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Abstract

BACKGROUND:

Cluster set plyometrics (CSP) promise greater muscular performance than traditional set plyometrics (TSP).

OBJECTIVE:

The purpose of this study was to compare the influence of TSP and CSP warm-ups via bounce drop jump (BDJ) on reactive strength index (RSI), leg stiffness (K ${}_{\text{leg}}$ ), ground contact time (CT), and jump height (JH).

METHODS:

Thirteen male rugby players (age, 20.92 $\pm$ 2.25 years; body mass, 82.88 $\pm$ 9.22 kg; rugby experience, 3.61 $\pm$ 3.04 years; training volume, 10.69 $\pm$ 3.75 h/week) were recruited for this study from two rugby clubs competing in the Second League in Turkey. The players completed 3 different set configurations of equal jump volume 72 h apart in a randomized, balanced order. The sets configurations were TSP (2 $\times$ 10 jump with 90 s interval), CSP-1 (4 $\times$ 5 jump with 30 s interval), and CSP-2 (10 $\times$ 2 jump with 10 s interval). Pre and post values of RSI, K ${}_{\text{leg}}$ , CT, and JH on each session were determined via the Myotest Pro system.

RESULTS:

Statistically significant changes were not found between pre- and post-values of RSI, K ${}_{\text{leg}}$ , CT, and JH in each session, either positive or negative ( $p>$ 0.05), as well as among the three different set configurations in any of the outcome variables according to post-results ( $p>$ 0.05). These findings indicated that low-volume TSP and CSP bounce drop jump had no effect on RSI, K ${}_{\text{leg}}$ , CT, and JH in male rugby players.

CONCLUSIONS:

Set configurations, jump volume, and intensity applied in this study may be too low to change muscle-tendon complex (MTC) stiffness and produce a post-activation potentiation (PAP) effect. Further research, with larger samples, is needed to determine which set configurations, jump volume ( $>$ 20 jumps/session), and intensity (box height $>$ 30 cm) are the best option for enhancement of RSI, K ${}_{\text{leg}}$ , CT, and JH.

Keywords

Cluster set traditional set bounce drop jump reactive strength index leg stiffness rugby

1. Introduction

Several minutes of low-intensity aerobic exercise followed by static stretching (SS) is generally recommended for athletes to enhance body temperature, muscle temperature, nerve conduction velocity, enzymatic cycling, and muscle compliance [1, 2]. Although SS has been considered an essential component of a warm-up [1, 2], several studies showed that sustained SS could impair subsequent explosive performance due to mechanical (e.g., alterations in muscle tendon unit stiffness) and neural alterations such as changes in reflex sensitivity and decreased motor unit activation [3, 4, 5].

Due to the possible detrimental effects of SS, dynamic warm-up (DW) such as high-intensity skip, hop and throw, complex training (heavy-resistance exercise with a plyometric exercise), and plyometrics have replaced SS in modern athletic warm-up [6, 7, 8, 9, 10, 11, 12, 13]. The positive effect of DS on explosiveness of the muscle is explained by post-activation potentiation (PAP) [6, 8, 14]. PAP is an acute and temporary enhancement of muscle performance as a result of its contractile history [14] via an increase in phosphorylation of myosin regulatory light chains [6].

Plyometrics is a dynamic warm-up type [9, 12]. Plyometric training is commonly used to develop the efficiency of an athlete’s stretch-shortening cycle (SSC) [15]. SSC is described as the successive combination of eccentric and concentric action, which is used in most sports movement such as throwing, sprinting, agility motion, and jumping as in rugby [15, 16]. Plyometrics enhances athlete’s SSC efficiency via increased muscle-tendon complex (MTC) stiffness and PAP of the muscle when applied acutely or chronically [6, 17, 18, 19]. Kubo et al. [20] reported that plyometric training significantly increased maximal Achilles tendon elongation and the amount of stored elastic energy, which led to improved SSC jumping performance.

Leg stiffness (K ${}_{\text{leg}}$ ) is considered a key attribute in the enhancement of SSC characteristics of the MTC [21]. Stiffness in the human body portrays its capability to withstand displacement once ground reaction forces (GRFs) are applied and can be defined as the ratio between peak GRFs and peak displacement of the center of mass [22, 23]. Komi [24] proposed that higher stiffness levels in the lower extremity muscle during SSC exercises lead to an advantage in terms of the larger amount of stored and reused elastic energy.

Another indicator of the SSC function of the MTC is the reactive strength index (RSI). RSI is described as an individual’s ability to change quickly from an eccentric to concentric contraction and can be considered as a measure of explosiveness [25]. The RSI also has been described as a simple tool to monitor stress on the MTC [26]. RSI and K ${}_{\text{leg}}$ are two different ways to describe the ability of an athlete to change with minimal possible delay from eccentric to concentric muscular contractions during dynamic jumping activity [26, 27].

In traditional set plyometrics (TSP), athletes should perform each repetition in a continuous fashion without rest between each repetition [28]. However, several researchers argued that in the traditional set configuration, power output decreases significantly during the last 5 of 10 repetitions [29, 30, 31]. A decreased power output after several repetitions is explained by the fatigue-induced alterations in the muscle depending on decreased adenosine triphospate (ATP) and phosphocreatine (PCr) and increased lactic acid production [15, 28, 29].

Recently, a new set configuration termed “cluster set” (CS), or inter-rep, rest training. This training structure involves the manipulation of work and rest periods, breaking sets into small clusters of repetitions, which may alter the training stimulus associated with a given resisted strength training session [32]. In this type of set configuration, an inter-repetition rest interval of 10–30 s is employed between repetitions [15]. This inter-repetition rest interval allows for some replenishment of PCr, which is vital for increasing muscle power and strength [15, 28, 32].

There seems to be a lack of information on the effects of cluster set configurations (CS) on plyometrics. Therefore, the purpose of this study was to compare the influence of TSP and cluster set plyometric (CSP) warm-ups via bounce drop jump (BDJ) on RSI, K ${}_{\text{leg}}$ , ground contact time (CT), and jump height (JH) in male rugby players. The hypothesis was that CSP warm-up would result in greater RSI and K ${}_{\text{leg}}$ than traditional plyometrics warm-up.

Figure 1.

Experimental design flowchart.

2. Methods

2.1 Subjects

Thirteen male rugby players (age: 20.92 $\pm$ 2.25 year; body mass: 82.88 $\pm$ 9.22 kg; rugby experience: 3.61 $\pm$ 3.04 years; training volume: 10.69 $\pm$ 3.75 h/week) were recruited for this study from two rugby clubs competing in the Second League in Turkey. Before the study, all players were informed about the potential risks and benefits associated with the participation and provided written informed consent. Approval was granted by the Medical Ethics Committee of the Medical Faculty of Trakya University (protocol number, TÜTF-BAEK 2017/257) in accordance with the Declaration of Helsinki. The inclusion criteria were as follows: age of 18 years or above, a minimum of 1-year experience in rugby, no history of lower body injury in the past 6 months and a rugby license for the 2017–2018 seasons. Those using ergogenic supplements such as creatine, amino acids, and protein powder were not permitted to participate in the study. Subjects were asked to abstain from heavy training at least 24 h before the test.

2.2 Procedure

The study consisted of 3 sessions with a 72-h interval in-between. All tests were performed by the same researcher at the same time of the day (11:00–13:00) to avoid the effect of circadian rhythms on the study results. Subjects performed one of 3 different exercise protocols called TSP, CSP-1, and CSP-2 for an equal duration in each session in a randomized, balanced order. The experimental design flowchart is presented in Fig. 1.

2.3 Warm-up

Each session started with a 5-min warm-up consisting of 3-min running on a motor-driven treadmill (SportsArt T630, USA) at 7 km/h speed and 1% slope and a series of 2-min dynamic stretching consisting of straight leg kicks, high knees, and butt kicks as fast as possible.

2.4 Devices

Reactive strength index (RSI), K ${}_{\text{leg}}$ , CT, and JH were determined via the Myotest Pro system (Myotest SA, Switzerland), which had previously been deemed valid for assessing these parameters [33]. The Myotest Pro system offers several performance tests, such as half-squat, jump-plyometric test, countermovement jump, and squat jump, to assess an athlete’s performance. We used the jump-plyometric test to assess K ${}_{\text{leg}}$ , RSI, JH, and CT, as in the study by Laffaye et al. [27]. After measuring the body mass of the subjects, the device was attached to the belt and fixed vertically. The subjects were asked to hop in their place five times as high as possible, while reducing CT. The instructions given were: “When the acoustic signal sounds, hop in place five times, with minimal knee flexion and maximal JH. After the last jump, the subject is to return to vertical standing posture and wait for the final acoustic signal.” [27]. After the final signal, jump-plyometric test was completed, and RSI, K ${}_{\text{leg}}$ , CT, and JH were automatically calculated by the device. This procedure was repeated twice, with a 2-minute rest in between. The mean value of the two treatments was accepted for statistical analysis. The body mass and height of the subjects were measured with a digital device (Seca 769, Turkey).

2.5 Jump box

Traditional set plyometric or cluster set plyometric bounce drop jump was executed using a box 30 cm in height. This height was determined according to Marshall and Moran [34]. Lees and Fahmi [35] argued that if optimal drop heights were to exist, they would be less, not greater, than 36 cm. For the bounce drop jump, subjects with hands on the hips were instructed to step from a box (30 cm) and, as quickly as possible, after touching the ground, jump for the maximal height [34].

2.6 Traditional set plyometric (TSP), Cluster set plyometric-1 (CSP-1), and Cluster set plyometric-2 (CSP-2) set configurations

Traditional set plyometric consisted of 2 $\times$ 10 bounce drop jump with 90 s rest between sets. CSP-1 and CSP-2 consisted of 4 $\times$ 5 jumps with 30 s rest and 10 $\times$ 2 jumps with 10 s rest between sets, respectively.

2.7 Statistical analysis

The statistical analysis was performed using the SPSS version 22.0 (IBM for OS X), initially using the Shapiro-Wilk normality and homoscedasticity tests. All the variables presented normal distribution and homoscedasticity. Statistical differences between pre- and post-values of the variables were examined paired $t$ -test. The effects of the three set configurations were analyzed using ANOVA for repeated measures (TSP $\times$ CSP-1 $\times$ CSP-2), with sphericity checked using Mauchly’s test. When the assumption of sphericity was met, the significance of F ratios was adjusted according to the Wilks’ lambda procedure. The findings are presented as mean $\pm$ SD (standard deviation), and an alpha level of $p<$ 0.05 was considered statistically significant for all analyses.

3. Results

Pre and post values of RSI, K ${}_{\text{leg}}$ , CT, and JH obtained from three different set configurations and statistical differences among the sets are presented in Table 1. There were no statistically significant improvements between pre- and post-values of RSI, K ${}_{\text{leg}}$ , CT, and JH in each session ( $p>$ 0.05). At the same time, no statistical differences determined 3 different set configurations (TSP, CSP-1, and CSP-2) in terms of RSI, K ${}_{\text{leg}}$ , CT, and JH values according to the post results ( $p>$ 0.05, see Table 1).

Table 1
Pre and post values of the variables and statistical differences among the set configurations

Parameters	Protocol	Paired T Test		Repeated ANOVA
		Baseline	Post	$F$	$p$
Jump height (cm)	Traditional set	34.0 $\pm$ 1.6	35.8 $\pm$ 1.3	1.246	0.306
		$t=$ $-$ 2.074; $p=$ 0.060
	Cluster set-1	31.4 $\pm$ 33.5	33.5 $\pm$ 1.6
		$t=$ 0.561; $p=$ 0.585
	Cluster set-2	34.1 $\pm$ 1.4	34.8 $\pm$ 1.1
		$t=$ $-$ 0.957; $p=$ 0.357
Ground contact time (ms)	Traditional set	193.7 $\pm$ 13.7	189.2 $\pm$ 12.6	0.567	0.574
		$t=$ 0.529; $p=$ 0.607
	Cluster set-1	162.2 $\pm$ 9.9	166.9 $\pm$ 12.7
		$t=$ $-$ 1.942; $p=$ 0.062
	Cluster set-2	170.2 $\pm$ 9.6	166.6 $\pm$ 8.5
		$t=$ $-$ 0.694; $p=$ 0.501
Reactive strength index	Traditional set	2.87 $\pm$ 0.14	2.95 $\pm$ 0.13	0.061	0.941
		$t=$ $-$ 0.820; $p=$ 0.428
	Cluster set-1	3.18 $\pm$ 0.13	3.27 $\pm$ 0.18
		$t=$ 0.817; $p=$ 0.430
	Cluster set-2	3.16 $\pm$ 0.15	3.28 $\pm$ 0.17
		$t=$ $-$ 1.058; $p=$ 0.311
Leg stiffness (N/m)	Traditional set	39.5 $\pm$ 5.0	37.1 $\pm$ 3.9	0.796	0.463
		$t=$ 0.815; $p=$ 0.431
	Cluster set-1	50.9 $\pm$ 7.8	46.6 $\pm$ 5.7
		$t=$ $-$ 1.277; $p=$ 0.226
	Cluster set-2	41.3 $\pm$ 4.4	43.9 $\pm$ 3.8
		$t=$ $-$ 0.742; $p=$ 0.472

4. Discussion

The aim of this study was to compare the influence of traditional set plyometric (TSP) and cluster set plyometric (CSP) warm-ups via bounce drop jump on RSI, K ${}_{\text{leg}}$ , CT, and JH in male rugby players. The main findings of the study are the following: i) either CSP (CSP-1 and CSP-2) or TSP has no effect in improving RSI, K ${}_{\text{leg}}$ , CT, and JH, acutely; ii) CSP warm-ups are not more effective than TSP warm-ups in improving RSI, K ${}_{\text{leg}}$ , CT, and JH. We hypothesized that cluster set bounce drop jump would result in greater RSI and K ${}_{\text{leg}}$ than traditional set bounce drop jump. According to the results of this study, our hypothesis is not confirmed.

It is common practice to perform a warm-up before any athletic activity since it has been shown to improve performance [36]. Static stretching (SS) is also commonly performed prior to exercise and athletic events [37]. It is believed that SS increases flexibility (by increasing range of motion of joints), promotes better performance, and reduces the risk of injury during strenuous exercise [37]. However, due to the possible detrimental effects of SS [36, 37, 38], dynamic warm-up (DW) is nowadays recommended as a pre-event routine because of demonstrated acute increase in power, sprint, or jump performance [39, 40, 41].

Two primary hypotheses have been developed to explain the possible detrimental effects of SS [37, 42]: (i) mechanical factors, such as changes in muscle stiffness, and (ii) neuromuscular factors, such as altered motor control strategies and/or reflex sensitivity. On the other hand, several mechanisms have been proposed to explain the acute enhancement in performance following DW, including [41](i) PAP, (ii) increased heart rate, (iii) increased core and muscle temperature, (iv) and rehearsal of movement.

Recently, there has been a renewed interest in what is termed as DW procedures that involve the performance of low-to-moderate, and high-intensity dynamic movements that are designed to elevate core body temperature, enhance motor unit excitability, improve kinesthetic awareness, and maximize active ranges of motion [2].

High-intensity skips, hops, jumps, throws, calisthenics, various movement-based exercises for upper and lower body, complex training (heavy-resistance exercise with a plyometric exercise), and plyometrics are known as DW exercises [6, 9, 12, 13, 43]. Recently, several studies argued that pre-event warm-up treatments that include TSP have been shown to positively influence explosive jump performance [43, 44], maximum squat performance [45], tennis serve performance [9], vertical jump, long jump [2, 13], and shuttle run performance [2].

Performance enhancement after the acute TSP warm-up is explained via PAP response by a majority of the researchers [6, 9, 13, 43, 44]. PAP consisted of two main mechanisms: fatigue and potentiation. Fatigue tends to diminish the contractile response and potentiation; however, potentiation tends to enhance contractile response. The effectiveness of the plyometrics depends on the balance between these two opposing effects [10]. Hilfiker et al. [43] argued that total muscle contraction time should be less than 10 seconds in order to avoid fatigue, which would reduce the positive effect of PAP. Masamoto et al. [45] also argued that potentiation was partially suppressed by fatigue in muscle contractions that are over 10 seconds in duration, which may explain why shorter duration plyometric exercises have previously improved performance.

Long-term plyometric exercises have been used widely as a training method to improve the efficiency of SSC as well as the muscle-tendon unit’s ability to tolerate stretch loads [46]. Improvement in RSI, K ${}_{\text{leg}}$ , and JH after long-term plyometric training may be caused by increased MTC stiffness and enhanced intramuscular coordination [18, 21]. However, the acute effects of plyometric warm-ups on MTC stiffness are not so well known. Tobin and Delahunt [6] speculated that the muscle-tendon unit behaves in the same way in both long-term and acute plyometric exercises.

As mentioned previously in traditional plyometrics, each repetition is applied without rest in between. However, some researchers argued that TSP might lead to muscle fatigue after the fifth repetition, depending on depletion of adenosine triphosphate (ATP) and PCr concentrations [8, 15, 28]. Moreno et al. [15] argued that a cluster set, specifically 10 sets of 2, allowed for greater maintenance of muscular power output (PW), take-off velocity (TOV), and JH than the traditional 2 sets of 10 when performing repeated body-weight plyometric squat jumps. According to Moreno et al. [15], this result may be caused by CSP, allowing for some replenishment of PCr, which is vital for muscular strength and power between intra-set rest intervals. Meanwhile, Boullosa et al. [8] reported that cluster set training seems to allow a more rapid improvement of jump height and a greater potentiation of various force-time parameters than a traditional set, probably as a consequence of the better fatigue-potentiation in the cluster condition.

To our knowledge, no other study has investigated the acute effects of plyometric exercise, designed in cluster set configuration, on RSI, K ${}_{\text{leg}}$ , CT, and JH. We argued that bounce drop jumps of 20 repetitions that applied either TSP or CSP set configuration from a box height of 30 cm in a warm-up session are not effective stimuli to produce an acute enhancement on RSI, K ${}_{\text{leg}}$ , CT, and JH.

The results of this study differ from studies of Moreno et al. [15] and Boullosa et al. [8]. Conflicting results may be caused by several factors including types of jumps (BDJ, countermovement drop jump, squat jump, etc.), jump volume (jumps/session), and jump intensity (box height) and subject characteristics (age, sex, fitness level, sport practice, etc.). Conversely, set configurations (TSP, CSP-1, and CSP-2), jump volume, and intensity, applied in this study, may be too low to change MTC stiffness and produce a PAP effect. The volume of plyometric exercise used in this study was kept low on purpose to prevent exercise-induced fatigue, which could lead to deterioration in neuromuscular performance.

There is no consensus about optimal box height to be used in plyometrics (15–100 cm) [21, 47, 49]. Struzik et al. [47] reported that a jump from 15 cm is relatively low to improve RSI. In contrast, if drop height is increased and jump technique is not controlled, subjects are likely to make a larger downward movement upon landing [47]. Marina et al. [48] reported the optimal height for drop jump to be between 40 and 60 cm. Matic et al. [49] suggested that drop height should be adjusted based on the subject’s neuromuscular capacity to produce maximal muscle strength.

Another contradictory issue among the studies is the jump volume that is to be applied in a session [17, 21, 50]. De Villarreal et al. [44] argued that a plyometric volume of 25–40 jumps is an effective stimulus to produce an acute enhancement in countermovement jump performance. However, Booth and Orr [17] suggested plyometric training volumes involving contacts higher than 50 feet per session. Eighty-foot contacts are recommended for track and field sports [17]. Braizer et al. [21] suggested that jumps to be added to high-intensity exercise are at least $>$ 40 jumps per session. According to Fouré et al. [50] a training session that includes jump should consist of 200–600 jumps.

This study has some limitations: a) although power analyses show that 15 subjects are needed to explain our aim and hypotheses, we only recruited 13 subjects; b) the box height was not adjusted based on the subject’s neuromuscular capacity; and c) we assumed that 30-cm box height, BDJ, and 20 jumps/session were suitable.

5. Conclusions

Further research, with larger samples, is needed to determine which set configurations, higher box height ( $>$ 30 cm), higher jump volume ( $>$ 20 jumps/session) are optimal for enhancement of RSI, K ${}_{\text{leg}}$ , CT, and JH. According to the results of the present study, TSP and CSP bounce drop jump demonstrated no negative effects on RSI, K ${}_{\text{leg}}$ , CT, and JH. Trainers, players, and fitness participants can apply both TSP and CSP as a dynamic warm-up tool; however, TSP and CSP do not lead to increase in RSI, K ${}_{\text{leg}}$ , CT, and JH.

Footnotes

Conflict of interest

The authors have no conflicts of interest to declare.

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Influence of traditional and cluster set plyometric warm-ups on reactive strength index and leg stiffness in male rugby players

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2.1 Subjects

2.2 Procedure

2.3 Warm-up

2.4 Devices

2.5 Jump box

2.6 Traditional set plyometric (TSP), Cluster set plyometric-1 (CSP-1), and Cluster set plyometric-2 (CSP-2) set configurations

2.7 Statistical analysis

3. Results

Table 1 Pre and post values of the variables and statistical differences among the set configurations

5. Conclusions

Footnotes

Conflict of interest

References

Table 1
Pre and post values of the variables and statistical differences among the set configurations