Clin Physiol Funct Imaging (2014)

doi: 10.1111/cpf.12158

Effect of set configuration on hemodynamics and cardiac autonomic modulation after high-intensity squat exercise
nchez-Otero1, Xian Mayo1, Xabier Eliseo Iglesias-Soler1, Daniel A. Boullosa2, Eduardo Carballeira1, Tania Sa 1 1 Castro-Gacio and Xurxo Dopico 1

Faculty of Sport Sciences and Physical Education, University of A Corun˜a, Oleiros, Spain and 2Post-Graduate Program In Physical Education, Catholic guas Claras, Brasılia, Brazil University of Brasılia, A

Summary Correspondence Eliseo Iglesias-Soler, Facultad de Ciencias del Deporte y la Educacio´n Fı´sica, Avda. E Che Guevara 121-Pazos-Lia´ns, 15179 Oleiros, A Corun˜a, Spain E-mail: [email protected]

Accepted for publication Received 11 October 2013; accepted 2 April 2014

Key words blood pressure; cluster training; heart rate variability; resistance training; work-to-rest ratio

The aim of this study was to compare the effect of two different high-intensity resistance exercise (RE) set configurations on the following: systolic blood pressure (SBP), rate pressure product (RPP), heart rate (HR) variability (HRV), and HR complexity (HRC). Ten well-trained males performed three parallel squat sets until failure (traditional training; TT) with the four repetitions maximum load (4RM), and a rest of 3 min between sets. Thereafter, participants performed a cluster training session (CT) of equated load but with resting time distributed between each repetition. Dependent variables were recorded before, during, and after RE. Mean SBP (25
7 versus 10
9% percentage increase; P = 0
016) and RPP (112
5 versus 69
9%; P = 0
01) were significantly higher in TT. The decrease in HRV after exercise and the drop of HRC during exercise were similar in CT and TT. Change of standard deviation of normal RR intervals after TT correlated with change in SBP (r = 0
803; P = 0
009) while the change of Sample Entropy during exercise correlated with the increment of RPP during CT (q = 0
667; P = 0
05). This study suggests that set configuration influences acute cardiovascular responses during RE. When intensity, volume and work-to-rest ratio are equated, CT is less demanding in terms of SBP and RPP. A greater hemodynamic response during exercise would be associated with a faster parasympathetic recovery.

Introduction Resistance training is traditionally designed with one or several sets of a number of repetitions in every exercise. Performance of successive repetitions within a set involves a progressive decline in mechanical performance (Izquierdo et al., 2006), with a simultaneous increase in arterial blood pressure (BP; McCartney, 1999; Gomides et al., 2010). Although peak systolic blood pressure (SBP) in the first repetition is higher with greater intensities (Sale et al., 1994), set duration has been suggested to be the main factor influencing on SBP workload (Lamotte et al., 2005b; de Souza Nery et al., 2010; Lovell et al., 2011). Thus, sets of low intensity with a higher number of repetitions produce greater cardiovascular responses when compared to sets of greater intensities, but with lower number of repetitions (Lamotte et al., 2005b; de Souza Nery et al., 2010; Lovell et al., 2011). Similarly, heart rate (HR) increases with the number of repetitions within a set, thus further increasing the rate pressure product (i.e. HR 9 SBP) (RPP; MacDougall et al., 1985; McCartney, 1999).

Therefore, at the same intensity, reducing the number of repetitions performed in every set may reduce loss of mechanical performance and limit the cardiovascular response to resistance exercise (RE). Traditional set configuration (TT) is performed in a continuous fashion where no rest is taken in between each repetition (Haff et al., 2008). An alternative set configuration consists of manipulating work and rest periods by breaking sets into small clusters of repetitions. This type of training has been termed cluster training (CT), inter-repetition rest training or intraset rest loading (Haff et al., 2003, 2008; Lawton et al., 2004; Hansen et al., 2011a,b) and has been proposed in order to improve mechanical performance during RE (Haff, 1997; Haff et al., 2003, 2008; Denton & Cronin, 2006; Hardee et al., 2012; Iglesias-Soler et al., 2012), thus increasing mechanical stimuli (Crewther et al., 2005; Denton & Cronin, 2006). Higher mechanical performance (e.g. higher velocity or power) during CT could be associated with a shorter time under tension and hence to a decrease in the time of mechanical compression of the skeletal muscle vasculature. This has

© 2014 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd

1

2 Set configuration and hemodynamics, E. Iglesias-Soler et al.

been proposed as an important factor underlying increases in BP during RE (McCartney, 1999). In this regard, previous studies have showed that the insertion of short relaxation pauses between repetitions is effective for reducing BP increments during RE (Baum et al., 2003; da Silva et al., 2007). However, in these previous studies, the work-to-rest ratio was not equated between conditions. This would be a limitation as training with pauses between each repetition was more timeconsuming, and total resting time has been shown to be a factor influencing on global hemodynamic response (Lamotte et al., 2006). However, to our knowledge, modulation of cardiovascular responses by set configuration when intensity, volume, and total resting time are equated has not been previously reported. Similar to SBP, previous studies on the acute effect of RE on cardiac autonomic modulation have suggested that fatigue induced by RE is associated with sympathetic activity and parasympathetic withdrawal (Heffernan et al., 2006; Rezk et al., 2006; Anunciacß~ao et al., 2011; Teixeira et al., 2011). However, the effect of set configuration on cardiac autonomic modulation during high-intensity RE has not been explored yet. Furthermore, it could be speculated that there is a relationship between changes in hemodynamic parameters during RE and changes in autonomic cardiac modulation as BP is a product of cardiac output and total peripheral resistance (Kenney & Seals, 1993), which are regulated by autonomic nervous system (ANS) activity (Iellamo, 2001). Therefore, the main objectives of this study were (i) to evaluate the effect of set configuration (CT versus TT) on cardiovascular responses [i.e. BP, RPP and heart rate variability (HRV)] and (ii) to analyse the relationship between changes in hemodynamics during RE and changes in HRV and heart rate complexity (HRC) after RE. We hypothesized that cardiovascular stress would be lower during CT when compared to TT of equated intensity, volume and work-to-rest ratio. We also expected a relationship between hemodynamic response and changes in cardiac autonomic modulation.

and not to consume alcohol or caffeine within 12 h. They were also asked to avoid strenuous physical activities the day before each session. They signed an informed written consent before participation. This study was conducted in conformity with the Declaration of Helsinki and was approved by the institutional review board committee. Study design Participants completed a 4-day experimental protocol, with at least 72 h between each session. Warm-up was the same for every session and consisted of 5 min of treadmill running at 50–70% of maximum HR, followed by 5 min of callisthenics. Day 1 and 2 were devoted to determine one repetition maximum (1RM) and the load corresponding to four repetition maximum (4RM) in parallel squat, respectively. The procedures to measure both 1RM and 4RM have been previously described (Iglesias-Soler et al., 2012). During the third day, a TT session was performed and consisted of performing three sets until failure with the 4RM load previously determined with 3 min of rest between sets. Failure was considered when a participant could not complete a repetition (i.e. muscular failure) and not reach the target position for two successive repetitions (i.e. technical failure). In the fourth session, which corresponded to CT, every participant lifted the same load as in the previous session, but in this occasion, the recovery was distributed between every repetition (i.e. one lift, rest, one lift, rest, etc.); thus, the volume, intensity and work-to-rest ratio were equated between sessions as previously described (Iglesias-Soler et al., 2012). In order to compare the acute effects of TT and CT on autonomic cardiac modulation, beat-to-beat intervals (R-R) were recorded at rest before, during, and after exercise. The auscultatory method was employed to obtain SBP before training and after each set during TT, or after each repetition during CT. A schematic representation of the study design and an example of the procedure to match work-to-rest ratio between TT and CT are presented in Fig. 1.

Methods Procedures

Participants Ten males volunteered for participation in this study. All of them were experienced judo fighters that had at least 18 months of experience in RT, with a minimum frequency of two sessions per week. Participants’ mean  SD age, height, and body mass were 23  4 years, 1
74  0
1 m, and 81
6  18 kg, respectively. This was a convenience sample of experienced athletes in order to minimize the possible influence of factors (e.g. psychological) other than training load on cardiovascular responses to physical stress. All the participants completed a familiarization period based on research procedures. Participants were instructed to remain fast for 3 h

SBP and RPP Systolic blood pressure was measured using a sphygmomanometer (Riester, Jungingen, Germany), and a phonendoscope (3M Health Care, ST. Paul, MN, USA). SBP was measured before RE and after each set in the case of TT, and after each repetition during CT. Subsequently, RPP was also calculated (i.e. Heart Rate 9 SBP) as a surrogate of cardiac energy demand. SBP was monitored over both TT and CT sessions by an experienced practitioner following standard recommendations (Perloff et al., 1993). SBP was obtained with participants in standing position with the arm flexed at the level of the

© 2014 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd

Set configuration and hemodynamics, E. Iglesias-Soler et al. 3

(a)

(b)

Figure 1 Schematic representation of the study (a) and example of evaluation of rest intervals in CT (b). The athlete performed nine repetitions in TT. In order to accumulate 360 s of recovery at the 9th repetition of CT, we needed to distribute the total resting time between the previous eight recovery intervals. reps. indicates repetitions; Total Volume = repetitions 9 load. TT, Traditional set configuration; CT, cluster training.

heart and rested on the shoulder of the researcher. Heart rate during the assessment of SBP was considered to calculate RPP. Heart rate and R-R intervals R-R recordings were collected before, during, and after TT and CT sessions, with a memory Belt (Suunto Ltd, Vantaa, Finland) at a sampling frequency of 1000 Hz. Validity and reliability of this device has been previously reported (Weippert et al., 2010). Before exercise, the participants remained seated during 10 min with spontaneous breathing. HR was also recorded during exercise. Finally, 8 min after performing the last repetition, participants sat and then HR was recorded during 10 min. Eight minutes was the time needed to obtain SBP immediately after the last repetition and to evaluate neuromuscular fatigue after each session as previously reported (Iglesias-Soler et al., 2012). Heart rate variability and heart rate complexity analyses Time domain, frequency domain, and nonlinear measures of HRV were used to estimate cardiac autonomic modulation. Time domain parameters were standard deviations of RRI (SDNN) and the root mean square of differences between adjacent RRI (RMSSD). Fast Fourier Transformation method was employed for spectral analyses of HRV. Power of high (HF, 0
15–0
4 Hz) and low frequency (LF, 0
04–0
15 Hz) bands were calculated in both absolute and normalized units (nu). The ratio of absolute LF to HF power was used as an indicator of sympatho-vagal balance (Task Force, 1996). Linear time domain and frequency domain measures of HRV have been previously employed during exercise (Richman & Moorman, 2000;

Makikallio et al., 2002; Heffernan et al., 2008), but the use of these methods has important limitations because the RRI time series is non-stationary during exercise. Thus, analysis of complexity of the signal by nonlinear methods such as sample entropy (SampEn) has been proposed as more suitable to evaluate cardiac autonomic modulation during exercise (Richman & Moorman, 2000; Makikallio et al., 2002; Heffernan et al., 2008). Therefore, frequency domain and absolute time domain measures were only considered for seated rest conditions (i.e. before and after sessions), while SampEn was used as a measure of HRC to evaluate cardiac autonomic modulation at rest and during exercise. In this case, an embedding dimension m (i.e. length of sequences to be compared) of 2 was used, while the filter parameter r (i.e. tolerance for accepting matches) was set at 20% of the standard deviation of the time series (Heffernan et al., 2008). Automatic artefact correction (i.e. strong correction level) and calculation of HRV and HRC measures were performed using the Kubios HRV software (The Biomedical Signal and Medical Imaging Analysis Group, Department of Applied Physics, University of Kuopio, Finland). The data were detrended with the smoothness priors method, which is a time-varying high pass filter with a cutoff frequency that can be adjusted with the Lambda parameter (the bigger the value of Lambda, the smoother is the removed trend). The Lambda value was fixed at 500. For data analyses at seated rest (before and after sessions), the first and the last minute were removed; thus, a period of 8 min was evaluated. First minute was removed to guarantee the stabilization of HR when participants changed from stand to seat position. Last minute was removed to avoid the influence of anticipation at the end of HR recordings. For analyses of HRC during exercise, a time window from the first to the

© 2014 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd

4 Set configuration and hemodynamics, E. Iglesias-Soler et al.

last repetition was selected. Mean length for exercise period was 535  66 s.

Results

Statistical analyses

Mean SBP, HR and RPP are presented in Table 1. Paired t-test revealed significant differences between training conditions for both SBP and RPP. Systolic blood pressure increased during exercise (Fig. 2a), showing significant differences between sessions at Set 2 (P = 0
014; d = 0
73; 95% CI = 0
44–1
01), and Set3 (P = 0
03; d = 0
67; 95% CI = 0
40–0
94), whereas a tendency was also detected for Set 1 (P = 0
059; d = 0
48; 95% CI = 0
24–0
72). Similarly, HR increased during exercise (Fig. 2b), with higher values in TT at Set 2 (P = 0
03; d = 0
72; 95% CI = 0
41– 1
03) and at Set 3 (P = 0
048; d = 0
66; 95% CI = 0
36–0
97) when compared to CT. Consequently, RPP (Fig. 2c) was higher during TT at set 2 (P = 0
009; d = 0
88; 95% CI = 0
54–1
21), and Set 3 (P = 0
028; d = 0
83; 95% CI = 0
46–1
20) when compared to CT.

SBP and RPP

SPSS version 15.0 software (SPSS, Inc., Chicago, IL, USA) was used to analyse the data. Descriptive parameters are shown as mean  SD. Normal distribution of parameters was tested using Kolmogorov–Smirnov (Lilliefors) test. If data violated the assumption of normality, logarithmic transformation was applied. In order to compare cardiovascular response to both types of set configuration (i.e. average values of SBP and RPP during exercises), paired t-test was used. To contrast the evolution of parameters over CT and TT, a two way repeated measures ANOVA (time 9 session) was used. Data after the end of each set in TT and data after the repetition corresponding to the last repetition of each set in TT were subsequently analysed (i.e. repetitions 4, 7 and 9 in the example of Fig. 1b). Post hoc analyses were conducted using paired t-test with Bonferroni adjustment. When simple effect of session factor was detected at a level of time factor, effect size was calculated using standardized mean differences (d). Ninety-five percent confidence intervals (CI 95%) of d were obtained according to asymptotic estimates of standard error for paired repeated measures (Nakagawa & Cuthill, 2007). The relationships between changes in HRV or HRC and changes in hemodynamics were evaluated by both Pearson product–moment correlation and Spearman’s rank correlation coefficient when appropriate. For each variable, percentage changes (Δ%) at the end of sessions were obtained as follows: [(measurement after exercise – measurement before exercise)/measurement before exercise] 9 100. Significance level was set at P≤0
05. A post hoc power analysis was calculated using the G Power software (version 3.1.4, Heinrich Heine University, Du¨sseldorf, Germany). For a sample size of 9, and a large effect, statistical power ranged from 0
56 to 0
75 depending on the statistical test. Additionally, we obtained the sensitivity of these tests (i.e. the minimum effect size the test was sufficiently sensitive to) for an alpha level of 0
05 and a power of 0
80. These analyses showed that a statistical power of 0
80 was achieved for a large effect size, while the tests employed had lower power to detect medium or small effect sizes.

HRV and SampEn Final sample size for HRV and HRC analyses was 9 because an important amount of missing data was detected in one participant’s HR recordings. Table 2 shows the results of the 2 9 2 ANOVA for HRV variables. Neither session nor the interaction between time and session factors were significant, while time factor was significant in most parameters with HRV reduced after exercise. The 2 9 3 ANOVA corresponding to natural logarithmic of SampEn is showed in Fig. 3. SampEn was reduced during exercise and returned to pre-exercise levels after training sessions (time effect, P