How cluster sets, rest-pause, and drop-sets affect strength, hypertrophy, and power (research review)


Written by
: Adam Tzur (primary author), Andrew Vigotsky, and Brandon Roberts.
Edited and reviewed by: Israel Halperin and Conrad Earnest
Acknowledgements: Greg Nuckols
Main article length: ~3000 words
Article length with tables: ~5500 words
Last updated: 04.05.2017

   

Summary


  • We have reviewed the cluster set (CS), rest-pause (RP), and drop-set (DS) literature (including Schoenfeld’s new DS study!). You can find overviews of the studies in graphs, tables and study analyses.

  • Cluster sets and rest-pause: CS/RP seems to be similar to traditional training for gaining strength and muscle mass. Cluster sets might be good for preventing form breakdown, reducing feelings of fatigue, and to build more volume with less discomfort. Long-term effects on power are unclear.

  • Drop-sets: There are few DS studies and they show conflicting information for strength and hypertrophy. Practically speaking, DS help you finish your workouts quicker and can be a good tool in that regard.

  • General conclusions: CS, DS, and RP can be good tools if programmed right.

  • Main limitations: CS/RP programs in many of the studies may not have been challenging enough to create a stimulus for optimal gains. Furthermore, several studies selectively reported data and had other methodological issues. Many of the studies had small sample sizes on average, aka. few subjects (n = ~27 ± ~11).

 

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What are rest-pauses, cluster sets and drop-sets?


It’s interesting to see how the terms rest-pause and cluster set are used colloquially. If we go online to see how people define the terms, then we get contradictory opinions (1, 2, 3, 4). Though most people seem to agree that the rest-pause method involves training to failure, resting, and then doing some more reps. Cluster sets are about dividing volume into multiple clusters, aka sub-sets, and resting in-between them.

In the scientific literature, rest-pause has many different definitions. Sometimes, it can seem like rest-pause is very similar to cluster sets. Yet, rest-pause generally relies on training to failure (Tufano et al., 2017). Clusters and rest-pause are used in a lot of crossfit style workouts or competitions especially those that use AMRAPS for time.

It should be said that many researchers use the terms rest-pause and cluster set in different ways. The terminology isn’t properly standardized yet (Tufano et al., 2017). However, we’ve kept the definitions consistent in this article, even if it means redefining terms that are used in some of the studies.

Drop-sets (DS) involve training to failure, dropping the weight, and immediately continuing to do more reps to failure. How many sets you want to drop consecutively is up to you. The idea behind drop-sets is that they would lead to greater muscle mass gains because they cause a lot of local stress to the muscle (i.e. metabolite buildup). Schoenfeld suggests that drop-sets lead to extended metabolic stress which might be good for hypertrophy (Schoenfeld, 2011).

Check out the examples in the next section for details on how to do CS, RP, and DS.

Why would you want to do cluster sets, rest-pause, and drop-sets?

Cluster sets help you keep fatigue low. Some researchers suggest that by using cluster sets, you can focus on form while maintaining velocity, and power output (Nicholson et al., 2016c; Tufano et al., 2017).

Rest-pause and drop-sets are good ways of accumulating fatigue quickly. For example, if you are short on time in the gym you could drastically reduce training time and still get in the same amount of total training volume (Prestes et al., 2017). This may not be ideal if you want to work at a high intensity (%1RM), but for hypertrophy it could be enough.

There are other benefits as well, check out the practical applications for more details.

Examples of the different training methods

Traditional sets

Figure based on materials from Tufano et al., 2017

This is pretty straightforward. You do 4 repetitions and rest for 2 minutes between sets.

Cluster sets

Figure based on materials from Tufano et al., 2017

In the example above, you:

  • Do 2 reps (sub-set #1 of the first cluster set)
  • Rest for 15 seconds
  • Do 2 reps (sub-set #2)
  • Rest for 120 seconds (this separates cluster set 1 from cluster set 2)
  • Do 2 reps (sub-set #1 of the second cluster set)
  • Rest for 15 seconds
  • Do 2 reps (sub-set #2)

You’ve now done a total of 2 cluster sets, 4 subsets, and 8 reps with the cluster set method. Remember that there is no rule for how many reps or clusters there should be in a cluster set, or how much rest you should use between clusters. Though, if you’re taking longer rests that last for minutes, you’re just doing regular sets. Reps are not generally done to failure and there is no specific cut off time for rests in the literature.

Rest-pause method

Figure based on materials from Tufano et al., 2017

In the first set from the example above, you:

  • Do 8 reps to failure
  • Rack the weight and rest for 30 seconds
  • Do 2 reps to failure
  • Rest for another 30s
  • Do 1 rep

You’ve now done a total of 11 reps; 8 traditional reps + 3 rest-pause reps. Since you went to failure on the first sub-set, you hit your 8RM. RP allows you to do more reps after failure.

  • [Then rest and do the next RP set]

Drop-sets

DS.png

In the set from the example above, you:

  • Do 12 reps to failure
  • Drop to a lighter weight
  • Do 8 reps to failure

  • Drop to a lighter weight
  • Do 5 reps to failure
  • Drop to a lighter weight
  • Do 3 reps to failure

You’ve now done a total of 28 reps; 1 traditional set + 3 drop-sets. Since you went to failure on the first set, you hit a 12RM. DS is a method that allows you to do more reps, at a lighter load, while still going to failure.

Inter-repetition rest sets

Figure based on materials from Tufano et al., 2017

With IRR sets, you take a short break in-between every repetition. This type of training is typically used to help focus on technique.

Graphs

Drop-sets: effects on strength

Drop-sets: effects on hypertrophy

 

Studies excluded from the graph, due to either major methodological issues and/or lacking outcome measurements:

  • Giessing et al., 2014
  • Fisher et al., 2016

You can find more information about the issues in the limitations section

Cluster sets and rest-pause: effects on strength

 

Studies excluded from the graph, due to major methodological issues:

  • Rooney et al., 1994

  • Giessing et al., 2016

You can find more information about the issues in the limitations section

CS and RP: effects on hypertrophy

CS and RP: effects on power

 

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Overall gains across studies


  • In the drop-set studies, the weekly strength gains of the DS groups were between ~1.5% and ~3%.

  • In the cluster-set studies, the CS groups gained somewhere between ~1.5% and ~4.5% per week.

  • All of the traditional groups in both CS and DS studies gained between ~2% and ~5% strength per week (except for Lawton et al., 2014 which is an outlier).

  • In terms of hypertrophy, CS and DS groups gained between ~0.2% and ~1.8% LBM or muscle CSA per week. However, this number is strongly influenced by Prestes et al., 2017 and their CSA measurement of the thigh musculature.

 

Interesting observations


Looking at this from a bird’s-eye-view, the traditional groups gained slightly more strength than the CS/DS groups. However, we should be careful about making definite conclusions because many of the studies had no statistically significant differences between groups even if the mean % changes were different. Also,  in studies with small groups, one participant can heavily influence the mean and few studies provided data about spread, outliers or individual data points.

An interesting observation is that the strength gains seemed to vary not only based on the training program, but also which exercise/muscle group was trained. For example, in Goto et al 2005, there was a large difference in gains made in knee extension (65.3% – Traditional; 38.1% – Cluster) compared to shoulder press(29.2% – Traditional; 21.2% – Cluster).

To add, in Prestes et al., 2017, the LBM gains of the traditional group were non-significantly larger than the CS group, but the thigh CSA changes were significantly larger for the CS group. This just goes to show how different hypertrophy measurements can give us different results. The thigh CSA change was the only significant hypertrophy change in that study. Strangely, the traditional group in the study had 12 kg less LBM than the CS group.

Should we train for power?


One of the first things we must ask ourselves when we do any type of training, is what is the purpose of the thing we’re doing? Why train for power? Is it to improve technique? Is it for a specific olympic lift? To maximize 1RM?

Fundamentally, we must question whether what we’re doing is an effective way to reach our goals.

Here’s what Nicholson et al., 2016b have to say about strength and power:

“(…) it would appear that the CL-1 regimen was not optimally designed for strength development since the ability to offset fatigue-induced reductions in repetition velocity and power did not translate to greater strength improvements. In some respects, this observation supports the specificity of training adaptations (Kaneko et al. 1983) since maximal strength assessments are not typically associated with higher velocities and power outputs. Instead, it seems logical that the slower repetitions in the STR and CL-2 regimens closely resembled the 1RM assessment and contributed to the larger strength improvements in these regimens”. (Nicholson et al., 2016b)

Is power important?

To understand if power is important, we must first understand the construct of mechanical power. Colloquially, the term power is often used to describe an action that is done forcefully, quickly, or explosively (Ayalon et al., 1974); for example, someone who can jump high or accelerate quickly is often described as “powerful”. However, mechanical power is different.

In mechanics, power, P, is the time-rate of doing work, W, (the rate at which work is done), and can be calculated as P = W/t. This formula can be rewritten as P = Fv, where F is force and v is velocity. Hence, power is equal to the component of force acting on a body that is in the same direction that it is moving times the velocity of that body.

This means that power, as used in research, is different from power as we use it in everyday speech. It’s important that we distinguish these two definitions so as to avoid confusion when talking about it.

When attempting to apply the purely mechanical definition of power, its usefulness becomes less clear than its colloquial meaning. Does someone need to apply a large force while traveling quickly in order to jump high? Does someone need to lift a heavy load quickly to be strong?

Fortunately, much has been written on misconceptions surrounding power (Knudson, 2009; Winter et al., 2015). For tasks like the vertical jump, impulse, rather than power, is what determines how high someone can jump (Ayalon et al., 1974). In fact, recent work has shown that jump height can improve without changing power output (Jiménez Reyes et al., 2017).

Of course, powerlifters lift loads slowly, and thus, ironically, display relatively low power output (Garhammer, 1993). That is not to say that power is totally useless, though. For sports like cycling, power is integral (Wilson, 2004). This is because drag (i.e., the force that resists one from moving forward) increases quadratically with the speed at which one travels, and thus, one must not only pedal faster, but also with increasing force; this is why gearing is so advantageous. In other words, tasks that involve high force output at high speeds require higher power.

Can we use power measurements that are taken during a training session to predict long-term power gains?

Most power research looks at short-term changes. This is is called acute research, and the studies we’ve reviewed usually last for less than a week. Sometimes the researchers train and test lifters for just one or two session. The issue with these studies is that they can’t predict long-term power changes. For example, a study might show that you can maintain better mean power output in one session when you use cluster sets. This doesn’t necessarily imply that the program they used will give you better power gains in the long-term.

Generally speaking, it’s hard to predict gains by looking at acute changes in, for example, hormones, mTOR, MPS, force, velocity, and power (1, 2, 3). However, this doesn’t mean that these variables are useless. It just means that we have to be careful about measuring something during or after a training session, and then concluding that the measurement will reflect long-term changes!

Therefore, we have to look at long-term power studies. Sadly, there aren’t many studies like that. If you want to look at the acute studies, you can find them listed here (spoiler alert: most acute studies find that cluster sets are better at maintaining power output compared to traditional sets). We haven’t included these studies in our analysis.

Our task here today is to inform you about how you can think of power. An in-depth discussion about power is a topic for another article, but like we’ve said, if you’re training for power then you should also justify why you are doing so, because it’s not given that more power translates to more strength, or better performance.

If you are interested in reading more about power, check out these articles

  • Strength and Power: A Definition of Terms (Harman, 1993)

  • ‘Correcting the use of the term “power” in the strength and conditioning literature (Knudson, 2009)

  • Misuse of “Power” and Other Mechanical Terms in Sport and Exercise Science Research (Winter et al., 2015)

  • Countermovement Jump Phase Characteristics of Senior and Academy Rugby League Players (McMahon et al., 2016)

SCR: “when athletes are making improvements from one level of sport to the next, jumping higher does not require them to increase the amount of force they exert in the jump. It requires them to produce a similar amount of force, but at a faster speed and over a longer distance.”

Practical applications for programming


 Should we use cluster sets and rest-pause?

Pros of cluster sets

Cons of cluster sets

In terms of strength and hypertrophy, the evidence is very mixed for cluster sets and rest-pause. Some studies support CS/RP for strength and/or hypertrophy, and some studies do not. This is probably because studies used different exercises, programs, subjects, durations, and so on.

When programming cluster sets, it’s probably a good idea to add more volume or intensity (Nicholson et al., 2016b). If you just take your old sets and splice them in half with a rest in-between, your muscles will probably be understimulated. This was an issue in several of the studies.

Furthermore, it’s possible that cluster sets should only be something you temporarily add during a phase in your training program (Nicholson et al., 2016b).

Should we use drop-sets?

Pros of drop-sets

  • Save time by making workouts shorte

Cons of drop-sets

  • Possibly decrease strength/muscle endurance? (Fink et al., 2017)
  • Fatiguing

There are few relevant studies on drop-sets and they have contradictory results for strength and hypertrophy. Hence, the jury is still out. However, drop-sets can be used strategically to increase training volume in a short amount of time. For example, some bodybuilders use drop-sets such as “running the racks” in order to accumulate volume or induce more fatigue. Yet, there is some debate over whether this technique accumulates “junk” volume or if it actually helps induce more hypertrophy.

There are currently no studies on this topic, but there is data showing that lifters can gain the same amount of muscle using high or low loads (Schoenfeld et al., 2016), so going to failure using a declining load during a drop-set likely induces hypertrophy. It could also be a good way to reduce the time you spend in the gym while still training to failure and getting enough volume.

If you want to get into the details of the DS studies, check out the DS table and single study analyses.

 

Table of CS and RP studies (long-term)


Legend

  • Green background = this group was better than the other group for this variable (statistically significant between-group differences)

  • Red background = this group did worse than the other group for this variable (statistically significant between-group differences)

  • Grey background = result was statistically significant for group x time, but not between-group

  • Purple background = no change or the result was not statistically significant (between-group or group x time)

  • Empty cells = this variable was not measured in the study

Strength and muscle mass

 

Study

Strength

Traditional training

Strength

Cluster sets

Muscle mass

Traditional training

Muscle mass

Cluster sets

Rooney et al., 1994
N = 44
Age
18-35 years
Duration
6 weeks

Experience
Untrained

Sex
Male & female

Tests

Elbow flexor 6RM

Measurement

1RM/isometric strength

Training program

(3x per week)

Volume equated?

Yes and no. They both did one set, but CS group had rests between each rep and didn’t go to failure. Hence, normal group did one hard set to failure, but CS group did not.

Number of working sets for the body part being measured/tested

Per session: 1

Weekly: 3

Rest

IRR group rested 30s between each rep, normal group did 6RM straight.

[Biceps curl]
Baseline

12.5 ± 8.4 kg

End measure

19.5 ± 10.2 kg

Mean

change

7.0 ± 0.9 kg

Mean change per week

1.17 kg

Mean % change

56%

According to Authors: 56.3%

“group means and SD are adjusted by analysis of covariance”

Mean % change per week

9.33%

According to the authors’ percentages: 9.4%

[Biceps curl]
Baseline

13.9 ± 8.6 kg

End measure

19.4 ± 12.2 kg

Mean

change

5.1 ± 0.8 kg

Mean change per week

0.85 kg

Mean % change

36.7%

According to Authors: 41.2%

“group means and SD are adjusted by analysis of covariance”

Mean % change per week

6.12%

According to the authors’ percentages: 6.87%

Folland et al., 2002
N = 23 (originally 30)
Age
18-29  years
Duration
9 weeks

Experience
Untrained

Sex
Male + Female (8)

Tests

Bilateral leg extension 1RM

Measurement

Training program

3x per week bilateral leg extension

Volume equation

Equated by total number of reps

Number of working sets for the body part being measured/tested

Per session: 4 (normal group) | 1 long set of 40 reps for IRR group

Weekly: 3×40 reps

Rest

IRR group rested 30s between each rep, normal group did 4 sets of 10 reps with drop-sets included when the given rep max was not hit.

[Bilateral leg extension]
Baseline

85 kg

End measure

114 kg

Mean

change

29 kg

Mean change per week

3.22 kg

Mean % change

34.12 %

Mean % change per week

3.8 %

[Bilateral leg extension]
Baseline

80 kg

End measure

112 kg

Mean

change

32 kg

Mean change per week

3.56 kg

Mean % change

40 %

Mean % change per week

4.44 %

Lawton et al., 2004
N = 26
Mean age
17-18  years
Duration
6 weeks

Experience
Novice (6+ months RT)

Sex
Male

Tests

Bench press 6RM | Bench throw for power

Training program

Only bench press 3x per week. Traditional group used higher intensities & went to failure

Volume equated?

Equated by total number of reps and by volume load(?):

Traditional: 4 sets x 6 repetitions)

CS: 8 sets x 3 repetitions

Rest

CS: about 100s rest between sets?

Traditional: about 248s rest?

[Exercise]
Baseline

±

End measure

±

Mean

change

Mean change per week

Mean % change

9.7 ± 3.5 %

Mean % change per week

1.62%

[Exercise]
Baseline

±

End measure

±

Mean

change

Mean change per week

Mean % change

4.9 ± 3.9%

Mean % change per week

0.82%

Goto et al., 2005
N = 26
Mean age
22-23  years
Duration
12 weeks

Experience
Untrained

Sex
Male

Tests

Shoulder press and leg ext. 1RM

Measurement

MRI of thigh

Training program

Lat pulldown, shoulder press and bilateral knee extension 2x per week

Volume equated?

Yes, they followed the same program

Number of working sets for the body part being measured/tested

Per session: 3-5 sets per exercise

Rest

CS group added 30s rest in the middle of every set. Everything else was identical.

Baseline

Shoulder press: 60.3 ± 3.9 kg

Knee extension: 69.3  ± 4.2 kg

End measure

Shoulder press: 77.9 ± 4.6 kg

Knee extension: 114.6 ± 6.7 kg

Mean

change

Shoulder press: 17.6 kg

Knee extension:

45.3 kg

Mean change per week

Shoulder press: 1.47 kg

Knee extension:

3.78 kg

Mean % change

Shoulder press: 29.2 %

Knee extension:

65,36%
(66.4 ± 5.2% according to researchers)

Mean % change per week

Shoulder press: 2.43 %

Knee extension:

5.45%
(5.53 % according to researchers)

Baseline

Shoulder press: 60.3 ± 3.9 kg

Knee extension: 71.9 ± 3.8 kg

End measure

Shoulder press: 73 ± 2.1 kg

Knee extension: 99.3 ± 4.5 kg

Mean

change

Shoulder press: 12.7 kg

Knee extension: 27.9 kg

Mean change per week

Shoulder press: 1.05 kg

Knee extension: 2.28 kg

Mean % change

Shoulder press:

21.2 %

Knee extension:
38.1%

(39.0 ± 3.7% according to researchers)

Mean % change per week

Shoulder press:

1.76 %

Knee extension: 3.18 %

(3.25 % according to researchers)

[Exercise]
Baseline

±

End measure

±

Mean

change

Mean change per week

Mean % change

12.9 ± 1.3 %

Mean % change per week

1.07 %

[Exercise]
Baseline

±

End measure

±

Mean

change

Mean change per week

Mean % change

4.0 ± 1.2 %

Mean % change per week

0.33 %

Hansen et al., 2011b
N = 18
Mean age

26.8 ± 4.5 years
Duration
8 weeks

Experience
Elite Rugby Union Players

Sex
Male
Tests

1RM calculated from 2-6 RM squat, jump squat, CMJ,

Training program

lower body resistance training 2x week (squat+clean+small supplementary exercises)

Volume equated?

Volume-load equated, but also in terms of total number of sets

Number of working sets for the body part being measured/tested

Per session for squat and clean: ~9.5 sets (both groups)

Weekly: ~19 sets

Rest

For CS, it depended on the week and set number. Usually 10-30s rest between clusters and 120s rest between sets.

Traditional used 180s rests.

Baseline (squat calculated 1RM)

SQ: 203.2 kg ± 16.6

End measure

SQ: 240.1 kg ± 25.0

Mean

change

36.9 kg

Mean change per week

4.6 kg

Mean % change

18.3 ± 10.1 %

Mean % change per week

2.3 %

Baseline (squat calculated 1RM)

SQ: 191.1 kg ± 25.0

End measure

SQ: 216.4 kg ± 25.3

Mean

change

25.3 kg

Mean change per week

3.2 kg

Mean % change

14.6 ± 18.0

Mean % change per week

1.8 %

Oliver et al., 2013

N = 22
Mean age
25 ± 5  years
Duration
12 weeks

Experience
Trained (6.5 ± 4.5 years)

Sex
Male

Tests

1RM strength & 60% 1RM power tests for squat + bench press
Measurement

DXA, weight scale

Training program

Whole-body PPL 4x per week

Volume equated?

Volume-load equated, and equated by total number of reps

Number of working sets

Traditional: 4 sets 10 reps  

CS: 8 sets of 5 reps

Rest

Trad: 120s

CS: 60s rest between every set

Baseline

BP: 104.1 ± 27.6 kg

SQ: 123.3 ± 39.3 kg

End measure

BP: 113.2 ± 27.3 kg

SQ: 171.8 ± 34.5 kg

Mean

change

BP: 9.1 kg

SQ: 48.5 kg

Mean change per week

BP: 0.76 kg

SQ: 4.04 kg

Mean % change

BP: 8.7 %

SQ: 39.3 %

According to figure 4:

BP: ~9.3 %

SQ: ~43 %

Mean % change per week

BP: 0.73 %

SQ: 3.28 %

If we use fig. 4 changes as reference:

BP: 0.78 %

SQ: 3.58 %

Baseline

BP: 110.9 ±  20.1 kg

SQ: 130.1 ± 25.1 kg

End measure

BP: 126.0 ± 22.8 kg

SQ: 193.9 ± 24.2 kg

Mean

change

BP: 15.1 kg

SQ: 63.8 kg

Mean change per week

BP: 1.26 kg

SQ: 5.32 kg

Mean % change

BP: 13.6 %

SQ: 49.0 %

According to figure 4:

BP: ~13.9 %

SQ: ~52 %

Mean % change per week

BP: 1.13 %

SQ: 4.08 %

If we use fig. 4 changes as reference:

BP: 1.16 %

SQ: 4.33 %

Baseline (LBM)

61.9 ± 8.9 kg

End measure

64.2 ± 8.5 kg

Mean

change (LBM)

2.3 kg

Mean change per week

0.19 kg

Mean % change

3.72 %

Mean % change per week

0.31 %

Baseline (LBM)

63.3 ± 7.0 kg

End measure

64.3 ± 6.8 kg

Mean

change (LBM)

1 kg

Mean change per week

0.08 kg

Mean % change

1.58 %

Mean % change per week

0.13 %

Iglesias-Soler et al., 2015
N = 13
Mean age
 22.5 ± 2.6  years
Duration
5 weeks

Experience
Sports science students

Trained >6 months

Sex
7 Male / 6 Female

Tests

Unilateral leg extension 1RM/Muscular endurance/MVC

Measurement

1RM/thigh girth

Training program

2x per week, leg extension only

Volume equated?

Volume-load equated +

Equated by total number of repetitions

Number of working sets for the body part being measured/tested

Traditional: 4

IRR: 1 continuous set

Rest

Traditional: 3 minutes between sets

IRR: 32 reps with 17.4s inter-repetition rest

Baseline (leg ext. 1RM)

63 ± 17.5 kg

End measure

72 ± 18 kg

Mean

change

9 kg

Mean change per week

1.8 kg

Mean % change

14.2 %

Mean % change per week

2.84 %

Baseline (leg ext. 1RM)

62 ± 17.5 kg

End measure

72 ± 19

Mean

change

10 kg

Mean change per week

2 kg

Mean % change

16.1 %

Mean % change per week

3.22 %

Baseline [thigh circumference]

47.7 ± 4.7 cm

End measure

49.3 ± 4.4 cm

Mean

change

1.6 cm

Mean change per week

0.32 cm

Mean % change

3.35 %

Mean % change per week

0.67 %

Baseline

47.7 ± 4.4 cm

End measure

49.1 ± 4.2 cm

Mean

change

1.4 cm

Mean change per week

0.28 cm

Mean % change

2.93 %

Mean % change per week

0.58 %

Nicholson et al., 2015

Skip

Nicholson et al., 2016b
N = 46
Mean age
 21.76 years
Duration
6 weeks

Experience
Trained

Sex
Male

Tests

Squat 1RM

Measurement

N/A

Training program

2x per week squat training

Volume equated?

Equated by total number of sets with the same number of reps

Number of working sets

Per session: 4 sets of 6 reps (CS rested between reps)

Rest

STR: 5 min

CS1: 5 min between sets, 25s IRR

CS2: 5 min between sets, 25s IRR

Baseline (squat)

120.56 ± 13.96 kg

End measure (squat)

135.83 ± 13.64 kg

Mean

change

15.28 kg

Mean change per week

2.55 kg

Mean % change

12.67%

According to abstract: 12.09 ± 2.75 %

Mean % change per week

2.11%

Baseline (squat)

CL1: 134.72 ± 21.88 kg

CL2: 121.39 ± 21.36 kg

End measure (squat)

CL1: 150.56 ± 23.78 kg

CL2: 138.61 ± 20.85 kg

Mean

change

CL1: 15.83 kg

CL2: 17.22 kg

Mean change per week

CL1: 2.64 kg

CL2: 2.87 kg

Mean % change

CL1: 11.75%

CL2: 14.19% (from abstract: 13.20 ± 2.18 %)

Mean % change per week

CL1: 1.96%

CL2: 2.37%

Giessing et al., 2016

Excluded from analysis due to methodological issues. See single study analyses.

Prestes et al., 2017
N = 18
Mean age
30 years
Duration
6 weeks

Experience
Trained

Sex
Male and Female

Tests

Leg press, bench, and biceps curl 1RM

Measurement

Skinfold and ultrasound

Training program

4x per week whole-body

Volume equated?

volume-load equated /

Equated by total number of sets /

Not equated

Number of working sets

Trad: 3 sets of 6 reps

RP: 1 set to failure then continue to do sets/reps with rests

Rest

Trad: 2 min rest

RP: go to failure then 20s rests between reps

Baseline

Bench: 80.74 ± 32.24 kg

Leg press: 289.08 ± 80.07 kg

Biceps curl: 36.55 ± 17.00 kg

End measure

Bench: 86.88 ± 35.79 kg

Leg press: 362.91 ± 70.71 kg

Biceps curl: 42.64 ± 16.24 kg

Mean

change

Bench: 6.14 kg

Leg press: 73.83 kg

Biceps curl: 6.09 kg

Mean change per week

Bench: 1.02 kg

Leg press: 12.31 kg

Biceps curl: 1.02 kg

Mean % change

Bench: 7.6%

Leg press: 25.5%

Biceps curl: 16.7%

Mean % change per week

Bench: 0.1%

Leg press: 4.3%

Biceps curl: 2.8%

Baseline

Bench: 91.80 ± 31.69 kg

Leg Press: 383.71 ± 92.55 kg

Biceps curl: 43.53 ± 12.69 kg

End measure

Bench: 104.50 ± 29.24 kg

Leg press: 473.14 ± 89.43 kg

Biceps curl: 51.27 ± 15.36 kg

Mean

change

Bench: 12.7 kg

Leg press: 89.43 kg

Biceps curl: 7.74 kg

Mean change per week

Bench: 2.12 kg

Leg press: 14.91 kg

Biceps curl: 1.29 kg

Mean % change

Bench: 13.8%

Leg press: 23.3%

Biceps curl: 17.8%

Mean % change per week

Bench: 2.3%

Leg press: 3.9%

Biceps curl: 3.0%

Baseline

LBM: 57.9 ± 13.1 kg

End measure

LBM: 59.8 ± 14.7 kg

Mean

change

LBM: 1.9 kg

Mean change per week

LBM: 0.32 kg

Mean % change

LBM: 3.3%

(3 ± 6 % according to researchers)

Thigh thickness: 1 ± 7%

Mean % change per week

LBM: 0.6%

Thigh thickness: 0.17%

Baseline

LBM: 70.0 ± 13.7 kg

End measure

LBM: 71.0 ± 12.4 kg

Mean

change

LBM: 1.0 kg

Mean change per week

LBM: 0.17 kg

Mean % change

LBM: 1.4%

(2 ± 4% according to researchers)

Thigh thickness: 11 ± 14%

Mean % change per week

LBM: 0.2%

Thigh thickness: 1.83 %

 

 

Technique and power

 

Study

Power
(lower body)

Traditional training

Power
(lower body)

Cluster

sets

Power
(upper body)

Traditional training

Power
(upper body)

Cluster

sets

Lawton et al., 2004
N = 26
Mean age
17-18  years
Duration
6 weeks

Experience
Novice (6+ months RT)

Sex
Male

Tests

Bench press 6RM | Bench throw for power

Measurement

Bench throw

Hansen et al., 2011b
N = 18
Mean age

26.8 ± 4.5 years
Duration
8 weeks

Experience
Elite Rugby Union Players

Sex
Male
Tests

Power: Jump-squat

2-6 RM lift on the squat, jump squat, CMJ,

Training program

lower body resistance training

Exercise

Squat

[Jump-squats] Baseline (peak power averaged from all measurements 0-60 kg)

4277 W

End measure (averaged)

4385 W

Mean

change

108 W

Mean change per week

13.5 W

Mean % change

2.5%

Mean % change per week

0.31 %

[Jump-squats]  Baseline (peak power averaged from all measurements 0-60 kg)

4053 W

End measure (averaged)

4236 W

Mean

change

183 W

Mean change per week

22.9 W

Mean % change

4.5 %

Mean % change per week

0,64 %

Oliver et al., 2013

N = 22
Mean age
25 ± 5  years
Duration
12 weeks

Experience
Trained (6.5 ± 4.5 years)

Sex
Male

Tests

1RM strength & 60% 1RM power tests for squat + bench press
Measurement

DXA, weight scale

Training program

[Squat]
Baseline

625 ± 245 W

End measure

830 ± 232 W

Mean

change

205 W

Mean change per week

17.1 W

Mean % change

32.8 %

(According to figure 2 in the study: ~37 %)

Mean % change per week

2.73 %

(If we use fig. 2 changes as reference: 3.08 %)

[Squat]
Baseline

632 ± 171 W

End measure

914 ± 207 W

Mean

change

282 W

Mean change per week

23.5 W

Mean % change

44.6 %

(According to figure 2 in the study: ~47 %)

Mean % change per week

3.72 %

(If we use fig. 2 changes as reference: 3.92 %)

[Bench]
Baseline

560 ± 122 W

End measure

593 ± 135 W

Mean

change

33 W

Mean change per week

2.75 W

Mean % change

5.89 %

(According to figure 2 in the study: ~ %)

Mean % change per week

0.49 %

(If we use fig. 2 changes as reference: %)

[Bench]
Baseline

575 ± 102 W

End measure

658 ± 113 W

Mean

change

83 W

Mean change per week

6.92 W

Mean % change

14.44 %

(According to figure 2 in the study: ~ %)

Mean % change per week

1.20 %

(If we use fig. 2 changes as reference: %)

Iglesias-Soler et al., 2015
N = 13
Mean age
 22.5 ± 2.6  years
Duration
5 weeks

Experience
Sports science students

Trained >6 months

Sex
7 Male / 6 Female

Tests

Leg extension

Rate of Force Development, mean propulsive velocity

Measurement

Skinfolds/thigh girth/BMI

Training program

2x per week leg extension only

[Leg extension]

Mean Max. Propulsive Power
Baseline

225 ± 100 W

End measure

295 ± 75 W

Mean

change

70 W

Mean change per week

14 W

Mean % change

31.1 %

Mean % change per week

6.2  %

[Leg extension]
Mean Max. Propulsive Power

Baseline

225 ± 100 W

End measure

295 ± 75 W

Mean

change

70 W

Mean change per week

14 W

Mean % change

31. 1$

Mean % change per week

6.2  %

[Exercise]
Baseline

±

End measure

±

Mean

change

Mean change per week

Mean % change

Mean % change per week

[Exercise]
Baseline

±

End measure

±

Mean

change

Mean change per week

Mean % change

Mean % change per week

 

Table of drop-set studies (long-term)

Study

Strength

Traditional training

Strength

Drop-sets

Muscle mass

Traditional training

Muscle mass

Drop-sets

Angleri et al., 2017
N = 32
Mean age
 27.0 ± 3.9 years
Duration
12 weeks

Experience
Trained

Sex
Male

Tests

1RM Leg Press & Leg extension

Measurement

Ultrasound

Training program

2x per week Leg press + Leg extension

Volume equated?

Total Training Volume equated

Number of working sets for the body part being measured/tested

Per session: 1 or 3-5

Weekly: 2 or 6-10

*Number estimated from graph, data presented as % in paper .

Baseline

[1RM Leg Press]

~225 kg*

End measure

~275 kg*

Mean

change

50 kg

Mean change per week

6 kg

Mean % change

25.9 %

Mean % change per week

2.15 %

Baseline

[1RM Leg Press]

~230 kg*

End measure

~280 kg*

Mean

change

50 kg

Mean change per week

6 kg

Mean % change

24.9 %

Mean % change per week

2.07 %

Baseline

~30 cm2*

End measure

~33 cm2*

Mean

change

3 cm2

Mean change per week

0.25 cm2

Mean % change

7.6 %

Mean % change per week

0.63 %

Baseline

~31 cm2*

End measure

~ 34 cm2*

Mean

change

3 cm 2

Mean change per week

0.25 cm2

Mean % change

7.8 %

Mean % change per week

0.65 %

Fink et al., 2017
N = 16
Mean age
  20 – 32 years
Duration
6 weeks

Experience
Untrained

Sex
Male

Tests

12RM Tricep Extension

Measurement

MRI

Training program

2x  per week

Tricep Pushdown

Volume equated?

Volume-load equated

Number of working sets for the body part being measured/tested

Per session: 1 or 3

Weekly: 2 or 6

[12RM Tricep Ext]

Baseline

99.25 ± 9.8 lb

End measure

124.3 ± 24.6 lb

Mean

change

25.05 lb

Mean change per week

4.18 lb

Mean % change

25. 2 %

Mean % change per week

4.2 %

[12RM Tricep Ext]

Baseline

101.5 ± 18.2 lb

End measure

117.9 ± 18.9 lb

Mean

change

16.4 lb

Mean change per week

2.73 lb

Mean % change

16.1 %

Mean % change per week

2.68 %

Baseline

6.9 ± 1.4 cm2

End measure

7.25 ± 1.4 cm2

Mean

change

0.35 cm2

Mean change per week

.058 cm2

Mean % change

5.1 %

Mean % change per week

0.85 %

Baseline

7.0 ± 1.3 cm2

End measure

7.7 ± 1.6 cm2

Mean

change

.7 cm2

Mean change per week

.117 cm2

Mean % change

10 %

Mean % change per week

1.67 %

Fisher et al., 2016
N = 36
Mean age
 ~ 34 – 38 years
Duration
12 weeks

Experience
Novice

Sex
Male & Female

Tests

Muscular Endurance (total volume)

Measurement

Bod Pod

Training program

2x per week whole-body

Volume equated?

Not volume equated (BD did less volume)

Number of working sets for the body part being measured/tested

Per session: 1

Weekly: 2

Strength not measured

Strength not measured

[LBM]

Baseline

75.77 ± 15.96

End measure

±

Mean

change

CON: -0.32 kg

Mean change per week

-0.027 kg

Mean % change

-0,42%

Mean % change per week

-0,035%

[LBM]

Baseline

BD: 68.81 ± 10.15

HLBD: 69.16 ± 13.36

End measure

±

Mean

change

BD: 0.41 kg

HLBD: -0.19 kg

Mean change per week

BD: 0.034 kg

HLBD: -0.016 kg

Mean % change

BD: 0.6 %

HLBD: -0.28 %

Mean % change per week

BD: 0.05 %

HLBD: -0.023 %

Giessing et al., 2014

Not reported here, see single study analyses

Johannsmeyer et al., 2016
LBM, DXA

Not reported here, see single study analyses

 

 

Acute cluster set studies


  • Acute measurements are generally not predictive of long-term changes
  • We’ve excluded acute studies from the article
  • If you’re interested in the acute studies, you can check them out here

 

Limitations


Heterogeneity between studies

“Indeed, comparisons between studies are confounded by the use of single – and multi – set training regimens, contrasting progression strategies (i.e. periodised vs. non – periodised) and strength test protocols (i.e. 1RM vs. 6RM). In addition, it is difficult to compare studies that have used different load – repetition schemes, exercises (e.g. compound vs. ballistic), subjects (e.g. trained vs. untrained), training durations (5 – 12 weeks) and contrasting inter – set and intra-set rest intervals.” – Nicholson et al., 2016b

Ecological validity

  • Some studies only used 1 set per session

  • Studies examined pure CS training vs traditional training, which isn’t reflective of how CS would be used in real life. It’s quite possible that results would be different if CS only consisted of 10% of a training program.

  • The CS programs were, in several of the studies, much easier than the traditional program. Real life approaches would probably use CS differently

Power testing

The test should be specific to the movement being trained in the program. Which raises questions why countermovement jump, squat jump, etc. are being used to test power gains:

“(…) for a valid assessment of strength gain from a resistance training regimen, the testing modality should closely resemble the training conditions (i.e. type of muscle action, etc.)”. – Nicholson et al., 2016b

Other issues

  • Large variance in definitions for CS/RP

  • A potential issue in several of the studies is that the CS/RP protocol was easier and less fatiguing. Less effort = less gains? In some of the studies, CS/RP programs required less effort compared to traditional training. Lifters in these groups may have been understimulated, hence less gains.

  • Varying volume matching

  • Poor reporting: Several of the studies did not report absolute values or standard deviations