Effects of cluster training on body composition and strength in resistance-trained men

Abstract

BACKGROUND:

Cluster Training (CL) is an alternative to traditional training where intra-set breaks are incorporated. Positive effects have been reported on sports performance. However, there is little research on body composition in trained subjects.

OBJECTIVE:

The aim of this study was to investigate the effects of three cluster training (CL) protocols comprised of different intra-set rest (RIntra) and blocks of repetitions (BK) on strength, power and body composition in individuals maintaining a high protein diet.

METHODS:

Twenty-nine resistance-trained male participants were randomized to RIntra 20 s and BK 3 RM ( $n=$ 8, CL1), RIntra 40 s and BK 3 RM ( $n=$ 7, CL2), RIntra 20 s and BK 6 RM ( $n=$ 7, CL3), and control group ( $n=$ 7, CG). All participants performed two sessions per week of lower-limb resistance training for 8 weeks.

RESULTS:

There were significant changes in FFM in CL1 (0.9 $\pm$ 0.5 kg, $P=$ 0.001, ES $=$ 0.17), CL2 (0.6 $\pm$ 0.5 kg, $P=$ 0.010, ES $=$ 0.14) and CL3 (0.6 $\pm$ 0.4 kg, $P=$ 0.011, ES $=$ 0.14) but not in CG (0.4 $\pm$ 1.1 kg, $P=$ 0.323, ES $=$ 0.13). Likewise, significant increases were found in the cluster groups (CL1, 14.5 $\pm$ 12.3, $P=$ 0.012, ES $=$ 0.80; CL2, 10.1 $\pm$ 4.3, $P=$ 0.001, ES $=$ 0.60; CL3, 9.5 $\pm$ 4.9, $P=$ 0.002, ES $=$ 0.45) but not in CG (9.0 $\pm$ 9.0, $P=$ 0.057, ES $=$ 0.55). There were no significant changes for any group in CMJ.

CONCLUSIONS:

We conclude that a RIntra of $\sim$ 20 s in CL protocols with 3 RM blocks in multi-joint exercises of the lower-limb is sufficient to elicit significant training adaptations; no additional benefits were obtained using longer rest intervals.

Keywords

Hypertrophy fitness exercise fat-free mass mechanical tension

1. Introduction

Resistance training is a well-established method for improving human health and performance. Traditionally, resistance training involves performing a set of repetitions with minimal pause resting for a prescribed period of time (also known as rest interval), performing another continuous set, and then proceeding along in this rest/work fashion until the prescribed number of sets is completed. Over the past decade, a strategy termed cluster training (CL) has gained popularity as an alternative to this traditional set configuration [26]. The concept of CL involves inserting preplanned rest intervals within a set (RIntra) after a prescribed block of repetitions are completed [26]. By allowing for partial recovery before the onset of complete fatigue, it is hypothesized that the strategy may increase performance, peak power output, barbell velocity, and displacement, among other parameters [10]. Current research has shown that CL allows greater maintenance of movement velocity and higher work outputs, which may translate into superior neuromuscular adaptations [22, 27, 28].

Current research suggests that CL increases strength similar to traditional protocols [21]. In fact, in a traditional set, longer rest intervals are related to an increased capacity to produce force [24]. The recovery of phosphocreatine (PCr) is established after 180 s, while incomplete pauses of between 60 and 90 s are associated with higher lactate [bLa ${}^{-}$ ] concentrations [15]. Thus, during training sessions in which volume and relative loads are matched, the incorporation of RIntra attenuates the degree of fatigue induced during resistance training (RT) compared to traditional set configurations [19].

The majority of CL research to date has focused on its effects on performance-related measures such as sprint and jumping ability [1, 20], as well as peak power and speed [11, 29]; however, there is little research on how CL effects fat-free mass (FFM). The limited research relates indirectly the variables of strength training to increased FFM and cross sectional-area (CSA). CL generates higher volume, and higher volumes are associated with more hypertrophy [16, 25]. In fact the CL results in a higher number of repetitions than a traditional protocol [13]. It can be speculated that variations in RIntra and the number of repetitions per block would differentially affect changes in FFM. Repetition blocks ranging between 1 to 5 RMs (repetition maximums) offer a higher mechanical load, and thus would conceivably result in greater strength increases [2, 11, 17]. However, alterations in the duration of the RIntra can also elicit different metabolic and mechanical responses [6, 9, 19], which in turn may impact FFM.

It has been shown that smaller RIntra (15 s vs. 30 s) can be more effective for the lower limb [9], and RIntra between 10–15 s for the upper limb [7]. However, these data reflect lactate production and loss of speed; factors that may influence hypertrophic outcomes. It therefore needs to be determined whether changes in FFM evaluation respond similarly; that is, the smaller intervals are more effective than the longer ones. For this reason, the purpose of our study was to evaluate body composition and performance outcomes in lower limb (LL) CL using a RIntra of 20 versus 40 s with repetition blocks comprised of 3 RM or 6 RM while matching the total training volume over an eight-week program in resistance-trained men. Based on the theory that increased metabolic stress may enhance muscular adaptations [30], we hypothesized that a duration of 20 s would optimize the increase in LL-FFM, squat strength and countermovement jump (CMJ). We hypothesized that blocks of 3 RM would be superior to 6 RM blocks for increasing strength without compromising gains in LL-FFM.

2. Methods

2.1 Participants

Twenty-nine male participants with more than two years of continuous RT experience volunteered to participate in this study (age $=$ 26.9 $\pm$ 8 years; height $=$ 176.2 $\pm$ 8.4 cm; body weight $=$ 75.5 $\pm$ 9.7 kg; BMI $=$ 24.2 $\pm$ 2.0 kg $\cdot$ m ${}^{-2}$ ; fat mass $=$ 12.2 $\pm$ 3.4 kg or 16.1 $\pm$ 3.1%). All individuals committed to adhere to their respective prescribed training protocol, with no other physical activity performed during the 8-week study period. Participants were made aware of the possible risks of the experiment and signed an informed consent form acknowledging their willingness to participate. The research protocol was approved by the Ethics Committee of the University of Málaga (code: 38-2019-H) in accordance with the ethical guidelines of the Declaration of Helsinki [31]. Participants who reported consuming exogenous doping substances (e.g. anabolic androgenic steroids) during the previous 2 years or consuming any type of dietary supplement during the study period were excluded from participation, as well as those who did not fall within the required age ranges (between 18 and 35 years). The Consolidated Standards of Reporting Trials (CONSORT) diagram outlining subject enrollment (Fig. 1).

Figure 1.

CONSORT diagram.

2.2 Study design

A randomized controlled trial with a parallel groups study design was used and the participants were randomly assigned to perform RT for the lower limbs under 1 of 4 conditions: (a) a RIntra 20 s approach and BK 3 RM (CL1) ( $n=$ 8); (b) a RIntra 40 s approach and BK 3 RM (CL2) ( $n=$ 7); (c) a 20 s approach and BK 6 RM (CL3) ( $n=$ 7), or; (d) a control group that trained in their usual and customary manner without supervision (CG) ( $n=$ 7).

2.3 Procedures

All experimental groups trained twice a week with 72 hours of rest between sessions. The training program lasted 8 weeks. All sets in the experimental groups were carried out to volitional failure.

2.4 Training protocol

All routines were directly supervised by the research team, which included certified personal trainers, to ensure proper performance of the respective routines. All participants were familiar with the CL protocols. The CL1 and CL2 groups performed the same training protocol except that CL1 rested 20 s between blocks and CL2 rested 40 s. The CL3 group performed the same training protocol as CL1, except that two blocks of 6 RM were performed each block, whereas CL1 and CL2 performed four blocks of 3 RM. In addition, CL3 performed an extra set of the leg press exercise to equate total volume load between experimental groups (Volume load: sets $\times$ repetitions $\times$ load). The 8-week investigation consisted of two weekly sessions of lower limb training with 72 hours of recovery afforded between sessions. The participants trained on Monday and Thursday to favor recovery, since all reported having lower energy levels and mechanical expenditure during the weekend. Three exercises (squats, deadlifts and leg press) were performed in this exact order, as reflected in Fig. 2. For the upper limbs, a standardized training program was stipulated for the participants of groups CL1, CL2 and CL3, which was not supervised (Fig. 2).

Figure 2.

Training protocols for cluster training.

2.5 Dietary intake

CL1, CL2 and CL3 groups were prescribed to consume a hyperenergetic diet ( $\sim$ 39 kcal $\cdot$ kg ${}^{-1}$ FFM per day) in order to optimize increases in LL-FFM [23]. To accomplish this, a macronutrient distribution of 5.0 g $\cdot$ kg ${}^{-1}$ FFM $\cdot$ d ${}^{-1}$ of carbohydrates, 2.5 g $\cdot$ kg ${}^{-1}$ FFM $\cdot$ d ${}^{-1}$ of proteins and 1.0 g $\cdot$ kg ${}^{-1}$ FFM $\cdot$ d ${}^{-1}$ of fats was followed to design all diets [14]. A sport nutritionist designed and supervised all individualized protocols to optimize dietary adherence to the total daily caloric values and the distribution of macronutrients, given that these factors are important to induce RT adaptations [12]. Similar foods were recommended for the diets of all the participants in the CL1, CL2 and CL3 groups, while the participants in the control group maintained their usual and customary diet (i.e. they were not asked to follow a specific diet).

2.6 Body composition

Total body and regional body composition were estimated using dual-energy x-ray absorptiometry (DXA). A certified technician scanned all participants and computer algorithms (software version APEX 3.0, Hologic QDR 4500, Bedford, MA) distinguished bone and soft tissue, edge detection, and regional demarcations. For each scan, participants wore light clothing and were asked to remove all materials that could attenuate the X-ray beam including jewelry items and underwear containing wire. Body composition testing was carried out in the early hours of the morning after an overnight fast. The coefficient of variation was less than to $<$ 1.5% for all whole body and segmental body composition measurements including bone mineral density (g $\cdot$ cm ${}^{-2}$ ), mineral content (g), FM (%), FM (g), FFM (g) and total body mass (g). The DXA was calibrated with phantoms as per the manufacturer’s guidelines each day before measurement. The scans were analyzed automatically by the software but regions of interest were subsequently confirmed by the technician.

Body composition was also analyzed regionally. The arm region included the hand, forearm, and arm, and was separated from the trunk by an inclined line crossing the scapulohumeral joint such that the humeral head was located in the arm region. The leg region included the foot, lower leg, and upper leg and was defined by an inclined line passing just below the pelvis crossing the neck of the femur. The head region comprised all skeletal parts of the skull and cervical vertebrae above a horizontal line passing just below the jawbone. The abdominal region was delineated by an upper horizontal border located at half of the distance between acromia and external end of iliac crests, a lower border determined by the external end of iliac crests, and the lateral borders extending to the edge of the abdominal soft tissue. All trunk tissue within this standardized height region was selected for analysis. To determine intertester reliability, two different observers selected the area for each subject manually.

2.7 Countermovement jump (CMJ) test

For measurement of variables related to muscular strength and power, participants were instructed to avoid vigorous exercise for 72 h before the tests in both the pre- and posttest periods. Prior to testing, participants performed a general warm-up consisting of light stretching and stationary cycling for 10–12 min. The CMJ test was performed on a jump mat (Smart Jump; Fusion Sport, Coopers Plains, Australia). Participants were instructed to initiate each jump by squatting to 90 ${}^{\circ}$ of knee flexion while keeping their hands at the waist and trunk erect, emphasizing that the movement should be performed without interruption from beginning to end. A total of 3–5 attempts were permitted for familiarization. Thereafter, two jumps were recorded with a rest interval of 1 min between each trial; the highest value was used for analysis (coefficient of variation of the technician was 4.65%).

Table 1
Baseline values of the anthropometric variables, body composition and strength of the study participants

	CL1		CL2		CL3		CON		$p$
BM (kg)	75.1	$\pm$ 11.7	75.8	$\pm$ 9.8	74.6	$\pm$ 10.2	75.2	$\pm$ 8.8	0.997
Height (cm)	173.3	$\pm$ 7.7	176.3	$\pm$ 9.5	178.9	$\pm$ 9.8	176.5	$\pm$ 7.5	0.667
BMI (kg $\cdot$ m ${}^{2}$ )	24.9	$\pm$ 2.7	24.3	$\pm$ 1.1	23.3	$\pm$ 2.4	24.1	$\pm$ 1.5	0.523
% FM	16.6	$\pm$ 3.3	16.8	$\pm$ 2.7	15.7	$\pm$ 4.0	15.2	$\pm$ 2.9	0.909
FFM (kg)	62.5	$\pm$ 9.0	63.1	$\pm$ 8.0	63.2	$\pm$ 7.9	63.2	$\pm$ 5.7	0.997
LL-FFM (kg)	30.0	$\pm$ 5.0	30.7	$\pm$ 4.4	31.1	$\pm$ 3.9	31.4	$\pm$ 3.0	0.916
Squat (kg)	113.0	$\pm$ 16.8	112.3	$\pm$ 15.6	100.9	$\pm$ 21.1	117.7	$\pm$ 11.2	0.324
CMJ (cm)	39.9	$\pm$ 6.1	35.5	$\pm$ 4.5	36.3	$\pm$ 6.1	38.5	$\pm$ 5.9	0.440

BM, body mass; BMI, body mass index; FM, fat mass; FFM, fat-free mass; LL-FFM, fat-free mass of lower limbs; CMJ, jump counter movement.

Table 2

Results of the study variables

		Before $\bar{X}$ $\pm$ SD		After $\bar{X}$ $\pm$ SD		$\Delta$ $\bar{X}$ $\pm$ SD; (CI)		$p$	ES	Time	Time $\times$ Group	Group
										F (P; $\eta_{p}^{2}$ )	F (P; $\eta_{p}^{2}$ )	F (P; $\eta_{p}^{2}$ )
FFM-LL	CL 1	30.0	$\pm$ 5.0	30.9	$\pm$ 5.3	0.9 $\pm$ 0.5	(0.5–1.3)	0.001 ${}^{*}$	0.17	30.27	0.26	0.26
(kg)	CL 2	30.7	$\pm$ 4.4	31.3	$\pm$ 4.5	0.6 $\pm$ 0.5	(0.2–1.1)	0.010 ${}^{*}$	0.14	( $<$ 0.01; 0.57)	(0.85; 0.03)	(0.85; 0.03)
	CL 3	31.1	$\pm$ 3.9	31.7	$\pm$ 4.2	0.6 $\pm$ 0.4	(0.2–1.0)	0.011 ${}^{*}$	0.14
	CG	31.4	$\pm$ 3.0	31.9	$\pm$ 3.4	0.4 $\pm$ 1.1	( $-$ 0.6–1.5)	0.323	0.13
Squat	CL 1	113.0	$\pm$ 16.8	127.5	$\pm$ 17.5	14.5 $\pm$ 12.3	(4.2–24.8)	0.012 ${}^{*}$	0.80	44.97	0.22	1.26
(kg)	CL 2	112.3	$\pm$ 15.6	122.4	$\pm$ 16.0	10.1 $\pm$ 4.3	(6.1–14.0)	0.001 ${}^{*}$	0.60	( $<$ 0.01; 0.66)	(0.88; 0.03)	(0.31; 0.14)
	CL 3	100.9	$\pm$ 21.1	110.4	$\pm$ 18.4	9.5 $\pm$ 4.9	(5.0–14.0)	0.002 ${}^{*}$	0.45
	CG	117.7	$\pm$ 11.2	126.7	$\pm$ 18.6	9.0 $\pm$ 9.0	( $-$ 0.4–18.5)	0.057	0.55
CMJ	CL 1	39.9	$\pm$ 6.1	39.0	$\pm$ 4.5	0.7 $\pm$ 4.3	( $-$ 3.3–4.6)	0.698	$-$ 0.17	0.01	0.37	0.55
(cm)	CL 2	35.5	$\pm$ 4.5	35.6	$\pm$ 3.7	0.1 $\pm$ 3.7	( $-$ 3.4–3.6)	0.941	0.02	(0.92; 0.00)	(0.78; 0.05)	(0.66; 0.07)
	CL 3	36.3	$\pm$ 6.1	37.1	$\pm$ 5.8	0.8 $\pm$ 4.5	( $-$ 3.3–5.0)	0.640	0.13
	CG	38.5	$\pm$ 5.9	37.6	$\pm$ 5.7	$-$ 0.8 $\pm$ 3.2	( $-$ 3.8–2.1)	0.517	$-$ 0.14

The results are expressed as average $\pm$ standard deviation. CL, Cluster; CG, Control; $\eta p^{2}$ , Eta partial square ES, Effect size; CMJ, Countermovement jump, $p$ , level of significance, ${}^{*}p$ -value less than 0.05 is statistically significant.

2.8 Repetition maximum (RM) test

The 1 repetition maximum (1RM) for the back squat (SQ) was evaluated in a Smith machine (Gervasport, Madrid, Spain) at the beginning and end of the study. Participants reported to the laboratory having abstained from any exercise other than activities of daily living for at least 72 h before the reference test and at least 72 h before post-study testing. In brief, participants performed a general warm-up prior to testing, which consisted of 7 to 10 minutes of light cardiovascular exercise. A specific warm-up set of the given exercise was then provided for 12 to 15 repetitions with approximately 40% of the 1RM perceived by the participants, with a load progression for each exercise of three to six load increments. The increases in each load were approximately 10% 1RM until reaching a mean propulsive velocity (MPV) of 0.5 ms [8] followed by increments of 5 to 10 kg until attainment of 1RM. A rest interval of three to five minutes was afforded between each successive attempt. Participants had to reach parallel in the 1RM SQ for the trial to be considered a successful attempt. The correct performance was validated by two assistants who also recorded the data by means of a linear encoder (SmartCoach Power Encoder SPE-35, SmartCoach Europe AB, Stockholm, Sweden), to guarantee the precision of squat depth. The protocol followed the recommendations described by McGuigan [18] and the technical execution of the squat according [4].

2.9 Statistical analysis

Data are expressed as the mean and standard deviation. The normality of the data was verified with the Shapiro-Wilk test and the homogeneity of the variance with the Levene test. The comparison of the mean (pretest vs. posttest) of the groups was performed with the paired Student’s $t$ -test, and the effect size was assessed with Hedges’s. Between-group comparisons were performed with the repeated measures generalized linear model (GLM) with repeated measures, considering the pre-test and the post-test as the intra-subject factor (Time), the groups as the inter-subject factor (Group) and the interaction between these factors (Time $\times$ Group); post-hoc comparison was made with Bonferroni correction. Additionally, the change in each study variable was calculated ( $\Delta=$ posttest $-$ pretest), and the change was considered significant when the confidence interval of the mean was above or below “0”. The one-way Anova test was used to establish the difference between the baseline variables and the difference of $\Delta$ between the groups. The level of significance was at 0.05 for all tests. The statistical procedures were performed using the Statistical Package for the Social Sciences (SPSS) software (SPSS 24.0, SPSS Inc., Chicago, USA).

3. Results

Data on the baseline values for anthropometric, strength, power, and body composition measurements of each group are outlined in Table 1. No statistical differences were noted at baseline (Table 1).

3.1 Fat free mass of lower limbs

A significant increase was found with a small effect size in the groups CL1 (0.9 $\pm$ 0.5 kg, $P=$ 0.001, ES $=$ 0.17), CL2 (0.6 $\pm$ 0.5 kg, $P=$ 0.010, ES $=$ 0.14) y CL3 (0.6 $\pm$ 0.4 kg, $P=$ 0.011, ES $=$ 0.14) however, there were no significant changes in the group CG (0.4 $\pm$ 1.1 kg, $P=$ 0.323; ES $=$ 0.13). According to the GLM analysis, differences were found by Time ( $F=$ 30.27, $P<$ 0.01, $\eta^{2}_{p}=$ 0.57), but not by Group ( $F=$ 0.26, $P=$ 0.85, $\eta^{2}_{p}=$ 0.03) or in the interaction Time $\times$ Group ( $F=$ 0.26, $P=$ 0.85, $\eta^{2}_{p}=$ 0.03). These results are displayed in Table 2 and Fig. 3.

Figure 3.

a. Changes in LL-FFM; b. Changes in squat; c. Changes in CMJ. Confidence intervals that occur entirely greater than or less than zero are considered significant ( ${}^{*}$ ).

3.2 Strength levels

In the SQ, the cluster groups expressed significant changes with a large effect in CL1 (14.5 $\pm$ 12.3 kg, $P=$ 0.012, ES $=$ 0.80) and a medium effect for CL2 y CL3 (10.1 $\pm$ 4.3, $P=$ 0.001, ES $=$ 0.60 and 9.5 $\pm$ 4.9, $P=$ 0.002, ES $=$ 0.45, respectively); the CG group did not show a significant increase but this was a medium effect (9.0 $\pm$ 9.0, $P=$ 0.057, ES $=$ 0.55). The GLM analysis expressed that there were differences in Time ( $F=$ 44.97, $P=<$ 0.01, $\eta^{2}_{p}=$ 0.66), but not by Group ( $F=$ 0.22, $P=$ 0.88; $\eta^{2}_{p}=$ 0.03) o Time $\times$ Group ( $F=$ 1.26, $P=$ 0.31, $\eta^{2}_{p}=$ 0.14). In the CMJ, no significant increases were found in CL1 (0.7 $\pm$ 4.3 cm, $P=$ 0.698, ES $=-$ 0.17), CL2 (0.1 $\pm$ 3.7 cm, $P=$ 0.941, ES $=$ 0.02) or CL3 (0.8 $\pm$ 4.5 cm, $P=$ 0.640, ES $=$ 0.13). For its part, the CG group tended to decrease, but this was not significant ( $-$ 0.8 $\pm$ 3.2 cm, $P=$ 0.517, ES $=-$ 0.14). Considering the factor analysis, differences were found by Time ( $F=$ 0.01, $P=$ 0.92, $\eta^{2}_{p}=$ 0.00), however, no differences by Group ( $F=$ 0.37, $P=$ 0.78, $\eta^{2}_{p}=$ 0.05) or in the interaction Time $\times$ Group ( $F=$ 0.55, $P=$ 0.66, $\eta^{2}_{p}=$ 0.07). These results are presented in Table 2 and Fig. 3.

4. Discussion

There is an emerging interest in CL as a potential strategy for increasing athletic performance. It is conceivable that the effectiveness of this method can be improved by optimizing the number of repetitions, the blocks and sets, and the RIntra intervals. Therefore, the objective of this study was to investigate the impact of different RIntra intervals during high-load work blocks on changes in strength and, importantly, body composition, where research is scarce.

The findings regarding athletic performance seem to indicate that shorter RIntra intervals can be equally effective as longer ones. González-Hernández et al. [9] recently investigated markers of metabolic stress and mechanical performance associated with different RIntra intervals during squat training. The study compared two traditional protocols, in addition to three CL protocols of three sets of 10 repetitions with RIntra intervals of 10, 15 and 30 s. The protocol with 30 s of RIntra yielded the lowest [bLa ${}^{-}$ ], while mechanical performance was similar between recovery periods of 15 and 30 s. Therefore, a RIntra of 15 s may provide optimal benefits from a mechanistic stand point by heightening the [bLa ${}^{-}$ ] while maintaining mechanical performance. The same research team performed a similar experiment for the upper limbs using the bench press. The protocols were the same, with the only difference being that RIntra values in CL were shorter (5, 10 and 15 s). As expected, the [bLa ${}^{-}$ ] was higher in the traditional protocol and in the CL that used a RIntra of 5 s, while the velocity loss was similar in the protocols that had a 10 or 15 s rest between repetitions, maintaining the same [bLa ${}^{-}$ ].

Similar results were obtained by Mora-Custodio et al., who compared a traditional protocol versus two CL protocols with RIntra intervals of 10 and 20 s, respectively [19]. The protocols were evaluated at different loading intensities (60, 70, 75 and 80% RM), measuring [bLa ${}^{-}$ ] and velocity loss. The CL protocols resulted in a smaller velocity loss and lower [bLa ${}^{-}$ ] concentrations compared to the traditional protocol. No between-group differences in [bLa ${}^{-}$ ] or velocity loss were noted in the CL conditions. Therefore, a RIntra of 10 s might be more efficient than 20 s, as it produces similar performance and physiological outcomes in a shorter timeframe.

Our research is consistent with the results obtained in the aforementioned studies [9, 19], providing further evidence that RIntra intervals longer than 20 s are superfluous for enhancing performance. Moreover, we expand these findings and show that the same prescription applies when the objective is to increase LL-FFM. Notably, these studies were performed on the LL (as in our research), and the volume used was limited to three sets in an acute protocol. Our study employed nine sets per session, with training carried out twice weekly frequency for eight weeks; therefore, it may be better to use 20 s, as shown in our research and indirectly in González-Hernández et al. [9], and not to reduce the RIntra to 10 s, as proposed by Mora-Custodio et al. [19]. In fact, González-Hernández et al. [9] employed a higher volume (3 $\times$ 10 RM) than Mora-Custodio et al. [19] (3 $\times$ 6 RM), and the greater volume may require an increase in the RIntra interval.

Alternatively, no significant differences were noted in CMJ for any of the groups. This result may be due to the lack of familiarity with the jumping movement because it was only performed before and after the study, rather than during the study. Interestingly, the CL1 group showed a large effect size (0.8) for the squat compared with the CL2 and CL3 groups (0.6 and 0.5), since the 40 s RIntra was less than the 20 s RIntra.

This study has several limitations that should be considered when interpreting the results. No rigorous assessment of energy and protein intake was performed, since the subjects did not record food throughout the research. Subjects were given individual amounts and foods to select. Dietary instructions were provided only to increase energy and protein ingestion.

Therefore, it is not possible to know if participants adhered to these instructions and it is possible there may have been variations in energy intake. Moreover, determination of 90-degree knee angle during CMJ testing was assessed by visual observation of the researchers. It has been shown that the depth in the counter movement can affect the propulsion of the jump, and thus it is possible that subjective error may have confounded findings [3].

5. Conclusions

Our results indicate that performing CT with blocks of 3 RM and 6 RM repetitions increased LL-FFM, with optimal benefits noted using a PIntra of 20 s. Increasing the rest interval between repetitions could decreases the [bLa ${}^{-}$ ], and also muscle activation [5]. Athletes with limited time may benefit from shorter PIntra intervals to optimize training efficiency. However, the fact that several participants reported discomfort and accumulated fatigue in the fifth and sixth week must be taken into account from an application standpoint. In this regard, it may be more feasible to manage training to failure loads, with 2–3 RIR when training with a load higher than usual, or introducing a drop-set or pause. In this way, eight consecutive weeks would not be necessary to concentric failure.

The results of the current study also suggest that RIntra values between 20 and 40 seconds elicit favorable improvements in LL-FFM and performance in trained men when performing lower body CL protocols using multiarticular exercises. In addition, an increased number of blocks with reduced repetitions, such as 3 RM vs. 6 RM, can optimize the increase in strength levels. However, further studies are needed to verify our results as well as investigating other ranges of block repetitions with the same RIntra values. In future investigations, it would be of value to verify this methodology with a self-controlled volume using the rating of perceived exertion for measuring repetitions in reserve, with no training to failure compared to the same conditions when training to failure. It would also be important to measure body composition every 3 weeks with the aim of clarifying when the CL ceases to be efficient and therefore the appropriateness of introducing a recovery period and protocol completion.

Author contributions

CONCEPTION: Salvador Vargas-Molina.

PERFORMANCE OF WORK: Salvador Vargas-Molina and Manuel García.

INTERPRETATION OR ANALYSIS OF DATA: Jorge L. Petro.

PREPARATION OF THE MANUSCRIPT: Salvador Vargas-Molina, Jorge L. Petro and Diego A. Bonilla.

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Salvador Vargas-Molina, Brad J. Schoenfeld, Diego A. Bonilla and Richard B. Kreider.

SUPERVISION: Salvador Vargas-Molina, Brad J. Schoenfeld, Manuel García, Jorge L. Petro, Fernando Martín-Rivera, Diego A. Bonilla, Javier Benítez-Porres, Ramón Romance and Richard B. Kreider.

Ethical considerations

Participants were made aware of the possible risks of the experiment and signed an informed consent form acknowledging their willingness to participate. The research protocol was approved by the Ethics Committee of the University of Málaga (code: 38-2019-H) in accordance with the ethical guidelines of the Declaration of Helsinki

Funding

The authors report no funding.

Footnotes

Acknowledgments

This research was supported by the University of Málaga (Campus of International Excellence Andalucía Tech).

Conflict of interest

The authors declare that they have no competing interests.

References

Asadi

Ramirez-Campillo

. Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance. Medicina (Kaunas). 2016; 52(1): 41-5.

Boullosa

Abreu

Beltrame

Behm

. The acute effect of different half squat set configurations on jump potentiation. J Strength Cond Res. 2013; 27(8): 2059-66.

Carroll

Wagle

Sole

Stone

. Intrasession and Intersession Reliability of Countermovement Jump Testing in Division-I Volleyball Athletes. Journal of strength and conditioning research. 2019.

Caulfield

Berninger

. Exercise technique for free weight and machine training. In: Haff

Triplett

, eds. Essentials of Strength Training and Conditioning. Champaign, IL: Human Kinetics; 2016. pp. 380-1.

Dankel

Mattocks

Jessee

Buckner

Mouser

Loenneke

. Do metabolites that are produced during resistance exercise enhance muscle hypertrophy? European Journal of Applied Physiology. 2017; 117(11): 2125-35.

Garcia-Ramos

Gonzalez-Hernandez

Banos-Pelegrin

Castano-Zambudio

Capelo-Ramirez

Boullosa

, et al. Mechanical and Metabolic Responses to Traditional and Cluster Set Configurations in the Bench Press Exercise. J Strength Cond Res. 2017.

Garcia-Ramos

Gonzalez-Hernandez

Banos-Pelegrin

Castano-Zambudio

Capelo-Ramirez

Boullosa

, et al. Mechanical and Metabolic Responses to Traditional and Cluster Set Configurations in the Bench Press Exercise. Journal of Strength and Conditioning Research. 2020; 34(3): 663-70.

Gonzalez-Badillo

Sanchez-Medina

. Movement velocity as a measure of loading intensity in resistance training. Int J Sports Med. 2010; 31(5): 347-52.

Gonzalez-Hernadez

Garcia-Ramos

Capelo-Ramirez

Castano-Zambudio

Marquez

Boullosa

, et al. Mechanical, metabolic, and perceptual acute responses to different set configurations in full squat. Journal of strength and conditioning research. 2017.

10.

Haff

Hobbs

Haff

Sands

Pierce

Stone

. Cluster training: a novel method for introducing training program variation. Strength & Conditioning Journal. 2008; 30(1): 67-76.

11.

Hansen

Cronin

Newton

. The effect of cluster loading on force, velocity, and power during ballistic jump squat training. Int J Sports Physiol Perform. 2011; 6(4): 455-68.

12.

Helms

Aragon

Fitschen

. Evidence-based recommendations for natural bodybuilding contest preparation: nutrition and supplementation. J Int Soc Sports Nutr. 2014; 11: 20.

13.

Iglesias-Soler

Carballeira

Sanchez-Otero

Mayo

Fernandez-del-Olmo

. Performance of maximum number of repetitions with cluster-set configuration. International Journal of Sports Physiology and Performance. 2014; 9(4): 637-42.

14.

Jager

Kerksick

Campbell

Cribb

Wells

Skwiat

, et al. International society of sports nutrition position stand: protein and exercise. J Int Soc Sports Nutr. 2017; 14: 20.

15.

Kraemer

Marchitelli

Gordon

Harman

Dziados

Mello

, et al. Hormonal and growth factor responses to heavy resistance exercise protocols. J Appl Physiol (1985). 1990; 69(4): 1442-50.

16.

Krieger

. Single vs. multiple sets of resistance exercise for muscle hypertrophy: a meta-analysis. Journal of Strength and Conditioning Research. 2010; 24(4): 1150-9.

17.

Lawton

Cronin

Lindsell

. Effect of interrepetition rest intervals on weight training repetition power output. J Strength Cond Res. 2006; 20(1): 172-6.

18.

McGuigan

. Administration, scoring, and interpretation of selected tests. In: Haff

Triplett

, eds. Essentials of strength training and conditioning. Fourth ed. Champaign, IL: Human Kinetics; 2016. pp. 266.

19.

Mora-Custodio

Rodriguez-Rosell

Yanez-Garcia

Sanchez-Moreno

Pareja-Blanco

Gonzalez-Badillo

. Effect of different inter-repetition rest intervals across four load intensities on velocity loss and blood lactate concentration during full squat exercise. Journal of Sports Sciences. 2018; 36(24): 2856-64.

20.

Moreno

Brown

Coburn

Judelson

. Effect of cluster sets on plyometric jump power. J Strength Cond Res. 2014; 28(9): 2424-8.

21.

Nicholson

Ispoglou

Bissas

. The impact of repetition mechanics on the adaptations resulting from strength-, hypertrophy- and cluster-type resistance training. Eur J Appl Physiol. 2016; 116(10): 1875-88.

22.

Oliver

Kreutzer

Jenke

Phillips

Mitchell

Jones

. Velocity drives greater power observed during back squat using cluster sets. J Strength Cond Res. 2016; 30(1): 235-43.

23.

Rozenek

Ward

Long

Garhammer

. Effects of high-calorie supplements on body composition and muscular strength following resistance training. J Sports Med Phys Fitness. 2002; 42(3): 340-7.

24.

Schoenfeld

Pope

Benik

Hester

Sellers

Nooner

, et al. Longer interset rest periods enhance muscle strength and hypertrophy in resistance-trained men. J Strength Cond Res. 2016; 30(7): 1805-12.

25.

Sooneste

Tanimoto

Kakigi

Saga

Katamoto

. Effects of training volume on strength and hypertrophy in young men. Journal of Strength and Conditioning Research. 2013; 27(1): 8-13.

26.

Tufano

Brown

Haff

. Theoretical and practical aspects of different cluster set structures: a systematic review. Journal of Strength and Conditioning Research. 2017; 31(3): 848-67.

27.

Tufano

Conlon

Nimphius

Brown

Banyard

Williamson

, et al. Cluster sets: permitting greater mechanical stress without decreasing relative velocity. Int J Sports Physiol Perform. 2017; 12(4): 463-9.

28.

Tufano

Conlon

Nimphius

Brown

Seitz

Williamson

, et al. Maintenance of velocity and power with cluster sets during high-volume back squats. Int J Sports Physiol Perform. 2016; 11(7): 885-92.

29.

Tufano

Conlon

Nimphius

Oliver

Kreutzer

Haff

. Different Cluster Sets Result In Similar Metabolic, Endocrine, And Perceptual Responses In Trained Men. J Strength Cond Res. 2017.

30.

Wackerhage

Schoenfeld

Hamilton

Lehti

Hulmi

. Stimuli and sensors that initiate skeletal muscle hypertrophy following resistance exercise. Journal of Applied Physiology. 2019; 126(1): 30-43.

31.

World Medical Association. World medical association declaration of helsinki: ethical principles for medical research involving human subjects. J Am Coll Dent. 2014; 81(3): 14-8.

Effects of cluster training on body composition and strength in resistance-trained men

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Methods

2.1 Participants

2.3 Procedures

2.4 Training protocol

2.6 Body composition

2.7 Countermovement jump (CMJ) test

Table 1 Baseline values of the anthropometric variables, body composition and strength of the study participants

2.9 Statistical analysis

3. Results

3.1 Fat free mass of lower limbs

4. Discussion

5. Conclusions

Author contributions

Ethical considerations

Funding

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Baseline values of the anthropometric variables, body composition and strength of the study participants