Velocity Based Training Q & A Part 3

Velocity training with Landyn Hickmott

I would like to start off this article by saying that the Velocity-Based Training Courses (both the VBT Theory Course and the VBT Application Course) are incredibly comprehensive to the powerlifting athlete, the powerlifting coach, and the strength enthusiast from an applied perspective. The format of the VBT Courses enables you to easily grasp the content and apply it directly into your training that same day! However, I’ve had numerous people reach out to me with some slightly more nuanced questions in relation to some of the free slides that I have shared; therefore, I thought I’d share some of the nuanced answers to those questions here. This article is long, but please feel welcome to read each individual question/answer in a separate sitting or navigate to the question(s)/answer(s) that are most applicable to you. Please also feel welcome to reach out to me on my website: landynhickmott.com. I hope you enjoy!

Section 1

Question

Which Causes Greater ‘Fatigue’ and Time Course of Recovery:

High Intensity and High RPE Training or 

High Volume and High Velocity Loss Training?

Answer

High Volume and High Velocity Loss Training Causes

Greater ‘Fatigue’ and Time Course of Recovery

than High Intensity and High RPE Training

Introduction

To my knowledge, it is sometimes suggested that high relative intensity training and high RPE training contribute the most to ‘fatigue’ (i.e., defined as greater time course of recovery). Certainly, when volume is equated, training to failure causes more ‘fatigue’ than not training to failure [1], but I don’t think any powerlifter ever trains to failure intentionally nor frequently trains to a 9.5 – 10 RPE. However, training at a moderate-high RPE (6 – 9 RPE) at a moderate-high percentage of 1RM is certainly less fatiguing than training at a high VL (>35% VL) at a high volume [2-4].

Before I address specific time course of recovery studies, I’ll provide two common measurements utilized to assess time course of recovery that are employed in these studies: countermovement jump height changes (for performance) and creatine kinase (for muscle damage). There are also several other measurements commonly utilized: load at a velocity of 1.00 m.s-1 in the respective lift, subjective descriptors of soreness, etc. I’ll primarily focus on the countermovement jump for two main reasons: 1) it is used in nearly all the studies that I will address; therefore, it is somewhat easier to make comparisons between studies (although this certainly has limitations); 2) Watkins et al. [5] found that a reduction in countermovement jump height of 8.41 ± 9.90% decreased the number of repetitions that were able to be performed in the back squat at 80% of 1RM by 28.32 ± 25.37%; therefore, it serves as a common potential tool for aiding to predict neuromuscular fatigue, readiness, and ultimately possibly performance (what we as powerlifters are arguably primarily interested in with reference to 1RM changes). I’ll also touch on creatine kinase since it is an enzyme that plays a role in skeletal muscle energy metabolism and is released in the blood during elevated muscle damage; therefore, creatine kinase’s appearance in the blood is frequently used as an indirect marker of muscle damage.

Training to Failure vs Training not to Failure on Time Course of Recovery

I’ll begin with perhaps one of the most well cited studies on time course of recovery: this study by Moran-Navarro et al. [1]. This study compared 6 sets of 5 repetitions at ~5 RPE to 3 sets of 10 repetitions at ~10 RPE with equated relative intensity (~10RM load) and relative volume (30 total repetitions at ~10RM load). The countermovement jump was recovered to baseline within 6 hours following the 6 sets of 5 repetitions protocol, whereas the countermovement jump required 72 hours to recover to baseline following the 3 sets of 10 repetitions protocol. This overall finding certainly isn’t surprising since the VL was considerably lower in the 6 sets of 5 repetitions protocol. The VL was likely ~20% in the 6 sets of 5 repetitions protocol, whereas the VL was likely >~40% or even up to ~60% in the 3 sets of 10 repetitions protocol (and this was also intended to be at ~10 RPE to failure). I wouldn’t suggest training to failure, nor do I know of anybody that recommends this either… I certainly don’t think this is anything new nor do I think most reading this will think that this is anything new either.

Is it High RPE or High Velocity Loss that is Primarily Contributing to Greater Fatigue?

I think the more interesting question is, “is it the high RPE or the high VL that is primarily contributing to greater ‘fatigue’?” I would argue that it is primarily the high VL, not necessarily the high RPE. To pose a slightly more specific question with respect to the demands of the sport of powerlifting, “is ‘higher’ RPE training (i.e., ≥7 RPE) necessarily that ‘fatiguing’ when VL is kept low-moderate?” This study by Pareja-Blanco et al. [3] is perhaps one of the most – if not the most – comprehensive study on time course of recovery to answer the question. 

In this study, it was demonstrated that at 24-hours following the intervention, the countermovement jump had returned to baseline when sets and VL were equated at 17.0 ± 5.3 – 22.7 ± 6.9% VL between comparison protocols: 4 RPE at 70%, 5 RPE at 75%, 6 RPE at 80%, 7 RPE at 85%, and 8 RPE at 90% of 1RM in the smith machine back squat. However, when sets and RPE were equated at ~10 RPE, training at higher VL thresholds (43.0 ± 6.3 – 45.2 ± 5.5% VL) consisting of 12RM (~70%), 10RM (~75%), and 8RM (~80%), resulted in the greatest elevations in creatine kinase concentrations at 48 hours post-intervention compared to training at lower VL thresholds (34.7 ± 8.8 – 36.0 ± 7.7% VL) consisting of 6RM (~85%) and 4RM (~90% of 1RM). Therefore, it appears that it is primarily the higher VL thresholds (i.e., >~35% VL) coupled with higher volume (this is obviously important to mention here) that are primarily causing the greatest ‘fatigue’ and time course of recovery, not necessarily the high RPE nor the high intensity.

This is likely where most athletes will be performing the majority of their training if employing the LRV Model (i.e., ~5 – 9 RPE at 70 – 90% of 1RM). There’s also certainly nothing wrong with hitting a 10 RPE set for 6 repetitions or fewer as a benchmark top set (as illustrated by the aforementioned recovery demands associated with this repetition and RPE scheme). However, I don’t think most athletes will be doing multiple working set repeats at a 10 RPE with 70 – 80% of 1RM which involves higher repetitions (8 – 12) and higher VL (≥~45% VL) for the majority of their training. As a side note, this investigation [3] also provides a small rationale of where the VL zones in the LRV Model manifest from with respect to neuromuscular fatigue. Specifically, if you reflect on the previous paragraph: the least fatigue was from ≤~25% VL, the moderate fatigue was from ~35% VL, and the greatest fatigue was from ≥~45% VL.

Is it High Intensity and High RPE or High Volume and High Velocity Loss that is Primarily Contributing to Greater Fatigue?

It’s also important to highlight that high volume and high VL training supersedes the ‘fatigue’ and time course of recovery of high intensity and high RPE training [4]. To illustrate, Bartolomei et al. [4] compared 8 sets of 3 repetitions at 90% of 1RM (high intensity, high RPE) to 8 sets of 10 repetitions at 70% of 1RM (high volume, high VL). The acute bout of high-intensity training comprised of 8 sets of 3 repetitions at 90% of 1RM had performance assessments (i.e., isometric squat), ultrasound measurements (i.e., cross-sectional area), biochemical measures (i.e., creatine kinase), and subjective ratings (i.e., soreness) recovered within 30-minutes post-intervention. Furthermore, all performance assessments for strength and power had returned to baseline within 24 hours following the 8 sets of 3 repetitions at 90% of 1RM protocol. However, measures of fatigue were still present at 72 hours following the 8 sets of 10 repetitions at 70% of 1RM protocol. For example, countermovement jump peak power was restored to baseline at 24 hours following the 8 sets of 3 repetitions at 90% of 1RM protocol; however, countermovement jump peak power required three-fold the time course to be restored to baseline (it required 72 hours) following the 8 sets of 10 repetitions at 70% of 1RM protocol. Therefore, it appears that it is primarily the higher volume coupled with higher VL that are primarily causing the greatest fatigue, suggesting that you don’t need to be as concerned about training with high RPE and high intensity if VL does not exceed moderate levels (i.e., ~25 – 35% VL) and volume is kept at that individual optimal dosage [6].

Finally, it is noteworthy to mention that an athlete’s ability to perform a strength-focused session recovers faster than an athlete’s ability to perform a hypertrophy-focused session [7]. To exemplify, Ferreira et al. [7] had subjects perform 8 sets to failure in the bench press, in which sets 1 – 4 involved 90% of 10RM and sets 5 – 8 involved a 20% load reduction. The results indicated that peak torque (which may be able to indirectly represent maximal strength) was significantly greater recovered than total work (which may be able to indirectly represent volume capabilities) at all time points assessed: 24-, 48-, 72-, and 96-hours post-intervention. Ultimately, this may have important implications for how you distribute and prioritize your frequency, intensity, volume, and sessions (and other training metrics) within sessions, a microcycle, and beyond.

Thoughts on Muscle Damage?

A question asked was, “what are my thoughts on ‘fatigue’ and skill practice?” This study by Leite et al. [8] is frequently cited to support that muscle damage attenuates motor skill learning and therefore harms skill practice of the powerlifts. This study compared a control condition (no exercise) to what the researchers called an exercise-induced muscle damage condition (weightlifting routine) on dart throwing ability (not on lifting ability), in which it was demonstrated that the exercise-induced muscle damage condition resulted in lower accuracy in the delayed post-test. I wouldn’t necessarily disagree that excessive muscle damage harms skill practice; however, in reference back to the studies by Pareja-Blanco et al. [3] and Bartolomei et al. [4], muscle damage is primarily caused by high volume training and high VL training; not from high relative intensity training and not from high RPE training as the question was asked.

Some may argue that the studies by Pareja-Blanco et al. [3] and Bartolomei et al. [4] were only equated for sets (and not equated for relative volume), which is a fair argument. However, these studies certainly highlight that training at high relative intensity and high RPE (which is going to be at a low-moderate VL simply because only few repetitions can be performed at high relative intensities) does not cause considerable muscle damage beyond 24 hours post-intervention. Think back to the 8 sets of 3 repetitions at 90% of 1RM in Bartolomei et al. [4] and the recovery response/demands… It wasn’t considerable at all. That’s a decent amount of volume to perform in a single session at 90% of 1RM (18 total repetitions), and a fast recovery (24 hours), especially for those ‘trained participants’ that are less trained than most reading this.

Thoughts on Training Quality?

I could cite some reviews here [9, 10]; however, I’d argue that there’s not necessarily any study to directly investigate training quality as it’s simply difficult to clearly define and measure training quality; thus, I’ll provide my anecdotal thoughts here. A question brought forth to me is, “what hinders overall training quality?” Anecdotally, I would argue that high VL training harms repetition quality and that high volume harms session quality. Anecdotally, I don’t think high relative intensity nor high RPE harms training quality; powerlifters should likely be good (and typically are good) at the skill of performing high relative intensity and high RPE sets since that is largely what the sport involves, although of course all of their training won’t be performed here and will be individualized.

When an athlete nears very high VL the repetition quality (i.e., lifting technique) typically worsens likely due to: 1) the accumulation of lactate and hydrogen ions, which may impair skeletal muscle contraction due to decreased central nervous system drive (although there is some conflicting evidence here) [11, 12]; 2) synergists activation likely increasing as the prime movers become increasingly fatigued [13, 14]; 3) sometimes the athlete may be focusing on getting through the set of many repetitions rather than focusing on each repetition as a single. When an athlete nears very high volume within a single session (not necessarily long sessions such as 4 hours or so) the session quality typically worsens likely due to: 1) simply becoming overly fatigued from excessively high volume [4], in which the latter portion of the session typically results in sub-optimal performance; 2) the excessively high volume session can cause greater time courses of recovery; sometimes hindering performance for the subsequent session(s) [2-4]; 3) sometimes the athlete may be focusing on just getting through the session of endless volume rather than focusing on quality, maximal intent, and performance of the session. For example, anecdotally I find that for myself and some athletes (although certainly not all) the overall training quality suffers more from doing a single set of 9 repetitions at an 8 RPE with for example 70% of 1RM compared to 3 sets of 3 repetitions at an 8 RPE with for example 85% of 1RM (since the VL is lower), but who knows that could just be me?

Velocity Loss Manipulated to Establish Velocity Loss Zones

Since high intensity and high RPE training don’t contribute to considerable time courses of recovery [2-4], and moderate-high intensity training is arguably most important for strength adaptations [15-17], whilst moderate-high RPE training is more specific to the sport and important for accumulating adequate volume efficiently [18], volume and VL become the two primary options available for manipulation to minimize unnecessary neuromuscular fatigue. However, an individual optimal dosage of relative volume is important for hypertrophy adaptations [6]; therefore, VL is manipulated and individualized VL zones are established to ‘optimize proximity to failure’, whilst LRV (and RPE) is used as the primary autoregulatory and monitoring prescription strategy. I’ll leave you on a cliff-hanger here, as this concept is addressed extensively in the VBT Courses; however, please observe Slide 1 for a summary. Due to the aforementioned studies and explanations, I’m more of an advocate of slightly higher frequencies than what most would typically prescribe for some athletes to ensure that sessional volume is kept more moderate when training at low-moderate VL (or else the set volume within a session may become incredibly high); however, this of course is also determined by individual rates of recovery [19, 20] and individual athlete preferences [21] as well.

Basic Examples for Potential Utility of Very Low RPE Training and True Failure Training

I typically advocate for moderate-high RPE (i.e., 6 – 9 RPE) on average for most athletes (with some at 10 RPE and some at 5 RPE) as illustrated in the LRV Model. Despite this, I’ll provide some potential utility of very low RPE training (i.e., <5 RPE) and true failure training (i.e., to actual concentric failure); however, I’m not a huge advocate of these extremist views, nor do I rarely ever prescribe this or suggest training here often. These examples certainly aren’t anything new and many reading this likely already know this and/or do this. Despite this, I thought these examples may be useful since most of this article focused on not training to true failure, and on not training at very low RPE either.

Very Low RPE (i.e., <5 RPE)

1) Appropriately disperse volume if following ultra-high frequency program (i.e., 6 times per week bench press or multiple bench press sessions per day)

2) Recovery sessions for active recovery [22] (i.e., if following ultra-high frequency program)

3) Power sessions to potentially potentiate performance [23] (i.e., prior to highest priority session)

4) Neuromuscular sessions [24, 25] (however, keep in mind that late rate of force development seems to be optimized at ~25% VL which is ~5 – 9 RPE at 70 – 90% of 1RM)

5) Perform a restoration block after multiple ultra-aggressive blocks

True Failure Training (i.e., to actual concentric failure)

1) Formulating the LRV Model

2) Testing sessions (although I typically recommend saving it mostly for competition)

3) Final set of easy supplementary hypertrophy exercises that you personally find aren’t very ‘fatiguing’ and that you can recover quickly from (i.e., rear delt flies, tricep extensions, etc.)

4) A session in which you have your lowest priority session during subsequent session (although I wouldn’t advocate for true failure training on the powerlifts nor similar compound lifts)

5) A session in which you have the most time prior to next highest priority session (although I wouldn’t advocate for true failure training on the powerlifts nor similar compound lifts)

Final Notes

Study Designs

These studies (i.e., studies investigating time course of recovery) are purposefully designed to induce considerable ‘fatigue’ to collect data on various measurements of time course of recovery. To be completely honest, although I greatly respect all participants, these ‘trained participants’ in these studies are considerably less trained than most reading this and are unaccustomed to these types of sessions; they’re in for a ‘long recovery’ with some of these time course of recovery studies. I would argue that many reading this would likely recovery considerably faster than these participants, and that many reading this (especially if you’ve stuck around and read this far) train much more frequently and ‘much harder’ than these participants. This is crucial to keep in mind when interpreting the results from these studies.

Repeated Bout Effect

Think about how sore you were the first time you trained or the first time you performed a style of training that you were very unaccustomed to. You were probably very sore the first session or so, but then you were less and less sore with each subsequent session thanks to the phenomenon recognized as the repeated bout effect [26]. The repeated bout effect will kick in depending on the approach or strategy that you are employing, and you’ll likely be fine (i.e., even if you perform a considerable amount of volume with high repetitions sets at high RPE which will be at high VL). Furthermore, you’ll likely be fine if you’re performing a bottom-up framework approach (i.e., an RTS Emerging Strategies framework or similarly inspired framework), since the prescription will be identical from microcycle-to-microcycle. Also, the concept that considerable fatigue consistently accumulates following training has recently been questioned [27]; thus, certainly warranting further investigation particularly in ‘more trained participants’.

Section 2

Question

Will Greater Strength Adaptations Result if an Individual Equates for Total Repetitions at a Given Percentage of 1RM and Performs More Sets with Lower Repetitions per Set Because They are Supposedly Performing a Greater Number of Repetitions that are the Most 

‘Strength Stimulating’ or ‘Count More for Strength’?

And Are the Final Five Repetitions Prior to Failure the Most ‘Hypertrophy Stimulating’ or ‘Count More for Hypertrophy’?  

Answer

Based on Two Recent Systematic Reviews and Meta-Analyses, 

there are No Differences in Strength Adaptations, and 

No Differences in Hypertrophy Adaptations between 

Traditional Sets and Alternative Set Structures When 

Total Repetitions and Percentage of 1RM are Matched

Introduction

To answer both questions with one word: No. Both questions were asked multiple times based on the previous Articles, and these are two questions that I debunked in the VBT Courses (although many others have already debunked both questions). The question asked with respect to strength was along the lines of, “will greater strength adaptations result if an individual equates for total repetitions at a given percentage of 1RM and performs more sets with lower repetitions per set because they are supposedly performing a greater number of repetitions that ‘count more for strength’ since the first repetition within the set supposedly produces the greatest strength stimulus and each subsequent repetition supposedly produces less and less of a strength stimulus?” The question asked with respect to hypertrophy was along the lines of, “are the final five repetitions prior to failure the most hypertrophy stimulating, or in other words ‘count more for hypertrophy’?” Individuals commonly used ‘strength-stimulating repetitions’ in reference to the first question and ‘hypertrophy-stimulating repetitions’ in reference to the second question; therefore, I will use this terminology for the remainder of this article; however, I will not use this terminology proceeding forward outside of this article, because both ‘concepts’ lack supporting evidence from the scientific literature.

The primary purpose of this article section is simply and solely to answer both questions based on the existing collated scientific data through an unbiased lens for those individuals that have asked or may be interested. Let’s dive into it…Two recent systematic reviews and meta-analyses (one conducted by Davies et al. [18] and one conducted by Jukic et al. [28]) published in January 2021 in Sports Medicine (arguably one of the best Journals in our field) demonstrated no difference in strength adaptations between traditional sets and alternative set structures, despite alternative set structures performing greater than or equal to twice the number of these ‘strength-stimulating repetitions’ compared to traditional sets. Similarly, Davies et al. [18] and Jukic et al. [28] demonstrated no difference in hypertrophy adaptations between traditional sets and alternative set structures, despite traditional sets typically performing well above greater than or equal to twice the number of these ‘hypertrophy-stimulating repetitions’ compared to alternative set structures. The good news is that you can perform either traditional sets or alternative set structures (or a combination of both) depending on your individual preference, context, and goals and likely get similar strength and hypertrophy adaptations. If you want the short answer, there you have it. If you want the long answer, continue reading. I’ll warn you… This is a long article! You may want to read it multiple times or in separate sittings to grasp the content. However, I think both questions certainly require considerable contextualization for further conceptual clarity as multiple individuals have asked both questions in which some individuals had some fair rebuttals. I appreciate and think that the rebuttals are totally appropriate. But in short, my response is that meta-analytic data is meta-analytic data [18, 28], and we can’t get much better than it, especially since these two systematic reviews with meta-analyses [18, 28] were published quite recently and included more studies than most systematic reviews and meta-analyses in our field. 

Illustration of Traditional Sets vs Alternative Set Structures

For those that are unaware of what traditional sets and alternative set structures are please observe Slide 2 for illustrated examples. Alternative set structures are comprised of intra-set rest, cluster sets, and inter-repetition rest [9]. Traditional sets and alternative set structures are typically slightly ‘higher repetitions’ in the literature than what I have illustrated in Slide 2. Rather than 3 sets of 6 repetitions for traditional sets vs 6 sets of 3 repetitions for intra-set rest, a more common comparison may comprise of 4 sets of 8 repetitions for traditional sets vs 8 sets of 4 repetitions for intra-set rest. I simply illustrated slightly ‘lower repetitions’ to fit on the slide more easily and depict repetitions more common in powerlifting contexts. For example, in the literature, traditional sets typically involve sets taken to a near 10 RPE or failure, whereas intra-set rest involves doubling the sets, halving the repetitions, and halving the rest periods compared to traditional sets; thereby, performing two-fold the number of ‘strength-stimulating repetitions’ compared to traditional sets. Since I believe that the ‘strength-stimulating repetitions’ question may have somewhat also derived from the velocity loss (VL) literature based on some of the sub-questions asked, I’ll admit that there may be slightly more VL accumulated from the first repetition of the first set to the last repetition of the last set during an intra-set rest protocol compared to if the rest period was kept identical to the traditional set (i.e., rather than have the alternative set structure comprised of 6 sets of 3 repetitions with 1 minute rest, have it comprised of 6 sets of 3 repetitions with 2 minutes rest). However, based on the sole study on alternative set structures that did record VL, the VL was still considerably lower in the alternative set structure groups (~8.61% VL and ~13.86% VL) compared to the traditional set group (~40.30% VL), and there was no significant differences in 1RM strength adaptations between groups [29]. Furthermore, based on the preliminary VL study where VL was first introduced into the scientific literature, VL increases relatively linearly as the repetitions within a set increase [11, 30]. Therefore, alternative set structures also involve training at considerably lower VL compared to traditional sets [31]. 

I would argue that the studies comparing traditional sets to alternative set structures are arguably the best to investigate both questions as there is typically no autoregulatory component; thus, it is simply investigating, “when total repetitions and percentage of 1RM are equated, is there a difference in strength adaptations when the repetitions are divided into additional sets to increase the number of these ‘repetitions that supposedly produce a greater strength stimulus’? And is there a difference in hypertrophy adaptations when these ‘repetitions that supposedly produce a greater hypertrophy stimulus’ are reduced?”

Ultimately, if you ‘count’ the number of these ‘strength-stimulating repetitions’, or ‘count’ the number of first repetitions, or allocate a point system (i.e., the first repetition with 0% VL counts 5 points, the second repetition counts 4 points, and so on and so forth), alternative set structures perform greater than or equal to twice the number of ‘strength-stimulating repetitions’ compared to traditional sets, yet the effect – despite negligible – favored traditional sets in both systematic reviews and meta-analyses [18, 28]. Similarly, if you ‘count’ the number of these ‘hypertrophy-stimulating repetitions’, or ‘count’ the number of repetitions near failure, or allocate a point system (i.e., the last repetition counts 5 points, the second last repetition counts 4 points, and so on and so forth), traditional sets typically perform well above greater than or equal to twice the number of ‘hypertrophy-stimulating repetitions’ compared to alternative set structures, yet the effect – despite negligible – favored traditional sets in both systematic reviews and meta-analyses [18, 28]. Overall, repetitions in and of themselves are certainly not any more nor any less ‘strength-stimulating’ [18, 28]; similar to the concept that repetitions in and of themselves are certainly not any more nor any less ‘hypertrophy-stimulating’ [18, 28, 32, 33].

Data for Strength Adaptations from Davies et al. [18] and Jukic et al. [28]

I selected the data in both systematic reviews and meta-analyses from the overall analysis (obviously this is important) and applicable sub-analyses/sub-group analyses for strength outcomes that I believed individuals reading this would be most interested in (i.e., sub-analysis of trained participants) [18, 28]. As illustrated in the tables below, there was typically no significant difference (p > 0.05) between traditional sets and alternative set structures. Keep in mind statistical significance was set at p < 0.05 here and is located in column 4. The effect typically favored traditional sets over alternative set structures; however, it was typically a negligible effect (<0.20). Keep in mind an effect of 0.20 – 0.49 would be considered a small effect based on the meta-analytic procedures conducted in these systematic reviews and meta-analyses and is located in column 2. I also included the 95% confidence intervals for those that may be interested and is located in column 3. Keep in mind that in a correctly conducted meta-analysis with a forest plot, if the confidence interval does not cross the zero point (does not go from a negative value to a positive value), then the data will be statistically significant (p < 0.05). Don’t worry if you’re not super up-to-speed on statistics; the takeaway is that there is typically no meaningful difference between traditional sets and alternative set structures for strength outcomes. Rest-redistribution (alternative set structures) resulted in significantly greater strength outcomes (see final row from Jukic et al. [28] table below) when rest-redistribution (intra-set rest) employed a higher load than traditional sets, which certainly isn’t a surprise as greater loads have been supported to result in greater strength adaptations than lower loads [15-17, 34]. As an important side note, there is also this sole relative volume equated longitudinal VL study [35] (all other VL studies are set equated) demonstrating no difference in 1RM strength and hypertrophy outcomes between 15% VL and 30% VL when total repetitions and percentage of 1RM are equated; however, 15% VL observed a medium effect size of 1.25, whereas 30% VL observed a large effect size of 1.82 for 1RM strength. 

Data is from Davies et al. [18] for strength outcomes

AnalysisandSub-Analyses Hedges’ g effect sizes ± standard error of the mean (ES ± SEM) 95% confidence intervals (95% CI) p-value
Overall  0.05 ± 0.10(favored traditional sets) -0.11 to 0.21 0.56
TrainedParticipants 0.07 ± 0.00(favored traditional sets) -0.16 to 0.30 0.55
Untrained Participants 0.05 ± 0.12(favored traditional sets) -0.19 to 0.28 0.69
CompoundExercises 0.04 ± 0.09(favored traditional sets) -0.14 to 0.23 0.64
IsolationExercises 0.14 ± 0.18(favored traditional sets) -0.22 to 0.49 0.44

Data is from Jukic et al. [28] for strength outcomes

AnalysisandSub-Group AnalysesandMeta-Regression Standardized mean difference(SMD) 95% confidence intervals (95% CI) p-value
Overall  0.06(favored traditional sets) -0.05 to 0.16 0.291
Cluster Set 0.07(favored traditional sets) -0.07 to 0.21 0.300
Rest-Redistribution(i.e., Intra-Set Rest) 0.04(favored traditional sets) -0.12 to 0.20 0.641
Load Employed Overall b = 0.01 -0.01 to 0.03 0.249
Load Employed Rest-Redistribution(when traditional sets used lower loads than rest-redistribution) b = 0.05(favored rest-redistribution) 0.01 to 0.10 *0.028(significant)

To summarize most simply, if one equates for total repetitions at a matched load, whether a traditional set, an intra-set rest, a cluster set, or an inter-repetition rest protocol is performed with the same training frequency and training length of the studies included in these systematic reviews and meta-analyses (i.e., ~2 – 3 times per week for ~8 weeks), there is no difference in strength adaptations [18, 28]. However, what’s important to note is that when you equate for sets, equate for repetitions per set, and compare RPE (i.e., compare 5 sets of 3 repetitions at 8 RPE to 5 sets of 3 repetitions at 4 RPE), in all studies in which there was a significant difference in relative intensity [34, 36, 37], the higher RPE groups resulted in significantly greater [34, 37] or favored small-to-moderate effect sizes [36] for 1RM strength adaptations, which certainly isn’t a surprise based on the load differences between say for example 5 sets of 3 repetitions at 8 RPE (high load) compared to say for example 5 sets of 3 repetitions at 4 RPE (low load) [15-17], further supporting that strength adaptations are primarily driven by load and not primarily driven by ‘strength-stimulating repetitions’. Now, let’s move onto hypertrophy adaptations.

Data for Hypertrophy Adaptations from Davies et al. [18] and Jukic et al. [28]

Please see the meta-analytic data/results for hypertrophy outcomes [18, 28] outlined below in tables with applicable sub-group analyses for intra-set rest and cluster sets from Jukic et al. [28]. As an important side note, there are also two other systematic reviews and meta-analyses from 2021 comparing training to failure vs not training to failure that demonstrated no differences in hypertrophy outcomes when volume was equated [32, 33], which further refutes ‘hypertrophy-stimulating repetitions’. 

Data is from Davies et al. [18] and Jukic et al. [28] for hypertrophy outcomes

AnalysisandSub-Group Analyses Hedges’ g effect sizes ± standard error of the mean (ES ± SEM)for Davies et al. [18]andStandardized mean difference (SMD)for Jukic et al. [28] 95% confidence intervals (95% CI) p-value
Davies et al. [18]Overall 0.05 ± 0.14(favored traditional sets) -0.23 to 0.32 0.73
Jukic et al. [28]Overall 0.03(favored traditional sets) -0.13 to 0.20 0.708
Jukic et al. [28]Sub-Group AnalysisIntra-Set Rest 0.06(favored traditional sets) -0.14 to 0.26 0.543
Jukic et al. [28]Sub-Group AnalysisCluster Sets 0.93^large SMD(favored traditional sets) -1.38 to 3.24 0.429

Similar to strength adaptations, if one equates for total repetitions at a matched load, whether a traditional set or an alternative set structure protocol is performed with the same training frequency and training length of the studies included in these systematic reviews and meta-analyses (i.e., ~2 – 3 times per week for ~8 weeks), there is no difference in hypertrophy adaptations [18, 28]; however, with the caveat of cluster sets. The sub-group analysis revealed a large effect in favor of traditional sets over cluster sets (SMD = 0.93 in favor of traditional sets) [28]. However, the sub-group analysis revealed no meaningful difference between traditional sets and intra-set rest (SMD = 0.06 in favor of traditional sets) [28]. Why did cluster sets reveal a meaningful difference in favor of traditional sets for hypertrophy, but intra-set rest didn’t, despite traditional sets, cluster sets, and intra-set rest being matched for relative volume? What’s important to recognize is that intra-set rest involves performing ~50% of the maximal number of repetitions that can be performed within the set (or simply ~50% of the repetition maximum), which corresponds to ~20% VL; however, cluster sets are likely at <~20% VL. Therefore, there may potentially be a caveat that ~20% VL may need to be achieved even when relative volume is utilized to optimize and quantify volume for hypertrophy. Please keep this in mind and I’ll re-visit it later. However, I will admit that for the cluster set sub-group analysis the degrees of freedom was one (the number of studies in this sub-group analysis was only 2) and the I2 (heterogeneity) was 87%; thus, interpret these results very cautiously (there certainly could be no meaningful difference). There you have it, if you look at the number of ‘strength-stimulating repetitions’ and ‘hypertrophy-stimulating repetitions’ from Slide 2 and then look at the meta-analytic data, both ‘concepts’ lack support. For those that want the nuanced discussion beyond the data, continue reading…

Single Studies, Cherry-Picking, Confirmation Bias, Systematic Reviews and Meta-Analyses

Some individuals asked me about individual studies and how much weight the results of an individual study that only has 8 – 16 participants per group really have? This depends on how many other studies exist in the literature that meet the inclusion criteria for a systematic review and meta-analysis. With respect to this question: studies that compare traditional sets to alternative set structures. Certainly, there will be additional more specific inclusion criteria that will be involved, and the systematic search will be explicitly stated as well so that anybody else can identically replicate the search to avoid bias. Furthermore, correct meta-analytic procedures (i.e., correctly devising a forest plot rather than incorrectly pooling percentage changes), consider the means and standard deviations of each group within each study and weight them appropriately within the pooled meta-analysis, while reporting p-values and effect estimates (i.e., between group effect sizes and/or mean differences) with confidence intervals.

Something that should be highlighted when bridging that gap between science and application is that if an individual has a bias towards a specific applied training approach, that individual can solely present scientific studies supporting that bias. Feel welcome to quickly Google search ‘cherry-picking data in academia’ for further details if you’re unaware of its meaning; however, ‘cherry-picking’ whether intentional or unintentional is an issue and mitigating ‘cherry-picking’ by presenting to you the meta-analytic data [18, 28] may aid to answer the questions. Feel welcome to also quickly Google search ‘confirmation bias in academia’, which can sometimes go hand-in-hand with ‘cherry picking’ and is commonly spotted when an individual presents a hypothesis and then seeks to find data to support that hypothesis whilst neglecting to discuss and address the totality of the scientific literature to date. For example, if one is biased towards traditional sets, one could solely cite Hansen et al. [38] and state that the one exercise investigated in this study (barbell back squat) resulted in significantly greater (p < 0.05) 1RM strength outcomes for traditional sets compared to cluster sets. Similarly, if one is biased towards alternative set structures, one could solely cite Oliver et al. [39] and state that in one of the two exercises investigated in this study – only the barbell back squat; not the barbell bench press – resulted in significantly greater (p = 0.041) 1RM strength outcomes for intra-set rest compared to traditional sets. However, it is also worth noting that the p-value in Oliver et al. [39] was 0.041; thus, with such a small sample size this may be a type-II error (i.e., a false positive; demonstrating significance when there actually isn’t significance… but who knows). To summarize, if a recent meta-analysis(es) (i.e., these two published in January 2021) [18, 28] has been conducted, we should first look at the meta-analysis(es) for the overall results of the literature to date, and subsequently contextualize/discuss that literature based on the overall results and applicable sub-analyses. Also, be cautious of simplified models that attempt to explain nuanced concepts. Models that aid with programming prescription (i.e., autoregulating and monitoring) are considerably easier to grasp and considerably more applicable in powerlifting contexts. Is an athlete/coach really supposed to count ‘strength-stimulating repetitions’ and count ‘hypertrophy-stimulating repetitions’? Does it provide an athlete/coach with any useful data for informing training? I don’t see how it would, especially since both ‘concepts’ are deeply flawed. Anyways, with that out of the way, let’s move onto the disadvantages and advantages of traditional sets and alternative set structures.

Disadvantages and Advantages of Alternative Set Structures 

Disadvantages

The two primary disadvantages of alternative set structures for powerlifters are: 1) ‘easy’ training style if using very low RPE sets (i.e., <5 RPE); 2) less efficient training due to more training time [18]. Davies et al. [18] noted that from the studies included in their review, traditional sets required an average of 11.2 ± 6.0 minutes per exercise per session, whereas alternative set structures required an average of 14.2 ± 7.3 minutes per exercise per session. Does this really matter that much? Maybe not. It’s only ~3 extra minutes per exercise for alternative set structures. I personally don’t care at all how long a training session takes, but it’s worth noting time requirements as many athletes have important time constraints and powerlifters typically take longer rest periods than what is employed in these studies. 

Davies et al. [18] highlighted that alternative set structures likely have less application when more traditional sets of ~1 – 6 repetition sets are performed as the intra-set fatigue (VL) is typically already low-moderate (again illustrating how VL is related to the number of repetitions performed within the set from Article 1). Therefore, alternative set structures may have more utility when higher repetition traditional sets are performed (as Jukic et al. [28] also demonstrated), which isn’t really a surprise, because who frequently does repeat sets of 10 repetitions to a 10 RPE in the competition lifts as a powerlifter? In other words, I strongly advocate for ~50% of the repetition maximum when using say a 10RM or 8RM load; however, this may have slightly less practical utility when using ≤~6RM load. For example, rather than perform 3 sets of 8 repetitions at a 10 RPE, I may opt for 6 sets of 4 repetitions at a 6 RPE. Furthermore, the authors explained how the requirement to re-rack, reset, and un-rack for additional repetitions may hinder the actual goal of the alternative set structure and/or overall training process to maintain maximal intent [18]. For example, maybe the athlete finds this stressful, annoying, or tedious since the sets aren’t necessarily very challenging or engaging at times but they’re performing endless ‘easy’ sets. From an athlete and competition perspective I certainly think that there is an advantage of ‘rising to the challenge’ and integrating more challenging ‘modified’ alternative set structures in powerlifting contexts (addressed in the Practical Applications of this Article).

Advantages

The primary advantage of alternative set structures for powerlifters are: 1) limiting excessive neuromuscular fatigue, which may have potential implications from a practical perspective (please see Section 1 of this Article for a more nuanced discussion). The primary advantages of alternative set structures for more main-stream sports/athletes (i.e., hockey, football) are: 1) limiting fatigue in-season when performance is most important [9, 40, 41]; 2) greater shifts towards velocity-oriented force-velocity profiles [28].

Although there was no significant difference in strength adaptations between set types in Jukic et al. [28], alternative set structures resulted in greater shifts towards velocity-oriented force-velocity profiles compared to traditional sets (SMD = 0.28), and potentially higher power and velocity with considerable submaximal loads (but the standardized mean difference estimate of effect size wasn’t even what is considered small (0.20); it was only 0.18; however, it was significant at p = 0.011). Anyways, what may potentially be happening to the force-velocity profile for traditional sets? I don’t know for certain, as it wasn’t explicitly reported; thus, this is purely my anecdotal thoughts. Since both alternative set structures and traditional sets resulted in nearly identical strength adaptations (SMD of 0.06 in favor of traditional sets in Jukic et al. [28]), my thought is that traditional sets are simply resulting in a right-ward shift of the force-velocity profile from pre- to post-test (essentially pasting the line from the profile and moving it to the right); not a shift towards more velocity-oriented or force-oriented force-velocity profiles per say. Essentially, the curves from alternative set structures and traditional sets are likely intersecting at high forces (at that 1RM), since there was no difference in strength adaptations. As a side note – in the VBT world – alternative set structures are considered more ‘velocity training’ (i.e., maintaining high force via maintaining high velocity repetitions to promote more velocity-specific profiles), compared to more ‘force training’ (i.e., training at high force via high loads on the bar to promote more force-specific profiles).

At a basic level, these force-velocity oriented profile shifts are primarily based on the SAID principle (specific adaptation to imposed demand initially conceptualized by Franklin Henry in 1958), specificity principle [42], and velocity principle [43]. This concept certainly isn’t anything new either; wherever an athlete performs most of the training on the force-velocity curve (illustration of force-velocity curve is located in Article 2 – Section 2 – Slide 2) is typically where the athlete will observe the greatest adaptations, with diminishing adaptations above and below that portion of the curve [15-17, 44, 45]. Training with ‘more force’ (i.e., higher velocities) at a given load for a total number of repetitions is going to promote slightly greater adaptations to those specific submaximal loads utilized. You may ask, “well wouldn’t this be a good adaptation for a powerlifter?” We certainly aren’t nearly as concerned about those submaximal loads compared to a 1RM load. Again, based on the Davies et al. [18] and Jukic et al. [28] systematic reviews and meta-analyses it doesn’t make a difference for strength due to the adaptations of the force-velocity curve intersection. Well then, how does a powerlifter train to promote greater shifts towards force-oriented force-velocity profiles? Primarily by training at high percentages of 1RM [15-17, 44, 45]. Well, what about the back-off volume work? Honestly, based on these systematic reviews and meta-analysis it certainly doesn’t make a meaningful difference for strength adaptations [18, 28]. Why? Because load remains constant across all total repetitions whether an individual is performing a traditional set or an alternative set structure. Let’s dissect traditional sets and alternative set structures from a specificity lens.

Traditional Sets vs Alternative Set Structures from a Specificity Lens

Some individuals asked the question along the lines of, “since high force training (i.e., training at high percentages of 1RM/load) results in greater strength adaptations than low force training (i.e., training at low percentages of 1RM/load) [15-17, 34], will training with higher intra-set force for a given load when total repetitions are matched also result in greater strength adaptations?” Some individuals that dove into the VBT narrative review by Weakley et al. [46] from Article 1 – Section 4 also asked about the terminology utilized from Weakley et al. [46] of ‘force-generating capacity’ and the terminology I utilized from my Bonus Seminar of ‘increased force production’. As both of us highlighted, these concepts do not directly relate to greater strength adaptations and are simply basic tenets of VL relating to one small basic component of many components of VBT and specificity. Ultimately, Davies et al. [18] and Jukic et al. [28] provide meta-analytic data to support that an athlete doesn’t increase force on the platform (i.e., 1RM strength) to a greater magnitude simply by maximizing the ‘repetitions that have the highest force’ when load and total repetitions are matched, since alternative set structures performed greater than or equal to twice the number of ‘repetitions that have the highest force’ compared to traditional sets. As a side note/somewhat relatable analogy, this somewhat reminds me of the ‘hormone hypothesis’ for hypertrophy [47], where it was previously hypothesized that acute transient increases in certain hormones (i.e., growth hormone and testosterone) would result in significantly greater hypertrophy adaptations [48], which similar to the ‘strength-stimulating repetitions’ question has also been debunked [49]. To clarify, acute responses are not necessarily indicative of chronic physiological adaptations. Both hypotheses certainly had some potential theoretical rationale; however, importantly, both hypotheses lack supporting data.

What are we most interested in as powerlifters? 1RM strength in the competition squat, bench press, and deadlift. Certainly, there are several other components to the sport that come with being an athlete and being able to perform one’s best on the platform on competition day, but in short, obviously the sport involves having and being able to put up a ‘big total’. Although acute responses are certainly important, we’re not interested so much in the acute response, as much as we are interested in the chronic adaptation of increased 1RM strength and performance outcome of optimizing 1RM strength at a powerlifting competition (in addition to the factors influencing competition performance: competition preparation, psychological skills… the factors/list is endless). For example, let’s take a set of 6 repetitions to failure and dissect it from an ‘acute specificity lens’. The first repetition is most specific to a 1RM with respect to force and VL; however, the first repetition is least specific to a 1RM with respect to RPE and velocity. Contrastingly, the last repetition is the least specific to a 1RM with respect to force and VL; however, the last repetition is the most specific to a 1RM with respect to RPE and velocity. Why did I address this? To highlight that the first repetitions nor the last repetitions are any more specific to a 1RM from a holistic specificity perspective (obviously one could take this considerably further with respect to specificity), and to also highlight that both traditional sets and alternative set structures address different components of 1RM specificity.

However, perhaps most importantly, the load on the bar remains constant across all total repetitions in a traditional set and alternative set structure, which largely explains the overall meta-analytic results from Davies et al. [18] and Jukic et al. [28], since load (i.e., relative intensity/percentage of 1RM one may say) is arguably the ‘primary driver’ of strength adaptations at the most foundational level [15-17, 34]. Please keep in mind that neither traditional sets nor alternative set structures are necessarily superior to the other for strength; both certainly have their advantages and limitations, but ultimately their utilization will be largely based on athlete preference, response, and what enables the greatest progress in the longer-term. A large role of the back-off volume work is of course volume, which is primarily contributing to strength by virtue of promoting hypertrophy [50] (via mechanical tension and perhaps 20% VL when sets are equated), since it has been suggested that hypertrophy has a contributory causal relationship with strength improvements [51]. However, the set and repetition schemes by which that relative volume is distributed has less importance from a strength perspective [18, 28] (perhaps more from a neuromuscular fatigue [31, 46], neuromuscular adaptation [24, 25], hypertrophy phenotypic adaptation perspective [13, 52]). Please see Article 2 Section 4 and this Article 3 Section 2 Practical Applications section for a detailed discussion. Arguably, an individualized and integrated approach may be the best avenue for most athletes [53, 54]. 

Where Might Have these Misconceptions Arisen from with Respect to the Velocity Loss Literature?

Strength Adaptations

To my knowledge, I believe that the ‘strength-stimulating repetitions’ misconception has partially arisen from the VL literature [13, 24, 25, 41, 55-60]. It’s crucial to recognize that intra-set VL thresholds are a method of volume autoregulation [61]. For each study, set volume is equated between groups, and each group performs repetitions to their respective VL threshold; therefore, total repetitions are un-equated between groups, and thereby relative volume is also un-equated between groups. To exemplify, Pareja-Blanco et al. [25] investigated VL thresholds of 0, 10, 20, and 40% in the smith machine back squat, reporting average repetitions per set of: VL0: 1.0 ± 0.0; VL10: 3.0 ± 0.8; VL20: 3.5 ± 1.0; VL40: 6.4 ± 1.7, and total repetitions of VL0: 48.0 ± 0.0; VL10: 143.6 ± 40.2; VL20: 168.5 ± 47.4; VL40: 305.6 ± 81.7. Consequently, since there is no difference in 1RM strength adaptations between groups in nearly all individual longitudinal VL studies [13, 24, 25, 55-59] (including this one just mentioned in the previous sentence [25]), I think that (based on the questions asked to me) it may have been mis-hypothesized that if a study was to equate for total repetitions between VL threshold groups, the lower VL thresholds groups would result in greater 1RM strength adaptations because these ‘lower VL repetitions contribute or count more towards 1RM strength.’

For example, if we take our previously mentioned study by Pareja-Blanco et al. [25], VL40 performed 3 sets of ~6.4 repetitions per set (let’s round to 6 repetitions for simplicity), and VL20 performed 3 sets of ~3.5 repetitions per set (let’s round to 3 repetitions for simplicity). To my knowledge, I believe it has been mis-assumed that since VL40 essentially performed 3 sets of 6 repetitions, while VL20 essentially performed 3 sets of 3 repetitions, and there was no significant difference between VL20 and VL40 in 1RM strength, that if VL20 were to perform 6 sets of 3 repetitions, VL20 would result in significantly greater 1RM strength adaptations than VL40. However, that’s simply an assumption. Let’s look at the available data for strength…

The sole longitudinal VL study to date that equated for total repetitions between different VL thresholds demonstrated no difference between VL thresholds in 1RM strength (15% VL to 30% VL was compared in a within-subject design) [35]. Furthermore, this hypothesis is ‘nearly identical’ to what the systematic review and meta-analyses conducted by Davies et al. [18] and by Jukic et al. [28] comparing alternative set structures to traditional sets investigated, in which both demonstrated no significant difference in strength adaptations between set type. The traditional sets are training at ‘higher VL thresholds’ in fewer sets and the alternative set structures are training at ‘lower VL thresholds’ in greater sets in which total repetitions at a given percentage of 1RM/load are matched.

One may ask, “why were there no significant differences in 1RM strength adaptations between VL20 and VL40 in this study [25] and in nearly all the longitudinal VL studies [13, 24, 25, 55-59]?” Strength is mainly driven by training with high percentages of 1RM [15-17], in which percentage of 1RM is equated between all VL threshold groups in each individual study [13, 24, 25, 41, 55-60]. I don’t see many (if any) high-level powerlifters making considerable progress to win high-level competitions performing say for example bench press twice per week for 3 sets of 3 repetitions at <5 RPE, nor do I see most of them always ‘polarizing training’ so extensively that they perform one high peak intensity exposure once in a while, and then perform back-off volume work that is incredibly submaximal with respect to percentage of 1RM and RPE (i.e., <5 RPE) for all sessions. What are arguably the two most basic methods to increase relative volume? One, increase the number of repetitions per set (i.e., go from 3 sets of 3 repetitions to 3 sets of 6 repetitions). Two, increase the number of sets and keep the number of repetitions per set the same (i.e., go from 3 sets of 3 repetitions to 6 sets of 3 repetitions). Obviously, there is extensively more to discuss here. Obviously, that’s not what their training is going to look like. Obviously, I wouldn’t suggest suddenly doubling training volume. And obviously, I wouldn’t suggest training to ~9.5 RPE – failure (but I don’t think any powerlifter trains here either). Most importantly what I simply want to highlight here with this example of the two basic methods to increase relative volume is that this is essentially comparing a traditional set to an alternative set structure; thus, I don’t see how either prescription would result in a significant difference for strength adaptations based on the systematic reviews and meta-analyses [18, 28]. 

In an incredibly basic sense, what would I prescribe of the two options: 3 sets of 6 repetitions at a 10 RPE or 6 sets of 3 repetitions at a 7 RPE? Keep in mind that load is matched between options. I’d likely prescribe 6 sets of 3 repetitions at a 7 RPE for most athletes, not because it will stimulate greater strength adaptations in and of itself, but simply to minimize VL to ~20% and thereby minimize neuromuscular fatigue [2, 3], which may possibly enable for greater longer-term progress. What would I prescribe of the two options: 6 sets of 3 repetitions at a 7 RPE or 6 sets of 3 repetitions at a 4 RPE? I’d likely prescribe 6 sets of 3 repetitions at a 7 RPE for most athletes, because strength adaptations are largely load-dependent [15-17, 34], and 3 repetitions at a 7 RPE would involve employing a higher load than 3 repetitions at a 4 RPE. 

Some individuals asked, “have ‘strength-stimulating repetitions’ been supported with reference to polarized training or top sets?” To answer in one sentence: integrating polarized training or top sets doesn’t support the terminology/definition utilized and simply magically change the repetitions of the back-off volume work so that the first repetitions of the back-off volume work magically become ‘more strength-stimulating/producing’ when total repetitions and percentage of 1RM/load are matched – the two previously referenced systematic reviews and meta-analyses refuted that [18, 28]. However, I’d certainly agree that integrating polarized training or top sets would be one of the primary methods to effectively utilize very low RPE alternative set structure back-off volume work in powerlifting programming contexts as one could cite some of the VL literature here (Gantois et al. 2021) or possibly the Lima et al. bicep curl study [62] or refer to some older powerlifting coaches that have employed this strategy previously. Honestly, these prescriptions will be individualized and primarily based on athlete preference, their belief in the training program, and perhaps most importantly: what enables the greatest progress for the best outcome in competition.

Hypertrophy Adaptations

Similar to the strength misconception, since there are typically greater hypertrophy adaptations for training at VL thresholds corresponding to ~9.5 RPE – failure [13, 24, 25], I think it may have also been mis-hypothesized that these ‘higher VL repetitions contribute or count more towards hypertrophy adaptations’ potentially due to metabolic stress. Although this misconception is certainly primarily based on other reasons such as motor unit recruitment (I am solely addressing the VL literature to keep this article somewhat related to VBT). Most importantly, it’s crucial to recognize that the higher the VL threshold the higher the corresponding relative volume.

However, I will essentially repeat below exactly what I stated for strength adaptations as the findings are nearly identical for hypertrophy adaptations. The sole longitudinal VL study to date that equated for total repetitions between different VL thresholds demonstrated no difference between VL thresholds in hypertrophy (15% VL to 30% VL was compared in a within-subject design) [35]. Furthermore, this hypothesis is ‘nearly identical’ to what the systematic review and meta-analyses conducted by Davies et al. [18] and by Jukic et al. [28] comparing alternative set structures to traditional sets investigated, in which both demonstrated no significant difference in hypertrophy adaptations between set type. Typically, the alternative set structure groups are performing for the most part zero, sometimes one, rarely two of these so-called ‘hypertrophy-stimulating repetitions’, whereas the traditional set groups are performing many and considerably more than the alternative set structure groups.       

Time to Discontinue the Terminology ‘Strength-Stimulating Repetitions’ and ‘Hypertrophy-Stimulating Repetitions’ to Prevent Confusion and Move Towards Clarity

Based on the scientific literature, I don’t think there’s much debate: ‘strength-stimulating repetitions’ and ‘hypertrophy-stimulating repetitions’ don’t exist. Perhaps most importantly, before I transition more into the VL literature and dive into some practical applications, from an academic integrity and academic communication perspective, it’s disappointing as continuing with these concepts after they have been refuted creates academic distrust and is disrespectful to those scientists that have conducted systematic reviews and meta-analyses when their manuscripts are disregarded because of three primary reasons: 1) they are incredibly systematic to avoid any such biases as much as possible; 2) on average they take over a year to conduct due to the extensive procedures and attention-to-detail that they require; 3) they have been through the peer-review and editor approval process to be published in the scientific literature. As a result, (nothing wrong if you have previously been using this terminology), but now it’s likely time that we discontinue the terminology of ‘strength-stimulating repetitions’ and ‘hypertrophy-stimulating repetitions’ completely to prevent confusion and move towards conceptual clarity, while giving credit to Davies and colleagues [18, 63, 64], Jukic and colleagues [28], Grgic and colleagues [33], Vieira and colleagues [32], Gantois and colleagues (2021), and those that conducted the individual studies to make up these systematic reviews and meta-analyses. 

Maybe this is a side tangent, but I’m also puzzled at what component of these questions are that new? And how does the ‘terminology’ of ‘strength-stimulating repetitions’ and ‘hypertrophy-stimulating repetitions’ aid to explain the findings from the scientific literature or is it just simply an ‘attempt’ to be ‘novel’? A lot of this research was conducted several years ago now, and nearly all the individual studies demonstrated no difference in strength outcomes between traditional sets and alternative set structures. Carl Miller (USA Weightlifting Coach) introduced cluster sets nearly 70 years ago in the 1950s in practical settings, Roll & Omer introduced cluster sets in 1987 in the scientific literature, and James Tufano [9] is one of the current lead researchers in this area demonstrating the efficacy of alternative set structures for maintaining velocity and power whilst limiting neuromuscular fatigue [28], which has possible practical implications for more main-stream sports (i.e., hockey, football, etc.) [65] and in clinical settings (when traditional protocols with high-exertions may be contraindicated) [10]. Cluster sets were incredibly commonplace 10 years ago when I played hockey and I frequently use them for individuals that I provide hockey consultations to, but this will also be based somewhat on initial force-velocity profiling [66, 67] and certainly primarily individualization [21, 68]. Anyways, now that we’ve addressed that there’s no difference in strength adaptations between traditional sets and alternative set structures (although I don’t think that’s anything new in academic research settings based on the data and if it was ‘novel and superior’ for strength and hypertrophy adaptations I think that individuals would have been discussing this several years ago), let’s move onto some of the potential advantages of VL.

What are the Potential Advantages of Low-Moderate Velocity Loss for Strength?

A large reason why I conceptualized the LRV Model back in May 2020 (which had been in conceptualization for a few years) and has now formed the ‘foundation’ for my PhD work was to outline not only how to integrate LRV (as many had certainly already been playing with similar concepts for years), but to also highlight the potential advantages of training at low-moderate VL for establishing VL zones to ‘optimize proximity to failure’ as this was gaining some interest in research and practical settings. Specifically, training at low-moderate VL values (i.e., 0 – 25% VL) has some potential advantageous practical implications [46] (Gantois et al. 2021). The POTENTIAL (the key wording here is ‘potential’) primary advantages are two-fold: 1) firstly, potentially preventing excessive and unnecessary neuromuscular fatigue [2, 3, 31]; 2) secondly, potentially promoting favorable and contributory neuromuscular adaptations [24, 25, 46]. Please note that as illustrated in the narrative review [46] and systematic review with meta-analysis (Gantois et al. 2021), neuromuscular adaptations and neuromuscular fatigue are best illustrated on a spectrum with respect to VL; hence, why the LRV Model has green, yellow, and red zones with respect to VL. Please be cognizant that just because the red zone is “red”, it certainly doesn’t mean that it is “bad”, but rather simply that this may be where the least amount of training will be allocated for most athletes (but certainly not all athletes) on average as a general heuristic. Please also remember that low-moderate VL doesn’t necessarily mean low RPE. Therefore, if an athlete is using LRV autoregulatory strategies (and LRV monitoring methods) within the VL zones, the athlete may be able to increase load more easily over the course of a block due to enhanced neuromuscular adaptations and lower neuromuscular fatigue (or accumulate more volume if that is required, or train at a higher frequency if that is beneficial, and other potential benefits specific to the athlete context and goals), but again it’s certainly not that repetitions in and of themselves have the ability to ‘stimulate strength’. Training with low-moderate neuromuscular fatigue (low repetition sets) has been incredibly commonplace for strength athletes and ‘power’ (i.e., velocity/speed) athletes for decades as most reading this likely know based on the aforementioned introduction of alternative set structures to practical and research settings, with VL coined 10 years ago in 2011 by Sanchez-Medina and Gonzalez-Badillo [11], and the first longitudinal VL study conducted 5 years ago (pre-printed in 2016/published in 2017) by Pareja-Blanco et al. [56].

Is There an Optimal Velocity Loss Value for 1RM Strength Adaptations?

Some individuals asked the question, “is there an optimal VL value for 1RM strength adaptations?” My response… If there is an optimal VL value for 1RM strength adaptations, I would hypothesize that it may likely be a VL zone within 0 – 25% VL, but unfortunately if that data exists it is unavailable in the scientific literature as I am writing this article, and to my knowledge none exists anywhere else that has been appropriately meta-analyzed. Some of the VL values I sometimes still hear are likely incorrect for powerlifters, such as: 1) an athlete should minimize VL as much as possible to 0% VL; or 2) an athlete should train at 15% VL. Why?

A very recent systematic review and meta-analysis (pre-printed in July 2021) by Gantois et al. compared three VL threshold ranges on 1RM strength, hypertrophy, and other performance parameters (i.e., countermovement jump height, sprint times, etc.). Please keep in mind that this is pre-printed (i.e., it hasn’t been through peer-review, editor approval, and published yet), although certainly much credit to the authors. It is currently available as a PDF online for those that may be interested: simply Google search the title (located at the very end of my reference section) and you should be able to access it. The VL threshold ranges (VL sub-groups) were: 1) <15% VL; 2) ≥15% to <30% VL; and 3) ≥30% VL. To summarize, all three VL sub-groups significantly increased 1RM strength (p < 0.00001) with no difference between sub-groups (p = 0.61). In other words, when equating for sets and percentage of 1RM, whether training is at <15%, ≥15% to <30% or ≥30% VL there is no meaningful difference in 1RM strength adaptations. These authors also reported within-group effect sizes for all three sub-groups; however, I would suggest: 1) within-group effect sizes should not be employed to make between-group comparisons; 2) the within-group effect sizes reported were very similar for all three sub-groups; 3) to my knowledge there was no meaningful between-group effect sizes (i.e., not at least a small between-group effect size, which is generally an effect size of 0.20 – 0.49 depending on the exact meta-analysis performed). I’m not going to address this too much here; however, there were some limitations with how this meta-analysis was conducted due to a lack of failing to report between-group effect sizes, among others.    

I also want to highlight that there doesn’t appear to be an ‘inverted U-curve’ between VL thresholds and 1RM strength adaptations at 10 – 20% VL that has sometimes been suggested, based on all existing evidence to date [13, 24, 25, 41, 55-60]. If you access the pre-print PDF of Gantois et al. 2021, I’ll turn your attention to Page 31 Figure 11 at the top left: 1RM strength. For 1RM strength, on the x-axis is the VL thresholds (%) and on the y-axis is the standardized mean difference (SMD; an estimate of effect). A regression equation was performed, providing an R2 value of 0.03. Specific R2 values corresponding to small, moderate, and large correlations vary slightly depending on the exact analysis performed; however, to provide context an R2 value of 1.00 is considered perfect, and again this was 0.03, which is pretty terrible. This is a large reason why percentage changes should not be pooled, and why I created conceptual VL zones to ‘optimize proximity to failure’, and LRV (with RPE) as the primary autoregulatory/monitoring prescription strategy back in May 2020, or else if it was as simple as plug x VL threshold into the LPT and perform repetitions until you hear a beep at this x VL threshold I would have just stated, ‘train to x VL threshold for optimal strength gains’, which simply isn’t the case based on the available collated meta-analytic data. Understanding how to use VBT appropriately is imperative to the coach and/or athlete success, and the LPT certainly won’t ‘tell you how to train’. We have RPE, which has been incredibly effective for powerlifters for years. I don’t think we need to change that, but rather start integrating LRV considerably more than it has been in the past with systematic strategies (as addressed in the VBT Courses) for optimal programming.

Why Might 20 – 25% Velocity Loss be Optimal for 1RM Strength Adaptations?

What may be that small VL zone for optimal 1RM strength adaptations based on the existing evidence? I don’t know for certain based on the evidence available to us at the time that I am writing this article; however, based on the preceding reasons and data from Gantois et al. 2021, I have a hunch that it may possibly be ~20 – 25% VL. Why? Training at low-moderate VL values promotes favorable neuromuscular adaptations that may play a role at aiding to increase 1RM strength [24, 25, 46]; however, these specific neuromuscular adaptations are rarely – if ever – discussed in detail. I address all the primary neuromuscular adaptations (including rate of force development shifting and force-time profiling) in the VBT Courses (i.e., increased rate coding, increased motor unit recruitment, synchronous motor unit firing, central nervous system drive… the list goes on) [69]. However, in this section, I’ll very briefly address the often-over-looked VL-specific adaptations to the force-time curve with respect to early and late rate of force development (RFD). 

Although powerlifters are primarily interested in optimizing 1RM strength, RFD may explain the rationale for the 1RM strength adaptations observed if this data was meta-analyzed as RFD is the primary neuromuscular adaptation that has been investigated in the most applicable VL studies [24, 25] that had the most wide-ranging VL thresholds investigated (0, 10, 20, 40% VL for smith machine squat; 0, 15, 25, 50% VL for smith machine bench press). It is crucial to understand that there is early rate of force development (early RFD) and late rate of force development (late RFD), and statements such as ‘increase RFD’ require further clarification and contextualization with respect to the sports and specific demands of the sport [70]. As powerlifters, with respect to adaptations to the force-time curve, we are primarily interested in increasing late RFD [44, 45]. Powerlifters want to be good at being able to generate a considerable amount of force over a long period of time (think about the sport of powerlifting: the force demands of a 1RM squat, and the time period (i.e., slow velocity) required to perform a 1RM squat).

Based on data from Pareja-Blanco et al. [25], 0% VL training at 70 – 85% of 1RM resulted in a significant increase in early RFD. This certainly isn’t a surprise to me, as in accordance with the SAID principle, specificity principle [42], and velocity principle [43], training with 0% VL involves very fast velocities and very low RPEs (<~5 RPE); individuals are performing training in which they are generating a considerable amount of force over a very short period of time. This has great implications for more main-stream athletes (i.e., hockey players), but this is of less benefit to powerlifters based on the respective demands of the sports. Contrastingly, 40% VL training resulted in a significant decrease in early RFD [24] (not really a surprise to me; the participants reached true failure here on ~56% of sets based on data from an identical investigation from the same researchers/lab [13]). Training with 15% VL resulted in no change in neither early RFD nor late RFD [25] (perhaps because it was in the middle ground; not incredibly fast nor slow velocities); therefore, I rarely ever prescribe 15% VL (although I used to sometimes recommend ~10 – 20% VL when I was basing recommendations off this single study pre-printed in 2019 [58] and this narrative review pre-printed in early 2020 [71], before I soon discovered the considerable flaws and lack of appropriately meta-analyzed data).

Perhaps most interestingly, 25% VL training at 70 – 85% of 1RM resulted in a significant increase in late RFD [25], which in accordance with the force-time curve may arguably optimize that absolute load obtained for a 1RM lift. In accordance with the SAID principle, specificity principle [42], and velocity principle [43], training with 25% VL enables the velocities to reach a point where they are ‘slower’ (but not with excessive fatigue) and the RPE is ‘higher’. The RPE is at ~5 RPE at 70% of 1RM and increasing to ~9 RPE at 85% of 1RM throughout the study; however, please be aware that this is a true 5 – 9 RPE based on the objective velocity with verbal encouragement, maximal effort, and velocity feedback. Does that mean that 25% VL is the optimal VL value for 1RM strength adaptations based on the literature? I will be honest and make it clear that in these two studies reporting RFD changes [24, 25], there was no significant difference between VL threshold groups in 1RM strength, which is what we are most concerned with as powerlifters. However, I still think it’s important to understand the difference between early RFD and late RFD while contextualizing the VL-specific adaptations to the force-time curve. This may be a potential explanation if a meta-analysis was conducted comparing different VL thresholds on 1RM strength adaptations and reported between-group differences demonstrating that 25% VL was “optimal”. But again, I want to make it explicitly clear that I could certainly be incorrect, and we certainly require an appropriate meta-analysis to support or refute this VL zone, since we just don’t know based on the data available to us. Just something to ponder…

Overall, it is interesting that:

1) 25% VL resulted in a significant increase in late RFD [24].

2) 25% VL was the threshold in which estimated 1RM was elevated to the greatest magnitude every session from session 4 through to session 15 (there was only 16 total sessions) [24].

3) In the sole longitudinal VL study in existence that was ≥5 weeks in length, that did not involve concurrent training, and that actually exhibited significant differences in 1RM strength adaptations, 25% VL resulted in significantly greater 1RM strength adaptations than 50% VL (50% VL was intended to be to failure) [60].

Why Might 20% Velocity Loss be Required to Optimize Hypertrophy Adaptations?

The data from Gantois et al. 2021 illustrated that there could possibly be a VL threshold around ~20% that might be needed for optimal hypertrophy adaptations. Although I just stated that Gantois et al. 2021 had some limitations by which the data was analyzed and not to compare within group effects, I’ll refer to it since it is the only meta-analyzed VL data that we have available to us from the literature at this time. Specifically, the SMD for low VL was 0.14 (didn’t reach 0.20, which is considered small); however, the SMD for moderate VL was 0.40 (small-moderate) and the SMD for high VL was 0.44 (small-moderate). Please keep in mind that the VL threshold groups in the individual studies included in the moderate VL group in the meta-analysis (≥15% to <30% VL) were both at 20% VL [13, 24]; therefore, to pose a question, “is ~20% VL required for optimal hypertrophy adaptations?”

Now, one may suggest, “simply add sets to the lower VL threshold groups to equate for relative volume to thereby result in the same hypertrophy as ~20% VL, because there was no significant difference between traditional sets and alternative set structures in hypertrophy in both systematic reviews and meta-analyses [18, 28]”. Honestly, I’m not sure if simply adding more relative volume to <~20% VL threshold groups would result in the same hypertrophy as those moderate VL threshold groups of ≥~20% VL, even if relative volume was equated. Why? Intra-set rest typically involves slightly higher repetition sets (i.e., 4 or 5 repetitions); thus, the estimated VL is typically ≥~20% VL based on estimations (explained in Article 1) from the number of repetitions performed and the load on the bar, but this is certainly a fair argument. However, take for example this study by Pareja-Blanco et al. [24], in which VL20 resulted in three-times greater hypertrophy improvements in comparison to VL10 even though nearly everything in both VL threshold groups’ prescription was identical: perhaps most importantly the relative volume was non-significantly different, the number of sets were identical, the percentage of 1RM was identical, the number of repetitions per set was nearly identical (VL10: 3.0 ± 0.8; VL20: 3.5 ± 1.0), and the RPE was nearly identical (~0.5 RPE difference between VL10 and VL20 in which they trained at ~4 – 7.5 RPE, and ~4.5 – 8 RPE, respectively). Does this mean that 20% VL is the minimal VL threshold required to optimize hypertrophy adaptations even when relative volume is equated? Maybe? Why? Let’s re-visit the meta-analytic sub-analyses from Jukic et al. [28] that I mentioned to keep in mind. The sub-group analysis revealed a large effect in favor of traditional sets over cluster sets (SMD = 0.93 in favor of traditional sets) [28]. However, the sub-group analysis revealed no meaningful difference between traditional sets and intra-set rest (SMD = 0.06 in favor of traditional sets) [28]. Why did cluster sets reveal a meaningful difference in favor of traditional sets for hypertrophy, but intra-set rest didn’t, despite traditional sets, cluster sets, and intra-set rest being matched for relative volume? What’s important to recognize is that intra-set rest involves performing ~50% of the maximal number of repetitions that can be performed within the set (or simply ~50% of the repetition maximum), which corresponds to ~20% VL; however, cluster sets are likely at <~20% VL.

You’ll see this study by Lasevicius et al. [72] sometimes cited to suggest that once an individual reaches a certain percentage of 1RM (i.e., 80% of 1RM) that the concept of proximity to failure or intra-set fatigue may play a minimal role at influencing hypertrophy; however, I’m not entirely convinced. Specifically, Lasevicius et al. [72] demonstrated no difference in cross-sectional area of the quadriceps femoris between two groups matched for volume load and matched for relative intensity at 80% of 1RM: one group performed 3.0 ± 0 sets and 12.4 ± 3.1 repetitions per set; the other group performed 5.5 ± 0.5 sets and 6.7 ± 1.6 repetitions per set. However, how does one explain the previously explained findings from Pareja-Blanco et al. [24]? Specifically, the 10% VL group performed 2.5 ± 0.9 repetitions per set at ~6 – 7 RPE (a higher estimated RPE than Lasevicius et al. [72]) at 80% of 1RM, which resulted in no significant increase in hypertrophy. Conversely, the 20% VL group performed 2.9 ± 0.8 repetitions per set at an estimated RPE of ~6.5 – 7.5 at 80% of 1RM, which resulted in a significant increase in hypertrophy. So why did the 20% VL group significantly increase hypertrophy, whereas the 10% VL group failed to significantly increase hypertrophy despite being matched for relative volume and only being ~0.5 – 1 RPE different in proximity to failure? Did intra-set fatigue (VL) play a role here? If you look back to the average repetitions per set from Lasevicius et al. [72], one group performed ~12.4 repetitions to failure, whereas the other group performed ~6.7 repetitions (i.e., ~50% of the maximal number of possible repetitions in the set). Does this data further support that an individual needs to be at ~20% VL or perform ~50% of the maximal number of repetitions in the set to maximize hypertrophy? 

In other words, based on the systematic review and meta-analysis by Gantois et al. (2021), the sub-group analyses from Jukic et al. [28], the study by Pareja-Blanco et al. [24], and the study by Lasevicius et al. [72], is 20% VL that minimal VL threshold that we may need to seek in order to augment/assist/mediate hypertrophy? Perhaps? However, the study by Andersen et al. [35] conflicts the aforementioned data, since Andersen et al. [35] demonstrated no significant difference in hypertrophy between 15% VL and 30% VL when relative volume was equated. Without getting into a ‘paralysis-by-analysis’ situation it would be interesting to compare 20% VL to <20% VL in a systematic review and meta-analysis to determine if there was a meaningful difference in hypertrophy adaptations. I will state that I’m certainly not 100% confident on this 20% VL value, and that we certainly require more data to either support or refute this possible 20% VL value. However, this is certainly something to ponder… As a side note, it’s also worth highlighting that the myosin heavy chain IIX pool is reduced when training at >~35% VL (40% VL to be more precise); however, when training at ≤~25% VL (20% VL to be more precise) the myosin heavy chain IIX pool is retained [13] (i.e., illustrating that spectrum of phenotypic adaptations to different VL thresholds [52]).

Finally, when addressing this topic, why is this question regarding hypertrophy framed in terms of ‘proximity to failure’? Why isn’t it also framed in terms of ‘intra-set fatigue’? When I think of proximity to failure, I think, “okay, how many RIR do I have?” When I think of intra-set fatigue, I think, “okay, what is my VL on the set?” Certainly, RIR (I would suggest integrating LRV for precision) is simpler and has greater practical utility in program design contexts; however, perhaps we shouldn’t forget about quantifying and reporting VL all together quite yet even when relative volume is matched between groups with respect to the concept of metabolic stresses potential role at mediating hypertrophy based on the previously explained data. I use that wording cautiously here… Potential role, because the role of metabolic stress at mediating hypertrophy has certainly been questioned in recent years [73], but based on the previously explained data perhaps lactate and hydrogen ions (since VL has a strong linear relationship with lactate accumulation) [11], or other metabolites, or hypoxic conditions, or acidotic conditions may assist/mediate/augment optimal hypertrophy adaptations?

Based on Schoenfeld’s 2010 proposed three-factor model of hypertrophy [74], I think mechanical tension still remains to be reasonably well supported as the ‘primary driver’ of hypertrophy [74], I think muscle damage has been refuted [75], and I think metabolic stress has certainly been challenged recently [73]. However, I’m not entirely convinced that it plays absolutely no role, but rather that there may be a potential ‘threshold’ of intra-set fatigue (VL) that exists on a spectrum that may assist/mediate/augment hypertrophy if an individual is seeking to maximize hypertrophy as much as possible. I don’t know, nor do I think the data thus far has entirely nor clearly elucidated the role of metabolic stress for promoting hypertrophy when everything else is equal (i.e., everything is equated between groups in a study). I’ll admit that the mechanistic underpinnings of hypertrophy is certainly not my area of focus with my research; therefore, I could be somewhat incorrect here. However, even if I am somewhat incorrect with respect to the mechanistic underpinnings, I think the previously explained data above suggests that ~20% VL may be something to keep in mind even when relative volume is equated, at least for the time being until we have more data. I would suggest that additional research is warranted investigating this possible 20% VL threshold and that researchers should use the ‘optimal’ tool at their disposal for quantifying and reporting proximity to failure and intra-set fatigue in future studies, which are LRV (integrated with RIR) and VL, respectively.

How do I primarily quantify volume for hypertrophy in powerlifting contexts? For a given lift, I suggest powerlifters utilize relative volume (and typically aim to be at ≥20% VL). In other words, I suggest quantifying volume as relative volume and simply recommend aiming to be at approximately ≥20% VL when hypertrophy is the focus simply to ‘ensure’ or ‘be safe’ that an athlete is likely not leaving any potential hypertrophy outcomes on the table. Finally, keep in mind that the ‘optimal’ dosage of relative volume must be individualized [6], and that there is a dose-response diminishing returns and inverted U-curve relationship between the ‘optimal’ dosage of relative volume and hypertrophy [76]. However, in closing this section, I want to repeat and make it clear that this potential 20% VL value remains to be supported or refuted and that if a certain magnitude of intra-set fatigue (i.e., a VL threshold) exists to mediate hypertrophy when relative volume is equated, it may have somewhat of a dose-response relationship with hypertrophy, rather than be a very specific value as often conceptualized.

Time to End the Proximity to Failure and Intra-Set Fatigue Debate for Strength and Hypertrophy?

Perhaps most interesting is:

1) What is the optimal VL zone to optimize 1RM strength adaptations? (Could it be ~20 – 25% VL based on late rate of force development-oriented force-time profiles?)

2) Does the minimal VL threshold required to optimize hypertrophy adaptations cross that VL zone for 1RM strength adaptations established in question #1 above? (Could it be ~20% VL?)

3) If this was the case, would it perhaps be time to start drawing closer to the ever-so desired answer on the proximity to failure and intra-set fatigue debate for strength and hypertrophy? 

It would be cool to have a published systematic review and meta-analysis reporting both between-group mean differences and effect sizes investigate this ever-so desired question…

Practical Applications

Alright, I know that was probably a lot of scientific literature. So, I’ll finish up with some very basic and very brief practical applications. Perhaps the simplest contextualization of how I may suggest integrating the LRV Model with what we’ve learned thus far with respect to the potential advantages of intra-set fatigue/VL and what is most applicable to the alternative set structure literature outlined in this Article is that when prescribing an individual optimal dosage of relative volume for a given percentage of 1RM within the Green Zone of the LRV Model: 1) for 20% VL, perform 50% of the repetitions of the repetition maximum; or 2) for 25% VL, perform 50% of the repetitions of the repetition maximum + 1 repetition. Please see the table below for an organized illustration. Please keep in mind that typically this may be based on percentages of 1RM from the warm-up and/or top set performance integrated with LRV (and RPE) and contain autoregulatory sub-strategies; however, I’ve illustrated repetition maximums (RMs) as I think that it is easier to understand in this particular example. Some might be thinking one of two things. One, this kind of looks like an intra-set rest protocol or an intra-set rest protocol +1 repetition. You’re correct; however, I typically employ longer rest periods than typical intra-set rest protocols in the literature. Two, this doesn’t look that far off from what most (certainly not all) athletes already do. You’re correct; however, I’ve integrated the collated VL data for 1RM strength to establish these specific VL zones to potentially optimize 1RM strength adaptations, muscle fiber phenotypic hypertrophy adaptations (i.e., preserve the “type II” fiber pool and reduce ‘conversion’ to “type I” fiber pool), maximize neuromuscular adaptations (i.e., late rate of force development-oriented force-time profiles), and minimize neuromuscular fatigue (i.e., maintain training quality and reduce time course of recovery) [13, 24, 25, 41, 55-60].

Generalized Repetition Schemes for Given Loads for Most (Certainly Not All) Athletes

Relative Volume Load 20% Velocity Loss 25% Velocity Loss
Individualized 10RM 5 reps (5 RPE) 6 reps (6 RPE)
Individualized 8RM 4 reps (6 RPE) 5 reps (7 RPE)
Individualized 6RM 3 reps (7 RPE) 4 reps (8 RPE)
Individualized 4RM 2 reps (8 RPE) 3 reps (9 RPE)
Individualized 2RM 1 rep (9 RPE) 0% VL 2 reps (10 RPE)

Some individuals have asked my thoughts on alternative set structures (or rest redistribution) in powerlifting contexts. Please see the table below for a summary of my generalized recommendations for alternative set structures contingent on lift components in powerlifting contexts. For example, the squat has set-up, un-rack, walk-out, set-up, execution, and re-rack components; therefore, I typically solely recommend intra-set rest, and I rarely recommend cluster sets or inter-repetition rest because the un-rack and walk-out components of a squat can hinder the actual goal of the alternative set structure and the overall training process with respect to maximal intent. For example, are you really going to set-up, un-rack, walk-out, set-up, perform an ‘easy’ single repetition, then re-rack, and then repeat for endless ‘easy’ singles… probably not in most circumstances. There are some caveats to how I recommend integrating ‘modified’ alternative set structures in powerlifting contexts: 1) typically I suggest using heavier loads than what is sometimes employed in the literature (i.e., 10RM load at the lightest for the most part) since 1RM strength adaptations are largely load-dependent [15-17, 34]; 2) typically I recommend keeping the RPE moderate-high (i.e., ~6 – 9 RPE) to mimic the sport and velocity/LRV of the sport more closely, whilst keeping the VL low-moderate (i.e., ~0 – 25% VL; most on average closer to ~20 – 25% VL) to limit unnecessary neuromuscular fatigue [2, 3] and due to the potential aforementioned rationale for strength and hypertrophy adaptations; 3) typically I recommend slightly longer rest periods to maintain performance (i.e., ~3 – 7 minutes) [77], although excessively long rest periods may not be required [78] and may be autoregulated [79].

For example, if 24 total repetitions with a 6 RM load is prescribed, rather than prescribe 4 sets of 6 repetitions at 10 RPE (a traditional set), what are some potential example prescriptions of ‘modified’ alternative set structures for the squat, bench press, and deadlift that I may prescribe? For the squat, I may prescribe intra-set rest comprised of 8 sets of 3 repetitions at a 7 RPE (based on the LRV). For the bench press, I may prescribe cluster sets comprised of 4 sets of 6 repetitions, in which each set is comprised of 3 clusters of 2 repetitions. For example, on cluster 1, it would be 2 repetitions at a 6 RPE (based on the LRV). On cluster 2, it would be 2 repetitions likely at a 7 RPE (based on the LRV), because the load may likely increase to ~5RM in this instance due to the short rest period from cluster 1 to cluster 2. On cluster 3, it would be 2 repetitions likely at an 8 RPE (based on the LRV), because the load may likely increase to ~4RM in this instance due to the short rest period from cluster 2 to cluster 3. For the deadlift, I may prescribe inter-repetition rest stipulating that a single repetition is performed every 60 seconds; therefore, the session (after the warm-up) would take 24 minutes to complete. Essentially, this is 24 single repetition sets at a 5 RPE (based on the LRV); however, typically the RPE may climb to say for example a 6 RPE (based on the LRV) or rarely sometimes even to a 7 RPE (based on the LRV) in this example. Would the bench press cluster set prescription and deadlift inter-repetition rest prescription result in less hypertrophy than if say an intra-set rest protocol was performed? Possibly. Therefore, these prescriptions in the bench press and deadlift may be prescribed during sessions when hypertrophy is of less importance.

Generalized Recommendations for Alternative Set Structures Contingent on Lift Components

Lift Set-Up Un-Rack Walk-Out Intra-Set Rest Cluster Sets Inter-Repetition Rest
Squat Checkmark with solid fill Checkmark with solid fill Checkmark with solid fill Checkmark with solid fill
Bench Press Checkmark with solid fill Checkmark with solid fill Checkmark with solid fill Checkmark with solid fill
Deadlift Checkmark with solid fill Checkmark with solid fill Checkmark with solid fill Checkmark with solid fill

Finally, how can we contextualize what we’ve learned and that is applicable to what I’ve discussed with a very basic example of the LRV Target Repetition Stop Strategy (as addressed in Article 2)? Please see the table below.

Generalized Recommendations to Employ a Basic LRV Target Repetition Stop Strategy

Step Description
1 Prescribe an individual optimal dosage of relative volume for a specific percentage of 1RM (i.e., a total number of repetitions at a specific percentage of 1RM)
2 Locate where that percentage of 1RM is in your Individualized LRV Model in the Green Zone at ~20 – 25% VL (i.e., 82.5% of 1RM for sets of 3 repetitions at 7 RPE)
3 Prescribe the set and repetition scheme (i.e., 5 sets of 3 repetitions at 7 RPE if 15 total repetitions at 82.5% of 1RM was selected as the optimal relative volume dosage and percentage of 1RM)
4 Employ LRV load autoregulation Strategies when the primary goal is hypertrophy/volume; employ LRV repetition autoregulation Strategies when the primary goal is strength/intensity; employ several other LRV Sub-Strategies
5 Discover endless other LRV Strategies and Monitoring Methods in the VBT Courses

Conclusion

If you got this far, there’s not much else to say other than thanks for sticking around to the end. I know this Article was ridiculously long, but I felt this much detail was necessary to answer the two questions comprehensively. Hopefully you learned something new and can take away some practical applications to apply into your own training. I understand that this may have raised additional questions and if the practical applications left you wanting more, feel welcome to enroll in the VBT Courses – I address extensively more extremely individualized practical applications that enables you to easily grasp the content and apply it directly into your training that same day! I hope you enjoyed!

References

1. Morán-Navarro, R., et al., Time course of recovery following resistance training leading or not to failure. Eur J Appl Physiol, 2017. 117(12): p. 2387-2399.

2. Pareja-Blanco, F., et al., Time course of recovery following resistance exercise with different loading magnitudes and velocity loss in the set. Sports (Basel), 2019. 7(3): p. 59.

3. Pareja-Blanco, F., et al., Time course of recovery from resistance exercise with different set configurations. J Strength Cond Res, 2018.

4. Bartolomei, S., et al., Comparison of the recovery response from high-intensity and high-volume resistance exercise in trained men. Eur J Appl Physiol, 2017. 117(7): p. 1287-1298.

5. Watkins, C.M., et al., Determination of vertical jump as a measure of neuromuscular readiness and fatigue. J Strength Cond Res, 2017. 31(12): p. 3305-3310.

6. Scarpelli, M.C., et al., Muscle hypertrophy response is affected by previous resistance training volume in trained individuals. J Strength Cond Res, 2020.

7. Ferreira, D.V., et al., Dissociated time course between peak torque and total work recovery following bench press training in resistance trained men. Physiol Behav, 2017. 179: p. 143-147.

8. Leite, C.M.F., et al., Does exercise-induced muscle damage impair subsequent motor skill learning? Hum Mov Sci, 2019. 67: p. 102504.

9. Tufano, J., L. Brown, and G. Haff, Theoretical and practical aspects of different cluster set structures: a systematic review. J Strength Cond Res, 2017. 31(3): p. 848-867.

10. Latella, C., et al., Strengthening the case for cluster set resistance training in aged and clinical settings: emerging evidence, proposed benefits and suggestions. Sports Medicine, 2021. 51(7): p. 1335-1351.

11. Sánchez-Medina, L. and J.J. González-Badillo, Velocity loss as an indicator of neuromuscular fatigue during resistance training. Med Sci Sports Exerc, 2011. 43(9): p. 1725-34.

12. Cairns, S.P., Lactic acid and exercise performance : culprit or friend? Sports Med, 2006. 36(4): p. 279-91.

13. Pareja-Blanco, F., et al., Effects of velocity loss during resistance training on athletic performance, strength gains and muscle adaptations. Scand J Med Sci Sports, 2017. 27(7): p. 724-735.

14. van den Tillaar, R., V. Andersen, and A.H. Saeterbakken, Comparison of muscle activation and kinematics during free-weight back squats with different loads. PLoS One, 2019. 14(5): p. e0217044.

15. Campos, G.E., et al., Muscular adaptations in response to three different resistance-training regimens: specificity of repetition maximum training zones. Eur J Appl Physiol, 2002. 88(1-2): p. 50-60.

16. Schoenfeld, B.J., et al., Strength and hypertrophy adaptations between low- vs. high-load resistance training: a systematic review and meta-analysis. J Strength Cond Res, 2017. 31(12): p. 3508-3523.

17. Schoenfeld, B.J., et al., Differential effects of heavy versus moderate loads on measures of strength and hypertrophy in resistance-trained men. J Sports Sci Med, 2016. 15(4): p. 715-722.

18. Davies, T.B., et al., Chronic effects of altering resistance training set configurations using cluster sets: a systematic review and meta-analysis. Sports Med, 2021.

19. Lievens, E., et al., Muscle fiber typology substantially influences time to recover from high-intensity exercise. J Appl Physiol (1985), 2020. 128(3): p. 648-659.

20. McLester, J.R., et al., A series of studies--a practical protocol for testing muscular endurance recovery. J Strength Cond Res, 2003. 17(2): p. 259-73.

21. Borresen, J. and M.I. Lambert, The quantification of training load, the training response and the effect on performance. Sports Med, 2009. 39(9): p. 779-95.

22. Bartolomei, S., et al., Upper-body resistance exercise reduces time to recover after a high-volume bench press protocol in resistance-trained men. J Strength Cond Res, 2019.

23. Tsoukos, A., et al., Delayed effects of a low-volume, power-type resistance exercise session on explosive performance. J Strength Cond Res, 2018. 32(3): p. 643-650.

24. Pareja-Blanco, F., et al., Velocity loss as a critical variable determining the adaptations to strength training. Med Sci Sports Exerc, 2020. 52(8): p. 1752-1762.

25. Pareja-Blanco, F., et al., Effects of velocity loss in the bench press exercise on strength gains, neuromuscular adaptations and muscle hypertrophy. Scand J Med Sci Sports, 2020.

26. McHugh, M.P., et al., Exercise-induced muscle damage and potential mechanisms for the repeated bout effect. Sports Med, 1999. 27(3): p. 157-70.

27. Kataoka, R., et al., Is there Evidence for the suggestion that fatigue accumulates following resistance exercise? Sports Medicine, 2021.

28. Jukic, I., et al., The effects of set structure manipulation on chronic adaptations to resistance training: a systematic review and meta-analysis. Sports Med, 2021.

29. Nicholson, G., T. Ispoglou, and A. 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): p. 1875-88.

30. Morán-Navarro, R., et al., Movement velocity as a measure of level of effort during resistance exercise. J Strength Cond Res, 2019. 33(6): p. 1496-1504.

31. Jukic, I., et al., Acute effects of cluster and rest redistribution set structures on mechanical, metabolic, and perceptual fatigue during and after resistance training: a systematic review and meta-analysis. Sports Med, 2020.

32. Vieira, A.F., et al., Effects of resistance training performed to failure or not to failure on muscle strength, hypertrophy, and power output: a systematic review with meta-analysis. J Strength Cond Res, 2021.

33. Grgic, J., et al., Effects of resistance training performed to repetition failure or non-failure on muscular strength and hypertrophy: A systematic review and meta-analysis. Journal of Sport and Health Science, 2021.

34. Graham, T. and D.J. Cleather, Autoregulation by “repetitions in reserve" leads to greater improvements in strength over a 12-week training program than fixed loading. J Strength Cond Res, 2019.

35. Andersen, V., et al., Resistance training with different velocity loss thresholds induce similar changes in strengh and hypertrophy. J Strength Cond Res, 2021.

36. Helms, E.R., et al., RPE vs. percentage 1RM loading in periodized programs matched for sets and repetitions. Front Physiol, 2018. 9: p. 247.

37. Shattock, K. and J.C. Tee, Autoregulation in resistance training: a comparison of subjective versus objective methods. J Strength Cond Res, 2020.

38. Hansen, K.T., et al., Does cluster loading enhance lower body power development in preseason preparation of elite rugby union players? J Strength Cond Res, 2011. 25(8): p. 2118-26.

39. Oliver, J.M., et al., Greater gains in strength and power with intraset rest intervals in hypertrophic training. J Strength Cond Res, 2013. 27(11): p. 3116-31.

40. Tufano, J.J., et al., Maintenance of velocity and power with cluster sets during high-volume back squats. Int J Sports Physiol Perform, 2016. 11(7): p. 885-892.

41. Held, S., et al., Improved strength and recovery after velocity-based training: a randomized controlled trial. Int J Sports Physiol Perform, 2021: p. 1-9.

42. Kraemer, W.J. and N.A. Ratamess, Fundamentals of resistance training: progression and exercise prescription. Med Sci Sports Exerc, 2004. 36(4): p. 674-88.

43. Behm, D.G. and D.G. Sale, Velocity specificity of resistance training. Sports Med, 1993. 15(6): p. 374-88.

44. Turner, A., et al., Developing powerful athletes, part 1: mechanical underpinnings. Strength and Conditioning Journal, 2020. 42: p. 1.

45. Turner, A.N., et al., Developing powerful athletes part 2: practical applications. Strength & Conditioning Journal, 2021. 43(1): p. 23-31.

46. Weakley, J., et al., Velocity-based training: from theory to application. Strength Cond J, 2020.

47. Kraemer, W.J. and N.A. Ratamess, Hormonal responses and adaptations to resistance exercise and training. Sports Med, 2005. 35(4): p. 339-61.

48. McCall, G.E., et al., Acute and chronic hormonal responses to resistance training designed to promote muscle hypertrophy. Can J Appl Physiol, 1999. 24(1): p. 96-107.

49. Morton, R.W., et al., Neither load nor systemic hormones determine resistance training-mediated hypertrophy or strength gains in resistance-trained young men. J Appl Physiol (1985), 2016. 121(1): p. 129-38.

50. Maden-Wilkinson, T.M., et al., What makes long-term resistance-trained individuals so strong? A comparison of skeletal muscle morphology, architecture, and joint mechanics. J Appl Physiol (1985), 2020. 128(4): p. 1000-1011.

51. Taber, C.B., et al., Exercise-induced myofibrillar hypertrophy is a contributory cause of gains in muscle strength. Sports Med, 2019. 49(7): p. 993-997.

52. Martinez-Canton, M., et al., Role of CaMKII and sarcolipin in muscle adaptations to strength training with different levels of fatigue in the set. Scand J Med Sci Sports, 2021. 31(1): p. 91-103.

53. Cronin, J., P.J. McNair, and R.N. Marshall, Developing explosive power: a comparison of technique and training. J Sci Med Sport, 2001. 4(1): p. 59-70.

54. Cronin, J.B., P.J. McNair, and R.N. Marshall, Force-velocity analysis of strength-training techniques and load: implications for training strategy and research. J Strength Cond Res, 2003. 17(1): p. 148-55.

55. Galiano, C., et al., Low-velocity loss induces similar strength gains to moderate-velocity loss during resistance training. J Strength Cond Res, 2020.

56. Pareja-Blanco, F., et al., Effects of velocity loss during resistance training on performance in professional soccer players. Int J Sports Physiol Perform, 2017. 12(4): p. 512-519.

57. Rodiles-Guerrero, L., F. Pareja-Blanco, and J.A. León-Prados, Effect of velocity loss on strength performance in bench press using a weight stack machine. Int J Sports Med, 2020.

58. Rodríguez-Rosell, D., et al., Velocity-based resistance training: impact of velocity loss in the set on neuromuscular performance and hormonal response. Appl Physiol Nutr Metab, 2020. 45(8): p. 817-828.

59. Rodríguez-Rosell, D., et al., Effect of velocity loss during squat training on neuromuscular performance. Scand J Med Sci Sports, 2021.

60. Sánchez-Moreno, M., et al., Effects of velocity loss during body mass prone-grip pull-up training on strength and endurance performance. J Strength Cond Res, 2020. 34(4): p. 911-917.

61. Larsen, S., E. Kristiansen, and R. van den Tillaar, Effects of subjective and objective autoregulation methods for intensity and volume on enhancing maximal strength during resistance-training interventions: a systematic review. PeerJ, 2021. 9: p. e10663.

62. Lima, B.M., et al., Planned load reduction versus fixed load: a strategy to reduce the perception of effort with similar improvements in hypertrophy and strength. Int J Sports Physiol Perform, 2018. 13(9): p. 1164-1168.

63. Davies, T., et al., Effect of training leading to repetition failure on muscular strength: a systematic review and meta-analysis. Sports Med, 2016. 46(4): p. 487-502.

64. Davies, T., et al., Erratum to: effect of training leading to repetition failure on muscular strength: a systematic review and meta-analysis. Sports Med, 2016. 46(4): p. 605-10.

65. Latella, C., et al., The acute neuromuscular responses to cluster set resistance training: a systematic review and meta-analysis. Sports Med, 2019. 49(12): p. 1861-1877.

66. Morin, J.B., P. Edouard, and P. Samozino, Technical ability of force application as a determinant factor of sprint performance. Med Sci Sports Exerc, 2011. 43(9): p. 1680-8.

67. Morin, J.B. and P. Samozino, Interpreting Power-Force-Velocity Profiles for Individualized and Specific Training. Int J Sports Physiol Perform, 2016. 11(2): p. 267-72.

68. Kiely, J., Periodization theory: confronting an inconvenient truth. Sports Med, 2018. 48(4): p. 753-764.

69. Semmler, J.G., Motor unit synchronization and neuromuscular performance. Exerc Sport Sci Rev, 2002. 30(1): p. 8-14.

70. Peltonen, H., et al., Increased rate of force development during periodized maximum strength and power training is highly individual. Eur J Appl Physiol, 2018. 118(5): p. 1033-1042.

71. Włodarczyk, M., et al., Effects of velocity-based training on strength and power in elite athletes-a systematic review. Int J Environ Res Public Health, 2021. 18(10).

72. Lasevicius, T., et al., Muscle failure promotes greater muscle hypertrophy in low-load but not in high-load resistance training. J Strength Cond Res, 2019.

73. Dankel, S.J., et al., Do metabolites that are produced during resistance exercise enhance muscle hypertrophy? Eur J Appl Physiol, 2017. 117(11): p. 2125-2135.

74. Schoenfeld, B.J., The mechanisms of muscle hypertrophy and their application to resistance training. J Strength Cond Res, 2010. 24(10): p. 2857-72.

75. Damas, F., C.A. Libardi, and C. Ugrinowitsch, The development of skeletal muscle hypertrophy through resistance training: the role of muscle damage and muscle protein synthesis. Eur J Appl Physiol, 2018. 118(3): p. 485-500.

76. Schoenfeld, B.J., D. Ogborn, and J.W. Krieger, Dose-response relationship between weekly resistance training volume and increases in muscle mass: A systematic review and meta-analysis. J Sports Sci, 2017. 35(11): p. 1073-1082.

77. Hernandez, D.J., et al., Effect of rest interval duration on the volume completed during a high-intensity bench press exercise. J Strength Cond Res, 2020.

78. Jukic, I. and J.J. Tufano, Traditional 3- to 5-Minute Interset Rest Periods May Not Be Necessary When Performing Fewer Repetitions Per Set: Using Clean Pulls as an Example. J Strength Cond Res, 2020.

79. Ibbott, P., et al., Variability and impact of self-selected interset rest periods during experienced strength training. Percept Mot Skills, 2019. 126(3): p. 546-558.

Gantois, P., Nakamura, F. Y., Alcazar, J., de Sousa Fortes, L., Pareja-Blanco, F., & de Souza Fonseca, F. (2021, June 29). The effects of different intra-set velocity loss thresholds on lower-limb adaptations to resistance training in young adults: A systematic review and meta-analysis. https://doi.org/10.31236/osf.io/v3tr9.