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Annals of Noninvasive Electrocardiology logoLink to Annals of Noninvasive Electrocardiology
. 2017 Jul 25;23(1):e12469. doi: 10.1111/anec.12469

Impact of functional training on geometric indices and fractal correlation property of heart rate variability in postmenopausal women

Marianne P da C de Rezende Barbosa 1,✉, Luiz C M Vanderlei 2, Lucas M Neves 3, Carolina Takahashi 2, Paula R dos S Torquato 2, Ana Claúdia de S Fortaleza 4, Ismael F Freitas Júnior 4, Isabel C E Sorpreso 5, Luiz C Abreu 1, Andrés R Pérez Riera 1
PMCID: PMC6931770  PMID: 28741870

Abstract

Background

To evaluate the influence of functional training on the geometric indices of heart rate variability (HRV) and fractal correlation properties of the dynamics of heart rate in menopausal women.

Methods

Of 39 women who were in the period of menopause for more than a year and who did not practice any regular physical activity were divided into: Functional training group (FTG = 50 ± 4.5 years; 67.64 ± 11.64 kg; 1.5 ± 0.05 m) that executed the functional training (FT) and all proposals by reviews and the Control group (58.45 ± 4.8 years; 66.91 ± 13.24 kg; 1.55 ± 0.05 m) who performed all assessments but not FT. The training consisted of 18 weeks (three times a week) and the volunteers performed three sets of 11 functional exercises followed by a walk in each of the sessions. The autonomic nervous system modulation was evaluated by analysis of HRV and the indices obtained were: RR intervals, RRTRI, TINN, SD1, SD2, SD1/SD2, qualitative analysis of Poincaré plot and DFA (alfa‐1, alfa‐2 and alfa‐1/alfa‐2). The Student's t‐test for unpaired samples (normal data) or Mann‐Whitney test nonnormal data) were used to compare the differences obtained between the final moment and the initial moment of the studied groups (p < .05).

Conclusion

Were observed in the FTG: increased SD1 (CG 0.13 ± 4.00 vs. 3.60 ± 8.43), beat‐to‐beat global dispersion much greater as an increased in the dispersion of long‐term RR intervals and increased fractal properties of short‐term (α1) (CG −0.04 ± 0.13 vs. 0.07 ± 0.21). FT promoted a beneficial impact on cardiac autonomic modulation, characterized by increased parasympathetic activity and short‐term fractal properties of the dynamics of the heart rate.

Keywords: activities of daily living, autonomic nervous system, cardiovascular parameters, menopause, nonlinear dynamics, resistance training

1. INTRODUCTION

Changes in heart rate defined as heart rate variability (HRV) are normal and expected and indicate a heart's ability to respond to multiple physiological and environmental stimuli as well as to compensate disorders induced by diseases (Silva et al., 2016; Vanderlei et al., 2001) . HRV is a simple and not invasive tool that describes the fluctuations between the consecutive heart beats intervals (RR Intervals) and can be used to identify phenomena related to the autonomic nervous system (ANS) (De Rezende Barbosa et al., 2016a,b; Silva et al., 2016; Vanderlei et al., 2001 Giacon et al., 2016).

Among the linear methods that assess HRV we highlight the geometric indices (triangular index [RRtri], triangular interpolation of RR interval histogram [TINN] and Poincaré plot) which convert RR intervals into geometric patterns and allow analyzing HRV through the geometric or graphic properties of the resulting pattern (De Rezende Barbosa et al., 2016a,b; Hoshi et al., 2013, Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 2002; Vanderlei et al., 2010).

However, considering the nonlinear nature of the body systems, HRV analysis by nonlinear methods has been gaining increasing interest (Boettger et al., 2010; De Rezende Barbosa et al., 2016a,b; Hoshi et al., 2013). These methods have shown a new vision on heart rate (HR) behavior abnormalities in various conditions, providing information to physiological and additional prognostic interpretations when compared to traditional methods (Boettger et al., 2010; De Rezende Barbosa et al., 2016a,b; Godoy, Takakura, & Correa, 2005; Khaled, Owis, & Mohamed, 2006). Therefore, nonlinear analysis of HRV is also necessary because information can be lost if only linear analyses are applied (Hoshi et al., 2013, Task Force of the European Society of Cardiology, the North American Society of Pacing and Electrophysiology, 2002).

Nonlinear techniques such as analysis of purified trend fluctuations (DFA), which quantifies the fractal correlation properties of the intervals between heartbeats, and thus enables detection of intrinsic self‐similarity embedded in nonstationary time series (Peng et al., 1994; Peng et al., 1995), has shown to be a powerful tool to characterize the complexity of the systems (Hoshi et al., 2013, Task Force of the European Society of Cardiology, the North American Society of Pacing and Electrophysiology, 2002). Previous studies in different populations suggest autonomic dysfunction association and loss of properties of fractals correlate to heart rate (Borghi‐Silva et al., 2009; Carvalho et al., 2010; Peng et al., 1995; Vanderlei et al., 2010; Ricci‐vitor et al., 2009).

Menopausal women, a normal period of the life of women (Abdollahi et al., 2013) that can cause in 50–85% of women substantial biological and psychosocial changes that generate great stress and disabilities (Yazdkhasti, Simbar, & Abdi, 2015), present a lower HRV when compared with women who are not in menopause (Jurca et al., 2004).

Studies show that after conducting physical training HRV is improved (Earnest, Blair, & Church, 2012; Earnest et al., 2008; Grant et al., 2012; Jurca et al., 2004; Paschoal, Polessi, & Simioni, 2008; Rossi et al., 2014; Rovere & Pinna, 2013; Silva et al., 2014) as well as improvement, at least in part, in the fractal nature of the heart rate in different populations (Heffernan et al., 2008; Karavirta et al., 2013; Millar et al., 2013; Ricci‐vitor et al., 2009). Among the different types of physical training, we highlight functional training, which was prepared from the imitation of everyday gestures and activities (such as bathing. preparing meals. climbing stairs. playing sports. etc.) (Pacheco et al., 2013; Tomljanovic′ et al., 1996) and has been widely used in clinical practice for maintaining health (Kibele & Behm, 2009; Lagally et al., 2009; Tomljanovic′ et al., 1996 Sorpreso et al., 2012), and in processes of rehabilitation to improve functional capacity in the elderly (Hosseini et al., 2012; Krebs et al., 2007; Pacheco et al., 2013), suggesting a good alternative for the population of menopausal women.

As women may have a lower HRV, which can be improved by physical training (Earnest et al., 2008, 2012; Jurca et al., 2004; Pacheco et al., 2013; Rossi et al., 2014), and the fractal correlation properties of HR (Heffernan et al., 2008; Karavirta et al., 2013; Millar et al., 2013; Ricci‐vitor et al., 2009) which makes the organism more chaotic and may reduce the risk of mortality (Godoy et al., 2005; Huikuri et al., 2000; Mäkikallio et al., 1999; Ricci‐vitor et al., 2009), it is understood that studying interventions, such as functional training, is of essential significance as they can reverse or minimize changes in the autonomic modulation induced by menopause.

Thus, this study aims to evaluate the influence of functional training on the geometric indices of HRV and fractal correlation properties of the dynamics of heart rate in menopausal women. We hypothesize that functional training can increase and restore HRV and, at least in part, the fractal correlation properties of the dynamics of heart rate in these women.

1.1. Population

There were 64 postmenopausal, apparently healthy women enrolled, aged 50–65 years to conduct this clinical trial, who were invited to participate in the study through television, radio and the news from the city of Presidente Prudente, São Paulo, Brazil. These 64 women were randomized into two groups consisting of 32 women each, and one group executed the functional training (FT) and all proposals by reviews, and in the other group the women performed all assessments but not FT (Control Group ‐ CG).

The sample size calculation was made from a study by Rezende Barbosa et al. (2016) (De Rezende Barbosa et al., 2016a,b) who was also assessed HRV but in a group of adult women who performed FT. The sample size calculation was based on the values obtained for the RMSSD index which showed a mean difference of −15.69 and standard deviation of 11.58. Adopting a power of 80% for a 2‐tailed test (to be increased or decreased with the intervention) and an alpha error of 5% (z = 1.96) the equation indicated the need for at least nine individuals in each group. We chose to increase this value to 32 women per group to maximize the community care and prevent sample loss in our study.

The inclusion criteria included: a medical certificate that would allow physical exercises; confirming the possibility of participation in the exercise program, a menopause period of more than one year; having follicle stimulating hormone level 54±21,01 IU/L not having practiced any regular physical activity or weight training in the last six months prior to the beginning of the clinical trial; not presenting known health problems or limitations that may prevent the realization of the proposed exercise; conducting all proposals by reviews and signing the consent form, thus being formally cleared to participate in the study.

Already the exclusion criteria encompassed: presenting any injury or limitation that would prevent them from performing the training exercises or having <85% attendance at training sessions.

This study was approved by the Ethics Committee of the Universidade Estadual Paulista ‐ Júlio de Mesquita Filho (FCT/UNESP)/ Presidente Prudente (CAAE No 11547013.2.0000.5402) and the Brazilian registration clinical trial (RBR ‐ 85vmkx). All women who agreed to participate in the program were informed about the procedures and objectives of the study and after agreeing they signed the Consent form.

2. EXPERIMENTAL DESIGN

The experimental protocol was composed of three steps: basal assessment, training protocol and final assessment. The assessment of cardiac autonomic modulation by HRV occurred in week zero as collecting baseline data and then they were repeated at the end of the training (after 18 weeks). In the initial evaluation to characterize the sample, body mass and height were also assessed (Rezende Barbosa et al., 2016).

The Functional training group performed a program of functional exercises associated with aerobic exercises with a weekly frequency of three days not being consecutive. The training sessions were composed of 11 exercise stations developed in a circuit format, whereby participants passed three times in each station with a break of 30 s. At the end of the exercise the participants performed an 18 to 30‐min walk depending on the week of training. The Borg scale (Borg, 1982) was used to control the intensity of functional training and the critical speed to stipulate the intensity of the aerobic part of the functional training (Takahashi et al., 2012). The strategy of loads and the training protocol performed by the functional training group (FTG) is described in Figure 1.

Figure 1.

Figure 1

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Progression loads strategy (overload) protocol used by the functional training group

3. ASSESSMENTS AND DATA COLLECTION

3.1. Heart rate variability analysis

The data collection of RR tracing (RR interval between two R waves where each R wave corresponds to ventricular depolarization) for HRV analysis was carried out between 07:00 am and 12:00 am to minimize possible interference of the circadian cycle in a room with temperature between 21 and 23°C and humidity between 40 and 60%.

Data were collected individually and the volunteers were instructed to remain in silence, in consonance and at rest throughout the collection period. In order to reduce interference during collection the movement of people were restricted on site. The volunteers were asked to stay 24 hr prior to the evaluation without consuming alcohol and / or ANS‐stimulating beverages such as coffee, tea and chocolate (De Rezende Barbosa et al., 2016a,b).

The capture of heart rate was performed using the heart rate monitor of Polar RS800 (Polar Electro OY, Kempele, Finland) (De Rezende Barbosa et al., 2016a,b) which is composed of a pickup belt and a heart rate receiver. After setting the heart rate monitor, the volunteers remained with spontaneous breathing for 30 min on a mat in a supine position.

The data obtained from the heart rate monitor were sent to the computer by infrared communication through the Polar Precision Performance SW software, version 3.0 (Polar Electro OY). 1000 consecutive RR intervals for data analysis were used that were selected from the more stable section of the tracing after undergoing digital filtering complemented manually to eliminate premature ectopic beats and artifacts. The only series with more than 95% of sinus beats was included in the study. The geometric figures were obtained through the Kubios HRV software ‐ version 2.0 (Kubios Biosignal Analysis and Medical Image Group Department of Physics University of Kuopio Finland) (De Rezende Barbosa et al., 2016a,b) and the contents of the DFA were generated by a software available in Physionet (http://www.physionet.org)—an online forum that merges the signals of Biomedical records and software programs to analyze these signals (Hautala et al., 2003).

Geometric indices were calculated from the construction of the histogram of RR intervals. The horizontal axis shows all possible values of RR intervals generally obtained with a sampling frequency of 128 Hz and the vertical axis how often each occurred. From this histogram indices were calculated: TINN: triangular interpolation of RR intervals and a triangular index corresponding to the base of the triangle and can be calculated by dividing the area (corresponding to the total number of RR intervals used to build the figure) by the height (corresponding to the number of RR intervals modal frequency) of the triangle (Acharya, Lim, & Joseph, 2002; Nunan et al., 2008; Rassi, 2000).

Also the indices obtained were processed by means of Poincaré plot representing a time series within a Cartesian plane in which each RR interval is correlated with the next range and defines a point in the plot. Quantitative evaluation was made by adjusting the ellipse of the figure formed by the attractor. It allows us to obtain three indices: SD1, SD2 and SD1/SD2. SD1: standard deviation of the instantaneous variability in continuous RR intervals determined by the width of the ellipse formed by the Poincare plot and; SD2: standard deviation of long‐term continuous RR intervals; it determines the length of the plot and SD1/SD2 shows the ratio between short and long variations of the intervals (Acharya et al., 2002; Tulppo et al., 2011; Vanderlei et al., 2010).

The qualitative analysis of the plot was made through the analysis of the figures formed by its attractor, which were described in (Tulppo et al., 2011; Vanderlei et al., 2010): (i) the Figure, in which an increase in dispersion of RR intervals is observed with increased intervals, characteristic of a normal plot; (ii) the small figure with beat‐to‐beat global dispersion and without increased dispersion of RR intervals in the long‐term.

Detrended Fluctuations Analyses (DFA) were also conducted. The DFA is a square root of analysis of modified average of a random walk (Krstacic et al., 2007), and based on the analysis of the fluctuations of the data after removal of trends in integrated time series which allows detection of self intrinsic similarities incorporated into nonstationary time series (Acharya et al., 2002).

The DFA graph basically consists of two distinct regions of different curves at a point separated, suggesting that there is a short‐term fractal scaling exponent (alpha‐1) which corresponds to a period of 4–11 beats; and the exponent of long‐term fractal scale (alpha‐2) that is longer than 11 beats. Therefore by the DFA graphs we obtained the following indices: Total DFA, alpha‐1, alpha 2 and alpha‐1/alpha‐2 ratio (Carvalho et al., 2010; Godoy et al., 2005; Souza et al., 2015).

3.2. Data analysis

For the population data profile analysis, a statistical method was used and the results were presented as mean values and standard deviations, median, minimum and maximum numbers and 95% confidence interval. For the assessment of the effects of training on cardiac autonomic modulation and the fractal properties of the dynamics of heart rate, the values of the differences found were compared between the values obtained before and after the training protocol, in both groups. First we tested the normality of the data by the Shapiro‐Wilk test and depending on the normality of the data, the Student's t test (normal data) or the Mann‐Whitney U test (non‐normal data) were used to compare the differences observed between the groups. The level of significance was set at p < .05 for all tests. The SPSS (version 13.0) (SPSS Inc. Chicago, IL, USA) was used for analysis.

4. RESULTS

At the end of the trial the group that underwent functional training consisted of 19 women with a mean age of 50 ± 4.5 years, mean weight 67.64 ± 11.64 kg and mean height 1.5 ± 0.05 m. The control group that did not receive the training ended the study with 20 volunteers with a mean age of 58.45 ± 4.8 years, average weight 66.91 ± 13.24 kg and mean height 1.55 ± 0.05 m. The flowchart losses are shown in Figure 2. No significant differences were found between the groups at baseline, as age (p = .308), weight (p = .856) and time (p = .361).

Figure 2.

Figure 2

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Consort ‐ flow diagram

The mean values ± standard deviation followed by median values and confidence interval of 95% and also p values of geometric indices of HRV in the control and training groups in the initial moment and the difference between the times are shown in Table 1. A significant increase in the SD1 index was observed for FTG compared to the control group (p = .033). In other indices differences were not observed between the groups after functional training.

Table 1.

Mean ± standard deviation values followed by median values and confidence interval of 95% and also p values of geometric indices of HRV in the control and training groups in the initial and final moments and the difference between times

Index Group Initial Final Difference p Value
Control 9.57 ± 2.51 10.05 ± 3.11 0.48 ± 2.83 .709
9.39 9.52 0.15
RR TRI (8.39–10.74) (8.59–11.51) (−0.84–1.81)
FTG 8.59 ± 2.73 9.42 ± 2.75 0.83 ± 2.91
8.13 8.77 0.23
(7.27–9.91) (8.09–10.75) (−0.57–2.23)
Control 139.75 ± 74.70 151.75 ± 68.83 12.00 ± 49.90 .292
157.50 160.00 7.50
TINN (104.79–174.71) (119.07–184.43) (−11.35–35.35)
FTG 117.10 ± 46.28 107.36 ± 78.03 −9.73 ± 73.77
125.00 105.00 −15.00
(94.79–139.42) (69.75–144.98) (−45.29–25.82)
Control 14.76 ± 6.26 14.63 ± 5.49 −0.13 ± 4.00 .033a
15 14.8 0.35
SD1 (11.83–17.69) (12.06–17.20) (−2.00–1.73)
FTG 13.16 ± 5.02 16.76 ± 8.65 3.60 ± 8.43
11.6 15.9 2.8
(10.74–15.58) (12.59–20.94) (−0.45–7.67)
Control 49.59 ± 15.83 50.68 ± 14.68 1.09 ± 13.43 .247
46.6 50.45 2.6
SD2 (42.17–57.00) (43.81–57.55) (−5.19–7.38)
FTG 40.45 ± 13.39 54.08 ± 39.09 13.62 ± 37.22
38.9 44 5.5
(34.0–46.91) (35.24–72.92) (−4.31–31.56)
Control 0.29 ± 0.11 0.29 ± 0.11 −0.0008 ± 0.10 .539
0.28 0.281 −0.002
SD1/SD2 (0.24–0.34) (0.23–0.35) (−0.05–0.04)
FTG 0.33 ± 0.11 0.35 ± 0.16 0.02 ± 0.13
0.33 0.364 0.024
(0.28–0.39) (0.27–0.43) (−0.04–0.08)
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RRTri, triangular index; TINN, triangular interpolation of RR intervals SD1, standard deviation of instantaneous beat‐to‐beat variability; SD2, standard deviation of the long‐term variability; SD1/SD2, ratio between SD1/SD2.

a

p < .05.

Figure 3 shows a pattern example of Poincaré plot before and after the functional training protocol executed by the FT and control groups.

Figure 3.

Figure 3

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Example of a Poincaré plot observed in the control group pre (a) and post (b), and in the experimental group pre (c) and post (d) the functional training program

Table 2 contains a comparison of the analysis of the indices that express the dynamics of the heart rate groups. We found that the α1 indices (p = .0002) and the ratio α/α2 (p = .021) showed a significant increase in the FT group. In the other indices differences were not observed between groups after the FT.

Table 2.

Mean ± standard deviation values followed by median values and confidence interval of 95% and also p values of the indices that express the dynamics of the heart rate in the control and training groups in the initial and final moments and the difference between times

Index Group Initial Final Difference p Value
Control 1.013 ± 0.114 0.988 ± 0.150 −0.024 ± 0.142 .771
0.99 1.006 −0.042
(0.95–1.06) (0.91–1.05) (−0.09–0.04)
DFA total FTG 0.954 ± 0.134 0.940 ± 0.146 −0.014 ± 0.117
0.93 0.95 −0.032
(0.88–1.02) (0.86–1.01) (−0.07–0.04)
Control 0.977 ± 0.219 0.927 ± 0.181 −0.049 ± 0.134 .002a
0.91 0.904 −0.028
(0.87–1.08) (0.84–1.01) (−0.11–0.01)
α1 FTG 0.885 ± 0.240 0.958 ± 0.286 0.073 ± 0.214
0.95 0.96 0.096
(0.76–1.001) (0.82–1.09) (−0.02–0.17)
Control 0.993 ± 0.134 0.990 ± 0.160 −0.002 ± 0.181 .580
0.99 0.98 −0.021
(0.93–1.05) (0.91–1.06) (−0.08–0.08)
α2 FTG 0.943 ± 0.133 0.911 ± 0.156 −0.031 ± 0.142
0.95 0.93 −0.04
(0.87–1.008) (0.83–0.98) (−0.10–0.03)
Control 1.00 ± 0.26 0.94 ± 0.20 −0.05 ± 0.20 .021a
0.92 0.92 −0.04
α1/α2 (1.12–0.87) (1.04–0.85) (0.04–0.14)
FTG 0.94 ± 0.27 1.08 ± 0.38 0.13 ± 0.27
0.94 0.99 0.09
(1.08–0.81) (0.89–1.26) (0.26–0.00)
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DFA, detrended fluctuations analysis; α1, short‐term fractal exponent; α2, long‐term fractal exponent; α1/ α2, ratio between the exponents.

a

p < .05.

5. DISCUSSION

This study examined the influence of FT on geometric indices of HRV and the fractal properties of the dynamics of heart rate in menopausal women with no regular physical activity. We note that FT performed for a period of 18 weeks was able to promote increased parasympathetic modulation of the ANS expressed by the significant increase in SD1 and increased fractal properties of short‐term (α1) of the heart rate causing an increase in system complexity.

We highlight the originality of this clinical trial as this is the first study to evaluate the influence of functional training on geometric indices of HRV and the fractal properties of the dynamics of heart rate in menopausal women and it can be seen as an alternative to the improvement of autonomic functions in this population.

In the quantitative analysis of Poincaré plot, the differences obtained between pre and post‐training were compared, and there was a rise in the values of the SD1 index in the voluntary FTG than in the CG, suggesting that functional training increased the parasympathetic modulation of the ANS. SD1 elevation was also observed in healthy young women who underwent functional training with emphasis on resistance training for 12 weeks (De Rezende Barbosa et al., 2016a,b).

Given that the decrease in vagal activity is associated with increased risk for morbidity and mortality from various causes and the development of risk factors for cardiovascular diseases and that menopause changes proper are observed in autonomic modulation promoting reductions in HRV (Souza et al., 2015), the results found in this study suggest that functional training can be a good ally in the prevention of these bouts in women in the period of menopause.

No significant differences were observed for the SD2, RRtri, TINN and SD1/SD2 indices; although for the SD2, RRtri and SD1/SD2 indices ratio values were greater after the completion of training. The study by Rezende Barbosa et al., 2016 (De Rezende Barbosa et al., 2016a,b) with healthy young women who underwent functional training with emphasis on resistance exercises, did not corroborate these results because significant differences were observed for the RRtri, SD1 and SD2 indices in the group that carried out the training.

In the qualitative analysis of Poincaré plot through the figure of the ellipse formed by its attractor, we can see that in the control group the figures before and after are very similar and it is not possible to observe differences in the dispersion of the points in the figures. However, when we compare the figures representing the volunteers who carried out the training, it is evident that after the FT beat‐to‐beat global dispersion was much greater as an increase in the dispersion of long‐term RR intervals, suggesting that training increases HRV.

As to the analysis of the exponents obtained by DFA, and the values of the average initial and final alpha 1 in the FTG and CG, we found that in the CG the change was 0.050 and in the FTG it was 0.073, with this being a significant increase. This same pattern was also observed by Heffernan et al., 2008 (Heffernan et al., 2008) in healthy men aged 20 and 30 who performed a strength training protocol lasting 6 weeks.

In the DFA analysis when values are closer to one (1) it indicates that the system is more fractal; whereas values near half (0.5) are associated with a more random time series feature (Acharya et al., 2002; Tulppo et al., 2011; Ricci‐vitor et al., 2009). Considering this aspect of an increased alpha 1 in the trained group, it suggests a restoration at least partial, of the fractal dynamic condition of the heart rate.

The changes in autonomic modulation may provide a marked reduction in the complexity of the dynamics of fluctuations in heart rate, accounting for a lower capacity to meet the demands of an environment continually changing (Lombardi, 2000; Marwan et al., 2009) and the reduction in alpha 1 has been shown to be a predictor of events and deaths related to cardiovascular disease (Godoy et al., 2005; Mäkikallio et al., 1999; Perkiömäki et al., 2001; Tapanainen et al., 2009). The observed increase in alpha 1 could indicate a better health condition and can reduce the risk of mortality.

Correlations between linear and nonlinear indices were analyzed by Hoshi et al., 2013 (Hoshi et al., 2013). They observed a strong correlation between the SD1 index and the α1/α2 ratio that can also be observed in our study, since both indices increased significantly in the trained group. These results suggest that increased parasympathetic activity (SD1) may be, at least in part, related to the gain observed in fractals correction properties of short‐term heart rate.

About alpha 2 and total DFA, statistically significant differences between groups were observed. However the alpha1/alpha2 ratio showed a significant increase (p = .021) in menopausal women who underwent functional training compared to women who did not undergo training. This increase reinforces the increasing complexity obtained by the group after the completion of functional training.

In this study, we chose to perform the analyzes using nonlinear methods, which allow a broader understanding of the physiological impact on autonomic modulation, since there is evidence that the mechanisms involved in cardiovascular regulation probably interact in a nonlinear manner (De Rezende Barbosa et al., 2016a,b). Besides that, a recent study by Ciccone et al., 2017 presented that RMSSD and SD1 are identical metrics of HRV and because the homology between them is not commonly known, the inclusion of both measures has been reported in many recent publications and the redundant data may affect the interpretation of HRV studies (Ciccone et al., 2017).

As study limitation was not being able to blind the assessors and volunteers to be even more certain of the reliability of our findings. The results obtained in this study, while adding the new data literature regarding nonlinear analysis of HRV in menopausal women after functional training, also encourages clinical researchers and professionals who work with this population to inspire these women to practice functional training as an effective strategy for improving autonomic modulation and the fractal correlation properties of short‐term dynamics of the heart rate.

The results of this study show that functional training conducted for 18 weeks in women in the menopause period promoted a beneficial impact on cardiac autonomic modulation, characterized by increased parasympathetic activity and short‐term fractal properties of the dynamics of the heart rate, and they may be a good tool for the improvement of autonomic condition in this population.

CONFLICT OF INTEREST

No conflict of interest to declare.

Rezende Barbosa MPDCD, Vanderlei LCM, Neves LM, et al. Impact of functional training on geometric indices and fractal correlation property of heart rate variability in postmenopausal women. Ann Noninvasive Electrocardiol. 2018;23:e12469 10.1111/anec.12469

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