Peripheral heart action (PHA) training as a valid ... - Springer Link

Eur J Appl Physiol (2015) 115:763–773 DOI 10.1007/s00421-014-3057-9

ORIGINAL ARTICLE

Peripheral heart action (PHA) training as a valid substitute to high intensity interval training to improve resting cardiovascular changes and autonomic adaptation Alessandro Piras · Michela Persiani · Nicholas Damiani · Monica Perazzolo · Milena Raffi

Received: 25 July 2014 / Accepted: 17 November 2014 / Published online: 27 November 2014 © Springer-Verlag Berlin Heidelberg 2014

Abstract Purpose The present study evaluated the effects of peripheral heart action training compared with high intensity interval training on changes in autonomic regulation and physical fitness. Methods Eighteen young adults (9 women, 9 men) (age 24 ± 3 years, BMI of 22.67 kg/m2, V′O2max 32.89 ml/kg/ min) were randomly assigned to either a high intensity interval training group (n = 8) or a peripheral heart action training (PHA) group (n = 10). Before and after training, maximal whole-body muscular strength, time series of beat-to-beat intervals for heart rate variability, and baroreflex sensitivity were recorded. Arterial baroreflex sensitivity and heart rate variability were estimated on both time and frequency domains. Physical fitness level was evaluated with maximum oxygen consumption test. Results The effects of PHA whole-body resistance training increased muscular strength and maximum oxygen consumption, with an effect on vagal–cardiac control and cardiovagal baroreflex sensitivity. Conclusions After 30 training sessions performed in 3 months, PHA resistance exercise promoted cardiovascular adaptations, with a decrease in the power spectral component of vascular sympathetic activity and an increase in the vagal modulation. Low-frequency oscillation estimated from systolic blood pressure variability seems to be a suitable index of the sympathetic modulation of vasomotor activity. This investigation also want to emphasize the

Communicated by Guido Ferretti. A. Piras (*) · M. Persiani · N. Damiani · M. Perazzolo · M. Raffi Department of Biomedical and Neuromotor Sciences, University of Bologna, Piazza di Porta S. Donato, 2, Bologna 40126, Italy e-mail: [email protected]

beneficial effects of this particular resistance exercise training, considering also that the increase in muscular strength is inversely associated with all-cause mortality and the prevalence of metabolic syndrome, independent of cardiorespiratory fitness levels. Keywords Heart rate variability · Baroreflex sensitivity · Resistance exercise · PHA peripheral heart action · HIIT high intensity interval training Abbreviations ANCOVA Analysis of covariance (a-v)O2 Artero-venous oxygen difference BRS Baroreflex sensitivity BP Blood pressure SBP Systolic blood pressure DBP Diastolic blood pressure ECG Electrocardiogram FFT Fast Fourier transformation HIIT High intensity interval training HRV Heart rate variability HF High frequency LF Low frequency NN50 The number of interval differences of successive R–R intervals greater than 50 ms N Newton PHA Peripheral heart action pNN50 Proportion derived by dividing NN50 by the total number of R–R intervals NN50 RM Maximum repetition %1-RM The percentage of one maximum repetition RMSSD The square root of the mean squared differences of successive R–R intervals SDNN Standard deviation of the R–R interval V′O2max Maximum oxygen consumption

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Introduction Resistance exercise has been recommended as a part of comprehensive exercise program to reduce cardiovascular risk in healthy (ACSM 2013) and in hypertensive populations (Pescatello et al. 2004). In healthy subjects, during rest, resistance exercise training has been shown to improve vagal control of the heart, with relative blood pressure reduction (Collier et al. 2009) and bradycardia (George et al. 1995). However, the cardiovascular and autonomic adaptation of this kind of exercise, especially on baroreflex sensitivity and heart rate variability, are not completely understood. In fact, Selig et al. (2004) found a decrease of the low frequency (LF) to high frequency (HF) ratio of HRV following 3 months of moderate resistance training in heart failure patients, but no changes were observed following dynamic resistance training in healthy older women (Forte et al. 2003). To our knowledge, no studies have investigated the potential resting cardiovascular changes and autonomic adaptation that may occur after exposure to particular resistance exercise such as peripheral heart action (PHA) training. PHA does not appear on fitness trend (Thompson 2013), it is a “system” that was developed by Dr. Arthur Steinhaus in the 1940s. The PHA method was specifically designed to keep blood circulating throughout the whole body during the entire workout, with few exercises (five– six) performed at low-intensity that stress the upper and lower body musculature, with the intention of alternating one exercise for the upper torso and one for the lower extremities with active pauses (no rest period) between exercises. With respect to the classical resistance training, PHA promotes blood flow throughout the body during the entire duration of the training session. It has been shown that low-intensity resistance training with short inter-set rest periods reduces arterial stiffness and improves vascular endothelial function, with an increase in resting arterial diameter (Okamoto et al. 2011). Moreover, plasma volume decreases linearly in relation to intensity (%1-RM) of weight lifting, due to shift of plasma liquid from blood to the interstitial space, a relation similar to that reported for dynamic, low-resistance exercise such as cycling and running (Collins et al. 1989). Rezk et al. (2006) reported that resistance exercise promoted post-exercise hypotension in normotensive subjects, with decrease in systolic blood pressure after both low- and high-intensities exercises, similar to those usually reported after aerobic exercise (Pescatello et al. 2004). The majority of studies on aerobic exercise have investigated the role of high intensity interval training (HIIT), which has been shown to reduce heart rate and arterial stiffness, increase aerobic fitness, stroke volume, limb vasodilatory capacity, heart rate variability, and baroreflex

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Eur J Appl Physiol (2015) 115:763–773

sensitivity in young healthy men after 12 weeks of training (Heydari et al. 2013). Now HIIT is the first top fitness trends and it reflects how this form of exercise has taken the fitness community in recent months (Thompson 2013). To date, the examination of the resting cardiac, vascular, and autonomic function responses to other forms of exercise has been limited. It is important to underline that the results of studies dealing with cyclic and resistance exercise training on circulation have been contradictory. Large-scale studies showed that both types of exercises had favorable effects on the central and peripheral hemodynamic parameters and metabolic risk factors for cardiovascular disease (Williams et al. 2007). Others studies reported that they have the opposite cardiovascular effects. For example, different types of myocardial hypertrophy (Pluim et al. 2000), arterial distensibility (Otsuki et al. 2006), heart rate variability (HRV) (Cooke and Carter 2005), and changes in baroreflex sensitivity (BRS) (Collier et al. 2009) were detected after comparing resistance versus aerobic training programs. The present study evaluated the effects of PHA training compared with HIIT on changes in autonomic regulation and physical fitness, with the intention to see whether PHA training brings benefits similar to those widely demonstrated after HIIT in young previously untrained people. We measured baroreflex activity by analyzing the simultaneous spontaneous variations of heart rate and systolic blood pressure, a non-invasive method that reflects a global measure of baroreflex activity (Pagani et al. 1986; Parati et al. 2000; Pinna et al. 2000). To this purpose, we applied a spectral analysis of RR interval and of systolic blood pressure (SBP) variability based on short-term analysis. Previous studies have already shown that this approach provides quantitative markers of sympathetic and vagal activities modulating the heart rate (Pagani et al. 1986) and of the sympathetic activity regulating vasomotor tone (Rimoldi et al. 1990).

Methods Subjects The participants of this study were 18 healthy volunteers (9 women, 9 men) (age 24 ± 3 years, BMI of 22.67 kg/ m2, V′O2max 32.89 ml/kg/min) who gave written informed consent to participate in the study. Participants were randomly assigned to either a HIIT group (n = 8) or a PHA group (n = 10). Subjects had no musculoskeletal or cardiorespiratory disease, were not smokers, and were not taking medications. Subjects were all recreationally active but not currently involved in specific training programs and were instructed to continue normal daily activities and to


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refrain from beginning any other training until the completion of the study. Subjects were instructed to avoid strenuous activity, caffeine and alcohol in the 24 h preceding the test (Doherty and Smith 2004). This study was approved by Ethics Committee of the University of Bologna.

(1-RM) values were calculated according to the following equation (Brzycki 1993): 1-RM = weight lifted/[1.0278 − (0.0278 × N° repetitions)]. Proper exercise form was taught and enforced by one of the investigators who supervised and documented each testing session.

Testing procedure

Cardiac autonomic regulation evaluation (HRV and BRS)

We performed two evaluations: a first measurement, called pre-test, was performed 3–4 days before the beginning of the training program. A second measurement, called posttest, was performed 3–4 days after the conclusion of the training program. All pre- and post-evaluation tested maximal oxygen uptake, muscular strength, and cardiac autonomic regulation. 30 training sessions of PHA and HIIT were performed over a 3-month period, with three sessions per week separated by 1–2 days of rest. Training was monitored by one of the investigators.

Pre- and post-tests were performed under a standardised protocol at the same time of the day to avoid circadian influence (Nakagawa et al. 1998; Collier et al. 2008) in a quiet room with stable temperature (22–25 °C). Participants were asked to stay in a supine position for 20 min, in a comfortable bed, without speaking or making any movements and the respiratory frequency were maintained on 12–15 breaths/min (Collier et al. 2008; van Zyl et al. 2008). Only 5 min, from 10′ to 15′, were used for analysis, as recommended by guidelines for heart rate variability analysis during short-term recording (Camm et al. 1996). Time series of beat-to-beat intervals were recorded using ECG signal (Cardioline® click, Milan, Italy) and extracted for HRV estimation. Data were analyzed with Kubios HRV software (v. 2.0, 2008, Biosignal Analysis and Medical Imaging Group, University of Kuopio, Finland). R–R intervals were visually inspected for artifacts; ectopic beats were identified and interpolated. A parameter set of time and frequency domain measures were calculated according to specifications by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (Camm et al. 1996). Arterial BRS was derived from the R–R intervals and beat-to-beat systolic arterial pressure computed from the finger arterial pressure waveform with Portapres (Portapres M2, TNO Biomedical Instrumentation). These data were then used to determine the coupling between fluctuations in heart rate and systolic arterial pressure.

Maximal oxygen consumption evaluation (V′O2max) Expired gases were analyzed using a Quark b2 breath-bybreath metabolic system (Cosmed, Rome, Italy) while subjects cycled on an ergometer (model H-300-R, Lode) at 30 watt for 3 min as a warm-up, followed by an instantaneous increase of 1 W every 2 s at a cadence above 70 rpm (Heydari et al. 2013). The maximal exercise test lasted until attainment of a V′O2 plateau or the attainment of at least two of the three additional criteria: (1) a plateau of heart rate despite an increased power, (2) inability to maintain the cycling cadence (i.e., dropped by >10 rpm), or (3) exercise cessation due to substantial fatigue. V′O2 plateau was defined as an increase in V′O2 ≤ 50 ml/min during the last 30 s despite increased power (Yoon et al. 2007). The highest V′O2 values reached during the exercise phase of the incremental test were considered as the maximal V′O2 uptake. Subjects were not given any visual, physiological, or temporal feedback during the test. Strength evaluation (1‑RM) Maximal whole-body muscular strength was assessed before and after 30 training sessions in both groups using the following six isotonic exercises in the order listed (with the primary muscle groups trained shown in parentheses): pectoral machine (pectoralis major and minor), leg extension (quadriceps), lat machine (latissimus dorsi), leg curls (hamstrings), shoulder press (deltoids), and calf machine (gastrocnemius) (Technogym®, Gambettola, Italy). Subjects first completed ten warm-up repetitions, followed by two or three additional sets with proper exercise form to become confident with exercises. After a suitable rest period in order to avoid fatigue, one repetition maximum

Time‑domain analysis To assess spontaneous baroreflex sensitivity, we used Beatscope version 1.1a (TNO Biomedical Instrumentation) with a BRS add-on module which calculates the timedomain cross-correlation BRS. This method is based on the computer identification in the time domain of spontaneously occurring sequences of four or more consecutive beats characterized by either a progressive rise in SBP and lengthening in R–R interval (+RR/+SBP sequences) or by a progressive decrease in SBP and shortening in R–R interval (−RR/−SBP sequences) (Parati et al. 2000). The slope of the regression line between SBP and R–R interval changes is taken as an index of the sensitivity of arterial baroreflex modulation of heart rate (BRS), as with the laboratory method based on i.v. injection of vasoactive drugs.

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This method for BRS computation gives more values per minute than the standard time-domain-based method and has lower within-patient variance than other methods (see Westerhof et al. 2006 for details). Time-domain indices for HRV analysis were: standard deviation of the R–R interval (SDNN), the square root of the mean squared differences of successive R–R intervals (RMSSD), the number of interval differences of successive R–R intervals greater than 50 ms (NN50), and the proportion derived by dividing NN50 by the total number of R–R intervals NN50 (pNN50). Spectral analysis To quantify these short-term modulations of BP and HR, spectral analysis has been used (Malliani et al. 1991), providing two main frequency components: low frequency (LF) ranging from 0.04 to 0.15 Hz, and high frequency (HF) centered at the breathing frequency (Camm et al. 1996). At rest, the quantification of spectral components gave indexes of the autonomic control of HR and BP. On the one hand, it has been shown that the HF spectral component of HR variability (HFR-R) is an index of the vagal tone (Camm et al. 1996), whereas both sympathetic and vagal activities contributed to the LF (LFR-R) spectral component of HRV (Camm et al. 1996; Casadei et al. 1996). Given that LFR-R does not provide an index of sympathetic modulation when measured in absolute units (Akselrod et al. 1981), we expressed the power in both absolute and normalized units (Camm et al. 1996). Such normalized units are obtained by dividing the power of each component by total variance from which the verylow-frequency component had been subtracted, and multiplying this value by 100 (Pagani et al. 1986; Malliani et al. 1991). Although their mixed origin (Malliani et al. 1991), low and high frequency (LFR-R and HFR-R) spectral components measured in normalized units, or as LF/HF ratio, provide quantitative markers of cardiac sympathetic and vagal modulation, respectively (Pagani et al. 1986). On the other hand, the LF (LF-SBP) spectral component of systolic BP variability only reflected the sympathetic activity to the α-adrenergic receptors of vasculature (Furlan et al. 1990; Japundzic et al. 1990), whereas HF-SBP probably reflected the mechanical effect of breathing on SBP (Pagani et al. 1986; Malliani et al. 1991). Then, BRS was analyzed by the alpha index, computed as square root of the ratio between the RR powers and the corresponding SAP spectral components (Pagani et al. 1988), representing a further global index to explore the sympathovagal modulation of sinoatrial node spontaneous activity (Pagani et al. 1986). Robbe et al. (1987) indicated that the modulus in the mid frequency band (0.07–0.14 Hz) between SBP and RR interval time gives equivalent results

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to those obtained using the phenylephrine method. Thus, it is a useful index for the BRS analysis. Exercise training protocols HIIT session Each HIIT session consisted of a 5-min warm-up followed by 1-min exercise at 100 % of V′O2max, monitored through the corresponding heart rate obtained during pre-test, followed by 2-min “loadless” cycling, and 5-min cool-down. This session was repeated five times on training days 1 and 2 and gradually increased in the following days depending on the subject’s heart rate, which was monitored with Polar Heart Rate Monitor (Polar Electro Oy; Kempele, Finland). Subjects were instructed to cycle at 70 rpm, strong verbal encouragement was given, and resistive loads were altered in accordance with the average pedal cadence to ensure that the average workload for each 1-min bout was 100 % of V′O2max. PHA session Each PHA session started with a 5-min warm-up and concluded with a cool-down. The conditioning phase of each session involved circuit weight training and consisted of six resistance exercise stations, as strictly ordered: pectoral machine, leg extension, lat machine, leg curl, shoulder press and calf machine. Subjects performed 15 repetitions of pectoral machine, and then moved to the next station (leg extension) with active pauses (e.g., subjects performed an exercise of the lower limbs as soon as they have finished one on the upper limbs, and vice versa), until the completion of the circuit training (calf machine). Such circuit training was performed four times, separated by 1-min of rest. Resistance was increased for the next exercise session if the subject could perform fifteen complete repetitions during the final set for each exercise. Subjects wore a heart rate monitor and maintained an intensity around 55–60 % of 1-RM, which corresponded approximately to 60–80 % of maximal heart rate calculated during the pre-test. Statistical analysis Data are presented as mean ± standard deviation (SD). The normal distribution of data was done with Shapiro–Wilk test. Variables with skewed distributions were log transformed (Ln) before analysis. To examine changes after intervention, an analysis of covariance (ANCOVA) was used to evaluate differences between the two groups (Van Breukelen 2006). Pre-test was used as a covariate, post-test was the dependent variable, and group (HIIT, PHA) the fixed factor. Data were analyzed with SPSS v13.0 (SPSS,


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Table 1 Characteristics of the two groups of participants at pre-test HIIT (n = 8) (6 women, 2 men)

PHA (n = 10) (3 women, 7 men)

Age (years) Height (cm)

25.37 ± 3.20 167.63 ± 10.90

22.90 ± 2.47 173.30 ± 9.60

BMI (kg/m2)

23.58 ± 2.74

21.94 ± 4.18

Maximum oxygen consumption

Values are presented as mean ± SD

Analysis showed a significant post-test main effect (F1,15 = 16.67, p 0.05).

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VO2max (ml/kg/min)

Fig. 1 Maximum oxygen consumption (V′O2max) changes after 30 training sessions of HIIT (filled diamond) and PHA (filled square) training. Conventions as in Table 1

HIIT PHA

41 39 37 35 33 31 29 27 25

Pre-test

Post-test

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BRS (ms/mmHg)

Fig. 2 BRS changes after 30 training sessions of HIIT (filled diamond) and PHA (filled square) training. Conventions as in Table 1

BRS changed significantly after 30 training sessions on both groups (F1,15 = 8.97, p = 0.009, ηp2 = 0.37), and showed a greater increase after HIIT than PHA (18.16 vs. 10.94 %) (Fig. 2). A decrease of SBP (F1,15 = 10.68, p = 0.005, ηp2 = 0.42) and mean arterial pressure (F1,15 = 6.62, p = 0.024, ηp2 = 0.33) was also found in both groups. Both pressures were reduced more after PHA than HIIT (−2.59 vs. −0.75, and −1.69 vs. −1.35) (Fig. 2). No significant difference was observed in diastolic blood pressure after training intervention. Frequency domain analysis of SBP showed significant post-test main effect for HF-SBP (F1,15 = 69.91, p = 0.000, ηp2 = 0.82) and for LF-SBP (F1,15 = 6.81, p = 0.020, ηp2 = 0.31). As shown in Table 2, after 30 training sessions, HF-SBP (mmHg2) were higher in both groups, instead LF-SBP was higher in HIIT group and lower after PHA training.

HIIT PHA

18 16 14 12 10 8

Pre-test

Post-test

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Table 2 Hear rate variability and BRS measures

All data are presented as mean ± SD On each variable the first row represents raw data, and the second row log-transformer data (Ln). Conventions as in Table 1 HR heart rate, R–R mean waves interval, RMSSD root mean square of successive differences, SDNN standard deviation of all R–R intervals, NN50 number of pairs of adjacent R–R intervals differing by more than 50 ms, pNN50 percentage of interval differences of successive R–R intervals greater than 50 ms, LF low frequency power, HF high frequency power, LF:HF ratio LF/HF, αLF and αHF alfa index of baroreflex sensitivity, SBP systolic blood pressure, n.u. normalized units, Ln natural logarithm

HRrest (beat/min) R–R (ms) RMSSD (ms) RMSSD (Ln/ms) SDNN (ms) SDNN (Ln/ms) NN50 (count) NN50 (Ln/count) pNN50 (%) pNN50 (Ln/%) LFR-R (ms2) LFR-R (Ln/ms2) LFR-R (n.u.) HFR-R (ms2) HFR-R (Ln/ms2) HFR-R (n.u.) LF:HF LF:HF (Ln) LF:HF (n.u.) αLF (ms/mmHg) αHF (ms/mmHg) LF-SBP (mmHg2) HF-SBP (mmHg2)

HIIT (n = 8) (6 women, 2 men)

PHA (n = 10) (3 women, 7 men)

Pre

Pre

Post

68.24 ± 11.82 63.33 ± 9.45 68.42 ± 10.81 62.66 ± 9.21 908.69 ± 160.93 971.86 ± 127.94 908.27 ± 145.60 989.15 ± 132.23 52.54 ± 21.59 59.64 ± 25.31 50.79 ± 22.87 57.29 ± 18.08 3.91 ± 0.39 4.30 ± 0.45 3.83 ± 0.48 4.86 ± 0.38 46.41 ± 15.37 54.66 ± 22.61 46.56 ± 19.26 53.60 ± 14.40 3.77 ± 0.32 3.92 ± 0.45 3.76 ± 0.44 3.97 ± 0.29 335.63 ± 165.77 396.00 ± 199.54 335.10 ± 213.04 349.30 ± 181.31 5.58 ± 0.53 5.86 ± 0.64 5.58 ± 0.99 5.73 ± 0.70 29.05 ± 19.21 31.75 ± 19.46 29.56 ± 21.16 27.90 ± 14.83 3.28 ± 0.67 3.28 ± 0.76 2.92 ± 1.10 3.08 ± 0.81 828.50 ± 792.64 1,511.12 ± 1,291.86 1,192.90 ± 1,000.00 1,188.70 ± 679.89 6.41 ± 0.80 6.96 ± 0.94 6.73 ± 0.93 6.94 ± 0.56 45.25 ± 13.35 48.61 ± 14.08 56.70 ± 16.55 54.49 ± 16.14 1,168.62 ± 774.65 1,584.75 ± 1,337.46 994.80 ± 854.37 1,031.30 ± 951.62 6.87 ± 0.66 7.02 ± 0.92 6.43 ± 1.16 6.58 ± 0.96 54.75 ± 13.35 51.39 ± 14.08 43.30 ± 16.55 45.51 ± 16.14 0.73 ± 0.48 1.10 ± 0.65 1.70 ± 1.21 1.90 ± 1.76 0.98 ± 0.03 0.99 ± 0.03 1.06 ± 0.04 1.07 ± 0.05 0.82 ± 0.46 0.95 ± 0.63 1.31 ± 0.51 1.21 ± 0.62 13.17 ± 3.39 16.88 ± 2.11 13.87 ± 2.37 16.73 ± 2.24 12.66 ± 2.20 13.65 ± 2.18 9.46 ± 1.99 10.58 ± 1.87 4.63 ± 1.01 5.63 ± 1.66 5.50 ± 1.12 5.00 ± 1.50 7.39 ± 1.71

Training significantly increased the gain of the RR-SBP relationship at rest, both for LF (F1,15 = 5.98, p = 0.022, ηp2 = 0.29) and HF (F1,15 = 6.76, p = 0.020, ηp2 = 0.31) components in both groups (see Table 2; Fig. 3). Heart rate variability indices The analysis revealed significant post-test main effect, with a decrease for resting heart rate (F1,15 = 12.41, p = 0.003, ηp2 = 0.45), and an increase for R–R (F1,15 = 17.44, p = 0.001, ηp2 = 0.54); for RMSSD (F1,15 = 6.48, p = 0.022, ηp2 = 0.30), for SDNN (F1,15 = 7.85, p = 0.013, ηp2 = 0.34), as well as for pNN50 (F1,15 = 4.97, p = 0.041, ηp2 = 0.24). Moreover, there was a significant post-test main effect, with an increase for LF power (F1,15 = 8.25, p = 0.012, ηp2 = 0.35), and HF power (F1,15 = 10.47, p = 0.006, ηp2 = 0.41) expressed in absolute values. Frequency domain analysis in normalized units showed a significant post-test main effect both for HFR-R (F1,15 = 6.15, p = 0.025, ηp2 = 0.29), for LFR-R (F1,15 = 6.15, p = 0.025, ηp2 = 0.29) and for the LF/HF index of sympathovagal balance (F1,15 = 20.29, p = 0.000, ηp2 = 0.57). As shown in Table 2, most of the heart

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Post

10.25 ± 3.85

9.70 ± 2.41

12.00 ± 2.94

rate variability indices have followed the same trend in both groups. Even if no significant group differences were found, a different trend was observed for LF and HF expressed in normalized units. PHA training spectral markers of autonomic activity controlling heart rate suggested, after inducing physiological adaptation to training, a simultaneous reduction of sympathetic modulation and increase of vagal modulation, as LFR-R was reduced and HFR-R was increased. The opposite was observed in the HIIT group. Strength indices The analysis revealed significant pre-test main effect for leg extension (F1,15 = 121.40, p