pNN50, percentage of Table 1 shows comparison between intervention and
adjacent RR intervals with a difference of duration greater control groups regarding rest HRV before slow breathing
than 50 ms) and frequency (LF, low frequency; HF, high exercise training. We noted no significant difference between
frequency; LF/HF, low-frequency/high-frequency ratio) groups.
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domain indices, where parameters were extracted from the According to Table 2, we noted a significant effect of the
Kubios HRV analysis software. The Poincaré plot analysis intervention with reduction of CR-Low in the 1st week, 4th
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(SDI, standard deviation of the instantaneous variability of week and 8th week during slow breathing. On the other
the beat-to-beat heart rate; SD2, standard deviation of hand, during slow breathing the CR-High increased in the
long-term continuous RR interval variability) had been 4th week and 8th week. In relation to the CR-Med, this
previously described. 12 parameter increased during slow breathing in the 4th week
We also assessed the following nonlinear parameters of and 8th week.
HRV: Approximate entropy (ApEn) ; Sample entropy Figure 2 shows the increase in cardiorespiratory
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(SampEnt) ; Multiscale entropy (MSE) ; Detrended coherence in one child from the experimental group
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fluctuation analysis (DFA, α1 and α2) ; Shannon entropy exemplary for the cohort. We observed a greater presence of
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(ShannEnt) ; mean line (Lmean) and maximum line (Lmax) regular/sinusoidal waves during respiratory exercise.
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of recurrence analysis. 18 Table 3 presents cardiorespiratory coherence parameters
in the control group in the 8th week after the slow breathing
Cardiorespiratory Coherence exercise in the experimental group. We reported no significant
We calculated cardiorespiratory coherence through changes between the two moments (spontaneous breathing
coherence ratio. First, the peak was identified in the range of vs. slow breathing).
0.04-0.26Hz (the frequency range in which coherence and When we compared cardiorespiratory coherence
synchronization of systems with external rhythms may occur). parameters between control and intervention group we
Peak power was then determined by calculating the integral in noted no significant difference in CR-low (P = .12, Cohen’s
a window of 0.03Hz, centered on the highest peak in that area. d = 0.28), CR-Med (P = .15, Cohen’s d = 0.18) and CR-High
Then, the total power of the entire spectrum was calculated. (P = 0.18, Cohen’s d = 0.31).
The coherence ratio is formulated as: [Peak Power / (Total We also performed HRV analysis during spontaneous
Power - Peak Power)]. Coherence data were extracted breathing and during RSA in the experimental group in the
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directly from the Institute of HeartMath’s emWave Pro software 8th week after the beginning of slow breathing training
(CR-Low: low coherence ratio; CR-Med: mean coherence (Table 4). Heart rate (HR), standard deviation of the
ratio; CR-High: high coherence ratio). instantaneous variability of the beat-to-beat heart rate (SD1),
approximate entropy (ApEn), MSE 1_5 area and DFAα1
Statistical analysis increased during RSA, while mean RR interval, SDNN,
Statistical methods were agreed for the computation of RMSSD, pNN50, SD2 and Lmean of recurrence analysis
means and standard deviations. Normal Gaussian distribution decreased during RSA in the experimental group.
of the data was verified by the Shapiro-Wilk goodness-of-fit We found no significant difference in HRV between
test (z value > 1.0). 19 spontaneous and RSA in the control group (Table 5).

16 ALTERNATIVE THERAPIES, JUL/AUG 2020 VOL. 26 NO. 4 Zuanazzi Cruz—Slow Breathing Exercise on Heart Rate