Introduction

Flywheel resistance (FRT) is an effective and efficient method for increasing strength, power, and measures of athletic performance across different populations (3). Much like traditional resistance training (TRT), FRT is typically divided into sets of pre-defined numbers of repetitions with several minutes of rest in-between. But there is growing interest in the use of cluster sets (CS), which break up each set into clusters of reps with short “intra-set” rest periods in-between.

As an example, if a traditional FRT set involves 8 consecutive reps, a CS variation may break the 8 reps into 4-rep clusters with 30 seconds of rest between. While either set structure can have benefits, there is some evidence to suggest that CS has some appealing features such as the ability to perform more reps at a given intensity, sustain higher intensities or outputs across a similar volume of work, and/or achieve similar adaptations with less fatigue (2,5,6).

Figure 1. Example of a Traditional Set structure (2 x 8 reps with 120 s rest) and CS structure (2 x 8 reps, broken up into 4 rep clusters with 30 s intra-set rest).

There is a rapidly growing body of research to support the use of CS in traditional training, however, only recently has it been explored as a potential method within FRT. Specifically, researchers suggest that CS may be valuable for:

1. Familiarizing new users to FRT
2. Maximizing training outputs within sessions

Familiarizing New Users to FRT

Like any new training method, there is usually a familiarization process when using FRT for the first time. There are some differences in the inertial loading offered by FRT compared to traditional methods, and this can take a few sessions to get used to before consistent and maximal outputs can be achieved (9).

Two recent studies by Ryan et al. (7,8) evaluated whether using CS structures can be an efficient method for familiarizing new users to FRT. The researchers also looked at whether providing instruction with external cues (“push the ground away”) or internal cues (“push through your heels”) influences the familiarization process.

The CS structure included 3 clusters of 5 reps and 45 seconds of intra-set rest for each set. The first two reps of each cluster were warm-up reps to build momentum into the flywheel followed by 3 high effort working reps. This was done with 4 different inertial loads, ranging from 0.025 to 0.10 kg.m² during each session.

Overall, they found that new users were able to become familiarized with FRT quarter-squats and RDLs within a couple sessions using this CS approach, evidenced by consistent power outputs across reps and sets. This was particularly true when external focus cues were used.

There are several potential reasons why CS may be valuable when familiarizing new users to FRT:

1. The added intra-set rest could reduce fatigue development, and fatigue can interfere with motor learning (1). By minimizing fatigue, the individual may be able to perform more high-quality reps across initial sessions, allowing them to rapidly build the skill of performing FRT exercises effectively. For example, researchers have found that CS was effective for maintaining power clean technique across a set compared to traditional training structures (4).
2. It is possible that the added rest within sets allows the new user to feel more comfortable during their initial experiences with FRT, allowing for them to adapt to the new training method and maximize outputs quicker than usual.

Regardless of the reason, it seems CS may be a valuable method to introduce new users to FRT, especially if proper technical instruction and cueing are provided as well.

Higher Velocity and Power Outputs During Training

A common finding in CS research is that the added intra-set rest helps individuals sustain higher velocities and power outputs across a set compared to traditional set structures (5). While less research has been done with CS and FRT, the studies by Ryan et al. (7,8) found that a CS approach resulted in consistent power outputs across each cluster of reps in FRT squats and RDLs, despite the users being new to the training method.

This may be particularly useful when considering the user-defined resistance that FRT offers. The resistance provided during the eccentric phase is dictated by the effort the user exerts during the concentric phase. So as fatigue sets in, the eccentric resistance will auto-regulate to the effort levels of the user. This unique quality of FRT may be particularly useful when combined with CS, as the added intra-set rest may minimize fatigue, allowing for greater efforts across the entire set and ultimately a higher quality stimulus!

I wanted to put this to the test, so I tracked some data (using the Exerfly App) across several training sessions where I used both traditional set (TS) and CS structures for multiple exercises in random order. The TS consisted of 2 warm-up reps to build momentum in the flywheel and 8 consecutive working reps with maximal concentric effort. The CS also had 8 working reps but divided into 4 rep clusters with 30-seconds of intra-set rest. Each cluster had 2 submaximal warm-up reps before the working reps.

Check out what I found below.

Figure 2. The distribution of rep concentric velocities across multiple sessions for the RDL (top), high pull (middle), and squat (bottom) for CS and Traditional Sets (TS).

Figure 2 displays the distribution of rep velocities that I measured with the Exerfly app across multiple sessions using either CS or TS. Over the course of several sessions, there were a few differences depending on the exercise. For the squat, I was consistently getting faster concentric velocities (which in turn provided a larger eccentric resistance) as shown by the CS density plot being further to the right. For the RDL and high-pull exercises, the biggest difference was that the velocities were more consistent with the CS, shown by the density plots being more condensed to a narrow range of velocities.

To dive deeper into what was happening with CS vs TS, I also looked at velocities across multiple sets of the same session rather than across all reps. An example of what I saw with the RDL can be observed in Figure 3.

Figure 3. Example of differences between cluster sets and traditional sets across multiple sets within the same session.

In this case, velocities were similar during set 1. The only small exception was a particularly slow rep in TS, which occurred towards the end of the set. However, larger differences emerged during subsequent sets (following 2 minutes of rest). Velocity tended to drop off to a larger degree when using traditional sets than CS.

This may indicate that I needed longer rest between sets when using TS schemes with the Exerfly RDL and/or that CS resulted in less fatigue and more sustained output across sets.

In conclusion, here is an overview of what I saw in my own mini-experiment:

1. Across several sessions, CS tended to have faster velocities in some exercises (e.g., squat), and more consistent velocities in others (RDL, high pull) compared to the traditional set structure.
2. When looking at multiple sets within a session, CS and TS often did not differ substantially in set 1, but there tended to be a more pronounced drop off across sets in the traditional sets than CS.
3. It is possible that longer rest is needed when using traditional sets than I used here (2 minutes), which could also mean that CS is a potentially useful option when the goal is maximizing outputs across sets.

It's important to note that this was my personal experience across a few individual sessions, but it does align with some of the research data on the advantages of CS.

Conclusions

While there are many set structures that can be effective with FRT, CS may be a viable option to consider. Particularly when the goal is to help familiarize new users or when attempting to maximize outputs across multiple sets and sessions. This does not mean CS should be used in all circumstances, but rather that it offers an additional tool in the toolbox when programming FRT.

References

1. Branscheidt, M., Kassavetis, P., Anaya, M., Rogers, D., Huang, H. D., Lindquist, M. A., & Celnik, P. (2019). Fatigue induces long-lasting detrimental changes in motor-skill learning.  Elife, 8, e40578.
2. Davies, T. B., Tran, D. L., Hogan, C. M., Haff, G. G., & Latella, C. (2021). Chronic effects of altering resistance training set configurations using cluster sets: a systematic review and meta-analysis.  Sports Medicine, 51, 707-736.
3. de Keijzer, K. L., Gonzalez, J. R., & Beato, M. (2022). The effect of flywheel training on strength and physical capacities in sporting and healthy populations: An umbrella review.  PLoS One, 17(2), e0264375.
4. Hardee, J. P., Lawrence, M. M., Zwetsloot, K. A., Triplett, N. T., Utter, A. C., & McBride, J. M. (2013). Effect of cluster set configurations on power clean technique.  Journal of Sports Sciences, 31(5), 488-496.
5. Latella, C., Teo, W. P., Drinkwater, E. J., Kendall, K., & Haff, G. G. (2019). The acute neuromuscular responses to cluster set resistance training: A systematic review and meta-analysis.  Sports Medicine, 49, 1861-1877.
6. Nagatani, T., Haff, G. G., Guppy, S. N., & Kendall, K. L. (2022). Practical application of traditional and cluster set configurations within a resistance training program.  Strength & Conditioning Journal, 44(5), 87-101.
7. Ryan, S., Ramirez-Campillo, R., Browne, D., Moody, J. A., & Byrne, P. J. (2023). Intra-and inter-day reliability of inertial loads with Cluster Sets When Performed during a Quarter Squat on a Flywheel Device. Sports, 11(6), 121.
8. Ryan, S., Ramirez-Campillo, R., Browne, D., Moody, J., & Byrne, P. J. (2023). Flywheel Romanian Deadlift: Intra-and Inter-Day Kinetic and Kinematic Reliability of Four Inertial Loads Using Cluster Sets.  Journal of Functional Morphology and Kinesiology, 9(1), 1.
9. Sabido, R., Hernández-Davó, J. L., & Pereyra-Gerber, G. T. (2018). Influence of different inertial loads on basic training variables during the flywheel squat exercise.  International Journal of Sports Physiology and Performance, 13(4), 482-489.