Dissertation Final

Reg Num: 115554 Gluteal Activation Warm Up 1

Gluteal Activation Warm Up Increases Subsequent Ground Reaction Force, Gluteal, Bilateral

Hamstring and Quadriceps Force Whilst Simultaneously reducing Gluteus Medius

Recruitment During the Hang High Pull in Elite Male Rugby Union Players.

Matthew Thomas Parr

Dr Daniel Cleather

June 2015

St Mary’s University, Twickenham, London.

School of Human Sciences

MSc Strength & Conditioning


Acknowledgments

Thank you to my wife Jemima and my children Maisie and Freya for sacrificing their time

with me in order for me to complete this research, further thanks must go to my supervisor Dr

Daniel Cleather and Phil Price for their insight and patience.


Abstract

Gluteal Activation Warm Up Increases Subsequent Ground Reaction Force, Gluteal, Bilateral

Hamstring and Quadriceps Force Whilst Simultaneously Reducing Gluteus Medius

Recruitment During the Hang High Pull in Elite Male Rugby Union Players.

The gluteal muscles play a material role in athletic performance and injury prevention thus

the purpose of this research was to identify the effect of a gluteal activation warm up on

gluteal motor unit (MU) recruitment and force output within the hang high pull (HHP),

endeavouring to increase athletic performance and reduce injury risk. Seventeen elite male

rugby union players undertook two separate warm ups, control and activation, prior to

executing three HHP. Electromyography (EMG) was employed to measure gluteus maximus

(GMAX) and gluteus medius (GMED) recruitment levels. The FreeBody Beta

musculoskeletal model (FBB) was used to measure the force output of the bilateral

hamstring, GMAX, GMED and quadriceps with a force plate measuring ground reaction

force (GRF). EMG results showed a statistically significant (p < 0.048) decrease in GMED

recruitment following the activation warm up with FBB data showing a strong trend of

increased force output following the activation warm up. This research concludes that the

implementation of a gluteal activation warm up may increase athletic performance and reduce

injury risk due to the increased gluteal and lower limb force output despite decreased GMED

motor unit recruitment.

Keywords: Isometric Contraction, Nervous System, Force, Preferential Recruitment


Table of Contents

Abstract p.3

Chapter One: Introduction

1.1 Introduction p.8

Chapter Two: Literature Review

2.1 Importance of the gluteal musculature within sports performance p.9

2.2 Importance of Gluteal Musculature in Injury Prevention p.11

2.3 Activation research p.12

2.4 Gluteal activation research p.14

2.5 Musculoskeletal modelling review p.16

2.6 Summary p.17

Chapter Three: Method


3.2 Research strategy p.19

3.3 Data Collection p.24

Chapter Four: Results


4.1 Results p.26

Chapter 5: Discussion


5.2 Research Objectives p.31

5.3 Recommendations and practical applications p.40

5.4 Contribution to knowledge p.41

5.5 Self Reflection p.41

Chapter 6

References p.42

Appendix p.66


List of Figures

Figure 1. Most effective GMAX and GMED recruitment exercises p.15

Figure 2. Research structure info graphic p.20

Figure 3. Control trial non-specific warm up. p.22

Figure 4. Intervention trial GMAX and GMED specific warm up. p.23

Figure 5. Peak motor unit recruitment rate in control and intervention trials p.29


List of Tables

Table 1. Cohort Anthropometrics Data Table p.19

Table 2. EMG Pilot Data in μν as a Percentage of MVIC p.26

Table 3. Group Mean Peak Recruitment as MVIC Percentage and p.27

Multiples of Body Mass

Table 4. FBB GMED, GMAX and GRF RFD as xBW/ms and EMGGMED p.28

and GMAX RFD as MVIC% μv/ms


Chapter One

2. Introduction

1.1 Introduction

Warm up protocols frequently endeavour to enhance force output (De Villarreal, Gonzalez-

Badillo & Izquierdo, 2007) especially as explosive movements are a key characteristic of

elite performance (Izquierdo, Hakkinen, Gonzalez-Badillo, Ibanez & Gorostiaga, 2002).

Activation warm ups are one such method, potential increases are believed to result from

increased axon potential discharge rate enabling a high coding rate to recruit high threshold

motor units (HTMU) (Aagaard et al., 2007) in turn producing powerful movements.

Increasing the coding rate and preferential recruitment of HTMU within musculature material

to athletic performance such as the gluteal musculature appears vital to increasing

performance and as such will be investigated. Within this research the gluteus maximus

(GMAX) and gluteus medius (GMED) will be targeted, as hip extension force has been cited

as a material factor in sporting performance (Nagano, Komura, Fukashiro & Himeno, 2005:

Newton & Kraemer, 1994: Mero & Komi, 1994), thus developing methods of increasing

sporting performance via increased gluteal force offers a rationale for the undertaking of this

research.

Considering the importance of HTMU activation and the impact of the gluteal musculature

on sporting performance, further research on the optimisation of these factors appears prudent

as the effects of gluteal activation warm ups on peak force output are currently unclear

(Buttifant, Crow, Kearney & Hrysomallis, 2011). Therefore further investigation of this

subject is required to provide an insight as to the efficacy of gluteal activation warm ups on


subsequent recruitment and force output. Within this literature review a rationale will be

given for the need of this research to investigate the effect of GMAX and GMED activation

warm up on recruitment and force output.

Chapter Two

2.1 Importance of Gluteal Musculature in Sporting Performance

Increased acceleration (Murphy, Lockie & Coutts, 2003: Kyrolainen, Avela & Komi, 2005:

Reilly & Borrie, 1992: Reilly, 1997), maximum velocity running (MVR) (Kyrolainen et al.,

2005: Meir, Colla & Milligan, 2001: Deutsch, Maw, Jenkins & Reaburn, 1998), change of

direction (CoD) (Young & Farrow, 2006: Holmberg, 2009) and jump performance have been

proposed as key sports performance indicators (KPI) (Izquierdo et al., 2002: Hoffman,

Cooper, Wendell & Kanf, 2004: Murphy et al., 2003: Penfold & Jenkins, 1996). All of these

are actions with high GMAX and GMED involvement (Nagano et al. 2005: Newton &

Kraemer, 1994: Mero & Komi, 1994), therefore it could be extrapolated that identifying

methods of improving GMAX and GMED recruitment and force output, such as activation

warm ups, may in turn increase performance and thus merits investigation. As previously

stated the GMAX and GMED play a material role in MVR (Kyrolainen et al., 2005: Meir et

al. 2001: Deutsch, 1998) and acceleration (Reily, 1996: Rienzi et al., 2000: Meir et al., 2001);

with the GMED stabilising the pelvis and femur (Gottschalck, Kourush & Leveau, 1989) and

abducts the hip (Boudreau et al., 2009), whilst the GMAX extends the hip, abducts and

external rotates the femur (Souza & Powers, 2009. Understanding the biomechanical

functions of GMAX and GMED is vital to performance enhancement as it enables greater

specificity of gluteal activation exercise selection.


Analysis of hip extension during running highlights that hip extension force correlates with

high stride frequency, with Schroter (1998) and Brown, Ferrigno and Santana (2000)

suggesting high stride frequency as vital during the crucial initial accelerative steps (Penfold

& Jenkins, 1996). Murphy et al. (2003) explored kinematic deviations between fast and slow

accelerators. Faster accelerators displayed a 9% greater stride frequency potentially due to

greater horizontal hip velocity, with Guskiewicz, Lephart and Burkholder (1993) proposing a

similar correlation between superior sprint performance and hip extensor power. Hip

extension torque produced by GMAX affects thigh velocity, with increased GMAX force

production increasing hip extension velocity thus reducing the thigh swing to stance duration

(Lieberman, 2006: Blazevich, 2000) enabling increased stride frequency and in turn running

velocity, thus investigation of the methods of enhancing hip extension force such as a gluteal

activation warm up appears valid. Similarly ground reaction force (GRF) is influenced by

hip extension force, Weyand, Sternlight, Bellizzi and Wright (2000) established that faster

runners apply greater GRF, GRF is the product of lower limb triple extension force, initiated

proximally by GMAX hip extension, thus GMAX contractile force greatly influences GRF

and thus running speed (Weyand et al., 2000), with Mann (1986) finding acceleration

velocity also related to horizontal force production of the hip extensors during ground

contact. This highlights the vital role GMAX plays in hip extension; increasing stride

frequency and in turn GRF resulting in increased running velocity, due to this association

identifying methods of optimally strengthening the GMAX such as a gluteal activation warm

up may facilitate improved running velocity within the sporting environment.

In addition to increased limb velocity and vertical GRF production, GMAX and GMED play

a vital role in femur and pelvic stability (Cutter & Kervorkain, 1999: Earle, 2005), as

insufficient GMAX strength reduces the hip’s ability to resist flexion (Lieberman et al., 2006)


with insufficient GMED strength reducing pelvic stability during stance (Gottschalck, 1989:

Lieberman et al., 2006) causing Trendelenburg hip drop and force leakage (Cutteret &

Kervorkain, 1999: Earle, 2005), consequently reducing ground contact force, reducing

vertical GRF thus decreased running velocity (Blazevich, 2000). Thus further highlighting

the importance of elevated GMAX and GMED force and recruitment on running velocity,

ergo by improving GMED force via increased recruitment in compound movements such as

the HHP may enable greater pelvic stability resulting in increased acceleration and MVR,

therefore providing further rationale for the investigation of the effect of a gluteal activation

warm up on GMAX and GMED force in the pursuit of increased athletic performance.

The GMAX and GMED function similarly within CoD (Neumann, 2010: Neptune, Wright &

Van Der Bogert, 1999) as within MVR and acceleration, with Neptune et al. (1999) finding

that the GMED displayed a constant EMG output during cutting movements and side

shuffling, highlighting GMED’s role in isometrically stabilizing the pelvis proximally,

creating a foundation for GMAX to produce and transfer force during CoD (Oliver & Keeley,

2010). The GMAX and GMED functional similarly in the counter movement jump (CMJ)

with Nagano et al. (2005) finding the GMAX to be maximally activated during take-off,

highlighting the differing functions of the gluteal muscles with GMAX as a force producer

and the GMED acting as a stabiliser (Bobbert & Van Ingen Schenau, 1988). The functions of

the GMAX and GMED in crucial performance measures discussed above provide a mandate

for this research in identifying methods of increasing gluteal force output to enable increased

sporting performance.


2.2 Importance of Gluteal Musculature in Injury Prevention

Further to the role of the GMAX and GMED in performance, decreased GMED strength has

been cited to increase Anterior Cruciate Ligament (ACL) injury risk (McLean et al., 2005:

Earle 2005), ACL injuries often occur during deceleration, landing and CoD (Powell &

Barber-Foss, 2000: Griffin et al. 2000: Griffin et al. 2006: Ireland, 1999: Olsen, Myklebust,

Engerbertsen & Bahr, 2004: Boden, Dean, Feagin & Garrett, 2000), due to decreased GMED

strength reducing frontal and transverse plane pelvic stability during gate and increasing hip

external rotation during cutting movements (Schmitz Riemann & Thompson, 2002: Earle,

2005: Heller, 2003: McLean, Huang & Van Den Bogert, 2004), insufficient GMED and hip

abductor strength creates a lack of frontal plane pelvic stability leading to Trendelenburg sign

(Cutter & KerVorkain, 1999: Earle, 2005). The Trendelenburg sign occurs when insufficient

GMED strength causes the GRF vector to shift laterally (Neumann, 2010) resulting in

increased knee valgus angle (Powers, 2010), increased knee valgus angle causes the

tibiofemoral joint stress within the frontal plane contributing to the relative ACL strain

(Withrow, Huston, Wojtys & Ashton Miller, 2006). Therefore considering these findings it

could be hypothesised that increasing GMED force via a gluteal activation warm up may

reduce the risk of increased knee valgus angle and in turn the risk of ACL injury. Therefore

investigation into methods of strengthening the GMED therapeutically such as within

activation warm ups, or via heavy resistance training which has been shown to produce high

levels of neuromuscular activation (Escamilla et al., 1998) and neural drive (Aagaard et al.,

2002: Hakkinen, Alen & Komi, 1985: Higbie, Cureton, Warren & Prior, 1996), appears a

valid rationale for the completion of this research.


Heavy resistance training has been shown as a stimulus which periodically will increase

hypertrophy (Aagaard et al., 2001: Andersen et al., 2005: Narici et al., 1996) and strength

gain (Aagaard et al., 2000: Andersen et al., 2005: Hakkinen & Komi, 1985). As GMED

strength has been associated with knee valgus angle and in turn ACL injury, increasing the

level of GMED neuromuscular stimuli within an a commonly used athletic training exercise

like the HHP, enabling increased cross sectional area and in turn strength via the use of a

therapeutic gluteal activation warm up appears advantageous.

2.3 Activation Research

Muscle activation describes the recruitment of individual motor units, governed by motor

neuron coding rate and the Henneman size principle (Zatsiorsky & Prilutsky, 2012), powerful

movements require a high coding rate recruiting high HTMU (Cormie, McGuigan & Newton,

2011), therefore establishing methods of innervating HTMU and increasing coding rate prior

to the requirement for powerful movement may facilitate decreased motor unit innervation

lag-time and accelerate recruitment of HTMU (Tsao & Hodges, 2008), subsequently

improving task performance. Within a therapeutic setting repeated activation of muscles with

delayed innervation patterns has been utilised to treat musculoskeletal dysfunctions

(Richardson, Jull, Hodges & Hides, 1999); the mechanism responsible for increasing the

excitation of the innervation afferent is believed to be plastic changes in the nervous system

modifying automatic muscle functions during tasks (Tsao & Hodges, 2008) and increased

efferent neural drive (Aagaard et al., 2002). Tsao & Hodges (2007) found that one session of

low intensity voluntary transverse abdominus (TA) activation training enabled the earlier

onset of TA EMG activity during subsequent trunk and upper limb movement, evidencing the

acceleration of feedforward motor patterns and the plasticity of automatic postural control


(Tsao & Hodges, 2008) in low velocity actions. Despite these findings in low velocity

movement, it is unclear if such mechanisms will result in increased force output within

explosive movements; the effect of low-level gluteal activation exercise on power output was

investigated by Crow, Buttifant, Kearny and Hrysomallis (2012), who discovered a 4.2%

increase in CMJ height after a therapeutic gluteal activation warm up in elite AFL players,

increases are believed to occur via increased efferent neural drive, preferential recruitment of

HTMU, affrent excitability and increased v-wave response (Aagaard et al., 2002: Milner-

Brown, Stein & Yemm, 1973) mechanisms described by Tsao and Hodges (2007). However

Crow et al (2012) reported no data suggesting the underlying mechanism for this increase;

CMJ height increased following a gluteal activation warm up but it is still unclear if increased

gluteal force production caused said increase. Therefore this window in the literature

provides a rationale for the use of EMG and MM to analyse the effects of a gluteal activation

warm up on GMAX and GMED recruitment and force output specifically, potentially

enabling a mechanical rationale for the cause and effect of any increase CMJ or force.

Minimal findings support the efficacy of low-level activation exercise at increasing the power

output of subsequent dynamic exercise (Crow et al. 2012) as such the effects of activation

exercise remain ambiguous, consequently the primary objective of this research is to establish

the effect of low-level therapeutic gluteal activation exercise on GMAX and GMED

recruitment and force output in the HHP.

2.4 Gluteal Activation Research

A number of studies (Ekstrom, Donatelli & Carp, 2007: DiStefano et al., 2009: Boren et al.,

2011) and literature reviews (Reiman, Bolgla & Loudon, 2012) have investigated the effect

of therapeutic GMAX and GMED exercise on motor unit recruitment, the research of


Ekstrom et al. (2007), DiStefano et al. (2009), Ayotte et al. (2007) and Bolgla (2005) and the

review of Reiman et al. (2012) highlighted the four exercises with the highest GMAX

activation MVIC% were compound exercises activating a large number of lower limb

muscles (Figure 1). Similarly these GMED exercises showed that the second and third

greatest activating exercises were also compound movements, however the highest GMED

activating exercise was the side bridge to neutral, a more isolated exercise exhibiting lateral

hip extension and thus qualifying for consideration of use within this research. Due to the

compound nature of the top ranked exercises evaluating the efficacy of the exercises based

solely on MVIC % may be ill-considered; as there is insufficient isolation of the GMAX and

GMED, which may potentially activate additional lower limb muscles therefore the

requirement for further review of activation literature is appropriate.

The following figure is adapted from the review of Riemann et al (2012)

Figure 1. Most effective GMAX and GMED recruitment exercises

0

10

20

30

40

50

60

70

80

Forward step up SL Deadlift SL Squat Wall Squat Side Bridge toNeutral

MV

IC %

Exercise

4 Most Effective GMAX & GMED EXERCISES (Reiman et al (2012)

GMAX

GMED


To this end Boren et al (2011) conducted cross comparisons research of the highest-ranking

gluteal musculature activation exercise from the work of DiStefano et al. (2009), Ayotte et al.

(2007) and Bolgla (2005), enabling improved testing protocol and cohort standardisation.

Standardising protocols and cohort offered a uniformity thus increasing validity, providing an

accurate comparison of targeted GMAX and GMED activation and their efficacy of

recruitment. Boren et al. (2011) found the side plank with hip abduction to be the most

effective GMED recruitment exercise with a 103% MVIC% and the Front plank with

unilateral hip extension as the most effective for GMAX recruitment at 106% MVIC

respectively, both are exercises with limited impact on the global musculature (…) and are

therefore appropriate for use within this research which endeavours to specifically target the

gluteal musculature. However when MVIC % exceeds 100% further scrutiny of the testing

protocol is required, Boren et al. (2011) offered two reasons for this; a lack of motivation and

an issue within the testing causing subjects the inability to exert maximal contraction. The

issue of incorrect electrode placement and ‘noise’ must also be examined; although it appears

the testing protocols appear in accordance with the other literature.

The side plank with hip abduction corresponds biomechanically with the function of the

GMED, GMED stabilises the pelvis and femur (Gottschalck et al., 1989), abducts the hip and

has elevated recruitment when combined with a minimal base of support (Boudreau et al.,

2009), and thus the relationship between anatomy, function and recruitment are apparent.

This relationship is also true of the actions of the GMAX within the front plank with

unilateral hip extension, GMAX is responsible for hip extension, abduction and femoral

external rotation (Souza & Powers, 2009), thus the biomechanics of these plank variation


correlates closely with the functions of the gluteal muscles during locomotion. Boren et al.

(2011) suggests that the increased MVIC % could be due to the high levels of trunk

musculature co-contraction resulting from the limited base of support, requiring the hip

musculature to contract to a greater extent to compensate for this increased demand.

The most appropriate GMAX and GMED exercises arising from this literature for the GMAX

and GMED activation warm up are; the side plank with hip abduction for the GMED

activation and the front plank with unilateral hip extension for the GMAX, these exercises

whilst being effective as GMAX and GMED activation exercises have the advantage of

having minimal impact upon the remaining lower limb musculature an important

consideration of this research, isolating the gluteal muscles provides a more accurate analysis

of the effect of gluteal activation upon recruitment and force output.

2.5 Musculoskeletal Modelling Research

MM is a growing field of research, particularly within medicine, much of this research

focuses on everyday activities such as sitting and standing exercises, activities performed at

slow velocities and as such differ from the high velocities of elite sporting actions, MM

provides a method of non-invasive measurement of muscular forces and as such is fitting for

this research as the evaluation of muscular forces within an elite population requires accurate

but non-invasive procedures. Considering this difference, the findings of the clinical MM

research has limited application within an elite performance population (Cleather, Goodwin

& Bull, 2013), however a small body of research has explored movements such as the squat

and push jerk (Cleather & Bull 2010). Considering the critical differences between clinical


and sports science research within MM the researcher believes further high velocity MM

research is required when estimating musculoskeletal forces, within elite population dynamic

exercises. A further review of MM literature can be found in Appendix 1.

2.6 Summary

The GMAX and GMED have been highlighted as muscles vital to athletic performance

(Kyrolainen et al., 2005: Meir et al., 2001: Deutsch, 1998: Reily, 1996: Rienzi et al., 2000:

Nagano et al., 2005: Newton & Kraemer, 1994: Mero & Komi, 1994), generating hip

extension force during locomotion (Murphy et al., 2003: Kyrolainen et al., 2005: Guskiewicz

et al., 1993: Lieberman, 2006: Blazevich, 2000) and maintaining a rigid and stable pelvis

during multi directional movement (Oliver & Keeley, 2010), GMAX and GMED

strengthening therefore demands substantial focus in the strive for improved performance.

One proposed method of increasing GMAX and GMED recruitment and thus strength gain

(Ayotte et al., 2007) is an activation warm up; providing feedforward motor programmes

(Tsao & Hodges, 2007), exciting the motor neurons and recruiting motor units (Aagaard et

al., 2002). Enabling decreased motor unit recruitment lag-time (Tsao & Hodges, 2008),

increased HTMU recruitment (Aagaard et al., 2007) in turn optimising the stimuli required

for strength gain (Ayotte et al., 2007). Therefore considering the issues emerging from this

reading the following research objectives will be investigated;

Identify the effect of the independent variable, a GMAX and GMED activation warm

up’s effect on the dependant variables GMAX and GMED recruitment and GMAX,

GMED and lower limb force output during the HHP.


Discover if GMAX and GMED recruitment and GMAX, GMED and lower limb force

output impacted total ground reaction force (GRF).

Investigate the effect of the gluteal activation warm up on GMAX, GMED and GRF

RFD.

Formulate recommendations for the application of this GMAX and GMED activation

warm up.

Chapter Three

Method

3.1 Introduction

The primary aim of this study was to identify the effect of the independent variable a GMAX

and GMED activation warm up’s effect, on the dependant variables GMAX and GMED

recruitment and force output during the Hang High Pull (HHP) within elite rugby union

players, the cohort comprised of Premiership and International players from London Irish

Rugby Club. The secondary objectives were to discover if GMAX and GMED recruitment

and force output impacted total ground reaction force (GRF) and the effect of the gluteal

activation warm up on GMAX, GMED and GRF rate of force development (RFD). GMAX

and GMED recruitment data was collected using EMG (Biopac MP150, Delsys monitor iv

wireless transmission and data logging system, Worcestershire, UK), with force output data

collected via a combination of kinematic data using Vicon motion capture (Vicon MX

system, Vicon Motion Systems Ltd, Oxford, UK) and kinetic force plate data (Kistler Type

9286AA, Kistler Instrumente AG, Winterthur, Switzerland), then amalgamated to within

FreeBody Beta MM (FBB) to run optimizations. The GMAX and GMED have been shown

to influence athletic KPI (Murphy et al. 2003), highlighting efficient methods of increasing

GMAX and GMED contractile force, as material to increasing athletic performance. Within


this research methods chapter the research strategy, data collection methods and data analysis

will be discussed.

3.2 Research Strategy

Table 1

Cohort Anthropometrics Data Table

EMG FBB Pilot

Subjects 17 10 4

Age (years) 26.0 (±3.8) 25.0 (±3.4) 26.2 (±5.4)

Height (cm) 181 (±25.1) 185.5 (±6.6) 186.5 (±6.4)

Body Mass (kg) 103.3 (±10.1) 102.8 (±9.6) 104.3 (±11.9)

Load (kg) 86.9 (±9.0) 88.2(±9.6) 87.5 (±12.6)

Prior to the primary crossover trial an EMG pilot study was undertaken to establish if any

activation carryover was elicited by the control warm up and HHP that could affect the

dependent variables prior to the activation warm up and HHP, thus invalidating the effect of

the activation warm up. The pilot consisted of 4 subjects with mean age 26.2 (±5.4), height

186.5 (±6.4) and body mass 104.3 (±11.9) (Table 1), participating in identical testing

procedures as the primary study (as described below), however only EMG data was analysed

to establish any increase in MU recruitment, pilot study EMG data can be found in Appendix

2. If any activation carryover was present a subsequent whole cohort pre-test would have

been undertaken to gather data on the level of EMG MU recruitment and FBB force output,

enabling the results of the primary study to be individually adjusted to account for increases


in dependant variables within subjects. Completion of the pilot tests reduced any inaccuracy

within study design and thus increased the research’s validity.

Figure 2. Research structure info graphic

A randomised crossover design was chosen to reduce bias related to confounding variables

(Mills, Kelly & Guyatt, 1984), allowing each subject to act as their own control, enabling

advantageous between and within group comparison (Elbourne et al., 2002: MacLure. 1991).

The minimal washout period of the independent variable removed the potential issue of

carryover, increasing statistical efficiency enabling a reduced cohort size, thus highlighting

the use of a crossover design as valid (Cleophas, 1990: Mills et al., 2009: Hopkins, 2000).

Conducting both control and intervention testing within one testing session removed issues of

inexact recreation of EMG electrode and Vicon marker placement and also reduced the time

demands on the cohort (minimal time demands were a significant consideration as it was

Pilot Test

Carryover Present

Full Cohort Pilot Study

No Carryover present

Main Control and Intervention Study


imperative the time and energy expenditure of the elite population was at a minimum during

the competitive season). Inexact EMG electrode placement can alter readings as the distance

from signal origin to detection site has been shown to alter EMG trace and thus negate

accurate EMG comparison (Mesin, Merletti & Rainoldi, 2009: Konrad, 2005). Similarly

EMG signal can be influenced by external factors such as depth of subcutaneous adipose

tissue and hydration levels; these factors affect EMG magnitude with an inverse relationship

between subcutaneous tissue depth and EMG magnitude (Kuiken, Lowery & Stoyokov,

2003: Nordander et al., 2003), therefore by minimising the time frame between data

collections negated these potential issues, enabling accurate peak EMG collection and MVIC

normalisation.

On arrival subjects were prepared for EMG electrode (Appendix 3) (Biopac MP150, Delsys

monitor iv wireless transmission and data logging system) and Vicon reflective marker

placement (Appendix 4), subjects wore tight fitting garments, with the skin beneath the EMG

electrode site prepared by shaving and cleaning the area with alcohol wipes, enabling better

skin contact resulting in increased amplitude characteristics, reduced electrical interference,

noise and signal disturbance (Hermens, Freriks, Disselhorst-Klug & Rau, 2000). Vicon

markers were attached using double sided adhesive tape and adhesive spray; the placements

on the right lower limb are shown in Appendix 4, the markers and electrodes remained in situ

until the completion of the final test thus preventing any previously mentioned inaccuracy

arising from repeated EMG electrode (Mesin et al., 2009) and Vicon Marker placement.

EMG electrodes were placed 2cm apart half way between and in the line of the second sacral

vertebrae to the greater trochanter of the femur in parallel with the muscle fibres of the

GMAX and half way between and in line from the crista iliac and the greater trochanter for

the GMED (Seniam.0rg) (Appendix 3).


Following electrode and marker placement individual subjects began the testing regime

(shown in Figure 2) initially undertaking the control warm up shown in Figure 3, subjects

performed the control warm up followed by a 1-minute rest period, post rest period subjects

performed 3 HHP at 80% 1RM, on a force plate within the Vicon testing area collecting

kinematic and kinetic data for use in the FBB, whilst synchronised GMAX and GMED EMG

was collected. HHP loads were tailored to the individual subject via extrapolation of

individual training loads, increasing the consistency of individual intensity. 80% 1RM was

selected as 70-80% 1RM to maximise force production and GMAX and GMED involvement,

as 70-80% 1RM is stated as the optimal range for lower body power production, activation

and hip extensor moment (Cormie et al., 2007: Kawamori et al., 2005: Haff et al., 1997:

Hakkinen et al., 1985: Beardsley & Contreras, 2014).

Exercise Reps Sets

Stationary Bike 3 minutes 1

Inch Worm 8

2

Bodyweight Squat 8

Leg Swing 5 E/L

Lunge 8

Press up 8

Figure 3. Control trial non-specific warm up.


Following the HHP subjects underwent 20 minutes of down time allowing any post activation

potentiating (PAP) from the activation warm up or HHP to subside, conjecture exists over the

evidence for PAP (Behm, Button, Barbour, Butt & Young, 2004), despite said debate PAP is

believed to dissipate from 1 to 16 minutes and thus sufficient time was prescribed to nullify

any PAP (Corrie & Hardin, 1964: Gullich & Schmidtbleicher, 1996: Trimble & Harp, 1998:

Kitago et al., 2004: Kilduf et al., 2007: Hodgson, Docherty & Robbins, 2005). Succeeding

the 20 minute down period, subjects completed the GMAX and GMED activation warm up

shown in Figure 4, following 1-minute rest subjects again performed 3 HHP at 80% 1RM on

a force plate within the Vicon MX motion capture area with synchronised EMG data

collection.

Exercise Reps Sets

Stationary Bike 3 minutes 1

Inch Worm 6 1

Bodyweight Squat 6

Leg Swing 6 E/L

Prone Plank with Hip Extension (Right Side) 6 (2 sec hold at top)

Prone Plank with Hip Extension (Left Side) 6 (2 sec hold at top)

Side Plank with Hip Extension (Dominant leg down) 6 (2 sec hold at top)

Side Plank with Hip Extension (Non-dominant leg down) 6 (2 sec hold at top)

Single Leg Squat 3 E/L


Figure 4. Intervention trial GMAX and GMED specific warm up.

Following the HHP subjects again underwent 20 minutes down time enabling any PAP

effects of the activation warm up or HHP to subside prior to the completion of Maximum

Voluntary Isometric Contraction (MVIC) testing (Corrie & Hardin, 1964: Gullich &

Schmidtbleicher, 1996: Trimble & Harp, 1998: Kitago et al., 2004: Kilduf et al., 2007:

Hodgson, Docherty & Robbins, 2005). MVIC testing enabled normalization and accurate

analysis of GMAX and GMED EMG data, elevating the result validity (Kellis &

Baltzopoulos, 1996). MVIC testing utilised the prone with 90 degree knee flexion exercise to

measure GMAX hip extension MVIC and side-lying with the test leg upper most hip

abduction to measure GMED MVIC, following the testing guidelines of Hislop and

Montgomery (2007) and was completed post HHP reducing the impact of issues of accurate

recreation of EMG test conditions such as; electrode placement, hydration and body

composition (DeLuca, 1997: Rainoldi, Melchiorri, Caruso, 2004: Mesin et al., 2009: Kuiken

et al , 2003: Nordander et al., 2003) and potential GMAX and GMED activation occurring if

MVIC testing had preceded the control warm up procedure.

2.3 Data collection

The subjects were recruited from London Irish Rugby club for this study; their mean age,

height and body mass are displayed in Table 1. Subjects were divided into 3 testing dates due

to the cohort’s size and the time constraints of the test and subjects. Ethical approval was

obtained from St Mary’s University Twickenham School of Sport, Human and Applied

Sciences (Appendix 5).


Data collection of GMAX and GMED occurred in synchronicity; recruitment was measured

via Electromyography (EMG), kinematic analysis data collected via Vicon motion capture

(Vicon MX system, Vicon Motion Systems Ltd, Oxford, UK) in conjunction with kinetic

GRF data via the FP (Kistler Type 9286AA, Kistler Instrumente AG, Winterthur,

Switzerland). The FBB data was utilised to identify peak hip extension angle, GMAX,

GMED, bilateral hamstring, quadriceps force and GRF, a more in depth description of the

data collection process is available in Appendix 6. Kinematic or position data of the markers

and kinetic GRF data was filtered from 1000Hz to 200Hz in Microsoft Excel and inputted to

FBB in line with the work of Cleather and Bull (2010).

Peak EMG amplitude data was collected and rectified and smoothed to an EPOCH of 50ms

via the average over samples algorithm; this time window is appropriate for high velocity

movements (Muthuraman, Govindan, Deuschl, Heute & Raethjen, 2008: Mustard & Lee,

1987) such as the HHP, as a reduced time window reduces the risk of phase shift in

contractions and thus increase accuracy of data capture (Konrad, 2005: Muthuraman et al.,

2008). Once smoothed the peak EMG data was normalised against MVIC, normalisation

provides a standard value against which subjects and muscle groups can be compared,

enabling accurate comparison of subject’s GMAX and GMED pre and post intervention

(Knutson, Soderberg, Ballantyne & Clarke, 1994). However one issue surrounding MVIC is

subjects’ inconsistency in achieving maximum contraction, despite this issue MVIC is a

robust method of normalising EMG data and thus an appropriate method within this research

(Westing, Seger & Thorstensson, 1990). Further discussion of MVIC can be found in

Appendix 3.


MM analysis offers a method of calculating individual muscle force and data, via

combination of kinetic and kinematic data collected from Vicon and force plate data. The

FBB model of Cleather and Bull (2010) is a comprehensive model building on the model of

Horsman et al., (2007), utilising muscle lines and linked rigid segments model of the foot,

shank, thigh and pelvis to calculate the inter-segmental moments during the HHP (Cleather &

Bull, 2010). FBB facilitates accurate estimation of lower limb muscle forces and total GRF

allowing the researcher to examine of the effect of the gluteal activation warm up on MU

recruitment and force output. Further discussion of MM is discussed in Appendix 1.

The utilisation of multiple methods of data collection enabled the triangulation of two data

sets, facilitating a global picture of the impact of the independent variables on the research

objectives. The use of simultaneous EMG and FBB data collection enabled comparison of

data; EMG data providing an insight into the level of motor recruitment, which reflects

contractile force (Konrad, 2005) in conjunction with FBB, which provided a mathematical

estimation of contraction force, thus offering two differing modalities of measuring the same

independent variable.

Once collected the data was statistically analysed within SPSS using separate dependant t-

tests to compare the means of HHP GMAX and GMED control and intervention peak EMG

amplitudes and FBB peak hip extension angle, GMAX, GMED, bilateral hamstring,

quadriceps peak force and GRF outputs. Dependant t-test’s enabled intra subject comparison,

appropriate for this research due to the crossover design, offering increased interpretation

accuracy of the independent variable’s effect by removing inter cohort base level variance


(Mills et al., 2009), with linear regressions employed to investigate the relationships between

GMAX, GMED and GRF rate of force development (RFD).

Chapter Four

Results

The EMG pilot results showed no significant effect from the control warm up, although a

decrease was visible within the second control HHP’s (Table 2) and thus was no requirement

to adjust the primary research results.

Table 2

EMG Pilot Data in μν as a Percentage of MVIC

Control 1 Control 2

GMAX 29.531 (±40.67) 0.745 (±0.22)

GMED

17.473 (±28.16)

1.131 (±0.20)

Mean (±SD)

Statistically Significant (p < 0.05) *

As denoted in the method, peak EMG amplitude results were normalised against MVIC to

provide a percentage and standardise recruitment level within the cohort, whilst MU

Recruitment rate was quantified by microvolts over milliseconds μv/ms.


The results show a statistically significant (p < 0.048) decrease in GMED peak recruitment

from control to activation warm up, Table 3 illustrates the variation in subject peak GMED

recruitment from control to intervention trial. With GMED recruitment in the control group

being 78.5 % (±0.39) compared to 66.6 % (±0.27) in the intervention trial. Contrastingly

FBB results show a strong trend of increasing force within the intervention trial (Table 3),

both trends are graphically illustrated in Appendix 7.

Table 3

Group Mean Peak Recruitment as MVIC Percentage and Multiples of Body Mass

Control Intervention

GMAX peak recruitment 85.2 % (±141.3) 82.4 % (±41)

GMED peak recruitment 75.9 % (±38.3) * 66.2 % (±27.8)

Adjusted cohort GMAX peak

recruitment

Adjusted cohort GMED peak

recruitment

FBB GRF

FBB Bi Ham

FBB GMAX

FBB GMED

FBB quadriceps

97% (±35.8)

84.7% (±37.3)*

1.588 x BM (±0.37)

2.243 x BM (±0.87)

1.123 x BM (±0.62)

3.151 x BM (±1.53)

1.729 x BM (±0.91)

96.4% (±36%)

66.5% (±25.6)

1.74 x BM (±0.34)

2.654 x BM (±1.08)

1.355 x BM (±1.08)

3.723 x BM (±1.78)

2.632 x BM (±2.24)

Mean (±SD)



The GMED had a greater significance (p < 0.006) when the EMG cohort was reduced to

identically match the FBB cohort, thus offering increased comparison accuracy. Table 3

shows both full and adjusted EMG cohort data.

Table 4

FBB GMED, GMAX and GRF RFD as xBW/ms and EMGGMED and GMAX RFD as

MVIC% μv/ms


FBB GMED RFD 0.219 xBW/ms (±0.33) 0.189 xBW/ms (±0.18)

FBB GMAX RFD 0.066 xBW/ms (±0.04) 0.267 xBW/ms (±0.56)

FBB GRF RFD 0.098 xBW/ms (±0.11) 0.201 xBW/ms (±0.21)

FBB GMAX & GRF RFD

regression p values

0.272 0.349

FBB GMED & GRF RFD

regression p values

0.059 0.267

Mean (±SD)


Once being filtered and run through the FBB RFD the results were statistically analysed via

dependent t-tests and linear regressions in SPSS, no statistically significant results (p ≤ 0.05)

were found via t-test (Table 4), however GMED RFD and GRF RFD showed a near


significant result (p < 0.059) using linear regression (Table 4). No statistically significant

results (p ≤ 0.05) or trends were found in the EMG data (Figure 5).

Figure 5. Peak motor unit recruitment rate in control and intervention trials

Chapter Five

Discussion

5.1 Introduction

The primary aim of this research was to further the understanding of the effects of gluteal

activation warm ups on the subsequent gluteal MU recruitment and force output, the specific

research objectives were to:

0

0.000005

0.00001

0.000015

0.00002

0.000025

0.00003

0.000035

0.00004

0.000045

0.00005

GMAX GMED

μv/

ms

Peak MU Recruitment Rate

control

Intervention


Identify the effect of the independent variable, a GMAX and GMED activation warm

up’s effect on the dependant variables GMAX and GMED recruitment and GMAX,

GMED and lower limb force output during the HHP.

Discover if GMAX and GMED recruitment and GMAX, GMED and lower limb force

output impacted total ground reaction force (GRF).

Investigate the effect of the gluteal activation warm up on GMAX, GMED and GRF

RFD.

Formulate recommendations for the application of this GMAX and GMED activation

warm up.

Within this chapter a discussion of the research findings in relation to the research

hypothesis, overall aim and specific research objectives will occur, resulting in practical

applications for the use of gluteal activation warm ups, a discussion of the possible rationale

behind the statistically significant decreased (p < 0.048) EMG peak GMED MU following

the activation warm. Following this discussion there will be a similar critique of why despite

this statistically significant (p < 0.048) decrease in MU recruitment during the intervention

HHP the FBB results show a non-significant but clear trend of increased force output in;

bilateral hamstring, GMAX, GMED, quadriceps and GRF following the intervention warm

up (Table 3 & Appendix 7). The next research objective regards the exploration of the

relationship between GRF and GMAX and GMED recruitment and force output, the FBB

results (Table 3 & Appendix 7) highlight a non-significant (p > 0.05) trend of increased GRF

following the gluteal activation warm up, this trend and the potential rationale will be

investigated along with the potential causes of increased force and GRF within the FBB data

(Table 3). Following the GRF debate a discussion of the differing trends within EMG and

FBB RFD (Figure 5 & Table 4) will occur, resulting in practical recommendation for the


application of this gluteal activation warm up will be given in light these findings and

previous research.

5.2 Research Objectives

As identified within the discussion introduction the primary research objective was to identify

the effect of the gluteal activation warm up on GMAX and GMED MU recruitment and force

output, lower limb force and GRF within the HHP. The peak GMED EMG amplitude results

(Table 3) of this research show a statistically significant (p < 0.048) decrease during the HHP

following the gluteal activation warm up, with a greater decrease in GMED MU recruitment

of p < 0.006 (Table 3) found when the EMG cohort was adjusted to identically match the 10

subjects employed within the FBB research, further supporting the relationship between

EMG and FBB results. These results initially suggest this gluteal activation warm up is

detrimental to MU recruitment and muscular force, a result contrary to the theory that

activation exercise increases the muscular innervation pattern via plastic changes in nervous

system (Tsao & Hodges, 2007), enabling greater firing rate and frequency (Van Cutsem,

Duchateau & Hainaut, 1998). However this may be simplistic as MU firing rates fluctuate

within different contraction types and do not always follow a linear correlation of increased

MU recruitment resulting in increased muscular force; as within high force isometric

contraction MU recruitment can reduce during high levels of force generation (Milner-Brown

et al., 1973).

Previous research investigating the effect of therapeutic activation exercise has resulted in

mixed conclusions; however Tsao and Hodges (2008) discovered that a specific TA

activation exercises facilitated faster activation of the TA within subsequent upper limb


exercise after only one session and over a 4 week training programme, similarly Crow et al.

(2012) identified an immediate 4.2% increase in CMJ height post gluteal activation warm up.

However despite the 4.2% increase in CMJ height the findings of Crow et al. (2012) did not

identify any specific increase in MU recruitment or force output of the lower limb muscles as

no data regarding increased muscle force and GRF was available to quantify the mechanism

of CMJ height increase, thus providing no rationale for CMJ height increase. The increase

was solely described in terms of a cause and effect relationship, that the activation warm up

resulted in increased CMJ height with no insight into the contributing factors of this increase,

thus this research attempted to fill said void. Despite the lack of explanation of their findings,

Crow et al. (2012) did find a statistically significant increase in CMJ height, which could be

extrapolated to have occurred due to increased GRF resulting from increased muscle force

and thus potentially facilitating increased CMJ height (Hammer, Seth & Delp, 2010), results

equivalent to those within the FBB data (Table 3) despite the statistically significant (p <

0.048) decrease in GMED MU recruitment following the gluteal activation warm up (Table

3).

These opposing trends in EMG and FBB data (Table 3) whilst initially puzzling provide an

interesting discussion surrounding the differing parameters measured within this research and

their relationship during dynamic actions. Peak EMG amplitude describes MU activity

during muscular contractions, in particular peak EMG amplitude describes the number of MU

recruited, with the number of MU being cited as modulating muscle force, with the firing rate

of recruited MU regulated by the higher centers of the central nervous system (CNS)

controlling the extent of force produced by each motor unit (DeLuca & Erim, 1994) and as

such makes quantifying these opposing findings challenging. However the behavior of MU

during high force isometric contractions may provide a rationale for these opposing findings,


Milner-Brown et al. (1973) states that at high levels of voluntary force generation during

isometric contractions a sharp decline occurs in the number of MU’s recruited whilst high

levels of force are generated, postulating that HTMU can generate increasing force despite

reduced MU recruitment and thus increased MU recruitment has reduced attribution to the

high generation of muscular force during high threshold isometric contractions. The ability

of the HTMU to generate increased force whilst the lower threshold MU (LTMU) have

reduced recruitment resulting in reduced total MU recruitment is hypothesized to be due to

the increased firing rate of the HTMU (Kernell, 1966: Granit et al., 1957: Kernell, 1965), a

theory appearing contrary to Henenman’s size principle, however this modulation of MU

recruitment is unique to isometric contractions with the strongest evidence for the

maintenance or increase in muscular force whilst MU recruitment decreases occurring in

contractions above 50% MVC in small muscles where MU recruitment has reached

completion (Kukulka & Clamann, 1981: De Luca et al., 1982) and 70-80% MVC in large

muscles (Kukulka & Clamann, 1981: De Luca et al., 1982), highlighting that high force

isometric contractions can be maintained despite decreasing firing rates with no additional

MU recruitment (De Luca, Foley & Erim, 1996). Therefore considering the rationale

provided by the previously discussed research and the results of this research, it could be

speculated that the activation warm up (Figure 4) facilitated increased GMED contraction

intensity to above 50% (Kukulka & Clamann, 1981: De Luca et al., 1982), thus breaching the

threshold required to trigger the cascade of plastic changes within the CNS resulting in

altered MU recruitment patterns and a statistically significantly (p < 0.048) decrease in

GMED MU recruitment despite increased force output.

This pattern of MU recruitment is unique to isometric contractions and despite the dynamic

nature of the HHP it is believed that the primary function of the GMED during the HHP is to


isometrically stabilize the pelvis. Considering the function of the GMED during the HHP in

conjunction with the research in isometric contractions and the trends discovered within this

research, it is speculated that the activation warm up elicited plastic changes within the motor

neuron pool, altering the firing patterns of the afferents from the central centers, thus

streamlining the neural circuitry required to preferentially activated the HTMU (De Luca &

Erim, 1994) enhancing contraction intensity to above 50% (Kukulka & Clamann, 1981: De

Luca et al., 1982), thus triggering the cascade of events in the CNS reduced the number of

LTMU recruited, resulting in the reduce requirement for total GMED MU recruitment. These

findings and extrapolations are specifically relevant to this research as the primary function

of the GMED during the HHP is isometric contraction to stabilize the pelvis, similar to its

function during the CMJ (Nagano et al., 2005), creating a stable platform for the GMAX and

lower limb muscles to produce force during concentric and eccentric contractions. Thus the

GMED EMG results (Table 3) suggest the gluteal activation warm up elicited plastic changes

in the CNS, facilitating preferential activation of the HTMU reducing the requirement of the

LTMU recruitment enabling greater force generation despite the reduction in total MU

recruitment (Table 3 & Appendix 7). This theory is further supported by virtually unchanged

GMAX MU recruitment of the matched FBB and EMG cohort (Table 3), as the function of

the GMAX is to act dynamically during the HHP to create hip extension and as such GMAX

function differs to the GMED.

The mechanisms believed to increase muscle force output in dynamic contractions of the

GMAX, bilateral hamstring and quadriceps is believed to center around; increasing coding

rate via increased afferent excitability, decreased MU innervation lag-time and increasing

HTMU recruitment. These mechanisms appear to increase the force output of the GMAX,

bilateral hamstring and Quadriceps during dynamic contractions resulting in increased GRF


(Hammer et al., 2010), increasing coding rate increases firing frequency, potentially

increasing the efficiency of the innervating axon, which carries the impulse to the MU

(Person & Kudina, 1971), enabling the greater firing rate of MU and thus increasing force.

Increasing muscle activation also increases the recruitment of HTMU (Tsao & Hodges, 2008)

via increasing the excitability of the afferent nerves potentially priming the innervating axon

of the HTMU’s, enabling increased HTMU recruitment efficiency during periods requiring

high levels of muscular force (Van Cutsem et al., 1998). These physiological processes

appear to be potential mechanisms of increasing force output and GRF elicited via the gluteal

activation warm up (Table 3 & Appendix 7) and as such provide a mandate for the utilization

of this gluteal activation warm up to increase performance KPI.

The FBB results highlight a trend of increased GRF, bilateral hamstring, GMAX, GMED and

quadriceps force (Table 3) following the activation warm up (Figure 4), despite statistical

analysis not find any statistically significant results (p ≤ 0.05) a strong trend is visible in

Appendix 7, suggesting that the gluteal activation warm up (Figure 4) elicited a positive and

relevant effect on force output. This trend mirrors the findings of Tsao & Hodges (2008) due

to the FBB results showing increased GRF following one activation intervention similar to

the extrapolation of the mechanisms responsible for the 4.2% increase in CMJ height found

by Crow et al. (2012). CMJ height is heavily influenced by GRF (Hori, Newton, Nosaka &

McGuigan 2006) as GRF is the result of lower limb muscular force (Hammer et al., 2010), it

could be extrapolated that despite no empirical evidence being provided for the increase in

CMJ height exhibited within the work of Crow et al. (2012), the increase may be the result of

similar physiological mechanisms as discussed within this research resulting in increased

lower limb force (Table 3) manifested as increased GRF (Hammer et al. 2010).


The statistically significant reduction (p < 0.048) in GMED MU recruitment resulting from

the activation warm up in Figure 4 could initially be view as having a detrimental effect on

both performance (Murphy et al., 2003) and injury risk (Presswood et al., 2008), however

when analysed in conjunction with the increased force outputs found in the FBB results this

conclusion appears limited, as the plastic changes in the CNS resulting in preferential HTMU

recruitment elicited by the gluteal activation warm up suggest that despite decreased GMED

MU recruitment the increased force facilitated by the activation warm up (Figure 4) enable

increased performance as a result of increased GRF and increased GMED and GMAX force

output which may potentially reduce ACL injury risk by reducing Trendelenburg sign (Cutter

& KerVorkain, 1999: Earle, 2005) and in turn knee valgus angle (Powers, 2010 Reiman et al.,

2009) and hip and tibial internal rotation moments within the transverse plane (Krosshaug et

al., 2007: Besier et al., 2001: Besier et al., 2001: McLean et al., 2005: Earle 2005). A similar

improvement in performance appears to be visible as a result of the gluteal activation warm

up due to the increased lower limb force and resultant increase in GRF and thus on sporting

KPI (Weyand et al., 2000: Murphy et al., 2003: Blazevich, 2000) discussed within the

literature review.

Further evidence for the improvement in athletic performance resulting from the activation

warm up in Figure 4 is increased pelvic stability during stance, resulting from elevated

GMED strength (Gottschalck, 1989: Lieberman et al., 2006) consequently reducing

Trendelenburg sign and in turn force leakage (Oliver & Keeley, 2010: Neptune et al., 1999

Cutteret al., 1999: Earle, 2005), resulting in increased GRF (Gottschalck, 1989: Lieberman et

al., 2006: Cutteret & Kervorkain, 1999: Earle, 2005) which has been previously cited as


increasing sporting KPI (Weyand et al., 2000: Murphy et al., 2003). As well as reducing

force leakage a stable pelvis enhances the ability of the GMAX and other hip extensors to

create proximal to distal force, resulting in increased GRF (Lieberman et al., 2006:

Blazevich, 2000: Weyand et al., 2000) and thus sporting KPI (Weyand et al., 2000: Murphy

et al., 2003).

Considering that FBB GMED and GMAX force output showed a non-significant trend of

increase following the gluteal activation warm up, despite the statistically significant (p <

0.048) decrease in EMG GMED peak MU recruitment (Table 3) which appears to arise from

the plastic changes in the CNS leading to preferential recruitment of the HTMU, it appears

the gluteal activation warm up (Figure 4) is an effective method of increasing lower limb

force and GRF during the HHP. Therefore the use of this activation warm up may increase

pelvic stability resulting in enhanced athletic performance (Murphy et al., 2003) and reduced

injury risk (Presswood et al., 2008).

The second research objective; exploring the relationship between GMAX and GMED

recruitment and force output and their relationship with GRF will now be discussed, the

relationship of GRF and GMAX and GMED recruitment and force output is relevant due to

the importance of GRF on athletic performance KPI (Murphy et al., 2003: Blazevich, 2000:

Weyand et al., 2000). Similarly to the previous discussion regarding on GMAX and GMED

recruitment and force output, the two methods of data collection show differing results and as

such create a challenging critique. Peak GRF was calculated using FBB and did not yield a

significant (p ≤ 0.05) increase; however despite this, a strong trend of increase is visible in

(Appendix 7) highlighting the positive impact of the gluteal activation protocol (Figure 4),


the previously discussed mechanisms of increased contractile force occurring in concert with

reduced MU recruitment (Milner-Brown et al., 1973) is also relevant to this discussion and as

despite the statistically significant decrease (p < 0.048) in GMED recruitment following the

activation warm up, the gluteal activation warm up in Figure 4 appears to have a positive

effect on GRF. This positive increase in GRF resulting from the activation warm up appears

to be the result of the trend of increased lower limb muscle force, the relationship between

GRF and lower limb force, which show increase following the activation warm up (Table 3),

has been established by Hammer et al. (2010) who states the accumulation of increased lower

limb forces results in increased GRF, thus there appears to be a strong relationship between

GMAX and GMED force and the resultant GRF. Therefore considering the positive impact

of the gluteal activation warm up on GRF, the relationship between increased lower limb

forces and GRF (Hammer et al. 2010) in conjunction with the 4.2% increase in CMJ height

found by Crow et al. (2012) following the activation warm up found in Appendix 8, provides

a rationale for the efficacy of activation warm up protocols at increasing athletic performance

via increased GRF (Weyand et al., 2000: Blavevich, 2000: Murphy et al., 2003).

The penultimate research objective was to investigate the effect of the gluteal activation

warm up on GMAX, GMED and GRF RFD; RFD describes the rate at which force is

produced (Δ force/Δ time), a quality cited as vital in explosive sports performance (Aagaard

et al., 2002: Hakkinen & Komi, 1986: Schmidtbleicher & Buehrle, 1987: Sleivert & Wenger,

1994: Thorstensson, Karlsson, Viitasalo, Luthanen & Komi, 1976). Within this research

RFD is defined as Δ force/Δ time from the onset of the contraction at which the peak

contraction occurred within the FBB and EMG data respectively. RFD data for both EMG

and FBB is shown in Figure 5 and Table 3 respectively, no significant change in means was


identified via dependent t-test across measurements in either FBB or EMG respectively,

however a linear regression highlighted FBB GMED RFD as having a near significant (p <

0.059) relationship with FBB GRF RFD (Table 3), as such any resultant increase in FBB

GMED RFD arising from the gluteal activation warm up may have a positive impact on FBB

GRF RFD. EMG RFD highlighted a trend of decreased RFD following the activation warm

up a trend correlating with the Peak EMG MU recruitment findings, which also show a

decreased peak following the activation warm up, both of these sets of results appearing to

follow a logical trend due to RFD being influenced by peak force, as increased absolute force

increases the ability to express force at a greater rate (Andersen & Aagaard, 2006). Therefore

both peak EMG MU recruitment and MU rate of recruitment show a decrease as a result of

the gluteal activation warm up (Figure 5) with only a limited trend being apparent within the

FBB data despite the near significant (p < 0.059) relationship between FBB GMAX RFD and

GRF RFD (Table 3), illustrating that the implementation of this gluteal activation warm up

(Figure 4) has no significant benefit on RFD within both FBB and EMG data collection

methods respectively, however due to the near significant (p < 0.059) relationship between

FBB GMED RFD and FBB GRF RFD and the statistically significant decrease (p < 0.048) in

GMED and increased FBB force output due to the isometric contraction of the GMED, the

implementation of the warm up in Figure 4 may warrant further investigation, as the

potentially greater GMED contraction of >50% (Kukulka & Clamann, 1981: De Luca et al.,

1982) eliciting the previously discussed plastic changes in the CNS and MU recruitment

could potentially facilitate a strong relationship between GMED RFD and GRF RFD, with

greater GMED isometric force output influencing increased GRF via the previously discussed

increases in pelvic stability. Despite maximum force output being important to sporting

performance, RFD is of greater importance (Aagaard et al., 2002) as high velocity

movements such as sprint ground contact times have a limited duration of 50-250ms


compared to ≥ 300ms for time to maximum force (Thorstensson et al., 1976), therefore the

minimal time window may be insufficient to exert maximal force and as such increased RFD

is important in facilitating a greater proportion of maximum force to be exerted during the

early phase of muscle contraction (Aagaard et al., 2002). Due to the importance of RFD on

sporting performance the implementation of the gluteal activation warm up show in Figure 4

may be advantageous to performance as, despite no significant improvements in RFD being

established the strong trend of increased lower limb force within the FBB results may

increase the total amount of force available for expression as GRF. Therefore despite this

gluteal activation warm up having a no statistically significant (p ≤ 0.05) effect on RFD, the

relationship between total force and RFD and the importance of RFD on sporting

performance (Aagaard et al., 2002) means further investigation of gluteal activation warm

ups on RFD appears warranted.

5.3 Recommendations and Practical application

The conclusion of the primary research objective is that the gluteal activation protocol

employed within this research (Figure 4) resulted in a significant reduction in GMED MU

recruitment, evidenced by a p < 0.048 decrease in GMED MU recruitment (Table 3),

however the strong trend of increase in the FBB muscular forces data highlights an

interesting occurrence unique to isometric contractions, thus speculation suggests the

implementation of this gluteal activation warm up appears beneficial in increasing lower limb

muscle forces and GRF. The secondary research objective investigating the effect GMAX

and GMED had on GRF discovered that despite the statistically significant reduction (p ≤

0.05) in GMED MU recruitment FBB GRF showed a non-significant increase, further

reinforced by the relationship between FBB GRF, GMAX and GMED force output showing


increase. Therefore considering these findings, the practical implementation of this protocol

could be prior to athletic performance; this performance could be gym based prior to a HHP

or similar compound exercises resulting in increased muscle strength and RFD over a training

cycle (Beachle & Earle, 2008). Further to this application, this protocol could be employed

prior to competitive performance similar to the practical application of Crow et al. (2012),

increasing GRF and thus potentially facilitating increased sporting KPI and sports

performance (Murphy et al., 2003: Blazevich, 2000: Weyand et al., 2000).

When evaluating this research it is imperative to understand its limitations, in particular those

concerning data collection, the use of both EMG and FBB enabled triangulation of results

and thus greater analysis of the gluteal activation warm up, however by limiting EMG

analysis to GMED and GMAX rather than aligning EMG muscle analysis with FBB

comparison was limited. EMG analysis was limited to the GMAX and GMED due to the

time constraints of the population, as one requirement of employing this elite population was

limiting the energy and time cost to a minimum during the competitive season, also the

potential for the increased EMG paraphernalia creating ghost markers within the kinematic

data was considered.

5.4 Contributions to knowledge

This research may have contributed to the knowledge of the use of therapeutic gluteal

activation as a method of increasing athletic performance in elite athletes, showing that an

extensive gluteal activation warm up (Figure 4) could enhance lower limb force output and

GRF despite a statistically significant decrease in GMED MU recruitment (p < 0.048); a


scenario believed to be due to plastic changes in the CNS enhancing preferential HTMU

recruitment and reducing the requirement for increased total MU recruitment during high

force isometric contractions (Milner-Brown et al., 1973). Further to the contribution of

knowledge to the field of gluteal activation this research broadens the scope of MM research,

due to the intervention based analysis of high velocity weightlifting movements, other

dynamic MM research exists, Cleather and Bull (2010) utilised the push jerk to investigate

inter-segmental moments of the foot, calf, thigh and pelvis, with Pandy and Zajac (1991) and

Anderson and Pandy (1999) investigating the CMJ and squat jump respectively, however this

was not interventionist research and therefore, it is the researcher’s belief that a minor

contribution to MM field has been made.

5.5 Self Reflection

On reflection the researcher incurred some problems during data collection; issues

surrounding data storage leading to differing FBB and EMG cohort numbers (Table 1),

resulting in the FBB data carrying reduced statistical power (Hopkins, 2009: Knudson, 2011),

however when the EMG cohort was limited to the identical 10 subjects utilised in the FBB

cohort the results showed a comparative if not a amplified effect. Therefore considering this

the researcher’s advice for future students would be to gain an increased proficiency using the

kinematic and kinetic data collection methods required for FBB to negate any potential issue,

building on the testing procedure shown in Appendix 6.


Despite this issue, the primary research objective found a statistically significant (p < 0.048)

decrease in GMED MU recruitment (Table 3) following this gluteal activation warm up, an

effect believed to be due to the previously discussed plastic changes in the CNS during

isometric contractions, elicited by the gluteal activation warm up, these changes contributed

to a strong trend of increase in GMAX, GMED, bilateral hamstring, quadriceps force and

GRF. Thus in conclusion the implementation of the activation warm in Figure 4 could

improve the force generation of the lower limb musculature, facilitating increased GRF, as

such in a research field yielding limited practical applications, these research findings

contribute to the body of evidence supporting the use of a therapeutic gluteal activation warm

ups to improve force generation in elite male rugby union players and wider elite populations.

References

Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P., & Dyhre-Poulsen, P. (2002). Increased

rate of force development and neural drive of human skeletal muscle following resistance

training. Journal of applied physiology, 93(4), 1318-1326.

Andersen, L. L., & Aagaard, P. (2006). Influence of maximal muscle strength and intrinsic muscle

contractile properties on contractile rate of force development. European journal of applied

physiology, 96(1), 46-52.

Andersen, L. L., Magnusson, S. P., Nielsen, M., Haleem, J., Poulsen, K., & Aagaard, P. (2006).

Neuromuscular activation in conventional therapeutic exercises and heavy resistance

exercises: implications for rehabilitation. Physical therapy, 86(5), 683-697.


Anderson, F. C., & Pandy, M. G. (1999). A dynamic optimization solution for vertical jumping in

three dimensions. Computer methods in biomechanics and biomedical engineering, 2(3), 201-

231.

Anderson, F. C., & Pandy, M. G. (2003). Individual muscle contributions to support in normal

walking. Gait & posture, 17(2), 159-169.

Ayotte, N. W., Stetts, D. M., Keenan, G., & Greenway, E. H. (2007). Electromyographical analysis of

selected lower extremity muscles during 5 unilateral weight-bearing exercises. Journal of

Orthopaedic & Sports Physical Therapy, 37(2), 48-55.

Baker, D., & Nance, S. (1999). The relation between running speed and measures of strength and

power in professional rugby league players. The Journal of Strength & Conditioning

Research, 13(3), 230-235.

Beachle, T. R., & Earle, R. W. Essentials of strength training and conditioning. Vol. 7. Champaign,

IL: Human kinetics, 2008.

Beardsley, C., & Contreras, B. (2014). The Increasing Role of the Hip Extensor Musculature With

Heavier Compound Lower-Body Movements and More Explosive Sport Actions. Strength &

Conditioning Journal, 36(2), 49-55.


Behm, D. G., Bambury, A., Cahill, F., & Power, K. (2004). Effect of acute static stretching on force,

balance, reaction time, and movement time. Medicine and Science in Sports and Exercise, 36,

1397-1402.

Berns, G. S., Hull, M. L., & Patterson, H. A. (1992). Strain in the anteromedial bundle of the anterior

cruciate ligament under combination loading. Journal of Orthopaedic Research, 10(2), 167-

176.

Besier, T. F., Lloyd, D. G., Cochrane, J. L., & Ackland, T. R. (2001). External loading of the knee

joint during running and cutting maneuvers. Medicine and Science in Sports and Exercise,

33(7), 1168-1175.

Blazevich, A. J. (2000). Optimizing Hip Musculature For Greater Sprint Running Speed. Strength &

Conditioning Journal, 22(2), 22.

Bobbert, M. F., & van Ingen Schenau, G. J. (1988). Coordination in vertical jumping. Journal of

biomechanics, 21(3), 249-262.

Boden, B. P., Feagin Jr, J. A., & Garrett Jr, W. E. (2000). Mechanisms of anterior cruciate ligament

injury. Orthopedics, 23(6), 573.


Boudreau, S. N., Dwyer, M. K., Mattacola, C. G., Lattermann, C., Uhl, T. L., & McKeon, J. M.

(2009). Hip-muscle activation during the lunge, single-leg squat, and step-up-and-over

exercises. Journal of sport rehabilitation, 18(1), 91.

Boren, K., Conrey, C., Le Coguic, J., Paprocki, L., Voight, M., & Robinson, T. K. (2011).

Electromyographic analysis of gluteus medius and gluteus maximus during rehabilitation

exercises. International journal of sports physical therapy, 6(3), 206.

Brown, L., & Ferrigno, V. (Eds.). (2014). Training for Speed, Agility, and Quickness, 3E. Human

Kinetics.

Buttifant, D., Crow, J., Kearney, S., & Hrysomallis, C. (2011). Whole Body Vibration vs. Gluteal

Muscle Activation: What are the Acute Effects on Explosive Power?. The Journal of Strength

& Conditioning Research, 25, S14-S15.

Carlock, J. M., Smith, S. L., Hartman, M. J., Morris, R. T., Ciroslan, D. A., Pierce, K. C., ... & Stone,

M. H. (2004). The relationship between vertical jump power estimates and weightlifting

ability: a field-test approach. The Journal of Strength & Conditioning Research, 18(3), 534-

539.

Cleather, D. J., & Bull, A. M. (2010). Lower-extremity musculoskeletal geometry affects the

calculation of patellofemoral forces in vertical jumping and weightlifting. Proceedings of the

Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 224(9),

1073-1083.


Cleather, D. J., & Bull, A. M. (2010). Influence of inverse dynamics methods on the calculation of

inter-segmental moments in vertical jumping and weightlifting. Biomedical engineering

online, 9(1), 74.

Cleophas, T. J. (1990). A simple method for the estimation of interaction bias in crossover studies.

The Journal of Clinical Pharmacology, 30(11), 1036-1040.

Čoh, M., Tomain, K., & Štuhec, S. (2006). The biomechanical model of the sprint start and block

acceleration. Facta Universitatis: Series Physical Education and Sport, 4, 103-114.

Cormie, P., McBride, J. M., & McCaulley, G. O. (2009). Power-time, force-time, and velocity-time

curve analysis of the countermovement jump: impact of training. The Journal of Strength &

Conditioning Research, 23(1), 177-186.

Cormie, P., McGuigan, M. R., & Newton, R. U. (2011). Developing maximal neuromuscular power.

Sports medicine, 41(1), 17-38.

Cormie, P., McCaulley, G. O., Triplett, N. T., & McBride, J. M. (2007). Optimal loading for maximal

power output during lower-body resistance exercises. Medicine and Science in Sports and

Exercise, 39(2), 340.


Corrie, W. S., & Hardin, W. B. (1964). Post-tetanic potentiation of H reflex in normal man:

quantitative study. Archives of neurology, 11(3), 317-323.

Crow, J. F., Buttifant, D., Kearny, S. G., & Hrysomallis, C. (2012). Low load exercises targeting the

gluteal muscle group acutely enhance explosive power output in elite athletes. The Journal of

Strength & Conditioning Research, 26(2), 438-442.

Cutsem, M., Duchateau, J., & Hainaut, K. (1998). Changes in single motor unit behaviour contribute

to the increase in contraction speed after dynamic training in humans. The Journal of

Physiology, 513(1), 295-305.

Cutter, N. C., & Kevorkian, C. G. (Eds.). (1999). Handbook of manual muscle testing. McGraw-

Hill/Appleton & Lange.

De Luca, C. J., LeFever, R. S., McCue, M. P., & Xenakis, A. P. (1982). Behaviour of human motor

units in different muscles during linearly varying contractions. The Journal of Physiology,

329(1), 113-128.

De Luca, C. J., & Erim, Z. (1994). Common drive of motor units in regulation of muscle force.

Trends in neurosciences, 17(7), 299-305.

De Luca, C. J., Foley, P. J., & Erim, Z. (1996). Motor unit control properties in constant-force

isometric contractions. Journal of Neurophysiology, 76(3), 1503-1516.


De Luca, C. J. (1997). The use of surface electromyography in biomechanics. Journal of applied

biomechanics, 13, 135-163.

Deutsch, M. U., Maw, G. J., Jenkins, D., & Reaburn, P. (1998). Heart rate, blood lactate and

kinematic data of elite colts (under-19) rugby union players during competition. Journal of

sports sciences, 16(6), 561-570.

Distefano, L. J., Blackburn, J. T., Marshall, S. W., & Padua, D. A. (2009). Gluteal muscle activation

during common therapeutic exercises. Journal of Orthopaedic & Sports Physical Therapy,

39(7), 532-540.

Ekstrom, R. A., Donatelli, R. A., & Carp, K. C. (2007). Electromyographic analysis of core trunk, hip,

and thigh muscles during 9 rehabilitation exercises. journal of orthopaedic & sports physical

therapy, 37(12), 754-762.

Elbourne, D. R., Altman, D. G., Higgins, J. P., Curtin, F., Worthington, H. V., & Vail, A. (2002).

Meta-analyses involving cross-over trials: methodological issues. International journal of

epidemiology, 31(1), 140-149.

Escamilla, R. F., Fleisig, G. S., Zheng, N., Barrentine, S. W., Wilk, K. E., & Andrews, J. R. (1998).

Biomechanics of the knee during closed kinetic chain and open kinetic chain exercises.

Medicine and science in sports and exercise, 30(4), 556-569.


Farina, D., Merletti, R., & Enoka, R. M. (2004). The extraction of neural strategies from the surface

EMG. Journal of Applied Physiology, 96(4), 1486-1495.

Fleming, B. C., Renstrom, P. A., Beynnon, B. D., Engstrom, B., Peura, G. D., Badger, G. J., &

Johnson, R. J. (2001). The effect of weightbearing and external loading on anterior cruciate

ligament strain. Journal of biomechanics, 34(2), 163-170.

Fukunaga, T., Miyatani, M., Tachi, M., Kouzaki, M., Kawakami, Y., & Kanehisa, H. (2001). Muscle

volume is a major determinant of joint torque in humans. Acta Physiologica Scandinavica,

172(4), 249-255.

Garhammer, J. (1993). A review of power output studies of Olympic and powerlifting: Methodology,

performance prediction, and evaluation tests. The Journal of Strength & Conditioning

Research, 7(2), 76-89.

Gentil, P., Oliveira, E., Junior, V. D. A. R., do Carmo, J., & Bottaro, M. (2007). Effects of exercise

order on upper-body muscle activation and exercise performance. The Journal of Strength &


Gottschalk, F, Kourosh, S., & Leveau, B. (1989). The functional anatomy of tensor fasciae latae and

gluteus medius and minimus. Journal of anatomy, 166, 179.


Granit, R., Phillips, C. G., Skoglund, S., & Steg, G. (1957). Differentiation of tonic from phasic alpha

ventral horn cells by stretch, pinna and crossed extensor reflexes. Journal of neurophysiology,

20(5), 470-481.

Gravel, D., Bélanger, A. Y., & Richards, C. L. (1987). Study of human muscle contraction using

electrically evoked twitch responses during passive shortening and lengthening movements.

European journal of applied physiology and occupational physiology, 56(6), 623-627.

Griffin, L. Y., Agel, J., Albohm, M. J., Arendt, E. A., Dick, R. W., Garrett, W. E., ... & Wojtys, E. M.

(2000). Noncontact anterior cruciate ligament injuries: risk factors and prevention strategies.

Journal of the American Academy of Orthopaedic Surgeons, 8(3), 141-150.

Griffin, L. Y., Albohm, M. J., Arendt, E. A., Bahr, R., Beynnon, B. D., DeMaio, M., & Yu, B.

(2006). Understanding and Preventing Noncontact Anterior Cruciate Ligament Injuries A

Review of the Hunt Valley II Meeting, January 2005. The American journal of sports

medicine, 34(9), 1512-1532.

Grimaldi, A. (2011). Assessing lateral stability of the hip and pelvis. Manual therapy, 16(1), 26-32.

Güllich, A., & Schmidtbleicher, D. (1996). MVC-induced short-term potentiation of explosive force.

Guskiewicz, K., Lephart, S., Burkholder, R., 1993. The relationship between sprint speed and hip

flexion/extension strength in collegiate athletes. Isokinetics and Exercise Science 3, 111–116.


Häkkinen, K., Alen, M., & Komi, P. V. (1985). Changes in isometric force‐ and relaxation‐ time,

electromyographic and muscle fibre characteristics of human skeletal muscle during strength

training and detraining. Acta physiologica scandinavica, 125(4), 573-585.

Häkkinen, K., & Komi, P. V. (1986). Training-induced changes in neuromuscular performance under

voluntary and reflex conditions. European journal of applied physiology and occupational

physiology, 55(2), 147-155.

Hakkinen, K., Pakarinen, A., Alen, M., Kauhanen, H., & Komi, P. V. (1988). Neuromuscular and

hormonal adaptations in athletes to strength training in two years. Journal of Applied

Physiology, 65(6), 2406-2412.

Haff, G. G., Stone, M. H., O’Bryant, H.S., Harman, C. N,. Dinan, R.. Johnson., & HAN, K. H.

(1997). Force-time dependent characteristics of dynamic and isometric muscle actions.

Journal of Strength and Conditioning Research, 11(4), 269-72

Heller, B. M., & Pincivero, D. M. (2003). The effects of ACL injury on lower extremity activation

during closed kinetic chain exercise. Journal of sports medicine and physical fitness, 43(2),

180.

Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000). Development of

recommendations for SEMG sensors and sensor placement procedures. Journal of

electromyography and Kinesiology, 10(5), 361-374.


Hewett, T. E., Myer, G. D., Ford, K. R., Heidt, R. S., Colosimo, A. J., McLean, S. G., ... & Succop, P.

(2005). Biomechanical measures of neuromuscular control and valgus loading of the knee

predict anterior cruciate ligament injury risk in female athletes A prospective study. The

American journal of sports medicine, 33(4), 492-501.

Hislop, H. J., & Daniels, M. J. (2007). Worthingham's Muscle Testing: Techniques of Manual

Examination. St Louis, MO: Saunders.

Hodgson, M., Docherty, D., & Robbins, D. (2005). Post-activation potentiation. Sports Medicine,

35(7), 585-595.

Hoffman, J. R., Cooper, J., Wendell, M., & Kang, J. (2004). Comparison of Olympic vs. traditional

power lifting training programs in football players. The Journal of Strength & Conditioning

Research, 18(1), 129-135.

Holmberg, P. M. (2009). Agility training for experienced athletes: A dynamical systems approach.

Strength & Conditioning Journal, 31(5), 73-78.

Hopkins, W. G. (2000). Measures of reliability in sports medicine and science. Sports medicine,

30(1), 1-15.


Hopkins, W. G. (2008). Research designs: choosing and fine-tuning a design for your study.

Sportscience, 12(1), 1-3.

Hopkins, W., Marshall, S., Batterham, A., & Hanin, J. (2009). Progressive statistics for studies in

sports medicine and exercise science. Medicine+ Science in Sports+ Exercise, 41(1), 3.

Hori, N., Newton, R. U., Nosaka, K., & McGuigan, M. R. (2006). Comparison of Different Methods

of Determining Power Output in Weightlifting Exercises. Strength & Conditioning Journal,

28(2), 34-40.

Horsman, M. K., Koopman, H. F. J. M., Van der Helm, F. C. T., Prosé, L. P., & Veeger, H. E. J.

(2007). Morphological muscle and joint parameters for musculoskeletal modelling of the

lower extremity. Clinical biomechanics, 22(2), 239-247.

Hunter, J. P., Marshall, R. N., & McNair, P. J. (2005). Relationships between ground reaction force

impulse and kinematics of sprint-running acceleration. J Appl Biomech, 21(1), 31-43.

Ikai, M., & Fukunaga, T. (1968). Calculation of muscle strength per unit cross-sectional area of

human muscle by means of ultrasonic measurement. Internationale Zeitschrift fuer

Angewandte Physiologie Einschliesslich Arbeitsphysiologie, 26(1), 26-32.

Ireland, M. L. (1999). Anterior cruciate ligament injury in female athletes: epidemiology. Journal of

Athletic Training, 34(2), 150.


Izquierdo, M., Häkkinen, K., Gonzalez-Badillo, J. J., Ibanez, J., & Gorostiaga, E. M. (2002). Effects

of long-term training specificity on maximal strength and power of the upper and lower

extremities in athletes from different sports. European Journal of Applied Physiology, 87(3),

264-271.

Jan, S. V. S. (2005). Introducing anatomical and physiological accuracy in computerized

anthropometry for increasing the clinical usefulness of modeling systems. Critical Reviews™

in Physical and Rehabilitation Medicine, 17(4).

Jull, G., Hodges, P., Hides, J., & Panjabi, M. M. (1999). Therapeutic exercise for spinal segmental

stabilization in low back pain: scientific basis and clinical approach (pp. 61-76). Edinburgh:

Churchill Livingstone.

Júnior, R., Valdinar, A., Bottaro, M., Pereira, M. C., Andrade, M. M., Júnior, P., ... & Carmo, J. C.

(2010). Electromyographic analyses of muscle pre-activation induced by single joint exercise.

Brazilian Journal of Physical Therapy, 14(2), 158-165.

Kankaanpää, M., Taimela, S., Laaksonen, D., Hänninen, O., & Airaksinen, O. (1998). Back and hip

extensor fatigability in chronic low back pain patients and controls. Archives of physical

medicine and rehabilitation, 79(4), 412-417.


Kawamori, N., Crum A. J., Blumert, P. A., Kulik, J. R., Childers, J. T., Wood, J. A., & Haff, G. G.

(2005). Influence of different relative intensities on power output during the hang power

clean: identification of the optimal load. The Journal of Strength & Conditioning Research,

19(3), 698-708.

Kellis, E., & Baltzopoulos, V. (1996). The effects of normalization method on antagonistic activity

patterns during eccentric and concentric isokinetic knee extension and flexion. Journal of

Electromyography and Kinesiology, 6(4), 235-245.

Kernell, D. (1965). The limits of firing frequency in cat lumbosacral motoneurones possessing

different time course of after hyper polarization. Acta Physiologica Scandinavica, 65(1‐ 2),

87-100.

Kernell, D. (1966). Input resistance, electrical excitability, and size of ventral horn cells in cat spinal

cord. Science, 152(3729), 1637-1639.

Kilduff, L. P., Bevan, H. R., Kingsley, M. I., Owen, N. J., Bennett, M. A., Bunce, P. J., ... &

Cunningham, D. J. (2007). Postactivation potentiation in professional rugby players: optimal

recovery. The Journal of Strength & Conditioning Research, 21(4), 1134-1138.

Kitago, T., Mazzocchio, R., Liuzzi, G., & Cohen, L. G. (2004). Modulation of H-reflex excitability by

tetanic stimulation. Clinical neurophysiology, 115(4), 858-861.


Knudson, D. V. (2011). Authorship, and sampling practice in selected biomechanics and sports

science journals. Perceptual and motor skills, 112(3), 838-844.

Knutson, L. M., Soderberg, G. L., Ballantyne, B. T., & Clarke, W. R. (1994). A study of various

normalization procedures for within day electromyographic data. Journal of


Konrad, P. (2005). The abc of emg. A practical introduction to kinesiological electromyography, 1

Krosshaug, T., Slauterbeck, J. R., Engebretsen, L., & Bahr, R. (2007). Biomechanical analysis of

anterior cruciate ligament injury mechanisms: three‐ dimensional motion reconstruction

from video sequences. Scandinavian journal of medicine & science in sports, 17(5), 508-519.

Kukulka, C. G., & Clamann, H. P. (1981). Comparison of the recruitment and discharge properties of

motor units in human brachial biceps and adductor pollicis during isometric contractions.

Brain research, 219(1), 45-55.

Kunz, H., Kauffman, D.A., (1981). Biomechanical analysis of sprinting: decathletes versus

champions. British Journal of Sports Medicine 15, 177–181.


Kuiken, T. A., Lowery, M. M., & Stoykov, N. S. (2003). The effect of subcutaneous fat on

myoelectric signal amplitude and cross-talk. Prosthetics and orthotics international, 27(1),

48-54.

Kyröläinen, H., Avela, J., & Komi, P. V. (2005). Changes in muscle activity with increasing running

speed. Journal of sports sciences, 23(10), 1101-1109.

Leinonen, V., Kankaanpää, M., Airaksinen, O., & Hänninen, O. (2000). Back and hip extensor

activities during trunk flexion/extension: effects of low back pain and rehabilitation. Archives

of physical medicine and rehabilitation, 81(1), 32-37.

Lieberman, D. E., Raichlen, D. A., Pontzer, H., Bramble, D. M., & Cutright-Smith, E. (2006). The

human gluteus maximus and its role in running. Journal of Experimental Biology, 209(11),

2143-2155.

Mann, R. (1986). The biomechanical analysis of sprinters. Track Technique, 94, 3000-3003.

Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of

acute events. American journal of epidemiology, 133(2), 144-153.

Markolf, K. L., Burchfield, D. M., Shapiro, M. M., Shepard, M. F., Finerman, G. A., Slauterbeck, J.

L. (1995). Combined knee loading states that generate high anterior cruciate ligament forces.

Journal of Orthopedic Research, (13), 930-935.


McLean, S. G., Huang, X., Su, A., & Van Den Bogert, A. J. (2004). Sagittal plane biomechanics

cannot injure the ACL during sidestep cutting. Clinical biomechanics, 19(8), 828-838.

McLean, S. G., Walker, K. B., & Van Den Bogert, A. J. (2005). Effect of gender on lower extremity

kinematics during rapid direction changes: an integrated analysis of three sports movements.

Journal of Science and Medicine in Sport, 8(4), 411-422.

McNair, P. J., Marshall, R. N., & Matheson, J. A. (1990). Important features associated with acute

anterior cruciate ligament injury. The New Zealand medical journal, 103(901), 537-539.

Meir, R., Colla, P., & Milligan, C. (2001). Impact of the 10-meter rule change on professional rugby

league: Implications for training. Strength & Conditioning Journal, 23(6), 42-46.

Mero, A., & Komi, P. V. (1994). EMG, force, and power analysis of sprint-specific strength exercises.

Journal of Applied Biomechanics, 10, 1-1.

Mesin, L., Merletti, R., & Rainoldi, A. (2009). Surface EMG: the issue of electrode location. Journal

of Electromyography and Kinesiology, 19(5), 719-726.

Mills, E. J., Chan, A. W., Wu, P., Vail, A., Guyatt, G. H., & Altman, D. G. (2009). Design, analysis,

and presentation of crossover trials. Trials, 10(1), 27.


Milner Brown, H. S., Stein, R. B., & Yemm, R. (1973). The contractile properties of human motor

units during voluntary isometric contractions. The Journal of physiology, 228(2), 285-306.

Moss, B. M., Refsnes, P. E., Abildgaard, A., Nicolaysen, K., & Jensen, J. (1997). Effects of maximal

effort strength training with different loads on dynamic strength, cross-sectional area, load-

power and load-velocity relationships. European journal of applied physiology and

occupational physiology, 75(3), 193-199.

Murphy, A. J., Lockie, R. G., & Coutts, A. J. (2003). Kinematic determinants of early acceleration in

field sport athletes. Journal of sports science & medicine, 2(4), 144.

Mustard, B. E., & Lee, R. G. (1987). Relationship between EMG patterns and kinematic properties for

flexion movements at the human wrist. Experimental Brain Research, 66(2), 247-256.

Muthuraman, M., Raethjen, J., Hellriegel, H., Deuschl, G., & Heute, U. (2008, August). Imaging

coherent sources of tremor related EEG activity in patients with Parkinson's disease. In

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International

Conference of the IEEE (pp. 4716-4719). IEEE.

Nagano, A., Komura, T., Fukashiro, S., & Himeno, R. (2005). Force, work and power output of lower

limb muscles during human maximal-effort countermovement jumping. Journal of



Narici, M. V., Binzoni, T., Hiltbrand, E., Fasel, J., Terrier, F., & Cerretelli, P. (1996). In vivo human

gastrocnemius architecture with changing joint angle at rest and during graded isometric

contraction. The Journal of Physiology, 496(1), 287-297.

Nelson-Wong, E., Gregory, D. E., Winter, D. A., & Callaghan, J. P. (2008). Gluteus medius muscle

activation patterns as a predictor of low back pain during standing. Clinical Biomechanics,

23(5), 545-553.

Nelson-Wong, E., Flynn, T., & Callaghan, J. P. (2009). Development of active hip abduction as a

screening test for identifying occupational low back pain. journal of orthopaedic & sports

physical therapy, 39(9), 649-657.

Neumann, D. A. (2010). Kinesiology of the hip: a focus on muscular actions. journal of orthopaedic

& sports physical therapy, 40(2), 82-94.

Newton, R. U., & Kraemer, W. J. (1994). Developing explosive muscular power: Implications for a

mixed methods training strategy. Strength & Conditioning Journal, 16(5), 20-31.

Neptune, R. R. (1999). Optimization algorithm performance in determining optimal controls in human

movement analyses. Journal of biomechanical engineering, 121(2), 249-252.


Nordander, C., Willner, J., Hansson, G. Å., Larsson, B., Unge, J., Granquist, L., & Skerfving, S.

(2003). Influence of the subcutaneous fat layer, as measured by ultrasound, skinfold calipers

and BMI, on the EMG amplitude. European journal of applied physiology, 89(6), 514-519.

Oliver, G. D., & Keeley, D. W. (2010). Gluteal muscle group activation and its relationship with

pelvis and torso kinematics in high-school baseball pitchers. The Journal of Strength &


Olsen, O. E., Myklebust, G., Engebretsen, L., & Bahr, R. (2004). Injury mechanisms for anterior

cruciate ligament injuries in team handball a systematic video analysis. The American journal

of sports medicine, 32(4), 1002-1012.

Pandy, M. G., & Zajac, F. E. (1991). Optimal muscular coordination strategies for jumping. Journal

of biomechanics, 24(1), 1-10.

Penfold, L., & Jenkins, D. (1996). Training for speed. Training for speed and endurance, 24-41.

Person, R. S., & Kudina, L. P. (1972). Discharge frequency and discharge pattern of human motor

units during voluntary contraction of muscle. Electroencephalography and clinical

neurophysiology, 32(5), 471-483.

Petering, R. C., & Webb, C. (2011). Treatment options for low back pain in athletes. Sports Health: A

Multidisciplinary Approach, 3(6), 550-555.


Pick, J., & Becque, M. D. (2000). The relationship between training status and intensity on muscle

activation and relative submaximal lifting capacity during the back squat. The Journal of

Strength & Conditioning Research, 14(2), 175-181.

Powell, J. W., & Barber-Foss, K. D. (2000). Sex-related injury patterns among selected high school

sports. The American journal of sports medicine, 28(3), 385-391.

Powers, C. M. (2010). The influence of abnormal hip mechanics on knee injury: a biomechanical

perspective. journal of orthopaedic & sports physical therapy, 40(2), 42-51.

Rainoldi, A., Melchiorri, G., & Caruso, I. (2004). A method for positioning electrodes during surface

EMG recordings in lower limb muscles. Journal of neuroscience methods, 134(1), 37-43.

Reilly, T. (1996) Motion analysis and physiological demands. In: Science and Soccer. Ed: Reilly, T.

E & F.N. Spon, London, 65-81.

Reilly, T. (1997). Energetics of high-intensity exercise (soccer) with particular reference to fatigue.

Journal of sports sciences, 15(3), 257-263.

Reiman, M. P., Bolgla, L. A., & Lorenz, D. (2009). Hip function's influence on knee dysfunction: a

proximal link to a distal problem. Journal of sport rehabilitation, 18(1), 33.


Reiman, M. P., Bolgla, L. A., & Loudon, J. K. (2012). A literature review of studies evaluating

gluteus maximus and gluteus medius activation during rehabilitation exercises. Physiotherapy

theory and practice, 28(4), 257-268.

Rienzi, E., Drust, B., Reilly, T., Carter, J. E. L. & Martin, A. (2000). Investigation of anthropometric

and work-rate profiles of elite South American international soccer players. Journal of Sports

Medicine and Physical Fitness 40, 162-169.

Schmidtbleicher, D., & Buehrle, M. (1987). Neuronal adaptation and increase of cross-sectional area

studying different strength training methods. Biomechanics XB, 6, 615-620.

Schmitz, R. J., Riemann, B. L., & Thompson, T. (2002). Gluteus medius activity during isometric

closed-chain hip rotation. Journal of Sport Rehabilitation, 11(3), 179-189.

Schroter, G. (1998). For Schools and Beginners-Basics of the Sprint Start. Modern Athlete and Coach,

36, 23-26.

Sforzo, G. A., & Touey, P. R. (1996). Manipulating exercise order affects muscular performance

during a resistance exercise training session. The Journal of Strength & Conditioning

Research, 10(1), 20-24.


Sleivert, G. G., & Wenger, H. A. (1994). Reliability of measuring isometric and isokinetic peak

torque, rate of torque development, integrated electromyography, and tibial nerve conduction

velocity. Archives of physical medicine and rehabilitation, 75(12), 1315-1321.

Souza, R. B., & Powers, C. M. (2009). Predictors of hip internal rotation during running an

Evaluation of hip strength and femoral structure in women with and without patellofemoral

pain. The American journal of sports medicine, 37(3), 579-587.

Suresh, K. P. (2011). An overview of randomization techniques: An unbiased assessment of outcome

in clinical research. Journal of human reproductive sciences, 4(1), 8.

Thorstensson, A., Karlsson, J., Viitasalo, J. H. T., Luhtanen, P., & Komi, P. V. (1976). Effect of

strength training on EMG of human skeletal muscle. Acta Physiologica Scandinavica, 98(2),

232-236.

Tricoli, V., Lamas, L., Carnevale, R., & Ugrinowitsch, C. (2005). Short-term effects on lower-body

functional power development: weightlifting vs. vertical jump training programs. The Journal

of Strength & Conditioning Research, 19(2), 433-437.

Trimble, M. H., & Harp, S. S. (1998). Postexercise potentiation of the H-reflex in humans. Medicine

and science in sports and exercise, 30(6), 933-941.


Tsao, H., & Hodges, P. W. (2007). Immediate changes in feedforward postural adjustments following

voluntary motor training. Experimental brain research, 181(4), 537-546.

Tsao, H., & Hodges, P. W. (2008). Persistence of improvements in postural strategies following motor

control training in people with recurrent low back pain. Journal of Electromyography and

Kinesiology, 18(4), 559-567.

Cutsem, M., Duchateau, J., & Hainaut, K. (1998). Changes in single motor unit behaviour contribute

to the increase in contraction speed after dynamic training in humans. The Journal of

physiology, 513(1), 295-305.

Van Sint Jan, S., & Della Croce, U. (2005). Accurate palpation of skeletal landmark locations: why

standardized definitions are necessary. A proposal. Clinical biomechanics, 20, 659-660.

de Villarreal, E. S. S., González-Badillo, J. J., & Izquierdo, M. (2007). Optimal warm-up stimuli of

muscle activation to enhance short and long-term acute jumping performance. European

journal of applied physiology, 100(4), 393-401.

Westing, S. H., Seger, J. Y., & Thorstensson, A. (1990). Effects of electrical stimulation on eccentric

and concentric torque‐ velocity relationships during knee extension in man. Acta physiologica

scandinavica, 140(1), 17-22.


Weyand, P. G., Sternlight, D. B., Bellizzi, M. J., & Wright, S. (2000). Faster top running speeds are

achieved with greater ground forces not more rapid leg movements. Journal of applied

physiology, 89(5), 1991-1999.

Winter, D. A., Prince, F., Frank, J. S., Powell, C., & Zabjek, K. F. (1996). Unified theory regarding

A/P and M/L balance in quiet stance. Journal of neurophysiology, 75(6), 2334-2343.

Withrow, T. J., Huston, L. J., Wojtys, E. M., & Ashton-Miller, J. A. (2006). The effect of an

impulsive knee valgus moment on in vitro relative ACL strain during a simulated jump

landing. Clinical Biomechanics, 21(9), 977-983.

Young, W., & Farrow, D. (2006). A review of agility: Practical applications for strength and

conditioning. Strength & Conditioning Journal, 28(5), 24-29.

Zatsiorsky, V., & Prilutsky, B. (2012). Biomechanics of skeletal muscles. Human Kinetics.

Champaigne, IL.


Appendix

Appendix

Appendix 1

Musculoskeletal Modelling Literature Review

Musculoskeletal modelling (MM) systems were first developed by Williams and Seireg,

(1977) and have advanced over the past decades to computer simulations and MM that are

now available in commercial formats, with MM now becoming an integral method of product

design and biomechanics research, an advance greatly due to advances in computer

capabilities and speeds (Delp et al, 1990: Lund, de Zee, Andersen & Rasmussen, 2012).

MM is a biomechanical analysis tool that enables analysis in situations where direct

measurements are inaccessible (Cleather & Bull, 2010), such as collecting in vivo measures

of muscular torque would be inappropriate for an elite population. MM utilises lines of

muscle action, inverse dynamics (ID) and optimization techniques (OT) to estimate

mechanical loading in the form of; joint moments, muscle forces, joint forces for specific

movements or tasks (Horsman et al, 2007: Nagano et al, 2005: Bryanton et al, 2012).

Offering an innovative method of understanding the mechanical loads of individual muscles

within weightlifting, enabling diagnosis of joint moments and muscular forces and the

limiting factors in completion of the exercises (Chiu & Salem, 2006: Flanagan and Salem,

2008). Complete calculation of these variables within a multibody system can establish the

relationship between bone motion and the inter-muscular forces, enabling mechanical


dynamic formulation (Erberhard & Schielon, 2006: Unda de Jalon, Losantos & Emparantza,

1987). These formulations produce descriptions of the functions of the musculoskeletal

system (Veeger, Van der Helm, Pronk & Rozendal, 1991), an advantageous tool for a

strength and conditioning coach when striving to maximise muscle torque and subsequent

force output. However a challenge to these MM’s is the complexity of human geometry and

systems, a relevant issue as the geometry of the musculoskeletal system determines the

moment arm of the muscle and therefore joint moment and muscular force in any given

position (Delp et al, 1990), therefore accurate measurement of tendon length and muscle size

and geometry are vital (Horsman et al, 2007), as musculotendon length determines

musculotendon force (Zajac, 1989). Variation in musculoskeletal geometry results in

complication surrounding reliable experimental data collection (Lund et al, 2012).

Complications regarding data collection and specifically the choice of MM pose questions of

best accuracy; the Delp MM (Delph, 1990: Delp et al, 1990: Delp & Loan, 1995) is a widely

used an amalgamation of previous work, this model contains 43 musculotendonous actuators

describing the musculoskeletal geometry and the musculotendonous parameters and lines of

action (Delp et al, 1990). However despite the greater number of muscle lines within the

Delp model, the Horsman model is believed to be a more comprehensive model (Cleather &

Bull, 2010).

Whereas Horsman’s data set is based upon cadaver evidence, employing a complete set of

muscle attachment sites and actuation parameters, joint properties, geometric information and

38 lines of muscle action (Horsman et al, 2007). Due to this, this model offers increased

detail of muscle lines of action (Cleather & Bull, 2010) over the Delp model. Lines of

muscle action represent each individual musculotendinous unit consisting of; origin and

insertion, underlying structures, cross-sectional area and a special awareness of proximity to


bone. This increased detail offers a more accurate analysis of the lower extremity (which as

previously mentioned is vital for accurate calculation), as this model was developed from one

consistent dataset no scaling of anatomical variations was needed (Horsman et al, 2007). The

Model developed by Cleather (2010) utilised within this research uses the Horsman model,

chosen due to the increased accuracy and consistency provided by the number of lines of

action and the exact replication of one cadaver.

The use of computers to conduct these calculations have grown in interest from the

development of the first MM software SIMM in the 1990’s to the more complex MM of

today, with three dimensional (3D) MM offering greater accuracy in mapping lines of muscle

actions and movement vectors as the 3D approach accounts for rotation and movements

outside of the sagittal plane (Nagano et al, 2005), considering this methodological feedback a

Vicon MX 3D motion capture device has been chosen. The MM FBB (Cleather & Bull,

2010) the MM is used within this research, FBB interprets the Kinematic data provided by

Vicon MX, EMG and the kinetic data provided by a portable force plate.

One commonly used method of inter-segmental force calculation within MM’s is the

Newton-Euler method (NE) (Nikravesh, 1988), the NE treats the segments of the skeleton as

individual bodies, allowing for all possible translations and rotations, following these

calculation the joint constraints are included in the calculation to allow only the relevant

muscular degrees of freedom, therefore tailoring the geometry and thus results to the

individual subject.


The validity of MM results have a high reliance on musculoskeletal geometry, mapping and

uniformity of subject, Dao et al, 2012 highlight the necessity for minimal subject movement

variation to reduce the parameter width of the cohort to enable greater accuracy of

comparison, however this research was conducted on polio patents who have large gait

discrepancies and thus this critique may not be applicable to an elite sporting population, who

despite varying anthropometrics possess similar gaits’. Thus accurate mapping of anatomical

structure and geometry is a material factor in the accuracy of the MM product (Pandy, 2001:

Herzog, 1992: Brand et al, 1994), therefore the use of generic MM for differing populations

may not always be optimal (Blankevoort & Huiskes, 1996). However despite these

limitations, alternative measurements such as computer tomography and magnetic resonance

imaging may be inadequate at capturing data at the frequencies required to capture muscle

contraction velocity and muscle activation, thus the use of ever more accurate MM powered

by faster computers indicates that the use of MM maybe optimal when studying high velocity

movement (Pandy, 2001).


Appendix 2

Pilot Data Pilot EMG Study Data

Subject GMAX Control 1 GMAX Control 2 MVIC GMAX Control 1 % MVIC GMAX Control 2 % MVIC GMED Control 1 GMED Control 2 MVIC GMED Control 1 % MVIC GMED Control 2 % MVIC

1 0.0211 0.0220 0.0125 0.0133 0.0105 1.1956 1.2648

2 0.0154 0.0148 0.0144 1.0726 1.0312 0.0156 0.0157 0.0119 1.3127 1.3155

3 0.7656 0.0062 0.0088 87.0419 0.7002 0.7673 0.0092 0.0116 66.2478 0.7963

4 0.0070 0.0074 0.0146 0.4776 0.5033 0.0156 0.0137 0.0119 1.1366 1.1492

Mean 0.2023 0.0126 0.0126 29.5307 0.7449 0.2028 0.0130 0.0115 17.4732 1.1314

SD 0.3253 0.0063 0.0027 40.6673 0.2178 0.3260 0.0023 0.0006 28.1601 0.2027

t-test 0.2113 0.1959

https://www.dropbox.com/sh/i5ci5n73l6s7uyq/AABUmOLsRe5BqQ0RtUaF71gLa?dl=0


Appendix 3

EMG Electrode Placement

GMAX GMED


Appendix 4

Vicon Marker Placement

Marker positions used for data capture.

Marker Location

FCC Calcaneus

FMT Tuberosity of the fifth metatarsal

FM2 Head of the second metatarsal

TF Additional marker placed on the foot

FAM Apex of the lateral malleolus

TAM Apex of the medial malleolus

C1, C2, C3 Additional markers placed on the calf segment

FLE Lateral femoral epicondyle

FME Medial femoral epicondyle

T1, T2, T3 Additional markers placed on the thigh segment

RASIS Right anterior superior iliac spine

LASIS Left anterior superior iliac spine

RPSIS Right posterior superior iliac spine

LPSIS Left posterior superior iliac spine


Appendix 5

Ethics Forms Ethics Documents

https://www.dropbox.com/sh/wuzvbaw0qqsso17/AACl_3uW0LdM7FIY4rV1UpgYa?dl=0



SCHOOL OF SPORT, HEALTH AND APPLIED SCIENCES (SHAS)

G) RESEARCH / DISSERTATION / PROJECT DECLARATION

In undertaking my Dissertation/ Research Project, I agree to adhere to the approved guidelines and procedures for the protocol I am using

and, inform my supervisor of any necessary changes to my protocol, which may require a revised Dissertation/ Research/ Project Form

DECLARATION SIGNATURE:

Individual undertaing the relevan Research/ Dissertation or Project activity

DATE: 13/01/15


School of Sport, Health and Applied Science (SHAS)

PRACTICAL HEALTH AND SAFETY RISK ASSESSMENT FORM (PRA1)

Name of Assessor(s):Matt Parr

Signature(s):Date Signed:

Total number of pages: 7Review date / notes:

15/01/2016 / or immediately in the event of a compulsory update.

SECTION 1: Identify Hazard types - Consider the activity or work area and identify if any of the hazards listed below are significant.

1 Fall of objects 7Heating, ventilation and humidity

13Pressure vessels - autoclave

19Biological hazards – micro-organisms, human samples or non-lab fieldwork

25 Working at heights

2Spillages, slips, Trips & Falls

8Layout , storage, space, obstructions

14 Noise or Vibration 20Fire hazards, flammable materials and explosion

26 Occupational stress

3Manual handling operations including repetitive movements

9 Electrical Equipment 15 Sharps – syringes, blades 21 Handling food 27Violence to staff / verbal assault

4 Display screen equipment 10Physical hazards –electrical, temperature

16Ergometers – rower, treadmill, bikes

22 Vehicles and driving 28Lone working / work out of hours

5 Work in public areas 11 Contractors 17 Ionising and non-ionising radiation

23 Physical Activity 29 Confined spaces

6 Lighting levels 12Mechanical (machinery) and use of portable tools / equipment

18Chemical hazards – toxic, corrosive, flammables

24 Outdoor work 30 Other(s) - specify

Assessment Reference No. 115554Activity assessed:

EMG, Vicon and force plate are used to assess the gluteal

activation levels and force output during the hang high pullAssessment date 15/01/2015Persons who may be affected by the activity (i.e. are at risk)

User and Users of activity assessed

Brief description of activity/procedure

Use of EMG, Vicon and a force plate to assess glutealmuscle activation during the hang high pull.Subjects will undergo a controlled warm up consisting of a general heart rate raising warm up and anintervention warm up specifically targeting the gluteal musculature. Following each warm up subjects will perform 3 hang high pulls at 80%1RM.Finally subjects will undergo GMAX and GMED MVIC testing to provide a comparison measure to assess gluteal activation.

Description of work to be done:

Please tick ( ) the following which applies:

Work to be done in designated areas

Work to be done under close supervision

Work to be done in the presence of at least 2 other workers

Work to be done within normal hours

Work not to be left unattended

15/01/2015


Health and Safety Risk Assessments – continuation sheetAssessment Reference No. 115554

Page 2





SECTION 2: Risk Controls - For each hazard identified in Section 1, complete Section 2. Please refer to the Risk Assessment Guidance notes on simmsCAPital folder for Risk Matrix. Please note that L refers to Likelihood; S refers to Severity and RS refers to Risk Score (L times S equals RS)

Hazard No.

Outcome due to Hazard description (Substance / equipment / procedure)

L SL X S

= RS

Initial risk Level (tick one)

Refer to the risk matrix Controls needed to eliminate or adequately reduce risks

L SL X S

= RS

Remaining Risk Level

(tick one)

0-5 0-5 0-25High

(13-25)Med

(5-12)Low(0-4)

0-5 0-5 0-25High

(13-25)Med

(5-12)Low(0-4)

1 Fall of objects 4 3 12

Ensure users have received adequatetraining to use equipment.Ensure units are securely connectedto the tripod head and the legs of thetripod provide an adequate base ofsupport for the unit attached. Ensurethe tripod weight is appropriate for theweight of the unit attached.Ensure set up is stable prior tocommencing.

1 3 3

2 Spillages, slips, Trips & Falls 4 3 12Use wire covering and tape to fix wireto floor or suitable safe positioning

1 3 3

3Manual handling operations incl uding repetitive movements

3 3 6

Ensure user is aware of correct handling protocol to set up the system.Provide and refer to Manual Handling Operations guide (HSPG 11).

1 3 3

4 Display screen equipment 2 2 4

Ensure users have received adequatetraining to use the equipment.Ensure the equipment is not adistraction for subjects.Equipment regularly checked toensure correct functioning

1 2 2

6 Lighting levels 4 4 16

Testing area inspected for additionalhazards.Ensure lighting is not too bright ordirectly in participant’s line of vision.Lighting only low when necessary.

2 4 8

15/01/2015



Page 3





SECTION 2: Risk Controls - For each hazard identified in Section 1, complete Section 2. Please refer to the Risk Assessment Guidance notes on simmsCAPital folder

for Risk Matrix. Please note that L refers to Likelihood; S refers to Severity and RS refers to Risk Score (L times S equals RS)

Hazard No.


L SL X S = RS

Initial risk Level (tick one)Refer to the risk matrix Controls needed to eliminate or

adequately reduce risks

L SL X S = RS

Remaining Risk Level(tick one)

0-5 0-5 0-25High

(13-25)Med

(5-12)Low(0-4)

0-5 0-5 0-25High

(13-25)Med

(5-12)Low(0-4)

8 Layout , storage, space, obstructions 3 3 9

Ensure users wear PPS (BS7184)

Provide and refer to Manual HandlingOperations guide (HSPG 11).Maintain storage of all items inrelevant cupboards / spaces (refer toroom floor plan)

1 3 3

9 Electrical Equipment 2 4 8

Ensure users are familiar with theequipment and have been inductedon how to setup equipment correctl y.Ensure equipment in Pat testedannually.Ensure no fluids are consumed nearthe equipment.Check wires for damage during setupand prior to use.Switch off equipment when not in use

1 4 4

12Mechanical (machinery) and use of portable tools / equipment

3 3 9

Ensure users have received adequatetraining to use the equipment. Ensureusers wear PPS (BS7184) whereappropriate.

1 3 3

15 Sharps – syringes, blades 4 5 20

Use a single shaving razor perperson, do not share blades. Theindividual being tested must shavethemselves under the supervision ofthe investigator. Dispose of the bladeby re-attaching the razor cover.

2 5 10

15/01/2015



Page 4





SECTION 2: Risk Controls - For each hazard identified in Section 1, complete Section 2. Please refer to the Risk Assessment Guidance notes on simmsCAPital folder

for Risk Matrix. Please note that L refers to Likelihood; S refers to Severity and RS refers to Risk Score (L times S equals RS)

Hazard No.


L SL X S = RS

Initial risk Level (tick one)Refer to the risk matrix Controls needed to eliminate or

adequately reduce risks

L SL X S = RS

Remaining Risk Level(tick one)

0-5 0-5 0-25High

(13-25)Med

(5-12)Low(0-4)

0-5 0-5 0-25High

(13-25)Med

(5-12)Low(0-4)

23 Physical Activity 3 4 12

All relevant equipment inspectedand calibrated before the start o fthe testing session. Any faults ormaintenance issues reported to a

member of technical services staff.Ensure the participant is aware ofthe testing protocol and theassociated risks (a wri tten protocolshould be provided).Ensure participant (s) hascompleted and signed an Informedconsent form (refer to Ethics Sub-Committee Suggested Format for aParticipant Consent Form) and(Physical Activity ReadinessQuestionnaire (PARQ)) prior tocommencing any procedure.Ensure students are supervised bya trained member of staff duringmaximal exercise protocols.Ensure student(s) has receivedappropriate training to administerthe protocols.Ensure user performs an adequatewarm-up and cool-down.

1 4 4

15/01/2015



Page 5





SECTION 3: Action Plan in the event of an emergency - For each hazard identified in Section 2, complete Section 3.

- Please refer to the Risk Assessment Guidance.

Hazard Number

Hazard Description –Substance / equipment /procedure

Action required (describe)

All hazards identified in section 1 Apply relevant First Aid and seek Medical Assistance where appropriate

SECTION 4: Arrangement for supervision and/or monitoring effectivenes s of control- For each hazard identified in Sections 2/3, complete Section 4.

- Please refer to the Risk Assessment Guidance notes.

Hazard No.

Hazard Description –Substance/equipment/procedure

Comments

All hazards identified in section 1Monitoring achieved through pre and post checks, continual supervision by test coordinator and/or separately recruited individual where further supervision is required.

15/05/2015



Page 6





SECTION 5: Further comments – If a more complex assessment is required, continue below:

IMPORTANT CONTACT DETAILS (including where activities are undertaken off campus):

St Mary’s University Security – 0208 240 4335 (advise in the event of calling the emergency services) Health and Safety Executive (HSE) Information line – 0845 345 0055 / www.HSE.gov.ukBASES (British Association of Sport & Exercise Science) – www.bases.org.ukCroner’s - 0208 547 2637 / www.croner.co.ukCLEAPPS - (0)1895 251496 / http://www.cleapss.org.uk/secfr.htm

GUIDELINES FOR REFFERAL (as a hard copy attachment, listed web link or other source):

(Examples of supporting information could be a Material Safety Data Sheet (MSDS) or a Qualification/Accreditation guideline).

Manual Handling Operations guide (HSPG 11) – see Health and Safety home page on Simms Space (students) or Staff Net (staff)Suggested Format for a Participant Consent Form – see Ethics Sub-Committee home page on Simms Space (students) or Staff Net (staff)Subject Information Sheet – see attachedSchool of SHAS Medical History and-or PARQ Form – see attachedR26 floor plan – See simmsCAPital biomechanics informationGeneral Risk Assessment information and guidance – see Ethics Sub-Committee home page on Simms Space (students) or Staff Net (staff)

15/01/2015



Page 7





SECTION 6: Period of cover – If a more complex assessment is required, continue below:

By signing this risk assessment I confirm that I have read and understood all of the risks associated with the activity specified on sheet 1, and that I will follow all of the specified controls to reduce the risks identified with the activity.

PERIOD OF COVER FOR TASK/EVENT

PRINT NAME OF TASK/EVENT LEADER SIGNATURE DATE SIGNED HAZARDS IDENTIFIED (mark with a tick or a cross)

FROM TO

15/01/2015


Participant Information Sheet

Waldegrave Rd

Twickenham

Greater London

TW1 4SX

Phone: 020 8240 4000

Participant Information Sheet

Research Title:

The Effect of Gluteal Activation Exercise on Gluteus Maximus and Gluteus Medius Recruitment and Force

Output During the Hang High Pull in Elite Rugby Union Players.

Purpose

The purpose of this study is to identify if gluteal muscle activation increases gluteal recruitment and force

output during subsequent exercise, this information will be valuable when designing training programmes

targeting gluteal adaptation.

Invite

You are invited to participate in this research, you do so in the knowledge that you are free to withdraw from

this research at any time and that your details will remain confidential, your anonymity will be respected and

that your legal rights will not be compromised in the event of incident.


Funding

Solely the organiser, Matt Parr, funds this research.

Why you

You have been invited to participate in this research, as you are an elite rugby union player at London Irish

Rugby Club, you have the right to refuse participation and can withdraw from this research at any time by

signing the bottom of your consent form “I wish to withdraw from this study”.

Research Method:

You will be asked to perform a control warm up followed by 3 hang high pulls, an intervention warm up

followed by 3 hang high pulls and maximum voluntary isometric contraction testing. During the hang high

pulls and maximum voluntary isometric contraction testing you will be asked to wear electromyography

electrodes and Vicon motion capture markers. Minimal risks are associated with this testing procedure and

everything will be done to minimise any associated risk, you will be performing weightlifting movements

which carry a small risk of injury, however due to a comprehensive warm up the risks have been reduced.

Clothing

Please bring with you tight fitting clothing to make data collection as accurate as possible.

Time demands

The research has been design to take no longer than 4 hours during 1 day of testing.

Print Name:


Signed: Date:

Organiser:

Contact Matt Parr for any further details: mobile 07737415422 and Email [email protected]


Institution Consent Letter


Appendix 6

Actual Testing Protocol Notes

Finding equipment

- Things like synch box, cables, tape, EMG electrodes are in Room 25 (equipment store)

Prepare area

- Arrange lifting boxes, bar and plates

- Put EMG units on floor against the wall

- Connect long Ethernet cable to computer (back bottom right and into the back of the

Biopac boxes

- Set up Physio bed near computers for electrode placement

- Stick all Vicon markers on to sticky tape and cut up

Turn on both computers (Vicon & EMG)

- Turn on all 3 Vicon boxes & other small box (on top and back)

- Turn on EMG Biopac units – check the sliding scale on top correlates to channels

Connect the synch box cables from splitter to EMG units and to Vicon Desktop

- Connect the cables from splitter to the correct ports

- Screw aerial into synch box

Vicon

- Load “Vicon Nexus”

- In “system” select “the pricep”

Cameras

- Highlight all cameras by clicking on press shift and highlight them all

- Now select each camera individually

- Click Paintbrush to get blue screen

- Remove any marks on screen by clicking on the cross icon, then click each mark which

should disappear.

Calibration

- Click “calibrate cameras”

- Move immediately to stand on FP with wand, wave wand around smoothly and at

different height – I must move around as well until all cameras show green lights

- Click stop to stop calibration or look at progress bar at bottom right corner

- Now place triangle marker at the Rear Right corner (when sitting at computer), ensuring

the metal guides are in the groove and its level

- Press “Set volume origin” and then “Set origin”


Data storage

- Create name titled folders with Your folder on the D drive

- Press F2 to bring up files

- Select “new database’ & save to the correct folder you created in the D drive

- Find and open the newly created file

- Create new ‘top level’, patient & session for each person with a new session for each trial

- Go back to main Vicon tab

- Click “subjects”

- Click create new subject from model template

- Select Freebody

EMG

On EMG computer load Acqknowledge

- Select the tab “MP150” from toolbar

Choose “setup acquisition”

- Set sample rate at 1000 & acquisition rate at 20,000,000 or 20 seconds

Back to MP150 and select “set up channel”

- Setup 1 channel per muscle and 1 for the synch

- Select analogue and select each tick box

- Label the channels

- Repeat for digital

- Now select “calculations”

- Highlight each channel individually and choose setup

- Ensure the channels match up to the analogue channels

- Choose source, select Average over samples, sample rate = 200, select “rectify”

- Once completed close box to save settings

EMG electrode placement

- Shave areas and clean with alcohol wipes

- Find correct placement (see attached image)

- Use RED electrodes

- Stick on straightaway once off strip

- Place overlapping so that only the metal nipples are 2cm apart - - 2 on GMED and 2 on

GMAX plus 1 on shin as an earth wire.

- Leave on for 5-10 mins before activity begins to allow good connection

Vicon marker placement (see attached image)

- May need to shave some of the marker positions

- Spray marker areas with adhesive spray

- Label markers

Vicon Orientation

- Select the capture button that looks like a “take clapper” (top right)

- Select session and then choose the correct ‘session’ to use as the profile

- Select “Dynamic trail with force”

- Right click on FP & select “zero level” DO THIS FOR EACH SUBJECT

- Subject stands still on FP


- Press start and collect 5 sec of data then click stop

Data Collection

- Zero FP

- Select “freebody’ in subject drop down for each subject

- Click start on both EMG (Green button) and Vicon (start)

- Press Red button on small portable black box (looks like a car remote) close before the

start of the lifts.

- Press stop after lifts

Vicon post activity

- On Vicon press F2 to bring the last trial forward

- Select the appropriate session and double click, it will then open in main Vicon tab

- Click on cog wheel (top right) or “pipeline’

- Select reconstruct label

- Click the play button (triangle)

- Select “Tools” then “Pipeline” then Phil March 2012

- Press Play and it is saved

- To find data Select D drive find file then copy and paste it to own storage device

- To begin next dynamic trail click “Go Live”

EMG post activity

- Select “Save As” for every trial one batch of lifts = 1 file

- Save as subjects name or number (uniform)

- Always as 2 separate Acqknowledge and excel files.

MVIC

- Use manual testing protocol (lying)

- Save as both text file and Acqknowledge file

Vicon marker labelling

- Label markers post testing to speed process up.

- Click the labels tag

- Click on each marker and label according to marker map


Appendix 7

Graphic illustration of the contrasting FBB and EMG trends

Figure 6. FBB muscular force outputs and EMG recruitment in control and intervention trials

0

0.5

1

1.5

2

2.5

3

3.5

4

GRF Bi Ham GMAX GMED Quad

Fo

rce

(xB

W)

FBB Combined Peak Force Means


0

10

20

30

40

50

60

70

80

90

100

% M

VIC

GMAX GMED

EMG Peak MU Recruitment



Appendix 8

Crow et al (2012) Gluteal Activation Warm Up Protocol

Exercise Repetitions Sets

Quadruped lower extremity

lift

10 1

Double leg bridge

10 1

Quadruped hip abduction 10 1

Side lying clam in 60 hip

flexion

10 1

Side lying hip abduction 10 1

Prone single leg hip

extension

10 1

Double leg stability ball

squat

10 1


Raw Data

EMG Cohort Data Part 1 of 4 Dropbox EMG Data

Function Subject Age Height (cm) Bodyweight (kg) Playing Position HHP 1RM HHP 1RM HHP 80%

1 30 195 114 Lock Elite 112.5 90

2 29 183 99 Wing Elite 115 92.5

3 19 185 103 Centre Elite 112.5 90

4 28 178 91 Fly half Int 95 75

5 22 188 101 Flanker Elite 105 85

6 30 193 110 Number 8 Int 105 85

7 24 178 96 Fullback Elite 112.5 90

8 23 185 95 Wing Elite 95 70

9 24 198 113.5 Flanker Int 112.5 90

10 28 185 112 Hooker Elite 125 100

11 28 183 93 Centre Elite 112.5 90

12 29 84 84 Fly half Elite 87.5 70

13 33 196 115 Lock Elite 112.5 90

14 25 194 110 Centre Elite 112.5 90

15 28 193 115.7 Flanker Int 120 95

16 21 182 114.2 Prop Elite 125 100

17 21 178 90.1 Scrum half Elite 95 75

Mean 26 181.0588235 103.3235294 #DIV/0! #DIV/0! 109.1176471 86.91176471

Median 28 185 103 #NUM! #NUM! 112.5 90

SD 3.819454843 25.09856004 10.12745421 #DIV/0! #DIV/0! 10.39446885 8.974203707



Part 2 of 4

GMAX MVIC GMED MVIC C Peak GMAX EMG C GMAX % MVIC C GMAX RFD % MVIC Int Peak GMAX EMG

0.00807312 0.024674683 0.01063446 1.31726771 0.000334996 0.010611572

0.019618835 0.03836731 0.008048401 0.410238462 1.72105E-05 0.02385437

0.0257483 0.0303476 0.0160657 0.623951873 3.67198E-05 0.0154276

0.007324219 0.01979126 0.006805725 0.929208333 1.85031E-05 0.006860046

0.017630005 0.035736694 0.015258789 0.865501125 2.71658E-05 0.013472595

0.010074768 0.021425476 0.009997559 0.992336352 2.33647E-05 0.008742371

0.014936829 0.0268307 0.00936798 0.627173294 8.54338E-06 0.00813049

0.014984741 0.040608215 0.013991699 0.933729787 2.49131E-05 0.01340332

0.011097717 0.021717529 0.010538635 0.949621889 3.98283E-05 0.008991394

0.016900024 0.015534363 0.01442749 0.853696414 1.11294E-05 0.010879211

0.0186362 0.0192331 0.0201373 1.080547537 3.06613E-05 0.0158847

0.0141653 0.0161777 0.0147937 1.044361927 1.1823E-05 0.0100766

0.029758606 0.014637756 0.007072754 0.237670874 1.46706E-05 0.007165222

0.015566406 0.034088745 0.010554504 0.678030897 5.35415E-05 0.009526367

0.009766235 0.017823792 0.009041748 0.925817136 2.56894E-05 0.008218079

0.007582703 0.160213013 0.005927734 0.781744275 1.79311E-05 0.006198425

0.005688477 0.009104004 0.010551147 1.854828326 3.4386E-05 0.010723572

0.014561911 0.032135996 0.011365607 0.88857213 4.30045E-05 0.011068584

0.014936829 0.021717529 0.010551147 0.925817136 2.49131E-05 0.0100766

0.006378651 0.033194651 0.003724412 0.346956932 7.3877E-05 0.004227216


Part 3 of 4

Int GMAX % MVIC Int GMAX RFD % MVIC C Peak GMED EMG C GMED % MVIC C GMED RFD % MVIC

1.3144326 4.14705E-05 0.018570557 0.752615826 7.77218E-05

1.215891232 1.74301E-05 0.02385437 0.621736848 7.62313E-05

0.599169654 2.56853E-05 0.0268582 0.885018914 4.01982E-05

0.936625 1.57789E-05 0.01340271 0.677203479 3.30985E-05

0.764185563 3.6336E-05 0.026880798 0.752190398 0.000115709

0.867749069 1.6043E-05 0.015126953 0.706026465 4.59408E-05

0.544325051 8.7191E-06 0.0161728 0.602772198 1.57731E-05

0.894464584 2.46234E-05 0.053282166 1.312103108 2.96323E-05

0.810202117 1.81729E-05 0.018608398 0.856837727 5.11359E-05

0.643739391 1.0777E-05 0.023111572 1.487770858 1.04162E-05

0.85235724 3.06633E-05 0.0191083 0.993511186 2.94914E-05

0.711358037 1.16123E-05 0.015387 0.951124078 1.16123E-05

0.240778152 1.34437E-05 0.009720764 0.664088398 5.10794E-05

0.611982434 3.82567E-05 0.013739014 0.403036651 4.54157E-05

0.841478658 3.77356E-05 0.013009644 0.729903262 7.60634E-05

0.81744275 1.77199E-05 0.008578491 0.053544285 4.24347E-05

1.885139485 2.09051E-05 0.010697632 1.175046929 3.92265E-05

0.85596006 2.2669E-05 0.019182904 0.801442977 4.654E-05

0.81744275 1.81729E-05 0.0161728 0.752190398 4.24347E-05

0.350318154 1.02816E-05 0.010099379 0.323939545 2.6449E-05


Part 4 of 4

Int Peak GMED EMG Int GMed % MVIC Int GMED RFD % MVIC

0.018527527 0.750871942 4.28621E-05

0.014719849 0.383656003 5.21964E-05

0.024826 0.818054805 4.9055E-05

0.015030212 0.759436872 4.23255E-05

0.021847229 0.611338833 0.000110918

0.014016418 0.654194026 3.93959E-05

0.0155298 0.578807113 1.29292E-05

0.036048279 0.887709014 3.5397E-05

0.016217957 0.746768029 5.7554E-05

0.014848938 0.955876864 2.206E-05

0.0139713 0.726419558 2.46187E-05

0.0145651 0.900319576 7.38129E-05

0.011072998 0.756468258 3.28218E-05

0.020202026 0.592630392 3.31192E-05

0.012684937 0.711685643 6.26335E-05

0.007224731 0.045094536 2.00206E-05

0.008859253 0.973116117 4.85321E-05

0.016481915 0.697202799 4.47207E-05

0.014848938 0.746768029 4.23255E-05

0.00647012 0.217838675 2.2677E-05


Adjusted EMG Cohort Dropbox EMG Data

Part 1 of 4

Subject Age Height (cm) Bodyweight (kg) Playing Position HHP 1RM HHP 1RM HHP 80%

Cox 30 195 114 Lock Elite 112.5 90

Fenby 29 183 99 Wing Elite 115 92.5

Gillsennsn 22 188 101 Flanker Elite 105 85

Guest 30 193 110 Number 8 Int 105 85

Homer 24 178 96 Fullback Elite 112.5 90

Lewington 23 185 95 Wing Elite 95 70

Low 24 198 113.5 Flanker Int 112.5 90

Mayhew 28 185 112 Hooker Elite 125 100

Mulchrone 28 183 93 Centre Elite 112.5 90

Smallbone 21 182 114.2 Prop Elite 125 100

Steele 21 178 90.1 Scrum half Elite 95 75

Mean 25.45454545 186.1818182 103.4363636 #DIV/0! #DIV/0! 110.4545455 87.95454545

Median 24 185 101 #NUM! #NUM! 112.5 90

SD 3.420888677 6.336304943 8.96460441 #DIV/0! #DIV/0! 9.523784927 8.71376796



Part 2 of 4

GMAX MVIC GMED MVIC C Peak GMAX EMG C GMAX % MVIC C GMAX RFD % MVIC Int Peak GMAX EMG

0.00807312 0.024674683 0.01063446 1.31726771 0.000334996 0.010611572

0.019618835 0.03836731 0.008048401 0.410238462 1.72105E-05 0.02385437

0.017630005 0.035736694 0.015258789 0.865501125 2.71658E-05 0.013472595

0.010074768 0.021425476 0.009997559 0.992336352 2.33647E-05 0.008742371

0.014936829 0.0268307 0.00936798 0.627173294 8.54338E-06 0.00813049

0.014984741 0.040608215 0.013991699 0.933729787 2.49131E-05 0.01340332

0.011097717 0.021717529 0.010538635 0.949621889 3.98283E-05 0.008991394

0.016900024 0.015534363 0.01442749 0.853696414 1.11294E-05 0.010879211

0.0186362 0.0192331 0.0201373 1.080547537 3.06613E-05 0.0158847

0.007582703 0.160213013 0.005927734 0.781744275 1.79311E-05 0.006198425

0.005688477 0.009104004 0.010551147 1.854828326 3.4386E-05 0.010723572

0.013202129 0.037585917 0.011716472 0.969698652 5.18299E-05 0.011899275

0.014936829 0.024674683 0.010551147 0.933729787 2.49131E-05 0.010723572

0.004658514 0.03986366 0.00375784 0.357861754 9.00001E-05 0.004599917


Part 3 of 4

Int GMAX % MVIC Int GMAX RFD % MVIC C Peak GMED EMG C GMED % MVIC C GMED RFD % MVIC

1.3144326 4.14705E-05 0.018570557 0.752615826 7.77218E-05

1.215891232 1.74301E-05 0.02385437 0.621736848 7.62313E-05

0.764185563 3.6336E-05 0.026880798 0.752190398 0.000115709

0.867749069 1.6043E-05 0.015126953 0.706026465 4.59408E-05

0.544325051 8.7191E-06 0.0161728 0.602772198 1.57731E-05

0.894464584 2.46234E-05 0.053282166 1.312103108 2.96323E-05

0.810202117 1.81729E-05 0.018608398 0.856837727 5.11359E-05

0.643739391 1.0777E-05 0.023111572 1.487770858 1.04162E-05

0.85235724 3.06633E-05 0.0191083 0.993511186 2.94914E-05

0.81744275 1.77199E-05 0.008578491 0.053544285 4.24347E-05

1.885139485 2.09051E-05 0.010697632 1.175046929 3.92265E-05

0.964539007 2.20782E-05 0.021272003 0.846741439 4.85193E-05

0.85235724 1.81729E-05 0.018608398 0.752615826 4.24347E-05

0.359932193 9.81354E-06 0.011369585 0.37341398 2.94108E-05


Part 4 of 4

Int Peak GMED EMG Int GMed % MVIC Int GMED RFD % MVIC

0.018527527 0.750871942 4.28621E-05

0.014719849 0.383656003 5.21964E-05

0.021847229 0.611338833 0.000110918

0.014016418 0.654194026 3.93959E-05

0.0155298 0.578807113 1.29292E-05

0.036048279 0.887709014 3.5397E-05

0.016217957 0.746768029 5.7554E-05

0.014848938 0.955876864 2.206E-05

0.0139713 0.726419558 2.46187E-05

0.007224731 0.045094536 2.00206E-05

0.008859253 0.973116117 4.85321E-05

0.016528298 0.664895639 4.24076E-05

0.014848938 0.726419558 3.93959E-05

0.007253338 0.25598991 2.56115E-05


FBB Data Dropbox FBB Data

Part 1 of 3

Function Subject Age Height (cm) Bodyweight (kg) Playing Position HHP 1RM HHP 1RM HHP 80%

1 30 195 114 Lock Elite 112.5 90

2 29 183 99 Wing Elite 115 92.5

3 22 188 101 Flanker Elite 105 85

4 24 178 96 Fullback Elite 112.5 90

5 23 185 95 Wing Elite 95 70

6 24 198 113.5 Flanker Int 112.5 90

7 28 185 112 Hooker Elite 125 100

8 28 183 93 Centre Elite 112.5 90

9 21 182 114.2 Prop Elite 125 100

10 21 178 90.1 Scrum half Elite 95 75

Mean 25 185.5 102.78 111 88.25

Median 24 184 100 112.5 90

SD 3.255764119 6.24899992 9.146671526 9.823441352 9.086390923



Part 2 of 3

C GRF C GRF RFD Int GRF Int GRF RFD C Bi Ham Int Bi Ham C GMAX C GMAX RFD

1.7604737 0.026513 9.17E-01 3.06E-01 2.7218488 8.71E-01 1.105637 0.12188

2.00E+00 0.0462876 2.00E+00 4.63E-02 3.1825462 2.72E+00 1.2335675 0.035185

1.46E+00 0.13275 1.46E+00 1.33E-01 1.75E+00 0.30094827 5.53E-01 0.042573

1.7496304 0.034329 2.1719277 0.187166 2.0343548 3.9264207 1.3806079 0.036334

1.62E+00 0.0199 1.84E+00 7.52E-02 3.03E+00 1.93E+00 1.28E+00 0.05265

1.67E+00 0.026513 1.86E+00 7.66E-02 3.27E+00 3.32E+00 2.63E+00 0.1064115

6.45E-01 0.322645 1.79E+00 1.18E-01 9.23E-01 3.28E+00 6.57E-01 0.0127775

1.91E+00 0.314295 1.82E+00 2.98E-02 9.23E-01 4.05E+00 6.99E-01 0.15406

1.77E+00 0.027915 1.53E+00 7.66E-01 2.99E+00 2.40E+00 1.36E+00 0.048075

1.28E+00 0.03050813 2.01E+00 2.76E-01 1.60E+00 3.74E+00 3.29E-01 0.0516694

1.587873283 0.098165573 1.739966194 0.201372815 2.24251863 2.653908085 1.123005025 0.06616154

1.7089494 0.032418565 1.8273488 0.12562 2.3781018 2.9997783 1.16960225 0.0498722

0.370409769 0.114442917 0.340000652 0.207885471 0.867689247 1.220840269 0.61561214 0.042925465


Part 3 of 3

Int GMAX Int GMAX RFD C GMED C GMED RFD Int GMED Int GMED RFD C Quad Int Quad

9.23E-01 2.96E-02 5.4672882 0.05958 3.00E+00 1.24E-02 0.40567547 6.59E-03

5.53E-01 3.52E-02 5.68E+00 0.07069 5.47E+00 7.07E-02 2.27E+00 4.06E-01

0.13938573 0.027877 3.43E+00 1.142197 0.25429196 0.082122 1.41E-02 0.33648865

2.3343267 0.214625 2.669167 0.027964 2.1565423 0.076287 2.3840857 2.7966903

1.89E-01 2.76E-02 2.89E+00 0.114313 2.98E+00 5.80E-01 2.38E+00 4.42E+00

1.61E+00 7.62E-02 3.90E+00 0.059579 4.86E+00 1.10E-01 2.38E+00 6.96E-01

8.61E-01 1.23E-01 8.97E-01 0.019726 3.11E+00 3.83E-02 1.76E+00 4.40E+00

3.86E+00 1.93E+00 1.37E+00 0.188527 5.02E+00 3.34E-01 2.91E+00 7.34E+00

1.11E+00 1.38E-01 3.57E+00 0.093726 3.54E+00 4.42E-01 1.89E+00 3.55E+00

1.98E+00 7.26E-02 1.64E+00 0.41004 6.85E+00 1.41E-01 8.94E-01 2.37E+00

1.355423888 0.26733464 3.151090724 0.2186342 3.723376396 0.18872876 1.728946377 2.63226972

1.01503332 0.074419 3.159800005 0.082208 3.3220944 0.095961 2.07978075 2.5825671

1.07916491 0.556596958 1.529502093 0.326404614 1.782347466 0.184086093 0.915338818 2.242945308


SPSS Data

SPSS Data

SPSS Outputs

SPSS Outputs

EMG t-tests

GMAX

Paired Samples Test

Paired Differences t df Sig.

(2-

taile

d)

Mean Std.

Deviation

Std. Error

Mean

95% Confidence Interval

of the Difference

Lower Upper

Pai

r 1

Pre

-

Po

st

2.7811437

77

23.7709779

12

5.7653089

86

-

9.4407652

93

15.0030528

47

.48

2

1

6

.636

GMAX Matched EMG Cohort

Paired Samples Test

Paired Differences t df Sig. (2-tailed)

Mean Std. Deviation

Std. Error Mean

95% Confidence Interval of the Difference

Lower Upper

Pair 1

GMAXPre - GMAXPost

.0051596445 .2829784069 .0853211999 -.1849478360

.1952671249 .060 10 .953




GMED

Paired Samples Test

Paired Differences t df Sig.

(2-

taile

d)

Mean Std.

Deviation

Std. Error

Mean

95% Confidence

Interval of the

Difference

Lower Upper

Pai

r 1

Pre

-

Po

st

9.6250884

33

18.5142083

87

4.4903551

03

.1059608

55

19.1442160

12

2.14

4

1

6

.048

GMED Matched EMG Cohort

Paired Samples Test

Paired Differences t df Sig. (2-

tailed) Mean Std.

Deviation

Std.

Error

Mean

95% Confidence

Interval of the

Difference

Lower Upper

Pair

1

GMEDPre -

GMEDPost

.18185 .17417 .05252 .06484 .29886 3.463 10 .006


RFD

Paired Samples Test


tailed) Mean Std.

Deviation

Std. Error

Mean

95% Confidence Interval of

the Difference

Lower Upper

Pair

1

PreGRF - GRF RFD

Pre

1.488344837 .457457535 .144660774 1.161099430 1.815590244 10.289 9 .000

Pair

2

PostGRF - GRF

RFD Post

1.538519955 .483807894 .152993490 1.192424637 1.884615273 10.056 9 .000

Pair

3

GRF RFD Pre -

GMED RFD Pre

-.120468627 .356143850 .112622574 -.375238589 .134301335 -1.070 9 .313

Pair

4

GRF RFD Post -

GMED RFD Post

.012644055 .253567966 .080185231 -.168747541 .194035651 .158 9 .878


FBB t-test

GMAX

Paired Samples Test

Paired Differences t df

Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair

1

Pre -

Post

-

.233190003

1.336908870 .422767705 -

1.189556996

.723176990 -.552 9

Paired Samples Test

Sig. (2-tailed)

Pair 1 Pre - Post .595

GMED

Paired Samples Test


Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair 1 Pre - Post -

.572737906

2.571074108 .813045021 -

2.411973525

1.266497713 -.704 9


Paired Samples Test

Sig. (2-tailed)


Bilateral Hamstring

Paired Samples Test


Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair

1

Pre -

Post

-

.411361917

1.798173341 .568632339 -

1.697697635

.874973801 -.723 9

Paired Samples Test

Sig. (2-tailed)



Quadriceps


Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair 1 Pre - Post -

.903390778

1.951436087 .617098274 -

2.299364060

.492582504 -1.464 9

Paired Samples Test

Sig. (2-tailed)


GRF

Paired Samples Test


Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair

1

Pre -

Post

-

.153382360

.542488751 .171550006 -

.541455435

.234690715 -.894 9


Paired Samples Test

Sig. (2-tailed)


Hip Extension Angle

Paired Samples Test


Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair

1

Pre -

Post

-

.101294000

9.966962062 3.151830147 -

7.231229142

7.028641142 -.032 9

Paired Samples Test

Sig. (2-tailed)



FBB RFD t-tests

Paired Samples Test


tailed) Mean Std.

Deviation

Std. Error

Mean

95% Confidence Interval of

the Difference

Lower Upper

Pair 1 Premed -

Postmed

.029905440 .415572119 .131415443 -.267376945 .327187825 .228 9 .825

Pair 2 Premax -

Postmax

-.201173100 .558304419 .176551359 -.600560022 .198213822 -1.139 9 .284

Pair 3 Pregrf - Postgrf -.103207242 .284426393 .089943523 -.306673627 .100259143 -1.147 9 .281


Linear Regressions

Control GMAX V Control GRF

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression .128 1 .128 .989 .349b

Residual 1.032 8 .129

Total 1.160 9

a. Dependent Variable: PostGRF

b. Predictors: (Constant), PostGMAX

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 1.598 .182 8.763 .000

PostGMAX .105 .105 .332 .995 .349


Intervention GMAX V Intervention GRF

ANOVAa

Model Sum of

Squares


1

Regression .128 1 .128 .989 .349b

Residual 1.032 8 .129

Total 1.160 9


b. Predictors: (Constant), PostGMAX

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 1.598 .182 8.763 .000

PostGMAX .105 .105 .332 .995 .349



Control GMAX RFD V Control GRF RFD

ANOVAa

Model Sum of

Squares


1

Regression .002 1 .002 .127 .731b

Residual .129 8 .016

Total .131 9

a. Dependent Variable: GRF RFD Pre

b. Predictors: (Constant), GMAX RFD Pre

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .076 .074 1.032 .332

GMAX RFD

Pre

.333 .935 .125 .356 .731


Intervention GMAX RFD V Intervention GRF RFD

ANOVAa

Model Sum of

Squares


1

Regression .025 1 .025 .489 .504b


Total .432 9

a. Dependent Variable: GRF RFD Post

b. Predictors: (Constant), GMAX RFD Post

Coefficientsa


Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) .225 .079 2.847 .022


GMAX RFD

Post

-.090 .128 -.240 -.700 .504


Control GMED V Control GRF

ANOVAa

Model Sum of

Squares


1

Regression .516 1 .516 4.825 .059b


Total 1.371 9

a. Dependent Variable: PreGRF

b. Predictors: (Constant), PreGMED

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 1.119 .237 4.727 .001

PreGMED .148 .068 .613 2.197 .059

a. Dependent Variable: PreGRF

Intervention GMED V Intervention GRF

ANOVAa

Model Sum of

Squares


1

Regression .176 1 .176 1.426 .267b


Total 1.160 9


b. Predictors: (Constant), PostGMED

Coefficientsa


Coefficients

t Sig.

B Std. Error Beta


1 (Constant) 1.463 .257 5.694 .000

PostGMED .074 .062 .389 1.194 .267


Control GMED RFD V Control GRF RFD

ANOVAa

Model Sum of

Squares


1

Regression .001 1 .001 .043 .840b


Total .131 9


b. Predictors: (Constant), GMED RFD Pre

Coefficientsa


Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .093 .049 1.905 .093

GMED RFD

Pre

.026 .124 .073 .208 .840


Intervention GMED RFD V Intervention GRF RFD

ANOVAa

Model Sum of

Squares


1

Regression .027 1 .027 .539 .484b


Total .432 9


b. Predictors: (Constant), GMED RFD Post

Coefficientsa


Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta


1

(Constant) .148 .102 1.451 .185

GMED RFD

Post

.284 .386 .251 .734 .484
