VELOCITY BASED TRAINING AND CLUSTER SET APPLICATION FOR THE BACK
SQUAT

By

Dylan S. Zangakis, B.S.
East Stroudsburg University of Pennsylvania

A Thesis Submitted in Partial Fulfillment of
the Requirements for the Degree of Master of Science in Exercise Science
to the office of Graduate and Extended Studies of
East Stroudsburg University of Pennsylvania

August 9, 2019

SIGNATURE/APPROVAL PAGE
The signed approval page for this thesis was intentionally removed from the online copy by an
authorized administrator at Kemp Library.
The final approved signature page for this thesis is on file with the Office of Graduate and
Extended Studies. Please contact Theses@esu.edu with any questions.

ABSTRACT
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master
of Science in Exercise Science to the office of Graduate and Extended Studies of East
Stroudsburg University of Pennsylvania.
Dylan S. Zangakis, B.S.
Title: Velocity Based Training and Cluster Set Application for the Back Squat
Date of Graduation: August 9, 2019
Thesis Chair: Gavin Moir, Ph.D.
Thesis Member: Shawn Munford, Ph.D.
Thesis Member: Brandon Snyder, M.S.
Abstract
Background: Velocity based training has been proposed as a method of periodization
through prescription of velocities rather than loads in training. However, specific velocity
ranges have not been studied for each exercise. Cluster sets serve as a form of set in
which intra-set rest periods are added for acute recovery periods. The purpose of the
study was to determine correlations between loads and velocities in the back squat, as
well as examine differences in velocities between cluster sets and traditional sets.
Methods: Fourteen participants completed three sessions of the back squat, including a
max test and two experimental sessions including sets under different loading conditions
(67%, 80%, 85%). Results: Trivial to moderate correlations were found when comparing
loads and velocities. Significant differences in recorded velocities by set type were only
found at the 67% 1RM condition. Conclusion: Weak correlations at any load imply the
need to individualize VBT programs.

TABLE OF CONTENTS
LIST OF TABLES…………………………………………………………………….....VI
LIST OF FIGURES……………………………………………………………………..VII
Chapter

I.

INTRODUCTION…………………………………………………………….…..1
Background…………………………………………………………..…..………..1
Purpose…………………………………………………………………..……..….3
Null Hypothesis………………………………………………………....………...3
Limitations……………...………………………………………………….……...3
Delimitations………………………………………………………………………4

II.

LITERATURE REVIEW…………………………………………………………5
Velocity Based Training…………………………………………...…………..…5
Cluster Set Application……………………………………..……………………6

III.

METHODOLOGY……………………………………………………………......8
Demographics…………………..……………………………………..….……....8
Equipment…………………………………………………………………..........8
Study Design…………………………………………………………………......8
Statistical Analysis………………………………………………...…....…..…..10

IV.

RESULTS………………………………………………………………………..11
Questionnaire Results………………………………………………...…………11
VBT Correlational Data…………………………………………………..…….12
Cluster Set Data……………………………………………………………...….15
IV

V.

DISCUSSION…………………………………..………………………………..19
Future Research ………………………………………………………………...21
Practical Application……………………………………………………………22

VI.

CONCLUSION…………………………………………………………………..23

REFERENCES…………………………………………………………………………..24
APPENDICES
Appendix A: IRB………………………………………………………………………...28
Appendix B: Informed Consent………………………………………………………….29
Appendix C: Questionnaire…………………………………………………………..…..31

V

LIST OF TABLES
Table
I.

Table 1. Questionnaire results……………………………………………………11

II.

Table 2. Means and Mean Differences…………………………..………………18

VI

LIST OF FIGURES
Figure
I.

Figure 1. Cluster Set Format……………………………………………………10

II.

Figure 2. 67% Load -Velocity Correlation………………………………………12

III.

Figure 3. 80% Load-Velocity Correlation…………………………………….…13

IV.

Figure 4. 85% Load-Velocity Correlation…………………………………….…14

V.

Figure 5. 67% Mean Recorded Velocities…………………………………….…15

VI.

Figure 6. 80% Mean Recorded Velocities…………………………………….…16

VII.

Figure 7. 85% Mean Recorded Velocities……………………………………….17

VII

CHAPTER 1
INTRODUCTION
Background
Velocity based training (VBT) has become a growing modality of training in
recent years with the introduction of linear position transducer technology. The ability to
measure barbell velocity allows for the creation and utilization of force-velocity
characteristics to define and assess training parameters. VBT has been proposed as an
auto-regulatory approach to training. Typically, training loads are determined using
percentages of a one repetition maximum. However, this may create issues on days where
an athlete is either fatigued or dealing with external stress factors which limit their ability
to perform when utilizing loads prescribed as a %1RM (Bryce, 2016).
Methods such as a modified rating of perceived exertion (RPE) have been used
for autoregulation, allowing load prescription to be determined as a response to how an
individual feels on a given day. Previously RPE measurement has been shown to have a
strong correlation with 1RM in powerlifters (Helms, 2017). However, RPE is not an
objective measurement and requires an athlete to be able to accurately gauge how

1

difficult a given workload is. VBT may be used as a form of objective
autoregulation, wherein prescribing velocities allows an individual to determine
workloads appropriate
for a given day. Previously, ranges of velocities have been presented to correlate
to specific percentages of a one repetition maximum but there is minimal research to back
up the presented numbers.
Lower body maximal force and power outputs are important for a wide variety of
athletes due to rapid movements in sport such as sprinting, rapid deceleration, and
jumping (Kubo, 2018). The back squat is a mainstay in periodized training programs for
any sport in which training for strength and power are important (Hester, 2014). Due to
the importance of rapid loading and force expression in the back squat, utilization of
VBT can be an important factor for developing and manipulating load velocity
characteristics to better develop an athlete. While general velocity ranges have been
prescribed for VBT, there is currently no research to determine specific ranges for
optimally training the back squat.
Cluster sets have been introduced as a method to further induce progressive
overloads in training through adding an intra-set rest period. Cluster sets allow for
manipulation of the set being performed through adjustments made to the frequency of
intra-set rest, length of intra-set rest, load, and total number of repetitions (Haff, 2008).
Cluster sets have been utilized to maintain acute mechanical performance while
systematically overloading resistance for a given exercise. Previous research has shown
their utilization to help with maintenance of kinematic variables such as force, power,
2

and velocity by adding intra-set rest periods (Tufano, 2017). Intra-set rest periods as short
as six seconds have been shown to decrease intra-set power output losses across varied
light and heavy loading conditions (Garcia-Ramos, 2016). In high volume sets, increasing
frequency of intra-set rest periods lead to a greater maintenance of power when compared
to lower frequency rest periods (Tufano, 2016). Additionally, chronic cluster training has
been shown to lead to greater increases in 1RM compared to other forms of training
perhaps due to increases in time under tension and greater impulse generation
(Nicholson, 2016). However, minimal research has been done to utilize cluster sets with
VBT.
Purpose
The purpose of the study is to determine the validity of the relationship between
prescribed velocities for a given %1RM when utilizing VBT for the back squat. In
addition, the study aims to use kinematic data to assess previously shown maintenance of
velocity and power output in cluster sets when compared to traditional loading
conditions.
Null Hypothesis
There will be no significant difference in recorded velocities between cluster and
traditional sets.
There will be no correlation between loads and velocities.

Limitations
Several limitations impacted the completion of the study. First, lack of experience
with cluster sets may have had an impact on data. While the participants were all
3

resistance trained and experienced in the back squat, they may not have had experience
training with cluster sets. The position of the linear position transducer may have affected
results due to its placement on the lateral end of the barbell. Any rotation of the barbell
would potentially alter recorded velocities, impacting total displacement and thus
velocity during a repetition.
Delimitations
Collegiate-aged males (ages: 21-23) were recruited for the study. The study
required all participants to have a minimum of one-year resistance training experience
with a training frequency of greater than or equal to three days per week. Participants
were required to have experience in the back squat as technique was not taught during the
study. While nutrition was not tracked participants were asked to maintain their normal
dietary intake throughout the duration of the study. In order to prevent external fatigue
adequate rest was provided between trials with a minimum of 72 hours after 1RM testing
followed by 48 hours between experimental sessions. Additionally, participants were
informed to avoid lower body physical activity for the duration of the study.

4

CHAPTER 2
LITERATURE REVIEW
Velocity Based Training
Velocity based training has many proposed benefits that have been outlined in
numerous studies. The use of velocity-based training introduces a new metric to track and
modify programming with. VBT may be used to identify day to day fluctuations in
training (Mann, 2015). Controlling loading by recording velocities may be particularly
useful as changes in daily readiness have been observed through estimating 1RMs during
warm up sets without interfering with daily training (Jovanovic, 2014).
When arranging data across loads and matching with RIR, previous studies have
shown similar velocity decrements in the smith-machine half squat while approaching
failure. This has very significant implications in training if it is assumed an athlete is
exerting maximal effort, it is possible to reliably track how many repetitions they can
complete before reaching failure. (Jovanovic, 2014.). Movement velocities may be used
to determine the level of effort during different resistance exercises. A study examining
the bench press, full squat, prone bench pull, and shoulder press found that velocities
associated with stopping a set before failure were highly reliable (coefficient of
5

variation: 4.4-8.0%) at 2,4,6,8 RIR when looking at 65, 75, and 85% 1RM in each
exercise (Morán-Navarro, 2019).
VBT may be used as a form of instantaneous feedback, by providing recorded
velocities in real time with the use of a linear position transducer. Feedback has
previously been shown to help improve acute performance. Use of VBT for feedback has
led to greater improvements in training and greater consistency in training when
compared to those not using VBT for feedback (Randell, 2011.).
A six-week training study was conducted using VBT for the bench press
investigating the possibility of using velocity to measure different loading intensities.
Fifty-six participants completed the study, and despite increasing their 1RM bench press
by an average of 9.3% across the population, velocities remained consistent at a given
%1RM (González-Badillo, 2010).
Monitoring velocity may potentially aid in estimating and potentially limiting
metabolic stress such as buildup with ammonia, which has been shown to increase
recovery times (Sanchez-Medina, 2011). Implementation of programs utilizing velocity
loss are a growing modality of periodization, where velocities are prescribed and a
percent velocity decrement is used to determine completion of a set (Banyard, 2019.).
Improvements in hypertrophy were found in both programs using 20% velocity loss and
40% velocity loss thresholds, with greater improvements found in the 40% group (ParejaBlanco, 2017).
Cluster Sets
Cluster sets have been utilized as a method to increase mechanical overload by
increasing total repetition volume in a set under heavier loading conditions than a
6

traditional set would allow (Haff, 2008). Maintenance of acute mechanical performance
has been demonstrated previously through comparison of cluster sets of two repetitions
and four repetitions when compared to traditional sets (Nicholson, 2016.).
Intra-set rest has been shown to help maintain acute mechanical performance.
When looking at high volume back squats maintenance of mean recorded velocities, and
power output were conserved in both two-repetition and four repetition cluster sets at
60% 1RM when compared to completing twelve repetitions in a traditional set. The study
also showed that the cluster sets of two repetitions demonstrated greater maintenance of
these variable, implying that increasing the frequency of intra-set rest periods also helped
to maintain performance (Tufano, 2016). Intra-set rest periods of varied lengths have also
been studied, with periods as short as six seconds demonstrating decreases in power loss
across varied loads with rest periods as short as six seconds (Garcia-Ramos, 2016). Intraset rest intervals may be manipulated depended on the goal outcome of the training
session., but it is important to note that the longer rest intervals may be less practical for
strength coaches due to constraints in time with athletes in the weight room. A study
observing changes in power output in the power clean clusters while manipulating intraset rest periods demonstrated that similar maintenance of power output was displayed
with twenty and forty second rest intervals and therefore shorter rest intervals may be
more practical (Hardee, 2012).

7

CHAPTER 3
METHODOLOGY
Demographics
Fourteen recreationally active males (age: 21.42±0.90 years; height: 1.75±0.06 m;
body mass: 84.29±7.91 kg) participated in the study. All participants exercised three or
more times per week for greater than one year. Participants were not required to have
experience with cluster sets.
Equipment
•

Olympic 20kg barbell

•

Kilogram plates (0.5-25 kg)

•

Squat stand

•

Linear position transducer (GymAware, Kinetic, AU)

Study Design
Prior to testing, all fourteen subjects signed an informed consent form. Height and
body mass data was gathered prior to testing. The initial testing day consisted of a 1RM
back squat test. All participants completed a standardized ten-minute dynamic warmup
8

prior to each session. After the warm-up participants were tested in the freeweight back squat using the NSCA 1RM protocol (Haff & Triplett, 2015). Upon
completion of 1RM testing, a minimum of 72 hours rest period was given to allow for
recovery before beginning the experimental sessions. The protocol included two separate
experimental trials. Participants were randomized into two groups following 1RM
sessions. Each testing sessions consisted of completing one set of ten repetitions at 67%
1RM, one set of
six repetitions at 80% 1RM, and one set of four repetitions at 85% 1RM. All
traditional sets were completed on one day while cluster sets were completed on the
other. One group completed traditional sets with all of the loads on the first day while the
other group completed their sets with the cluster sets first. Traditional sets consisted of all
repetitions being completed in sequence followed by a five-minute rest period between
sets, beginning when the participant placed the barbell back into the rack. Cluster sets
were completed with thirty second intra-set rest periods following every two reps while
matching the same total volume as the traditional sets for each condition. Thirty second
timers were started when the participant placed the barbell back into the rack and a
countdown was given to ensure the participant continued the set after the thirty second
rest periods. Five-minute rest periods were included between each set. All participants
were instructed to squat to parallel, and to move each rep as fast as they could.
Fatigue was monitored using a questionnaire to assess hours of sleep, quality of
sleep, muscle strain, and stress levels prior to each session (Gastin, 2013.). During each
trial mean concentric velocities were recorded with a linear position transducer
(GymAware, Kinetic, AU) to measure mean concentric velocities of each repetition.
9

Participants were not informed of their repetition velocities during testing.

Figure 1. Cluster Set Format
Figure 1 displays the format of sets during the cluster set day. Each cluster of two
repetitions will be proceeded by a thirty second intra-set rest period until total repetitions
matches with the traditional sets.

Statistical analysis
Statistical analysis was completed using the statistics package for the social
sciences (SPSS 25.0). Correlations were determined with an alpha of p≤0.05. The fastest
repetition from each loading condition was used for analysis. Two-way ANOVAs were
used to determine differences between mean velocities in the cluster set and traditional
set conditions. Two repetition averages were used for each loading condition to compare
each cluster with pairs of repetitions. Paired t-tests were used to assess differences in
questionnaire results by testing day.
10

CHAPTER 4
RESULTS
Table 1. Questionnaire Results
Questionnaire Results
Traditional Day

Cluster Day

Hours of Sleep

6.54

6.75

Sleep Quality

2.21

1.71

Fatigue

2.57

2.43

Muscle Strain

1.93

2.21

Stress

3.36

3.28

This table displays mean results from the wellness questionnaire. Sleep quality, muscle
strain, and stress were all measured on a 1-5 scale with 1 being the highest score and 5
being the lowest score. No significant differences were observed in questionnaire scores
between testing days (p>0.05)

11

VBT Correlational Data

67% 1RM Load-Velocity Correlation
0.9
0.8

Velocity (m/s)

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

20

40

60

80

100

120

140

160

Load (kg)

Figure 2. 67% 1RM Load-Velocity Correlation
A trivial correlation was displayed between loads and velocities in the 67%
condition (r=-0.096). Outliers on both ends deviated from the trendline shown in the
figure.

12

80% 1RM Load-Velocity Correlation
0.7

Velocity (m/s)

0.6
0.5
0.4
0.3
0.2
0.1
0
0

50

100

150

200

Load (kg)

Figure 3. 80% 1RM Load-Velocity Correlation
A weak negative correlation was displayed between loads and velocities in the
80% 1RM condition (r=-0.242).

13

85% 1RM Load-Velocity Correlation
0.7

Velocity (m/s)

0.6
0.5
0.4
0.3
0.2
0.1
0
0

50

100

150

200

250

Load (kg)

Figure 4. 85% 1RM Load Velocity Correlation
A moderate negative correlation was found between loads and velocities in the
85% 1RM condition (r=-0.41).

14

Cluster Set Data

67% 1RM Mean Recorded Velocities
0.66

Traditional

Mean Velocity (m/s)

0.64

Cluster

0.62
0.60
0.58
0.56
0.54
0.52
0.50
0.48
1

2

3

4

5

Pair

Figure 5. 67% 1RM Mean Recorded Velocities.
Figure 5 displays two repetition averages for mean concentric velocities in the
67% 1RM condition for traditional and cluster sets. A significant difference was found
between the recorded velocities in the two sets (p<0.05).

15

80% 1RM Mean Recorded Velocities
0.54

Traditional

Mean Velocity (m/s)

0.52

Cluster

0.50
0.48
0.46
0.44
0.42
0.40
1

2

3

Pair

Figure 6. 80% 1RM Mean Recorded Velocities.
Figure 6 shows two repetition averages for mean concentric velocities in the 80%
1RM condition for traditional and cluster sets. No significant difference was found
between the two groups (p>0.05). However, pairs two and three did display an increasing
mean difference in the cluster condition when compared to the traditional sets.

16

85% 1RM Mean Recorded Velocities

Mean Velocity (m/s)

0.49
0.48

Traditional

0.47

Cluster

0.46
0.45
0.44
0.43
0.42
0.41
0.40
0.39
0.38
1

2

Pair

Figure 7. 85% 1RM Mean Recorded Velocities.
Figure 7 shows two repetition averages for mean concentric velocities in the 85%
1RM condition for traditional and cluster sets. There was no significant difference
between the two set types (p>0.05).

17

Table 2. Means and Mean Differences
Means and Mean Differences
Cluster Set (m/s)

Traditional Set
(m/s)

Mean Differences
(m/s)

67% Pair 1

0.63

.64

-0.01

67% Pair 2

0.62

0.62

0.00

67% Pair 3

0.61

0.60

0.01

67% Pair 4

0.60

0.58

0.02

67% Pair 5

0.59

0.54

0.05

80% Pair 1

0.52

0.53

-0.01

80% Pair 2

0.50

0.49

0.01

80% Pair 3

0.47

0.45

0.02

85% Pair 1

0.47

0.48

-0.01

85% Pair 2

0.43

0.42

0.01

Table 2 displays means and mean differences for each pair of repetitions under all
loading conditions. While significant differences were only observed between set types in
the 67% condition, Similar mean differences were shown across %1RM conditions in
pairs 1-3 at 80% 1RM as well as pairs 1-2 at 85% 1RM.

18

CHAPTER 5
DISCUSSION
The questionnaire results displayed no significant differences between testing
sessions. However, participants showed high levels of stress on both testing days,
potentially affecting the movement velocity of each repetition. Using the questionnaire
for inclusion criteria may have been a way to mitigate any issues with fatigue by
excluding participants when they answered with a four or five on any question.
No significant correlations were found under any of the three loading conditions
(67%, 80%, 85% 1RM). This goes against previously suggested evidence and the ability
to generalize velocities into training zones. While the correlations ranged from trivial to
moderate across the measured loads, there were large groupings of values that fell around
the trendline with a few outliers deviating from the trend. With a greater subject pool, the
outliers would have a lesser affect, potentially leading to a stronger correlation than the
ones demonstrated in this study.
The lack of correlations between load and velocity across participants implies that
individualization may be necessary when determining velocities for VBT. This has also
been shown to be true with load-velocity profiling as different muscle and training
19

characteristics may have effects on movement velocity from one individual to
another. Individualized load-velocity profiles (LVPs) have previously been shown to
have high reliability with the free-weight back squat (Banyard, 2018). Individualized
LVPs may allow for more accurate velocity prescriptions for VBT instead of using
generalized ranges which have been designated in previous studies. Using LVPs will
allow for appropriate development and monitoring of each individual exercise as well
rather than relying on general prescriptions across multiple exercises. Using a
combination of methods may also be ideal to further gain insight into an individual’s
performance in an exercise such as combining VBT with set RPEs, RIRs, or session
RPEs to help monitor fatigue and auto-regulate as intended with a VBT program.
Tracking velocities over a prolonged period could be considered as tool for monitoring
fatigue rather than solely for use with prescription of loading.
Maintenance of velocity was not shown as previous research has concluded when
comparing traditional and cluster sets. However, this may partially be due to the
inexperience of the participants with cluster sets as some research has stated that cluster
sets show benefits with chronic training when compared to traditional sets (Nicholson,
2016). Additionally, when looking at table 2, mean differences trend similarly across all
conditions, which eventually led to a significant difference in the 67% condition due to
an increased number of pairs. With increased repetition volume at greater loads a similar
difference may have been displayed when comparing the traditional and cluster sets.
Looking at maintenance of power output as well as force data may help to reveal more
information about cluster sets and their usage. Recently, rest-redistribution protocols have
been shown to maintain peak force output in an even greater manner than cluster sets
20

(Tufano, 2017). This perhaps calls to the notion that cluster sets should not serve as a
replacement for traditional sets but may be used to train a specific capacity when
compared to other methods of set structures such as traditional sets or utilization of rest
redistribution.

Future Research
In the future, it may be worth controlling repetitions through a true 2 RIR rather
than estimating RIR by using estimated repetition maximums at a given %1RM. By
reaching a true 2 RIR it may be possible to see if the trends in the 80% and 85% cluster
sets continued to match those in the 67% set. Additionally, completing sets to 2 RIR in
both the cluster and traditional sets may potentially yield greater total repetition and load
volume through clusters if the acute intra-set recovery periods allow for greater total
repetitions in each set when compared to the traditional sets. Matching total intra-set rest
time across conditions may be worth investigating in the future, as the additional rest
time at the 67% 1RM cluster sets (120s) compared to the intra-set rest at 85% 1RM (60s)
may have an impact on the results demonstrated in this study. Further investigation of
cluster sets may include undulating cluster sets, in which loads can be varied within a
cluster set to further induce mechanical overload and potentiation within a single set.
Examining other mechanical variables including power output and force output may
provide further insight on the benefits of cluster sets. Looking at joint moments and
muscle activation may provide more information regarding the mechanism through which
mechanical variables are maintained in cluster sets.
21

Practical Application
VBT is gaining popularity with more widespread availability of commercial linear
position transducers. While the present study did not display strong correlations between
loads and velocities, using velocity to monitor daily changes in training may still be a
useful tool in assessing an individual’s fatigue. Estimating 1RMs with VBT prior to
training sessions may be an effective way to evaluate an individual’s ability to perform
which varies with day to day fluctuations (Jovanovic, 2014). Developing individual loadvelocity profiles for specific exercises will allow for appropriate prescription of velocities
in training. Loads can be prescribed in terms of velocity to match the more traditional
%1RM load prescription or velocity loss thresholds may be used to further control fatigue
by terminating a set when velocity decreases by a set percentage (Pareja-Blanco, 2017).
Cluster set implementation may be used to provide further mechanical overload in
a set when compared to traditional sets. By introducing acute recovery periods, greater
overload may be achieved through increased loading or total repetition volume. Previous
studies have shown maintenance of power output, making cluster sets useful when power
is the capacity that is being focused on (Tufano, 2016). Furthermore, while cluster sets
have been shown to maintain power output, they may still be utilized for multiple phases
of training. When focusing on hypertrophy or conditioning, the use of cluster sets will
allow for greater total volume through added repetitions with shorter intra-set rest periods
when compared to cluster sets with heavier loads.

22

CHAPTER 6
CONCLUSION
No correlations were found between loads and velocities for the given group
implying that individualization may be key when prescribing periodized programs which
utilize VBT. Significant differences were only measured in the 67% condition when
comparing recorded velocities between traditional and cluster sets. However, additional
repetitions under heavier loads may lead to greater maintenance in velocity as displayed
in previous studies

23

REFERENCES
1. Banyard, H. G., Nosaka, K., Vernon, A. D., & Haff, G. G. (2018). The Reliability
of Individualized Load–Velocity Profiles. International journal of sports
physiology and performance, 13(6), 763-769.
2. Banyard, H. G., Tufano, J. J., Delgado, J., Thompson, S., & Nosaka, K. (2018).
Comparison of Velocity-Based and Traditional 1RM-Percent-Based Prescription
on Acute Kinetic and Kinematic Variables. International Journal of Sports
Physiology and Performance.
3. Banyard, H. G., Tufano, J. J., Delgado, J., Thompson, S. W., & Nosaka, K.
(2019). Comparison of the Effects of Velocity-Based Training Methods and
Traditional 1RM-Percent-Based Training Prescription on Acute Kinetic and
Kinematic Variables. International Journal of Sports Physiology and
Performance, 14(2), 246-255.
4. Bryce, A. (2016). A Comparative Study of Strength Improvements in
Autoregulatory Training (Doctoral dissertation, University of Kansas).
5. García-Ramos, A., Nebot, V., Padial, P., Valverde-Esteve, T., Pablos-Monzó, A.,
& Feriche, B. (2016). Effects of Short Inter-Repetition Rest Periods on Power
Output Losses During the Half Squat Exercise. Isokinetics and Exercise
Science, 24(4), 323-330.

24

6. Gastin, P. B., Meyer, D., & Robinson, D. (2013). Perceptions of Wellness to
Monitor Adaptive Responses to Training and Competition in Elite Australian
Football. The Journal of Strength & Conditioning Research, 27(9), 2518-2526.
7. González-Badillo, J. J., & Sánchez-Medina, L. (2010). Movement Velocity as a
Measure of Loading Intensity in Resistance Training. International Journal of
Sports Medicine, 31(05), 347-352.
8. Haff, G. G., Hobbs, R. T., Haff, E. E., Sands, W. A., Pierce, K. C., & Stone, M.
H. (2008). Cluster training: A Novel Method for Introducing Training Program
Variation. Strength & Conditioning Journal, 30(1), 67-76.
9. Haff, G., & Triplett, N. T. (2015). Essentials of Strength Training and
Conditioning.
10. Hardee, J. P., Triplett, N. T., Utter, A. C., Zwetsloot, K. A., & Mcbride, J. M.
(2012). Effect of Interrepetition Rest on Power Output in the Power Clean. The
Journal of Strength & Conditioning Research, 26(4), 883-889.
11. Helms, E. R., Storey, A., Cross, M. R., Brown, S. R., Lenetsky, S., Ramsay, H., &
Zourdos, M. C. (2017). RPE and Velocity Relationships for the Back Squat,
Bench Press, and Deadlift in Powerlifters. The Journal of Strength &
Conditioning Research, 31(2), 292-297.

25

12. Hester, G. M., Conchola, E. C., Thiele, R. M., & DeFreitas, J. M. (2014). Power
Output During a High-Volume Power-Oriented Back Squat Protocol. The Journal
of Strength & Conditioning Research, 28(10), 2801-2805.
13. Jovanović, M., & Flanagan, E. P. (2014). Researched Applications of Velocity
Based Strength Training. Journal of Australian Strength & Conditioning, 22(2),
58-69.
14. Kubo, T., Hirayama, K., Nakamura, N., & Higuchi, M. (2018). Influence of
Different Loads on Force-Time Characteristics during Back Squats. Journal of
Sports Science & Medicine, 17(4), 617.
15. Mann, J. B., Ivey, P. A., & Sayers, S. P. (2015). Velocity-Based Training in
Football. Strength & Conditioning Journal, 37(6), 52-57.
16. Morán-Navarro, R., Martínez-Cava, A., Sánchez-Medina, L., Mora-Rodríguez,
R., González-Badillo, J. J., & Pallarés, J. G. (2019). Movement velocity as a
Measure of Level of Effort During Resistance Exercise. The Journal of Strength
& Conditioning Research, 33(6), 1496-1504.
17. Nicholson, G., Ispoglou, T., & Bissas, A. (2016). The Impact of Repetition
Mechanics on the Adaptations Resulting from Strength-, Hypertrophy-and
Cluster-Type Resistance Training. European Journal of Applied
Physiology, 116(10), 1875-1888.

26

18. Pareja‐Blanco, F., Rodríguez‐Rosell, D., Sánchez‐Medina, L., Sanchis‐Moysi, J.,
Dorado, C., Mora‐Custodio, R., & González‐Badillo, J. J. (2017). Effects of
Velocity Loss During Resistance Training on Athletic Performance, Strength
Gains and Muscle Adaptations. Scandinavian Journal of Medicine & Science in
Sports, 27(7), 724-735.
19. Randell, A. D., Cronin, J. B., Keogh, J. W., Gill, N. D., & Pedersen, M. C. (2011).
Effect of Instantaneous Performance Feedback During 6 Weeks of VelocityBased Resistance Training on Sport-Specific Performance Tests. The Journal of
Strength & Conditioning Research, 25(1), 87-93.
20. Sanchez-Medina, L., & González-Badillo, J. J. (2011). Velocity Loss as an
Indicator of Neuromuscular Fatigue During Resistance Training. Medicine and
Science in Sports and Exercise, 43(9), 1725-1734.
21. Tufano, J. J., Conlon, J. A., Nimphius, S., Brown, L. E., Petkovic, A., Frick, J., &
Haff, G. G. (2017). Effects of Cluster Sets and Rest-Redistribution on Mechanical
Responses to Back Squats in Trained Men. Journal of Human Kinetics, 58(1), 3543.

27

APPENDIX A

28

APPENDIX B

Informed consent for scientific study

Title of investigation: Velocity Based Training and
Cluster Set Application for the Back Squat
.
.

Principle investigator: Dylan Zangakis

Overview of study
Velocity based training (VBT) has been proposed as an auto-regulatory approach to training. Typically,
training loads are determined using percentages of a one repetition maximum (%1RM). However, this
may create issues on days where an athlete is either fatigued or dealing with external stress factors which
limit their ability to perform. The purpose of the study is to determine relationship between prescribed
velocities for a given %1RM when utilizing VBT for the back squat. In addition, the study aims to use
kinematic data to assess maintenance of velocity and power output in cluster sets.

Testing sessions
There will be three testing sessions during the study. The testing sessions will be performed in the
Biomechanics Laboratory of East Stroudsburg University. During the testing session you will be asked to
perform repetitions of the squat under various loads (67%, 80%, 85%) based on one repetition maximum
testing. Prior to all testing sessions you will be taken through a standardized warm-up.

Although you will be undergoing physical testing, there is very little risk if you are a normal healthy
individual. Individual information obtained from this study will remain confidential. Non-identifiable data
will be used for scientific presentations and publications. You may withdraw from the study at any time. If
you have any questions please ask Dr Moir before signing this consent form.
If you have any additional questions during or after the study, Dr Moir can be contacted at:

gmoir@po-box.esu.edu

Tel: (570) 422 3335
29

YOU ARE MAKING A DECISION WHETHER OR NOT TO PARTICIPATE. YOUR SIGNITURE
INDICATES THAT YOU HAVE READ THE INFORMATION PROVIDED AND YOU HAVE
DECIDED TO PARTICIPATE IN THE STUDY.
I have read and understood the above explanation of the purpose and procedures for this study and agree to
participate. I also understand that I am free to withdraw my consent at any time.

Print name

Signature

Witness signature

30

Date

APPENDIX C
Questionnaire
Testing Day (Cluster/Traditional):_____________________

How many hours of sleep did you get last night?

How would you rate your quality of sleep (1 being the best-5 being the worst)?
1

2

3

4

5

What is your current level of fatigue?
1

2

3

4

5

What is your current level of muscle strain?
1

2

3

4

5

How would you rate your current stress?
1

2

3

4

5

31