A Thesis Entitled Effects of Playing Surface on Muscle Activation and Plantar Pressure in Collegiate Football Players by Ema

Home , Peroneus brevis, Peroneus longus, Peroneus tertius

A Thesis

entitled

Effects of Playing Surface on Muscle Activation and Plantar Pressure in Collegiate

Football Players

Ema Kossin

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Masters in Science Degree in Exercise Science with a Concentration in Athletic Training

______Dr. Neal Glaviano, Committee Chair

______Dr. Grant Norte, Committee Member

______Dr. Cindy Bouillon, Committee Member

______Dr. Amanda Bryant-Friedrich, Dean College of Graduate Studies

The University of Toledo

May 2018

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of

Effects of Playing Surface on Muscle Activation and Plantar Pressure in Collegiate Football Players

Ema Kossin

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Exercise Science

The University of Toledo May 2018

During the jogging task, the gluteus medius differed on the laboratory and the turf

(p=.016), and the peroneus longus between the grass and the turf (p=.029). During the cutting task, the peroneus longus differed on the grass compared to the turf (p=0.038).

iii During the sprinting task, the biceps femoris differed on the grass compared to the turf

(p=.011). During the cutting task, the PTI differed between the turf and the grass

(p=.044). Conclusions: There were differences in muscle activation in the lower extremity across all three surfaces. Greater differences were seen in the distal muscles.

There was no significance in the mean pressure of the plantar pressure force distributions.

This new information could influence how clinicians rehabilitate lower extremity injuries and return to play decisions. More research will need to be done to identify if these differences observed match up with previous studies on injury rate differences.

Word Count 291

Table of Contents

Abstract iii

Table of Contents v

List of Tables vii

List of Figures viii

List of Abbreviations ix

List of Symbols x

I. Manuscript 1

A. Introduction 1

Methods 2

a. Study Design 2

b. Participants 2

c. Instrumentation 3

d. Procedures 4

e. Data Analysis 5

f. Statistical Analysis 5

C. Results 6

D. Discussion 6

E. Conclusion 11

References 12

Appendices

A. The Problem 21

B. Literature Review 25

C. Additional Methods 36

D. Additional results 62

E. Back Matter 82

F. Bibliography 85

List of Tables

Table 1 Differences in EMG Activity During the Jogging Task ...... 15

Table 2 Differences in EMG Activity During the Cutting Task ...... 16

Table 3 Differences in EMG Activity During the Sprinting Task ...... 17

Table 4 Differences in Plantar Pressure Activity During the Functional Tasks ...... 18

Table 5 Weather Log during Collection ...... 19

vii

List of Figures

Figure 1 Flowchart of functional tasks across the three playing surfaces ...... 20

viii

List of Abbreviations

ACL...... Anterior Cruciate Ligament ATFL...... Anterior Talofibular Ligament

CAI ...... Chronic Ankle Instability CFL ...... Calcaneofibular Ligament

EMG ...... Electromyography

FIFA ...... Fédération Internationale de Football Association FTCL ...... Fibulotalocalcaneal Ligament

IER ...... Inferior Extensor Retinacula IMU ...... Inertial Measurement Units IT ...... Illiotibial kPa Kilopascal kPa*s ...... Kilopascal per second

LCL ...... Lateral Collateral Ligament LTCL...... Lateral Talocalcaneal Ligament

MCL ...... Medial Collateral Ligament

NCAA ...... National Collegiate Athletic Association NFL ...... National Football League

PCL ...... Posterior Cruciate Ligament PTFL ...... Posterior Talofibular Ligament PTI...... Pressure Time Integral

List of Symbols

#...... p<0.05 when compared to the lab *...... p<0.05 when compared to the grass †...... p<0.05 when compared to the turf

Chapter One

Manuscript

Introduction

Every day millions of people participate in athletic related activities; from professional athletes completing at the elite level, to weekend warriors just having fun with friends. Several sports such as football, soccer, and lacrosse are played predominantly outdoors; however, the playing surface is not always the same. The playing surface can change between natural grass and artificial turfs. With athletes changing playing surfaces during the course of a season, it has been found that injury rates change based on the playing surface.1,2 With more injuries occurring on artificial turf than on grass.1,2

In sports such as football, soccer, and lacrosse there are two types of injuries that can occur: contact injuries and noncontact injuries.3 Contact injuries are a result of player-to-player contact, and most likely not influenced too much by the playing surface.3 Noncontact injuries are when the player is injured during participation and it is not due to collision with another player. Many factors can lead to this type of injury such as: overuse, sport specific movements, and force distributions on different joints due to muscle activation and plantar pressure distributions.4,5 Majority of noncontact injuries that occur are lower extremity injuries. Ninety-two percent of all muscle injures and fifty-three percent of all injuries occur to the lower extremity.3,6 Previous studies have found a five percent increase in lower extremity injuries when soccer was played on artificial turf when compared to grass.7 As well as a thirty-eight percent increase in overuse injuries when played on artificial turf when compared to natural grass.2 This could be due to differences in muscle activation occurring on the turf compared to the natural grass or any other training surface.

Knowing that players are occurring injuries at different rates when they play on artificial turf as compared to playing on natural grass, something has to be changing with how the athlete’s body is responding to the playing surface. However, there is no literature when it comes to how a person’s body responds to different playing surfaces; this includes the laboratory setting, where most rehabilitation will

occur if a person is injured. It is still unclear in the literature if there is a difference in plantar pressure distributions and muscle activation on different playing surfaces. Hence the purpose of this study is to determine if performing different functional tasks on artificial turf, natural grass, and in the laboratory setting affects the muscle activation and plantar pressure of the lower extremity in collegiate football players.

Methods

Study Design:

This study was a crossover study design with the interventions being the same for all participants.

The independent variables were playing surface conditions: (1) natural grass, (2) artificial turf, and (3) laboratory setting; during three different functional tasks: (1) 10-yard jog at 50%, (2) a ten-yard jog at

100%, and a (3) ten-yard jog at 50% with a cutting task being performed off the dominant leg. The surfaces were tested in a randomized predetermined order, and the functional tasks were performed in the randomized predetermined order. The dependent variables were electromyography (EMG) peak and mean for eight lower extremity muscles, and plantar pressure readings of mean pressure and pressure-time integral.

Participants

Volunteers were recruited from the University of Toledo athletics department. A convenience sample of 9 male participants (age: 20.42.0 years, height: 72.9  2.5 cm, and mass: 93.712.3 kg) were enrolled. Participants were enrolled if they met the inclusion criteria: between 18 and 25 years old, and participated on the University of Toledo football team. Exclusion criteria were any lower extremity injury within the past six months, any low back injury within the past six months, any history of having a lower extremity surgery, and a men’s shoe size over 15. A single researcher (E.L.K.) reviewed participants’ lower extremity medical history to identify possible exclusion criteria before participants were enrolled in the study. The dominant leg was decided upon by the participant, based on which leg they would kick a football with. The study received approval from the institutional review board (IRB # 202073), and all participants provided written informed consent before enrollment.

Instrumentation:

Electromyography

Surface electromyography (EMG) was collected using Noraxon DTS wireless system and analyzed in Myomotion ResearchPro. A 2000 Hz sampling rate, with 10-500Hz band pass filter was used for collection. The electrodes used were Ag/AgCl electrodes with a standardized inter-electrode distance of 2 cm. The electrode locations were shaved with a disposable razor, debride with gauze, and then cleaned with an alcohol pad. Electrodes were placed over the selected muscles on the dominant limb with the proper pad placement based off the recommendations by SENIAMS, and then a manual muscle test performed to verify proper electrode placement.8 Surface EMG was collected from the gluteus maximus, gluteus medius, biceps femoris, vastus lateralis, vastus medialis, lateral head of the gastrocnemius, peroneus longus, and anterior tibialis.

Inertial Measurement Units (IMUs)

An inertial measurement unit (IMU) was collected using Noraxon Inertial Measurment Unit and analyzed in sync with the EMG data in Myomotion ResearchPro. IMU was used as a reference to align kinematics of the lower extremity to up the timing of the EMG and plantar pressure with the functional tasks. IMU sensors were placed on the pelvis, mid shank and mid-thigh of the dominant limb and fastened down with straps.

Plantar Pressure

Plantar pressure was recorded using Teckscan® F-Scan® VersaTek Wireless System with F-

Scan® 3000E in-shoe sensors. Data was collected at a sampling rate of 100Hz and was analyzed with

Teckscan® research software (v7). Teckscan F-Scan 7 has been showed good to high reliability in the toe and forefoot region when tested on healthy active population walking on a treadmill9. This data was consistent with other in-shoe plantar pressure systems.9 Plantar pressure inserts were prepared to fit in the participants shoe size, and placed into the athletes shoe, Nike Metcon3 (Nike, Beaverton, OR). Once the plantar pressure system was set up on the participant with the data transmission boxes strapped down to the ankles, the cords wrapped to the thighs, and the home unit securely fastened around their waist.

Procedures

Participants who were eligible and met the inclusion and exclusion criteria were enrolled in the study. EMG electrodes were placed over the eight muscles of interest and IMU sensors were secured on the pelvis, shank and thigh. Plantar pressure inserts were then selected to the individuals shoe size and inserted so it sat flush in the shoe. Participants were instructed to place their foot carefully in the shoe, trying to minimize any movement of the insert. All wires were connected to the transmitting box that sat on the individual’s pelvis. Once participant setup was completed, a 5-second quiet standing trial was collect for EMG normalization and simultaneously calibrated with the IMUs. Calibration of the plantar pressure was then performed by having the participant stand on the right leg, and then switch to the left, and then repeated by starting on the left foot and switching to the right. This is done to allow the system to obtain the individual participants base single limb stance, as well as non-weight bearing readings. Once all the instrumentation was calibrated the participant was given as much time as they needed to move around and become comfortable with the equipment. No practice trials were done, due to participants familiarity with the tasks.

Following calibration of all instruments, a concealed envelop was opened and playing surface and task order was identified for the participant. The participants were tested in the laboratory-based setting, on natural grass, and on artificial turf; order was randomized and determined before trials began.

Participants completed three functional tasks: 10-yard jog at 50% of their maximal sprint speed, 10-yard sprint at 100% of their maximal sprint speed, and a cutting task which was a 10-yard jog at 50% of their maximal speed with a cut off the dominant leg once reached 10 yards.10 (Figure 1) Standardized instructions were provided prior to all participants and three practice trials were provided on each playing surface. The tests were performed in the same order on all three surfaces, the order was randomized among participants and determined before the trial began. All tests were performed three times on each surface to obtain an average value for each surface tested. A 30-second rest was provided to all participants between trials and 1-minute rest between tasks. There was also a 5-minute break between

surfaces. Once data collection was completed on all surfaces, the instrumentation was all removed from the participant and they were dismissed from the study.

Data Analysis

Outcome measures were obtained from EMG and from the plantar pressure insoles to analyze the differences between force distribution and muscle activation among the different surfaces while performing functional tasks. The EMG readings were analyzed to calculate the mean of muscle activation of each lower extremity muscle during each task. The plantar pressure readings were analyzed to calculate: the mean pressure (KPa) and the pressure-time integral (PTI) (KPa*s).

EMG data for the three tasks was normalized to quiet standing trials for all 8 muscles, which were expressed as times greater than quiet standing muscle activity. The mean muscle activity of each muscle was calculated during the three tasks over the three surfaces. EMG data during the functional tasks were identified with kinematic data from the IMU units. The jog EMG activity was defined as the terminal knee extension at heel contact of the second step until terminal knee extension of heel contact of the third step. Plantar pressure of the jog was analyzed using steps two through four. The sprint EMG activity was defined as the terminal knee extension at heel contact of the second step until terminal knee extension of heel contact of the third step. Plantar pressure of the sprint was analyzed using steps two and three. The cut EMG activity was defined as the terminal knee extension at heel contact of the cut until terminal knee extension of heel contact of the following step. Plantar pressure of the cut was analyzed using the step of the cut. EMG data was rectified, filtered with a 10-500 Hz band pass filter and a 60 Hz notch filter, and smoothed with a 50 ms window.

Statistical Analysis

All data was assessed for normality with skewness and kurtosis. Separate repeated analysis of variance (ANOVA) were conducted for lower extremity muscle activity 9eight muscles) and plantar pressure (two levels) between the three playing surfaces (jog, sprint, and cut). Alpha was set a priori at p<.05 and Tukey post-hoc testing was conducted when appropriate. All statistics were performed in

SPSS 23.0 (IBM SPSS, Armonk, NY).

Results

EMG

There were statistical differences in the mean EMG activity for three muscles depending on the task that was being performed. During the jogging task, the gluteus medius had significance between the laboratory setting and the turf (p=0.016), with the laboratory setting resulting in greater activation. (Table

1) The peroneus longus also had a significant difference during the jogging task, between the natural grass and the turf (p=0.029), with an increase in activity on the turf. (Table 1) During the cutting task, the peroneus longus had significant difference between the natural grass and the turf (p=0.038), with increased activity on the grass. (Table 2) During the sprinting task, the biceps femoris had a significant difference, with an increase activity on the turf when compared to the natural grass (p=0.011). (Table 3)

Plantar Pressure

There were no differences in the mean plantar pressure during the three functional tasks across the three surfaces. There was a statistical difference in the pressure time integral during the cutting task, with a greater KPa*second on the turf compared to the natural grass (p=0.044). (Table 4)

Discussion

The purpose of this study was to evaluate how playing surface influences muscle activation and plantar pressure in collegiate football players during three functional tasks. We found some differences in muscles activation, mainly in the distal shank, across the three playing surfaces. The most notable differences seen were when comparing grass and turf playing surfaces across the tasks. Plantar pressure did not have much significant difference across the playing surface, with the exception of PTI during the cutting task between the grass and turf surfaces.

EMG

The EMG data showed significant differences in the means of three of the muscles of the lower extremity, the gluteus medius, peroneus longus, and biceps femoris. Peroneus longus was found to be more active in two of the three functional tasks when comparing grass and turf surfaces. Interestingly, there was an increase in peroneal longus activity on the turf during the jogging task; however, the grass

resulted in greater activity during the cutting task. There did not appear to be a consistent trend in muscle activity during the functional tasks across the three surfaces. Previous research supports the findings that uneven surfaces, such as grass and turf, have higher muscle activation when compared to a laboratory or treadmill.11,12 The higher muscle activation on the grass and turf settings could be due to an unanticipatory response by the muscles as they are coming in contact with the surface and the physical response to the ground reaction forces being absorbed by the body.13,14 This could help explain why muscles have a higher mean EMG activation on the uneven surfaces (grass and turf) compared to the lab.

The grass and turf had the greatest mean muscle activation across all three functional tasks in the tested muscles, besides the vastus medialis and gluteus medius. The turf specifically had the highest mean activation across the most distal muscles during the jog, run and cut. This is consistent with previous research that has found that when overground running (indoor track) was compared to treadmill running the tibialis anterior, soleous, and peroneus longus activated more in overground running.11 The greater activity in the distal muscles may be a strategy to provide stability during activity. Previous research has found that when comparing uneven surface (uneven foam and even foam) to even surface (treadmill) the rectus femoris, vastus lateralis, vastus medalis, and medial hamstring muscles activated more on uneven surfaces.12 Another study compared uneven surface treadmill walking and running to even surface treadmill walking and running, and found that uneven surfaces have a decreased dynamic stability when compared to even surfaces.15 In a previous study done on tennis players it was found that when comparing clay courts to acrylic courts, clay courts had greater ankle inversion, ankle dorsiflexion, and knee flexion than acrylic courts.16 This study also found that players that usually played on clay still had increased kinematics on clay compared to acrylic and that they made compensations elsewhere in the kinetic chain to accommodate for the clay surface.16

The gluteus medius had significantly higher activation in the laboratory than the turf during the jogging task, which is inconsistent with previous research. This could possibly be explained by the tennis study that found altered mechanic on familiar surfaces.16 The participants in this study usually practice on a turf field and the increased distal activation could have been partially due to accommodations that their

body naturally makes, which may have reduced gluteus medius activation on that surface during a simple task.15 However, when they were in the lab setting their body knew the ground reaction forces it would be absorbing and the distal segments were not required to work as much forcing the gluteus medius to activate more.

The biceps femoris had increased muscle activation on the turf field when compared to the grass field during the sprinting task. This difference in muscle activation is likely due to the increased intensity of the task, sprinting requires much more hamstring activation due to the increased knee flexion and hip extension than walking and jogging.17 Previous studies have found that biceps femoris muscle activity is greatest during the early stance phase and the late swing phase, indicating that the biceps femoris has the greatest activation directly before and after foot contact with the ground.18 A previous study found that when athletes practice on a surface they make kinematic accommodations to play on that surface, which may help explain this difference that was observed.16 Being that the participants usually practice on turf, the increase in biceps femoris activity may be as a result of accommodations more distal on the kinetic chain, that were not made on the grass field.

The peroneus longus muscle activation differences could be as a result of how the foot is contacting the ground, and how the body is requiring altered muscle activation compared to a firm surface when reacting to the change in playing surface. There was greater activation on uneven surfaces then in the laboratory setting, which is consistent with previous literature.11,12 Past studies have found that in a healthy population, tasks with increased complexity such as foot inversion, single limb balance, or maintaining control of knee or ankle stability will result in greater activation of the lower limb.19 This study also found that increased activation of one lower leg muscle usually occurs simultaneously with increased activation of multiple lower leg muscles.19 Other studies have found that in a population of subjects with chronic ankle instability (CAI) the lower leg muscles activate quicker than a healthy population. In particular the tibialis anterior and the peroneus longus activate quicker, and the tibialis anterior is activated for a longer period of time.20

Changes in muscle activity on different playing surfaces may play a role in clinical practice.

Sports such as football and soccer may require athletes to change playing surface during their athletic season. Some teams attempt to train their athletes on the playing surfaces specific to their upcoming competitions, resulting in practices across different surfaces. Since differences seen occurred during the grass and turf surfaces, this might be a clinical recommendation in an effort to provide some training and familiarity on the different surfaces.

It may also influence return to sport protocols following injury. Athletes commonly perform rehabilitation during a stable surface, similar to the lab setting. Introducing athletes to different playing surfaces during their return to sport protocols, most specifically with sports specific exercises, may provide some benefit to provide variability to complete the tasks on different surfaces they may utilize during their sport. We saw these muscle activity differences in a healthy population on different surfaces, and we know that there are differences in lower extremity muscle activity in the CAI populations, specifically of the peroneus longus and tibias anterior.20 It is unknown how differences in muscle activity transfer over to an unhealthy population that already presents with altered muscle activity.

Knowing that the artificial turf and natural grass have increased muscle activity when compared to the lab, could lead us to future research. One question is if changes in lower extremity muscle activation on different surfaces could explain the differences in injury rates that has already been observed on different playing surfaces.1,2,6 Evidence exists that higher injury rates occur during athletic participation on turf surface.1,7 While playing surface has been suggested to influence injury rates, the underlying mechanisms for increased injury rate is unknown. While the current study did not assess injury rates, the differences in muscle activity may be one potential influence. These differences in muscle activity occurred during controlled tasks where the participations received input when the task was to be completed by the research team. This controlled environment decreases additional factors that are seen during actual athletic participation, such as opposing players, unplanned tasks, and decreased preparation time to completion of the task. It could be speculated that as these factors are added, the

difficulty of the tasks increases and might provide more significant differences in both muscle activity and plantar pressure.

Plantar Pressure

Plantar pressure data only showed significance in the pressure time integral during the cutting task on the natural grass compared to the artificial turf, with the artificial turf being significantly higher.

(table 4). This is different from what previous research has stated about plantar pressure and changing surfaces. Previous study done on tennis players showed that there was increased mean pressure on greenset when compared to clay courts.21,22 The PTI difference in the cut could possibly be explained some by a tennis study that found that clay had a longer contact time than greenset.21 This could potentially be problematic when it comes to returning athletes from any type of lower extremity injury.

An increase in PTI on turf, means that the amount of force being placed on the foot is substantially higher than the grass over a shorter period of time. With a higher force over less time, it is most likely affecting the force distribution up the kinetic chain, which could lead to injury if an athlete is already compensating due to return from an injury. More research would need to be done on how the force distribution in the foot alters force distributions higher up the kinetic chain during sport specific tasks. Future research could also be done on if the cleat changes the plantar pressure distribution and if that would intern also alter the muscle activation. More research should assess how different type of cleats (high top, mid-cut, and low- cut) change the pressure distribution and muscle activation, as well as brand type (Nike, Under Armor,

Adidas, ect).

Limitations

One limitation of this study is that we had no way to control the weather. Temperature, heat index, and rain/snow the previous day was recorded (Table 5) to try and limit this factor as much as possible, however it is not known how this may alter the findings. Another limitation was the instrumentation used, as the equipment is often used in the laboratory setting, it is unknown how temperature may influence surface EMG or plantar pressure. The tasks that we used were chosen based off previous research, the participants were not given minimal directions such as how far to run, what

percent to run at, and when to make the cut with the goal of the tasks being as natural as possible. In the future, more research could be done with different tasks, sport specific skills, and unanticipated reactions.

We did have a small sample size in this study, but due to the cross over design of the study, we were able to make comparisons across individuals performing the tasks on each surface. While the lower sample size may be responsible for larger levels of variability, future studies could use a larger sample size and possibly incorporate different levels of collegiate football players. Finally, the EMG data was normalized to a quiet standing for data analysis. While this is a common normalization procedure, the variability of some of the subjects was greater than anticipated, possibly due to the demands of the tasks. Future data could normalize the data to MVIC or peak activation in an effort to improve the variability.

Conclusion

We identified that there are significant differences in muscle activation across different playing surfaces. EMG activation on average is higher on grass and turf than in the laboratory setting, while plantar pressure distributions only differed in the PTI between grass and turf. Clinicians should be aware of these differences in muscle activity during the rehabilitation process, return to play decisions, and when working with athletes who alter playing surface during the athletic season.

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Table 1: Differences in EMG Activity During the Jogging Task (mean  SD) Muscle Lab Grass Turf Gluteus Medius 115.9 ± 195.6 † 97.8 ± 118.1 53.4 ± 80.3 # Gluteus Maximus 53.5 ± 56.4 123.5 ± 201.8 162.7 ± 215.1 Biceps Femoris 121.4 ± 98.5 136.0 ± 150.6 151.9 ± 136.1 Vastus Lateralis 47.2 ± 54.0 110.2 ± 128.5 177.6 ± 216.2 Vastus Medialis 118.5 ± 181.0 117.0 ± 146.5 97.3 ± 77.8 Oblique Lateral Gastrocnemius 108.0 ± 170.8 76.2 ± 88.2 111.8 ± 195.8 Peroneal Longus 17.4 ± 12.5 17.3 ± 12.4 † 27.4 ± 23.9 * Tibialis Anterior 26.3 ± 13.7 28.5 ± 18.0 33.3 ± 18.0 Values are presented as times greater than quiet standing muscle activity # p<0.05 compared to Lab * p<0.05 compared to Grass † p<0.05 compared to Turf

Table 2: Differences in EMG Activity During the Cutting Task (mean  SD) Muscle Lab Grass Turf Gluteus Medius 100.9 ± 141.8 148.8 ± 190.8 77.2 ± 112.1 Gluteus Maximus 85.1 ± 108.6 168.1 ± 219.9 208.0 ± 258.7 Biceps Femoris 67.6 ± 54.8 75.7 ± 107.2 88.3 ± 77.7 Vastus Lateralis 65.3 ± 60.9 106.8 ± 103.9 160.3 ± 204.1 Vastus Medialis 147.5 ± 190.0 121.2 ± 131.3 109.1 ± 106.1 Oblique Lateral Gastrocnemius 106.4 ± 172.3 116.8 ± 191.9 109.8 ± 221.7 Peroneal Longus 24.7 ± 21.7 36.1 ± 27.8 † 32.2 ± 26.9 * Tibialis Anterior 35.6 ± 21.4 33.7 ±22.3 38.0 ± 20.1 Values are presented as times greater than quiet standing muscle activity # p<0.05 compared to Lab * p<0.05 compared to Grass † p<0.05 compared to Turf

Table 3: Differences in EMG Activity During the Sprinting Task (mean  SD) Muscle Lab Grass Turf Gluteus Medius 118.7 ± 184.3 178.8 ± 224.9 84.0 ± 117.1 Gluteus Maximus 67.8 ± 42.0 236.7 ± 229.7 119.5 ± 216.8 Biceps Femoris 71.1 ± 57.3 93.7 ± 50.7 † 101.3 ± 93.9 * Vastus Lateralis 64.9 ± 36.2 143.3 ± 168.8 157.4 ± 186.7 Vastus Medialis 103.7 ± 86.3 145.4 ± 97.7 182.3 ± 209.2 Oblique Lateral Gastrocnemius 155.8 ± 235.9 150.5 ± 200.7 160.4 ± 242.2 Peroneal Longus 25.8 ± 17.0 35.9 ± 22.2 48.2 ± 25.0 Tibialis Anterior 40.6 ± 24.6 42.9 ± 25.6 53.0 ± 29.0 Values are presented as times greater than quiet standing muscle activity # p<0.05 compared to Lab * p<0.05 compared to Grass † p<0.05 compared to Turf

Table 4: Differences in Plantar Pressure Activity During the Functional Tasks (mean  SD) Functional Task Lab Grass Turf Jog Mean (kPa) 234.2 ± 187.2 249.3 ± 121.3 146.6 ± 73.8 Jog PTI (kPa*s) 78.9 ± 45.9 86.2 ± 18.7 89.1 ± 52.7 Cut Mean (kPa) 260.9 ± 165.2 329.3 ± 168.8 254.0 ± 159.8 Cut PTI(kPa*s) 74.3 ± 38.8 79.6 ± 21.3 † 277.0 ± 161.0 * Sprint Mean (kPa) 287.1 ± 263.0 344.1 ± 184.5 353.3 ± 273.3 Sprint PTI (kPa*s) 80.8 ± 44.0 96.7 ± 21.1 93.5 ± 40.9 PTI: Pressure Time Interval, kPA: kilopascal, s: second # p<0.05 compared to Lab * p<0.05 compared to Grass † p<0.05 compared to Turf

Table 5: Weather Log during Collection Participant Temperature (F) Heat Index (F) Precipitation 1 74 73 yes 2 95 96 no 3 43 46 no 4 43 46 no 5 44 40 no 6 38 29 yes 7 37 28 yes 8 36 27 yes 9 34 25 no Temperature and Heat Index recorded in Fahrenheit (F) Precipitation includes rain or snow 24 hours prior to collection

Figure 1: Flowchart of functional tasks across the three playing surfaces

Appendix A

The Problem

Problem Statement

Over seventy-five percent of Americans claim to participate in some form of organized sport. Some of the more popular sports such as soccer and football play on different playing surfaces. Forcing the athletes to switch between artificial turf and natural grass when traveling for different competitions. Different playing surfaces have been shown to cause different lower extremity injuries and the rates at which these injuries occur. There has been research done on injury rates on artificial turf compared to natural grass, but there is a lack in literature when it comes to why these injuries are occurring. There could many different factors that influence the differences in injury rates occurring; it could be weather related, due to the playing surface being played on, or due to the shoe wear being used. Previous research has evaluated the influence of shoe wear on muscle movement, muscle activation, and force distribution. The type of shoe wear has been found to alter muscle activation and force distribution, however these studies were conducted in a controlled laboratory setting. Since distal factors have an influence on the entire lower extremity during functional tasks, it is important to also assess other potentially contributing factors such as playing surface. Therefore, the goal of this study is to investigate differences occurring in muscle activation, movement patterns, and force distribution on different playing surfaces using EMG and plantar pressure readings on both natural grass and artificial turf compared to a lab setting during different functional activities.

Research Question

Does changing playing surface (turf, grass, and laboratory setting) affect lower extremity muscle movement, muscle activation, and force distribution measured using

EMG and plantar pressure readings during different functional tests in healthy active adults?

Experimental Hypothesis

• Peak and average pressure measured with plantar pressure will be greatest in the

laboratory setting, followed by the artificial turf and then the natural grass.

• mean activation measured with EMG will be activated greatest on natural grass,

followed by artificial turf, and then the laboratory setting.

Assumptions

• Subjects will provide maximum effort on tasks given

• Subjects will provide the same effort on all playing surfaces

• The equipment involved will not hinder the subjects’ performance

• Subjects will complete the functional tasks in similar manners during the three

playing surface conditions

Delimitations

• Subjects will be from a healthy active population

• Subjects will be students from the University of Toledo aged 18 - 23

• No previous Hx of lower extremity injuries that required surgery

• Subjects must partake in all three environments

List of operational definitions

• Electromyography: EMG, a non-invasive neuromuscular assessment that can be

used to evaluate muscle activation, motor coordination, and treatment efficacy.

• Kilopascal: kPa, a widely used unit of pressure measurement.

• Mean Pressure: the pressure averaged over the entire measurement period.

• Peak Pressure: max pressure obtained during the stance (kPa). Can also be given

as the highest pressure that each sensor obtained during the course of the stance

phases.

• Pressure-Time Integral: area under the peak pressure curve, total pressure divided

by the time spent in the stance phase.

• Contact Area: total time that the region was in contact during the stance period.

Innovation

• There is research finding that there are differences in injury rates on different

playing surfaces

• There is no research as to what the differences are on the body between different

playing surfaces

• Can influence clinical practice, because if there are drastic differences between

surfaces rehabilitation may need to be done outside of the clinical setting more

• Could influence clinical practice, with how teams prepare and train for travel

when knowing they are going to a different playing surface

• Could influence clinical practice, with modified bracing and preventative

rehabilitation for upcoming change in playing surface

• Athletes do not all wear the same type of shoes

• Rehabilitation and training does not occur in the lab setting,

• There are many different factors that could be influencing the changes seen in

injury rates, however there is not any current research on how a difference in

playing surface may affect muscle activation, muscle movement, and force

distribution.

Appendix B

Literature Review

Anatomy

Anatomy of the lower extremity is very intensive, but can be made simpler when broken down into smaller sections. This article will break it down to the foot, ankle, knee, and the thigh and hip. The bones and muscles of each section combine to make different movements at each joint.

Foot

The foot is very complex and has many structures that all work together to allow the foot to function as it needs to. The foot contains twenty-six bones: calcaneus, talus, navicular, cuboid, medial cuneiform, intermediate cuneiform, lateral cuneiform, five metatarsals, and fourteen phalanges. These bones interlock to make up the three arches of the foot; the medial longitudinal arch, lateral longitudinal arch, and the transverse arch.

The medial and lateral longitudinal arches are located between the calcaneus and the metatarsal heads, and the transverse arch is located posterior to the metatarsal heads.

These arches are supported by the plantar aponeurosis (plantar fascia), long and short plantar ligaments, and the plantar calcaneonavicular (spring) ligament. There are also many muscles that run through the foot to assist in different motions. The muscles of the foot can be divided by what joint they act on. The hallux has the flexor hallucis brevis and longus, extensor hallucis brevis and longus, abductor hallucis, adductor hallucis. The metatarsal phalangeal joint for toes two through five have the flexor digitorum brevis, lumbricals, extensor digitorum longus, dorsal interossei, abductor digiti minimi, plantar interossei. The proximal interphalanageal joint contains the flexor digitorum brevis,

lumbricals, interossei, and abductor digiti minimi. The distal interphalangeal joint contains the flexor digitorum longus, lumbricals, interossei, and adductor digiti minimi.

The subtalar joint and transverse tarsal joint contain the tibialis anterior, gastrocnemius, soleus, tibialis posterior, peroneus longus, and peroneus brevis. 1 These bones, arches, ligaments, and muscles are work together to form the movements that occur at the foot of pronation and supination. Pronation is a combination of abduction, dorsiflexion, and eversion, where supination is a combination of adduction, plantar flexion, and inversion. 2

Ankle

The ankle is made up of three articulations that work together to allow coordinated movements at the rearfoot. The talocural joint is made up of the dome of the talus, medial malleolus, tibial plafond, and the lateral malleolus. This joint is also known as the “mortise” allowing plantar flexion and dorsiflexion to occur. The talocural joint is stabilized by ligamentous support from the anterior talofibular ligament (ATFL), posterior talofibular ligament (PTFL), calcaneofibular ligament (CFL), and the deltoid ligament. The ATFL, PTFL, and CFL support the lateral ankle and the deltoid ligament supports the medial ankle. 3 The subtalar joint is made up of articulations between the talus and the calcaneus, allowing motions of pronation and supination 4. The subtalar joint is supported by ligaments and retinacula consisting of the cervical and interosseous ligaments stabilizing the subtalar joint, fibers of the inferior extensor retinacula (IER) providing support to the lateral aspect, and the CFL, lateral talocalcaneal ligament

(LTCL), and fibulotalocalcaneal ligament (FTCL) preventing excessive inversion and internal rotation of the calcaneus on the talus. The last articulation is the distal tibiofibular joint, which is made up of the distal end of the tibia and fibula. This joint

allows very limited motion, stabilization occurs by a thick interosseous membrane and the anterior and posterior inferior tibiofibular ligaments. 3

There are also twelve muscles that cross the ankle joint and assist in movement of the ankle joints and they are divided into four compartments. The anterior compartment contains four muscles that aid in dorsiflexion, these muscles are the tibialis anterior, extensor digitorum longus, extensor hallucis longus, and the peroneus tertius. The tibialis anterior and extensor digitorum longus also aides in inversion and the peroneus tertius also aides in eversion. The lateral compartment is made up of the peroneus longus and the peroneus brevis which are responsible for plantarflexion and eversion. The posterior compartment is containing the gastrocnemius, soleus, and plantaris, which all contribute to plantarflexion. The deep posterior compartment has the tibialis posterior, flexor digitorum longus, and flexor hallucis longus, which produce plantarflexion and inversion.

Knee

The knee joint is comprised of two joints the tibiofemoral joint and the patellofemoral joint that interact to allow flexion and extension to occur, along with some internal-external rotation and varus-valgus angulation. The bones that make up this joint are the femur superiorly, the tibia and fibula inferiorly, and the patella anteriorly. The primary stabilizers of the knee are four ligaments. The anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) are intra-articular ligaments and the medial collateral ligament (MCL) and lateral collateral ligament (LCL) and extra-articular ligaments. The ACL prevents anterior translation of the tibia on the femur, while the

PCL prevents posterior translation of the tibia on the femur. The MCL is the primary

restraint against valgus forces, and the LCL is the primary restraint against varus forces. 5

The muscles of the knee do not act solely on the knee, but cross the knee joint and give support and aid in movement. On the medial side is the pes anserine attachment of the sartorious, gracilis, and semitendinosus. On the lateral side, there is the Iliotibial (IT) band. Posterior the gastrocnemius cross along with the hamstring muscles

(semimembranosus and biceps femoris). Anteriorly the quadriceps muscles cross the joint, splitting around the patella to form the patellar tendon. (goldblatt)

Thigh and Hip

The hip is made up of a ball-and-socket joint consisting of the femur and the hip bone (ilium, ischium, and pubis, the acetabulum is the part that articulates with the head of the femur). The hip can move in flexion, extension, external rotation, internal rotation, adduction, and abduction. There are many muscles that stabilize this joint and allow for movement to occur. The best way to divide up the muscles is to separate them by action performed. The flexors consist of iliopsoas, sartorius, tensor fascia latae, rectus femoris, adductor longus, pectineus, adductor brevis, gracilis, and the gluteus minimus. The extensors are the gluteus maximus, adductor magnus, biceps femoris, semitendinosus and semimembranosus. The external rotators are the gluteus maximus, piriformis, obturator internus, gemellus superior, gemellus inferior, quadratus femoris, gluteus medius, gluteus minimus, obturator externus, sartorious, and biceps femoris. The internal rotators include the gluteus minimus, gluteus medius, tensor fasciae latae, adductor longus, adductor brevis, pectineus, and adductor magnus. The adductors are the pectineus, adductor longus, gracilis, adductor brevis, adductor magnus, biceps femoris, gluteus maximus, quadratus femoris, and the obturator externus. The abductors consist of the gluteus

medius, gluteus minimus, tensor fascia latae, piriformis, sartorius, and the rectus femoris.

Common Injuries

There are many different factories that can lead a person to becoming injured while participating in athletic activities. The kinematics of a person’s movements while they are performing athletic type activates or the muscle activation and force distribution on the joints and plantar pressure during weight-bearing tasks can have a large influence on injury rate. It has been found that even though activities appear to be similar such as, a single leg squat and a step down task. The kinematics involved may be different, predisposing the person to different positions that could lead to potential injury. 7 One study found that when comparing a squatting and lunging task between males and females both sexes were quadriceps dominant. However, each sex recruited different muscles of the quadriceps during the different tasks to allow movement to occur at the knee. 8 These factors can influence injury rates in many ways, one being contact injuries versus non-contact injuries.

There are many injuries that occur during athletic participation, of which can be divided between contact and non-contact based off the mechanism of the injury. Contact injuries are a result of impact with other players or equipment and may lead to required medical care and a decrease in athletic participation. These injuries have led to the development of recent rules by different organizations (such as NCAA, NFL, and FIFA) to limit those injuries as much as possible 9. Contact injuries account for about eighty- five percent of injuries that occur during soccer participation by female athletes at the elite level; and of those contact injuries about sixty percent of them were lower extremity

injuries10. The study also found that the person being tackled was only slightly more at risk for injury than the person performing the tackle, and majority of the tackles resulting in injury came from the side not the front or back. 10

Most athletic related injuries are due to player-to-player contact; however, many noncontact injuries still occur during athletic participation. The most common noncontact injuries are muscle strains and joint sprains 9. Noncontact injuries can be caused by many factors such as: overuse injuries, influence of illness/infections, sport specific movements, and force distributions of different joints 11. In a study that looked at football injuries among National Collegiate Athletic Association football players they found that of all the injuries suffered during competition about fourteen percent were noncontact injuries11 With about one percent of those being occurred from overuse and about one percent of them being from illness/infection11. Thirty percent of these injuries suffered during practice were noncontact, with about five percent of them being from overuse and about six percent of them being from illness/infection 11. Sports such as soccer see many muscle based injuries, with about thirty-seven percent of players missing time do to a muscle related injuries 12. In soccer ninety-two percent of all muscle injuries were lower extremity, with majority of them consisting of the hamstring (37%), adductor (23%), quadriceps (19%), or calf (13%) 12. Of the injuries that do occur during practice and competition over fifty-three percent of them are lower extremity injuries, with majority of them occurring at the knee and ankle joints 9. In a study done on sixteen different sports football (63.9%) and soccer (3.2%) were in the top three sports with the most injuries, with knee injuries being the most common injury among athletes especially females 13.

Playing Surfaces

Over the years there has been a bad image of artificial turf. The first and second generations of artificial turf had their disadvantages in that they changed performance characteristics such as cutting and planting, and injury pattern with more injuries being recorded on turf than natural grass 14. The new third generation of turf is designed to closely mimic natural grass, with long fiber length, rubber synthetic infill, and increased shock absorbency 15. When third generation turf was compared to natural grass with elite soccer players, majority of the injuries occurred to the lower extremity with eighty-six percent occurred on grass and eighty-seven percent occurred on turf in males, while in females eighty-one percent occurred on grass and eighty-seven percent occurred on turf for females 14. Another study found that when the different surfaces where compared sprains, strains, and contusions were the most common injuries and showed that there were fewer sever injuries (injuries resulting in greater than twenty-eight days of missed athletic participation) on grass, but the severity of injuries was not worse because of the turf 16. Further studies were performed on Nordic soccer clubs and showed that playing on artificial turf had higher rates of contusions and muscle/tendon injuries, and showed a thirty-eight percent increase in overuse injuries when compared to natural grass 17. The literature shows that there is a difference between surfaces and that injures do occur at different rates, but why is this happening. Is it actually because of the surface that is being played on, is it because of the body’s reaction to the surface, or is it because of something altogether such as shoe or mental perception that is influencing play?

Distal Factors

It has been found that distal factors can influence plantar pressure and muscle activation in healthy active individuals. Hähni et al. found that forefoot cushioning

significantly reduced the plantar pressure readings during running, especially when compared to foot orthosis with a metatarsal pad18. Another study done by Nigg, et. al. found that changing the shoe can alter muscle activation in runners19. They found that when the shoe was changed the muscle activation of medial head of the gastrocneuis, vastus medalis, and the hamstring muscle group was significantly different19. Fu et al. found that when comparing high-top to low-top shoes, participant was more likely to incur an inversion ankle sprain20. The muscle activation of the tibialis anterior and peroneus brevis was significantly lower in high-tops as compared to low-tops, especially at fifteen and twenty-five-degree inversion20.

Plantar Pressure System

The TekScan F-Scan 7 system is used for in-shoe pressure measurement. Being that the foot is the only part of the body in contact with the ground during the gait cycle and standing, the foot can encounter a lot of problems. The in-shoe system designed by

TekScan F-Scan 7 has ninety-nine capacitive sensors that can be calibrated to 500 kPa using the trublu calibration device. The software for TekScan F-Scan 7 can calculate information on eighteen parameters including: peak pressure, mean pressure, contact area, pressure-time integral, and force-time integral. The repeatability of the Pedar system has been tested and taking into consideration the fact that no two steps even by the same person are identical because of normal sway during gait, ninety-three percent of the parameters tested had a coefficient of repeatability (CR) of less than ten percent with the highest CR being 15.3 percent21. Overall the TekScan F-Scan 7 system is accurate at having repeatable results and is an effective way to measure different pressure placement being placed on the foot. 22

There has been some research done on comparing different playing surfaces through the use of in shoe pressure measurement systems. One study compared clay and greenset courts and the differences between plantar loading when players changed surfaces. It showed that the clay courts reduced the mean force applied to the whole foot, but increased the contact time with the ground as compared to the greenset playing surface23. It also found that when playing on clay additional pressure is applied to the midfoot, where when playing on greenset additional pressure is applied to the hallux and lesser toes; this can help athletes when trying to choose the type of shoe wear need when changing surfaces. 23

Peak Pressure

Peak pressure is the highest pressure value experienced during the measurement.

It is usually expressed in kPa, but can be recorded as PSI, N/cm2, and bar. Most software applications will give a peak pressure map, which will show the maximum pressure value that was obtained by each sensor.24 Reference ranges for the peak pressure has been determined for the ten different regions of the foot. The ranges are recorded in kPa as follows: heel (177.9-350.7), mid foot (3.54-184.5), first metatarsal head (110.6-385.4), second metatarsal head (151.8-341.2), third metatarsal head (125.9-323.5), fourth metatarsal head (63.59-258.4), fifth metatarsal head (27.1-256.1), hallux (117.7-443.1), second toe (30.51-247.3), third through fifth toe (32.12-210.5).22

Mean Pressure

Mean pressure for each sensor is the pressure value averaged over the measurement period. This can be done by averaging the pressure over the entire

measurement time period or only over the time period the specific sensor had been loaded.24

Contact Area

Contact area is the instantaneous value of loaded pressure measurement device area. Usually expressed in a curve, with points plotted to show contact area evolution along the measurement period.24

Pressure-Time Integral

The pressure-time integral or impulse represents the area under the peak pressure curve. This is calculated as the sum of the products instantaneous pressure by sampling interval, higher sampling rates will give more accurate values.24

Force-Time Integral

The force-time integral or impulse is a parameter that is obtained by calculating the area under the force curve. This measurement is usually presented as kPa or kPa*s.24

EMG

Surface electromyography (EMG) is a non-invasive neuromuscular assessment that can be used to evaluate muscle activation, motor coordination, and treatment efficacy. In order to receive accurate feedback from EMG the electrodes have to be placed properly. If the electrodes are placed over the innervation zones (where the never attaches to the muscle), musculotendinous junctions, and tendon regions at the attachments all have poor conduction and will result in inaccurate readings. To receive accurate readings, the electrode must be placed between the innervation zone and the distal/proximal muscle attachment.25 Table 1 shows values for lower extremity muscle’s innervation zones and the signal quality of the different muscles.26 There are many

different variables that can be measured with surface EMG, however usually only time and intensity are desired the rest of the factors only intensifies the variability of the EMG.

Some factors that can affect the EMG reading are: timing and intensity of the muscle contraction, electrode placement, properties of the overlying tissue (thickness, amount of adipose), electrode and amplifier properties, quality of the contact between the electrode and the skin.27,28

Table 1: Absolute values of IZ location semi-range (maximal value − minimum value)/2 for lower extremity muscles and signal quality26 (Max − min)/2 Muscle IZ uniformity Signal quality (mm) Biceps femoris 25 Fair Excellent Semitendinosus 15 Good Excellent Vastus lateralis 20 Good Excellent Tensor faciae Good 20 Good latae Gastrocnemius 25 Fair Good medialis Gastrocnemius 25 Fair Good lateralis 25 Gluteus maximus Fair Excellent

Vastus medialis 20 Good Excellent obliquus Tibialis anterior 20 Good Good 25 Soleus Fair Fair

30 Gluteus medius Fair Fair

20 Peroneus longus Good Poor

Appendix C

Additional Methods

Executive Summary

Title:

Influence of playing surface on muscle activation and plantar pressure in collegiate

football players.

Principal Investigator:

Neal Glaviano, PhD, AT, ATC (Assistant Professor School of Exercise and

Rehabilitation Sciences)

Research Team:

Ema Kossin, AT, ATC (Masters student in athletic training)

Grant Norte, PhD, AT, ATC, CSCS (Assistant Professor School of Exercise and

Rehabilitation Sciences)

Cindy Bouillon, PT, PhD (Associate Professor School of Exercise and Rehabilitation

Sciences)

Annie Tomten (Bachelors student in athletic training)

Purpose:

To determine if the ground surface influences the muscle activation and force

distribution patterns of the lower extremity.

Participants:

University of Toledo DI Football Players

Inclusion Criteria:

Play football for the University of Toledo

Exclusion Criteria:

1) Any history of lower extremity surgery

2) Any history of lower extremity injury in the past six months that withheld them

from practice or football related activities for more than three days

3) Any history of low back injury in the past six months that withheld them from

practice or football related activities for more than three days.

4) Shoe size over men’s 14

Study Design:

Crossover (all participants perform same tasks on all surfaces)

Independent Variable:

Playing surface

1) Natural grass

2) Artificial turf

3) Laboratory setting

Dependent Variables:

Muscle activation assessed with EMG readings for each functional task

1) mean of activation

Force distribution assessed with Plantar Pressure readings for each functional task

1) mean pressure

2) pressure-time integral

Procedures:

1) Recruit University of Toledo football players that meet the criteria

2) Complete informed consent form

3) Screen using health history questionnaires

4) Wireless EMG set up on patient (muscles of interest are Gluteus maximus,

Gluteus medias, Biceps Femoris, Vastus lateralis, Vastus medialis oblequis,

lateral head of the Gastrocnemius, Peronius Longus, and Anterior Tibialis )

5) Test EMG placement with manual muscle tests

6) IMU set up on patient (posterior pelvis, lateral mid-thigh, lateral lower leg mid

shank)

7) Plantar Pressure set up

8) Calibrate wireless EMG (quite standing) and IMU with patient standing in neutral

position

9) Calibrate plantar pressure with patient alternating single leg balance

10) Move to the first playing surface as predetermined for the randomized order

11) Have patient perform functional tasks in predetermined randomized order (each

task is done three times to obtain an average).

a. Single limb squat on dominant leg

b. Ten-yard jag at 100%

c. Ten-yard jog at 50% with cutting task on dominant leg

12) Go to the second playing surface as predetermined for the randomized order and

repeat step 11

13) Go to the third playing surface as predetermined for the randomized order and

repeat step 11

14) Remove the wireless EMG, IMU, and Plantar Pressure sensors and insoles

15) Dismiss subject

IRB Protocol: UT Biomedical IRB# 0000202073

Statistical Analysis:

For each dependent variable measure, a 1x3 repeated measure ANOVA will assess

the influence of playing surface. Significance level will be set at a priori P<0.05.

Will also calculate 95% CI and Cohen’s D effect size for each measure.

Research Hypothesis:

1) EMG activation will be higher on grass than any other surface.

2) Plantar pressure will be higher in the laboratory setting than any other surface.

1. EMG Setup and electrode placement

a. Software set up

o Noraxon DTS wireless EMG

o TeleMyo DTS receiver

 Turn the receiver on

 Plug the receiver into the laptop

 Turn the software program on

o Receiver: Communicate through Bluetooth to MyoMotion

ResearchPro

 Processed in same software

b. Electrode placement

 Clean and debride the skin where electrodes will be

attached

 Place the electrodes over the appropriate portion of the

selected muscle of interest.

 Place the wireless transmitter close to the electrodes

 Plug the wireless transmitter into the electrodes

 Confirm placement with manual muscle test

 Collect 10 seconds of quite standing at the same time as

IMU

o Gluteus Medius (transmitter 1)

 electrodes were placed 20 mm apart at 50% on the line

from the crista iliac to the trochanter, in the direction of the

line from the crista iliaca to the greater trochanter



 having the participant lying on their side with their legs

spread resisting pressure from the examiner o Gluteus Maximus (transmitter 2)

 electrodes were placed 20 mm apart at 50% on the line

between the sacral vertebrae and the greater trochanter, in

the direction of the line from the posterior superior iliac

spine to the middle of the posterior aspect of the thigh



 having the participant lay prone and lift their leg up against

resistance

o Semitendinosus (transmitter 3)

 electrodes were placed 20 mm apart at 50% on the line

between the ischial tuberosity and the medial epicondyle of

the tibia, in the direction of the line between the ischial

tuberosity and the medial epicondyle of the tibia



 having the participant lying prone try and flex the knee

from a partially flexed position against resistance o Vastus Lateralis (transmitter 4)

 Electrodes were placed 20 mm apart at 2/3 on the line from

the anterior spina iliaca to the lateral side of the patella, in

the direction of the muscle fibers



 having the participant seated off the end of the table try and

extend the knee without rotating the hip from a flexed

position against resistance o Vastus Medialis Oblique (transmitter 5)

 Electrodes were placed 20 mm apart at 80% on the line

between the anterior spina iliaca superior and the joint

space in front of the anterior border of the medial ligament,

in the direction of the muscle fibers



 having the participant seated off the end of the table try and

extend the knee without rotating the hip from a flexed

position against resistance o Lateral head of the gastrocnemius (transmitter 6)

 electrodes were placed 20 mm apart at 1/3 of the line

between the head of the fibula and the heel in the direction

of the line between the head of the fibula and the heel



 having the participant plantar flex the foot with emphasis

on pulling the heel upward more than pushing the forefoot

downward o Peroneus Longus (transmitter 7)

 electrodes were placed 20 mm apart at 25% on the line

between the tip of the head of the fibula to the tip of the

lateral malleolus, in the direction of the muscle fibers



 Support the leg above the ankle joint. Evert the foot with

plantar flexion of the ankle joint while applying pressure

against the lateral border and sole of the foot, in the

direction of inversion of the foot and dorsiflexion of the

ankle joint.

o Anterior Tibialis (transmitter 8)

 electrodes were placed 20 mm apart at 1/3 on the line

between the tip of the fibula and the tip of the medial

malleolus, in the direction of the muscle fibers



 Support the leg just above the ankle joint with the ankle

joint in dorsiflexion and the foot in inversion without

extension of the great toe. Apply pressure against the

medial side, dorsal surface of the foot in the direction of

plantar flexion of the ankle joint and eversion of the foot.

2. IMU Set Up

a. Software setup

o Noraxon Inertial Measurement Unit

o Receiver: Communicate through Bluetooth to MyoMotion

ResearchPro

 Plug the receiver into the laptop

 Turn the software program on

o Data collected in MyoResearch MR3 b. Marker placement

o Place the sensors on the dominant limb as stated on the sensor

 Lateral right shank, half way between lateral epicondyle

and lateral femoral condyle

 Lateral left shank, half way between lateral epicondyle and

lateral femoral condyle

 Lateral right thigh, half way between lateral femoral

condyle and the greater tubercle of the femur

 Lateral left thigh, half way between lateral femoral

condyle and the greater tubercle of the femur

 Pelvis, half way between the right PSIS and left PSIS

c. Calibration

o Have patient stand in neutral position for 10 seconds

o Also collect quiet standing of the EMG at the time

3. Plantar Pressure Set up

a. Software setup

o F-scan VersaTek Wireless System

o Receiver: f-scan research 7.0

o Data collected in Teckscan

 Turn the software program on

 Connect to the wireless VersaTek unit

b. Insole setup

o Select proper insole based off the subject’s shoe size

o Attach insoles to the VersaTek wireless system

c. System calibration

o Have the patient stand on the right leg, then switch to the left

o Repeat the process starting on the left leg and switching to the right

4. Data collection

a. Have subject go to first surface that was randomly assigned pretrial and

perform each task three times

b. Subject Positioning for Tasks

o Single leg squat

 Arms crossed on chest

 Squat down on dominant leg, with non-dominant leg bent

back

a. Dominant leg is the leg the subject would kick a

football with

 Squat down as low as the participant can go while keeping

control

 Repeat the test 3 times and obtain the average

 o 10-yard jog

 Have subject jog 10 yards at 50% of their maximal effort

 o 10-yard jog with cut

 Have the subject jog 10 yards at 50% of their maximal

effort and then cut (plant and turn) off their dominant leg

a. Dominant leg is the leg the subject would kick a

football with



c. Move to the second surface randomly decided pretrial and preform each

task three times

d. Move to the third location that was randomly assigned and preform each

task three times

e. Remove the sensors and electrodes

5. Data processing

a. Plantar Pressure

o Peak Pressure

o Pressure Time Integral

b. EMG

o Peak Value

o Integral (Root Square Mean)

6. Save File

Appendix D

Additional Results

Descriptive Statistics

N Mean Std. Deviation Age 9 20.4444 2.00693 Height 9 72.8889 2.52212 Mass 9 93.7423 12.31818 Valid N (listwise) 9

Descriptive Statistics

Mean Std. Deviation N GMed_L_Sq_Peak 65.9906 107.73495 9 GMed_G_Sq_Peak 95.6549 158.76164 9 GMed_T_Sq_Peak 26.6108 11.61705 9 GMed_L_Sq_Mean 14.1150 14.51784 9 GMed_G_Sq_Mean 14.1823 14.34916 9 GMed_T_Sq_Mean 7.5647 3.89255 9 GMed_L_Jog_Peak 240.8017 324.19876 9 GMed_G_Jog_Peak 281.6955 312.24603 9 GMed_T_Jog_Peak 151.4376 224.44760 9 GMed_L_Jog_Mean 115.8805 195.60373 9 GMed_G_Jog_Mean 97.7544 118.05345 9 GMed_T_Jog_Mean 53.4456 80.30694 9 GMed_L_C_Peak 237.3802 288.09339 9 GMed_G_C_Peak 396.3955 419.04571 9 GMed_T_C_Peak 226.5718 306.27699 9 GMed_L_C_Mean 100.9332 141.79040 9 GMed_G_C_Mean 148.8008 190.77806 9 GMed_T_C_Mean 77.2109 112.09029 9 GMed_L_Sp_Peak 268.9251 319.92118 9 GMed_G_Sp_Peak 375.5151 427.55205 9 GMed_T_Sp_Peak 234.2980 366.06840 9 GMed_L_Sp_Mean 118.6712 184.33985 9 GMed_G_Sp_Mean 178.7967 224.85214 9 GMed_T_Sp_Mean 84.0004 117.13427 9 GMax_L_Sq_Peak 35.3531 22.68090 9

GMax_G_Sq_Peak 141.4565 225.31540 9 GMax_T_Sq_Peak 104.3322 116.57590 9 GMax_L_Sq_Mean 9.6249 5.58287 9 GMax_G_Sq_Mean 13.9838 8.65584 9 GMax_T_Sq_Mean 15.4620 12.06538 9 GMax_L_Jog_Peak 158.5414 147.53680 9 GMax_G_Jog_Peak 272.8714 305.85488 9 GMax_T_Jog_Peak 405.4481 541.22397 9 GMax_L_Jog_Mean 53.4729 56.43477 9 GMax_G_Jog_Mean 123.5201 201.75278 9 GMax_T_Jog_Mean 162.7351 215.12474 9 GMax_L_C_Peak 251.2265 313.21914 9 GMax_G_C_Peak 346.2676 373.77326 9 GMax_T_C_Peak 515.6632 698.98746 9 GMax_L_C_Mean 85.0524 108.62233 9 GMax_G_C_Mean 168.0656 219.93211 9 GMax_T_C_Mean 208.0432 258.65931 9 GMax_L_Sp_Peak 175.2985 96.71521 9 GMax_G_Sp_Peak 510.2072 444.48643 9 GMax_T_Sp_Peak 401.0616 412.71647 9 GMax_L_Sp_Mean 67.8044 41.97213 9 GMax_G_Sp_Mean 236.6561 229.73683 9 GMax_T_Sp_Mean 199.5032 216.80871 9 BF_L_Sq_Peak 20.3571 16.15394 9 BF_G_Sq_Peak 29.1962 12.99799 9 BF_T_Sq_Peak 29.6244 17.07230 9 BF_L_Sq_Mean 6.2538 4.31435 9 BF_G_Sq_Mean 8.2827 3.91056 9 BF_T_Sq_Mean 9.0201 5.19385 9 BF_L_Jog_Peak 121.4429 98.52116 9 BF_G_Jog_Peak 136.0480 150.59314 9 BF_T_Jog_Peak 151.9073 136.10755 9 BF_L_Jog_Mean 44.0054 35.14526 9 BF_G_Jog_Mean 51.3151 55.56217 9 BF_T_Jog_Mean 56.1437 49.65067 9 BF_L_C_Peak 200.4983 142.88824 9 BF_G_C_Peak 244.4221 328.57259 9 BF_T_C_Peak 203.8834 170.50068 9 BF_L_C_Mean 67.6248 54.84722 9 BF_G_C_Mean 75.6952 107.20963 9

BF_T_C_Mean 88.3151 77.70028 9 BF_L_Sp_Peak 176.5150 147.06322 9 BF_G_Sp_Peak 211.7561 103.55813 9 BF_T_Sp_Peak 214.3248 178.82503 9 BF_L_Sp_Mean 71.0980 57.30094 9 BF_G_Sp_Mean 93.7061 50.69238 9 BF_T_Sp_Mean 101.3325 93.88113 9 VL_L_Sq_Peak 124.9439 78.62984 9 VL_G_Sq_Peak 112.1164 65.28569 9 VL_T_Sq_Peak 110.2666 64.97268 9 VL_L_Sq_Mean 37.4036 27.64059 9 VL_G_Sq_Mean 32.8459 24.62050 9 VL_T_Sq_Mean 29.6032 19.30759 9 VL_L_Jog_Peak 142.6078 97.68187 9 VL_G_Jog_Peak 226.1724 253.60162 9 VL_T_Jog_Peak 405.2073 422.95198 9 VL_L_Jog_Mean 47.2573 54.09049 9 VL_G_Jog_Mean 110.2009 128.54866 9 VL_T_Jog_Mean 177.6370 216.19018 9 VL_L_C_Peak 193.7060 135.76279 9 VL_G_C_Peak 366.2443 318.54748 9 VL_T_C_Peak 353.0427 393.76044 9 VL_L_C_Mean 65.2847 60.86199 9 VL_G_C_Mean 106.8044 103.94312 9 VL_T_C_Mean 160.2968 204.09964 9 VL_L_Sp_Peak 242.1706 162.33990 9 VL_G_Sp_Peak 383.9241 394.22596 9 VL_T_Sp_Peak 390.7645 369.98954 9 VL_L_Sp_Mean 64.9317 36.22141 9 VL_G_Sp_Mean 143.2737 168.77357 9 VL_T_Sp_Mean 157.3509 186.65037 9 VMO_L_Sq_Peak 176.3280 145.55074 9 VMO_G_Sq_Peak 191.2890 152.77874 9 VMO_T_Sq_Peak 172.6143 156.17220 9 VMO_L_Sq_Mean 51.5165 37.33889 9 VMO_G_Sq_Mean 47.5310 36.76768 9 VMO_T_Sq_Mean 43.3621 39.01025 9 VMO_L_Jog_Peak 466.5281 703.36761 9 VMO_G_Jog_Peak 501.4205 710.33828 9 VMO_T_Jog_Peak 378.2348 304.22220 9

VMO_L_Jog_Mean 118.4908 181.01674 9 VMO_G_Jog_Mean 117.0242 146.51864 9 VMO_T_Jog_Mean 97.2681 77.82938 9 VMO_L_C_Peak 553.4812 807.05670 9 VMO_G_C_Peak 525.4305 650.60668 9 VMO_T_C_Peak 308.8767 198.63305 9 VMO_L_C_Mean 147.5224 189.98197 9 VMO_G_C_Mean 121.2475 131.27215 9 VMO_T_C_Mean 109.0665 106.07912 9 VMO_L_Sp_Peak 402.4708 358.85032 9 VMO_G_Sp_Peak 447.5750 328.31637 9 VMO_T_Sp_Peak 441.7252 416.06675 9 VMO_L_Sp_Mean 103.6514 86.26674 9 VMO_G_Sp_Mean 145.3895 97.72022 9 VMO_T_Sp_Mean 182.2771 209.20354 9 LGAS_L_Sq_Peak 1210.3210 2862.49689 9 LGAS_G_Sq_Peak 1389.4548 2869.03662 9 LGAS_T_Sq_Peak 37.5507 43.95236 9 LGAS_L_Sq_Mean 162.2468 373.63825 9 LGAS_G_Sq_Mean 72.2367 112.56611 9 LGAS_T_Sq_Mean 128.2143 198.91936 9 LGAS_L_Jog_Peak 187.7335 230.53259 9 LGAS_G_Jog_Peak 283.2519 286.60708 9 LGAS_T_Jog_Peak 188.2132 243.94099 9 LGAS_L_Jog_Mean 107.9706 170.75338 9 LGAS_G_Jog_Mean 76.2419 88.19547 9 LGAS_T_Jog_Mean 111.7915 195.75667 9 LGAS_L_C_Peak 217.8925 265.54529 9 LGAS_G_C_Peak 178.3573 134.22400 9 LGAS_T_C_Peak 211.5457 284.39307 9 LGAS_L_C_Mean 106.3825 172.35292 9 LGAS_G_C_Mean 116.7614 191.93624 9 LGAS_T_C_Mean 109.8361 221.65568 9 LGAS_L_Sp_Peak 192.8828 239.14827 9 LGAS_G_Sp_Peak 248.5651 259.27264 9 LGAS_T_Sp_Peak 241.7918 260.86765 9 LGAS_L_Sp_Mean 155.8470 235.93688 9 LGAS_G_Sp_Mean 150.4837 200.65364 9 LGAS_T_Sp_Mean 160.4227 242.16130 9 PLONG_L_Sq_Peak 56.4840 53.90102 9

PLONG_G_Sq_Peak 60.3191 46.15812 9 PLONG_T_Sq_Peak 78.8574 74.40970 9 PLONG_L_Sq_Mean 12.2168 9.29293 9 PLONG_G_Sq_Mean 14.9263 11.10753 9 PLONG_T_Sq_Mean 15.9722 10.11215 9 PLONG_L_Jog_Peak 66.3213 50.91908 9 PLONG_G_Jog_Peak 62.4724 47.48219 9 PLONG_T_Jog_Peak 87.0961 82.91392 9 PLONG_L_Jog_Mean 17.3851 12.50890 9 PLONG_G_Jog_Mean 17.3434 12.35404 9 PLONG_T_Jog_Mean 27.4065 23.92651 9 PLONG_L_C_Peak 89.0219 92.87709 9 PLONG_G_C_Peak 129.5957 130.02976 9 PLONG_T_C_Peak 99.0762 73.92579 9 PLONG_L_C_Mean 24.6642 21.66775 9 PLONG_G_C_Mean 36.1396 27.84359 9 PLONG_T_C_Mean 32.2368 26.88446 9 PLONG_L_Sp_Peak 75.3421 54.86755 9 PLONG_G_Sp_Peak 96.9641 59.48488 9 PLONG_T_Sp_Peak 115.1128 55.47395 9 PLONG_L_Sp_Mean 25.8119 17.00903 9 PLONG_G_Sp_Mean 35.8876 22.17027 9 PLONG_T_Sp_Mean 48.1718 24.95437 9 TANT_L_Sq_Peak 144.5027 108.52531 9 TANT_G_Sq_Peak 110.7009 72.60194 9 TANT_T_Sq_Peak 134.4246 84.21337 9 TANT_L_Sq_Mean 41.5178 33.66283 9 TANT_G_Sq_Mean 29.8812 20.45566 9 TANT_T_Sq_Mean 34.4589 21.20531 9 TANT_L_Jog_Peak 59.6523 30.35388 9 TANT_G_Jog_Peak 67.9011 45.56623 9 TANT_T_Jog_Peak 76.4091 39.06971 9 TANT_L_Jog_Mean 26.3248 13.72760 9 TANT_G_Jog_Mean 28.5081 17.98257 9 TANT_T_Jog_Mean 33.3044 18.00952 9 TANT_L_C_Peak 91.9236 58.82178 9 TANT_G_C_Peak 92.9266 76.09033 9 TANT_T_C_Peak 101.0670 63.73840 9 TANT_L_C_Mean 35.5801 21.35692 9 TANT_G_C_Mean 33.7265 27.26819 9

TANT_T_C_Mean 37.9835 20.09801 9 TANT_L_Sp_Peak 88.2829 47.89349 9 TANT_G_Sp_Peak 89.6850 48.50345 9 TANT_T_Sp_Peak 109.0867 56.62938 9 TANT_L_Sp_Mean 40.5974 24.61414 9 TANT_G_Sp_Mean 42.8809 25.59178 9 TANT_T_Sp_Mean 53.0368 29.01739 9

67 Pairwise Comparisons

95% Confidence Interval Mean for Differenceb (I) (J) Difference Std. Lower Upper Measure Surface Surface (I-J) Error Sig.b Bound Bound GMed_Sq_peak 1 2 4.443 4.631 .439 -15.482 24.368 3 -31.784 29.791 .398 -159.967 96.398 2 1 -4.443 4.631 .439 -24.368 15.482 3 -36.227 26.179 .301 -148.867 76.412 3 1 31.784 29.791 .398 -96.398 159.967 2 36.227 26.179 .301 -76.412 148.867 GMed_Sq_mean 1 2 -.347 .952 .751 -4.445 3.752 3 .080 1.388 .960 -5.891 6.050 2 1 .347 .952 .751 -3.752 4.445 3 .426 .452 .445 -1.518 2.370 3 1 -.080 1.388 .960 -6.050 5.891 2 -.426 .452 .445 -2.370 1.518 GMed_Jog_peak 1 2 -62.304 75.871 .498 -388.751 264.143 3 -75.259 69.975 .395 -376.335 225.817 2 1 62.304 75.871 .498 -264.143 388.751 3 -12.955 7.243 .216 -44.117 18.207 3 1 75.259 69.975 .395 -225.817 376.335 2 12.955 7.243 .216 -18.207 44.117 GMed_Jog_mean 1 2 -17.158 9.199 .203 -56.738 22.422 3 -6.699* .852 .016 -10.364 -3.035 2 1 17.158 9.199 .203 -22.422 56.738 3 10.459 9.592 .389 -30.813 51.730 3 1 6.699* .852 .016 3.035 10.364 2 -10.459 9.592 .389 -51.730 30.813 GMed_C_peak 1 2 -132.701 61.050 .162 -395.378 129.976 3 -22.483 21.856 .412 -116.522 71.555 2 1 132.701 61.050 .162 -129.976 395.378 3 110.218 58.317 .199 -140.700 361.136 3 1 22.483 21.856 .412 -71.555 116.522 2 -110.218 58.317 .199 -361.136 140.700 GMed_C_mean 1 2 -27.405 14.323 .196 -89.033 34.223 3 -8.962 7.699 .365 -42.088 24.165 2 1 27.405 14.323 .196 -34.223 89.033 3 18.444 7.542 .134 -14.005 50.892 3 1 8.962 7.699 .365 -24.165 42.088 2 -18.444 7.542 .134 -50.892 14.005

GMed_Sprint_peak 1 2 29.967 44.637 .571 -162.089 222.024 3 34.220 41.731 .498 -145.335 213.776 2 1 -29.967 44.637 .571 -222.024 162.089 3 4.253 12.713 .770 -50.447 58.953 3 1 -34.220 41.731 .498 -213.776 145.335 2 -4.253 12.713 .770 -58.953 50.447 GMed_spring_mean 1 2 7.832 16.357 .679 -62.548 78.212 3 3.930 10.958 .754 -43.217 51.077 2 1 -7.832 16.357 .679 -78.212 62.548 3 -3.902 6.124 .589 -30.251 22.447 3 1 -3.930 10.958 .754 -51.077 43.217 2 3.902 6.124 .589 -22.447 30.251 GMax_Sq_peak 1 2 1.704 6.696 .823 -27.104 30.513 3 -34.297 65.590 .653 -316.509 247.914 2 1 -1.704 6.696 .823 -30.513 27.104 3 -36.002 59.110 .604 -290.332 218.329 3 1 34.297 65.590 .653 -247.914 316.509 2 36.002 59.110 .604 -218.329 290.332 GMax_Sq_mean 1 2 -.905 .999 .461 -5.203 3.393 3 -.498 9.912 .965 -43.144 42.148 2 1 .905 .999 .461 -3.393 5.203 3 .407 9.265 .969 -39.458 40.272 3 1 .498 9.912 .965 -42.148 43.144 2 -.407 9.265 .969 -40.272 39.458 GMax_Jog_peak 1 2 -83.239 37.471 .156 -244.464 77.985 3 -1.254 31.367 .972 -136.215 133.707 2 1 83.239 37.471 .156 -77.985 244.464 3 81.985* 13.806 .027 22.582 141.388 3 1 1.254 31.367 .972 -133.707 136.215 2 -81.985* 13.806 .027 -141.388 -22.582 GMax_Jog_mean 1 2 -3.938 11.850 .771 -54.924 47.048 3 -199.795 108.860 .208 -668.182 268.592 2 1 3.938 11.850 .771 -47.048 54.924 3 -195.857 113.869 .228 -685.795 294.081 3 1 199.795 108.860 .208 -268.592 668.182 2 195.857 113.869 .228 -294.081 685.795 GMax_C_peak 1 2 9.910 60.144 .884 -248.868 268.688 3 -233.759 369.316 .591 -1822.796 1355.277

2 1 -9.910 60.144 .884 -268.688 248.868 3 -243.669 398.647 .603 -1958.909 1471.570 3 1 233.759 369.316 .591 -1355.277 1822.796 2 243.669 398.647 .603 -1471.570 1958.909 GMax_C_mean 1 2 -8.141 12.082 .570 -60.128 43.845 3 -70.979 104.491 .567 -520.569 378.611 2 1 8.141 12.082 .570 -43.845 60.128 3 -62.837 116.404 .643 -563.681 438.007 3 1 70.979 104.491 .567 -378.611 520.569 2 62.837 116.404 .643 -438.007 563.681 GMax_Sprint_peak 1 2 -18.448 63.972 .800 -293.699 256.803 3 -112.255 188.592 .612 -923.701 699.192 2 1 18.448 63.972 .800 -256.803 293.699 3 -93.807 221.143 .713 -1045.309 857.695 3 1 112.255 188.592 .612 -699.192 923.701 2 93.807 221.143 .713 -857.695 1045.309 GMax_Sprint_mean 1 2 2.491 24.443 .928 -102.679 107.661 3 -53.238 78.390 .567 -390.521 284.045 2 1 -2.491 24.443 .928 -107.661 102.679 3 -55.729 98.778 .629 -480.735 369.278 3 1 53.238 78.390 .567 -284.045 390.521 2 55.729 98.778 .629 -369.278 480.735 BF_Sq_peak 1 2 -.651 2.810 .838 -12.743 11.440 3 -.115 1.883 .957 -8.218 7.988 2 1 .651 2.810 .838 -11.440 12.743 3 .536 1.209 .701 -4.666 5.739 3 1 .115 1.883 .957 -7.988 8.218 2 -.536 1.209 .701 -5.739 4.666 BF_Sq_mean 1 2 -.916 1.426 .587 -7.052 5.221 3 -.378 .892 .713 -4.215 3.459 2 1 .916 1.426 .587 -5.221 7.052 3 .538 .751 .549 -2.695 3.771 3 1 .378 .892 .713 -3.459 4.215 2 -.538 .751 .549 -3.771 2.695 BF_Jog_peak 1 2 31.640 40.624 .518 -143.153 206.433 3 20.741 29.003 .549 -104.048 145.531 2 1 -31.640 40.624 .518 -206.433 143.153 3 -10.899 11.825 .454 -61.778 39.981

3 1 -20.741 29.003 .549 -145.531 104.048 2 10.899 11.825 .454 -39.981 61.778 BF_Jog_mean 1 2 2.504 8.134 .787 -32.495 37.502 3 4.166 4.976 .491 -17.246 25.577 2 1 -2.504 8.134 .787 -37.502 32.495 3 1.662 3.602 .690 -13.835 17.159 3 1 -4.166 4.976 .491 -25.577 17.246 2 -1.662 3.602 .690 -17.159 13.835 BF_C_peak 1 2 93.420 112.185 .493 -389.271 576.112 3 91.825 80.193 .371 -253.218 436.869 2 1 -93.420 112.185 .493 -576.112 389.271 3 -1.595 33.244 .966 -144.632 141.441 3 1 -91.825 80.193 .371 -436.869 253.218 2 1.595 33.244 .966 -141.441 144.632 BF_C_mean 1 2 17.663 18.523 .441 -62.036 97.363 3 9.100 7.461 .347 -23.001 41.201 2 1 -17.663 18.523 .441 -97.363 62.036 3 -8.563 11.115 .522 -56.389 39.262 3 1 -9.100 7.461 .347 -41.201 23.001 2 8.563 11.115 .522 -39.262 56.389 BF_Sprint_peak 1 2 29.582 34.015 .476 -116.773 175.937 3 15.046 48.785 .787 -194.860 224.952 2 1 -29.582 34.015 .476 -175.937 116.773 3 -14.536 16.202 .464 -84.247 55.175 3 1 -15.046 48.785 .787 -224.952 194.860 2 14.536 16.202 .464 -55.175 84.247 BF_Sprint_mean 1 2 18.477 16.893 .388 -54.209 91.163 3 10.906 16.150 .569 -58.581 80.392 2 1 -18.477 16.893 .388 -91.163 54.209 3 -7.571* .791 .011 -10.974 -4.168 3 1 -10.906 16.150 .569 -80.392 58.581 2 7.571* .791 .011 4.168 10.974 VL_Sq_peak 1 2 4.480 4.003 .379 -12.745 21.706 3 -.767 .884 .477 -4.569 3.034 2 1 -4.480 4.003 .379 -21.706 12.745 3 -5.247 3.425 .265 -19.983 9.488 3 1 .767 .884 .477 -3.034 4.569 2 5.247 3.425 .265 -9.488 19.983

VL_Sq_mean 1 2 1.638 1.443 .374 -4.569 7.844 3 .198 2.039 .932 -8.575 8.971 2 1 -1.638 1.443 .374 -7.844 4.569 3 -1.440 2.169 .575 -10.774 7.894 3 1 -.198 2.039 .932 -8.971 8.575 2 1.440 2.169 .575 -7.894 10.774 VL_Jog_peak 1 2 -2.597 10.462 .827 -47.609 42.416 3 -11.163 15.928 .556 -79.696 57.369 2 1 2.597 10.462 .827 -42.416 47.609 3 -8.567 6.418 .314 -36.181 19.048 3 1 11.163 15.928 .556 -57.369 79.696 2 8.567 6.418 .314 -19.048 36.181 VL_Jog_mean 1 2 -.351 3.460 .928 -15.239 14.536 3 1.369 1.266 .392 -4.076 6.815 2 1 .351 3.460 .928 -14.536 15.239 3 1.721 2.355 .541 -8.410 11.852 3 1 -1.369 1.266 .392 -6.815 4.076 2 -1.721 2.355 .541 -11.852 8.410 VL_C_peak 1 2 -28.168 31.552 .466 -163.927 107.591 3 -32.305 26.411 .346 -145.944 81.335 2 1 28.168 31.552 .466 -107.591 163.927 3 -4.136 9.507 .706 -45.040 36.767 3 1 32.305 26.411 .346 -81.335 145.944 2 4.136 9.507 .706 -36.767 45.040 VL_C_mean 1 2 -2.571 8.402 .789 -38.720 33.578 3 -7.602 5.001 .268 -29.122 13.917 2 1 2.571 8.402 .789 -33.578 38.720 3 -5.031 3.787 .315 -21.323 11.261 3 1 7.602 5.001 .268 -13.917 29.122 2 5.031 3.787 .315 -11.261 21.323 VL_sprint_peak 1 2 20.060 27.262 .538 -97.241 137.360 3 -2.563 1.008 .126 -6.900 1.775 2 1 -20.060 27.262 .538 -137.360 97.241 3 -22.622 28.249 .507 -144.170 98.925 3 1 2.563 1.008 .126 -1.775 6.900 2 22.622 28.249 .507 -98.925 144.170 VL_sprint_mean 1 2 6.831 6.858 .424 -22.678 36.341 3 1.353 2.345 .622 -8.736 11.443

2 1 -6.831 6.858 .424 -36.341 22.678 3 -5.478 7.647 .548 -38.378 27.423 3 1 -1.353 2.345 .622 -11.443 8.736 2 5.478 7.647 .548 -27.423 38.378 VMO_Sq_peak 1 2 -6.292 3.491 .213 -21.311 8.727 3 -72.655 65.726 .384 -355.450 210.140 2 1 6.292 3.491 .213 -8.727 21.311 3 -66.363 63.615 .406 -340.075 207.350 3 1 72.655 65.726 .384 -210.140 355.450 2 66.363 63.615 .406 -207.350 340.075 VMO_Sq_mean 1 2 1.833 2.576 .551 -9.251 12.917 3 -7.183 8.070 .467 -41.906 27.539 2 1 -1.833 2.576 .551 -12.917 9.251 3 -9.016 10.246 .472 -53.101 35.068 3 1 7.183 8.070 .467 -27.539 41.906 2 9.016 10.246 .472 -35.068 53.101 VMO_Jog_peak 1 2 -292.302 349.865 .491 -1797.650 1213.046 3 -109.918 282.319 .735 -1324.640 1104.805 2 1 292.302 349.865 .491 -1213.046 1797.650 3 182.384 606.607 .792 -2427.633 2792.402 3 1 109.918 282.319 .735 -1104.805 1324.640 2 -182.384 606.607 .792 -2792.402 2427.633 VMO_Jog_mean 1 2 38.707 38.428 .420 -126.634 204.047 3 113.698 103.851 .388 -333.139 560.535 2 1 -38.707 38.428 .420 -204.047 126.634 3 74.991 66.511 .377 -211.180 361.163 3 1 -113.698 103.851 .388 -560.535 333.139 2 -74.991 66.511 .377 -361.163 211.180 VMO_C_peak 1 2 118.985 152.850 .518 -538.674 776.644 3 118.100 160.263 .538 -571.457 807.658 2 1 -118.985 152.850 .518 -776.644 538.674 3 -.885 22.230 .972 -96.531 94.761 3 1 -118.100 160.263 .538 -807.658 571.457 2 .885 22.230 .972 -94.761 96.531 VMO_C_mean 1 2 73.969 72.545 .415 -238.166 386.104 3 182.876 184.608 .426 -611.428 977.180 2 1 -73.969 72.545 .415 -386.104 238.166 3 108.906 112.085 .434 -373.357 591.170

3 1 -182.876 184.608 .426 -977.180 611.428 2 -108.906 112.085 .434 -591.170 373.357 VMO_Sprint_peak 1 2 57.368 50.511 .374 -159.964 274.700 3 271.229 265.957 .415 -873.091 1415.548 2 1 -57.368 50.511 .374 -274.700 159.964 3 213.861 221.834 .437 -740.613 1168.334 3 1 -271.229 265.957 .415 -1415.548 873.091 2 -213.861 221.834 .437 -1168.334 740.613 VMO_Sprint_mean 1 2 -6.702 7.442 .463 -38.721 25.317 3 62.636 62.470 .422 -206.152 331.423 2 1 6.702 7.442 .463 -25.317 38.721 3 69.338 67.210 .411 -219.842 358.518 3 1 -62.636 62.470 .422 -331.423 206.152 2 -69.338 67.210 .411 -358.518 219.842 LGas_Sq_peak 1 2 -72.457 56.946 .331 -317.476 172.562 3 99.666 198.044 .665 -752.448 951.779 2 1 72.457 56.946 .331 -172.562 317.476 3 172.123 141.114 .347 -435.041 779.286 3 1 -99.666 198.044 .665 -951.779 752.448 2 -172.123 141.114 .347 -779.286 435.041 LGas_Sq_mean 1 2 -75.452 77.933 .435 -410.772 259.868 3 36.921 35.705 .410 -116.704 190.545 2 1 75.452 77.933 .435 -259.868 410.772 3 112.372 113.636 .427 -376.563 601.308 3 1 -36.921 35.705 .410 -190.545 116.704 2 -112.372 113.636 .427 -601.308 376.563 LGas_Jog_peak 1 2 33.890 44.007 .522 -155.455 223.236 3 -.066 24.847 .998 -106.973 106.842 2 1 -33.890 44.007 .522 -223.236 155.455 3 -33.956 31.527 .394 -169.607 101.695 3 1 .066 24.847 .998 -106.842 106.973 2 33.956 31.527 .394 -101.695 169.607 LGas_Jog_mean 1 2 27.281 32.848 .494 -114.055 168.616 3 10.482 9.432 .382 -30.099 51.062 2 1 -27.281 32.848 .494 -168.616 114.055 3 -16.799 23.916 .555 -119.702 86.105 3 1 -10.482 9.432 .382 -51.062 30.099 2 16.799 23.916 .555 -86.105 119.702

LGas_C_peak 1 2 180.962 190.761 .443 -639.815 1001.740 3 66.087 71.987 .456 -243.649 375.824 2 1 -180.962 190.761 .443 -1001.740 639.815 3 -114.875 122.086 .446 -640.170 410.420 3 1 -66.087 71.987 .456 -375.824 243.649 2 114.875 122.086 .446 -410.420 640.170 LGas_C_mean 1 2 14.696 19.309 .526 -68.385 97.776 3 12.190 16.095 .528 -57.062 81.443 2 1 -14.696 19.309 .526 -97.776 68.385 3 -2.505 3.813 .579 -18.912 13.902 3 1 -12.190 16.095 .528 -81.443 57.062 2 2.505 3.813 .579 -13.902 18.912 LGas_Sprint_peak 1 2 -111.570 128.755 .478 -665.560 442.420 3 -87.011 76.580 .374 -416.510 242.488 2 1 111.570 128.755 .478 -442.420 665.560 3 24.559 192.139 .910 -802.150 851.267 3 1 87.011 76.580 .374 -242.488 416.510 2 -24.559 192.139 .910 -851.267 802.150 LGas_Sprint_mean 1 2 21.334 15.877 .311 -46.980 89.648 3 .658 38.601 .988 -165.430 166.746 2 1 -21.334 15.877 .311 -89.648 46.980 3 -20.676 24.063 .481 -124.209 82.858 3 1 -.658 38.601 .988 -166.746 165.430 2 20.676 24.063 .481 -82.858 124.209 PL_Sq_peak 1 2 -.899 2.103 .711 -9.947 8.148 3 -4.137* .456 .012 -6.098 -2.176 2 1 .899 2.103 .711 -8.148 9.947 3 -3.237 1.760 .207 -10.811 4.337 3 1 4.137* .456 .012 2.176 6.098 2 3.237 1.760 .207 -4.337 10.811 PL_Sq_mean 1 2 -.437 1.335 .775 -6.181 5.307 3 -2.744 1.962 .297 -11.185 5.696 2 1 .437 1.335 .775 -5.307 6.181 3 -2.307 .971 .141 -6.485 1.870 3 1 2.744 1.962 .297 -5.696 11.185 2 2.307 .971 .141 -1.870 6.485 PL_Jog_peak 1 2 -194.156 53.696 .069 -425.193 36.881 3 -101.618 63.823 .252 -376.225 172.989

2 1 194.156 53.696 .069 -36.881 425.193 3 92.539 117.460 .513 -412.852 597.929 3 1 101.618 63.823 .252 -172.989 376.225 2 -92.539 117.460 .513 -597.929 412.852 PL_Jog_mean 1 2 2.751 1.770 .260 -4.863 10.366 3 .400 1.911 .854 -7.822 8.622 2 1 -2.751 1.770 .260 -10.366 4.863 3 -2.351* .406 .029 -4.098 -.605 3 1 -.400 1.911 .854 -8.622 7.822 2 2.351* .406 .029 .605 4.098 PL_C_peak 1 2 -2.579 2.593 .425 -13.734 8.575 3 -22.634 21.286 .399 -114.222 68.954 2 1 2.579 2.593 .425 -8.575 13.734 3 -20.055 19.376 .409 -103.424 63.313 3 1 22.634 21.286 .399 -68.954 114.222 2 20.055 19.376 .409 -63.313 103.424 PL_C_mean 1 2 .846 2.674 .782 -10.657 12.350 3 -5.714 3.979 .288 -22.834 11.407 2 1 -.846 2.674 .782 -12.350 10.657 3 -6.560* 1.318 .038 -12.229 -.891 3 1 5.714 3.979 .288 -11.407 22.834 2 6.560* 1.318 .038 .891 12.229 PL_Sprint_peak 1 2 3.299 2.943 .379 -9.366 15.963 3 -25.696 26.875 .440 -141.331 89.939 2 1 -3.299 2.943 .379 -15.963 9.366 3 -28.995 26.746 .392 -144.075 86.085 3 1 25.696 26.875 .440 -89.939 141.331 2 28.995 26.746 .392 -86.085 144.075 PL_Sprint_mean 1 2 .320 .858 .745 -3.372 4.012 3 -7.215 7.646 .445 -40.114 25.684 2 1 -.320 .858 .745 -4.012 3.372 3 -7.535 7.582 .425 -40.158 25.088 3 1 7.215 7.646 .445 -25.684 40.114 2 7.535 7.582 .425 -25.088 40.158 TA_Sq_peak 1 2 -73.567 69.878 .403 -374.230 227.095 3 -249.855 58.879 .051 -503.189 3.479 2 1 73.567 69.878 .403 -227.095 374.230 3 -176.288 65.023 .113 -456.061 103.485

3 1 249.855 58.879 .051 -3.479 503.189 2 176.288 65.023 .113 -103.485 456.061 TA_Sq_mean 1 2 -.096 2.776 .975 -12.041 11.849 3 -4.804 4.847 .426 -25.657 16.049 2 1 .096 2.776 .975 -11.849 12.041 3 -4.708 6.922 .567 -34.489 25.073 3 1 4.804 4.847 .426 -16.049 25.657 2 4.708 6.922 .567 -25.073 34.489 TA_Jog_peak 1 2 -85.931 43.386 .186 -272.608 100.745 3 -226.120 65.026 .074 -505.904 53.664 2 1 85.931 43.386 .186 -100.745 272.608 3 -140.189 59.447 .142 -395.969 115.592 3 1 226.120 65.026 .074 -53.664 505.904 2 140.189 59.447 .142 -115.592 395.969 TA_Jog_mean 1 2 5.069 1.855 .112 -2.914 13.052 3 2.355 3.765 .596 -13.847 18.556 2 1 -5.069 1.855 .112 -13.052 2.914 3 -2.714 3.574 .527 -18.094 12.665 3 1 -2.355 3.765 .596 -18.556 13.847 2 2.714 3.574 .527 -12.665 18.094 TA_C_peak 1 2 7.208 3.127 .148 -6.244 20.661 3 -1.145 1.567 .541 -7.889 5.599 2 1 -7.208 3.127 .148 -20.661 6.244 3 -8.353 4.255 .189 -26.659 9.953 3 1 1.145 1.567 .541 -5.599 7.889 2 8.353 4.255 .189 -9.953 26.659 TA_C_mean 1 2 4.802 2.266 .168 -4.945 14.550 3 -2.765 3.866 .549 -19.401 13.870 2 1 -4.802 2.266 .168 -14.550 4.945 3 -7.568 5.342 .292 -30.552 15.417 3 1 2.765 3.866 .549 -13.870 19.401 2 7.568 5.342 .292 -15.417 30.552 TA_sprint_peak 1 2 77.665 63.964 .349 -197.550 352.880 3 66.827 67.929 .429 -225.446 359.100 2 1 -77.665 63.964 .349 -352.880 197.550 3 -10.838 14.220 .526 -72.024 50.347 3 1 -66.827 67.929 .429 -359.100 225.446 2 10.838 14.220 .526 -50.347 72.024

TA_sprint_mean 1 2 3.336 2.232 .274 -6.266 12.938 3 -2.146 2.688 .508 -13.712 9.421 2 1 -3.336 2.232 .274 -12.938 6.266 3 -5.482 4.918 .381 -26.643 15.680 3 1 2.146 2.688 .508 -9.421 13.712 2 5.482 4.918 .381 -15.680 26.643 PPRESS_Sq_Peak 1 2 -125.264 50.121 .130 -340.919 90.391 3 -122.819 70.430 .223 -425.853 180.215 2 1 125.264 50.121 .130 -90.391 340.919 3 2.444 33.669 .949 -142.419 147.308 3 1 122.819 70.430 .223 -180.215 425.853 2 -2.444 33.669 .949 -147.308 142.419 PPRESS_Sq_Mean 1 2 -35.320 31.002 .373 -168.710 98.071 3 -44.097 38.724 .373 -210.714 122.519 2 1 35.320 31.002 .373 -98.071 168.710 3 -8.778 39.611 .845 -179.211 161.656 3 1 44.097 38.724 .373 -122.519 210.714 2 8.778 39.611 .845 -161.656 179.211 PPRESS_Sq_PTI 1 2 -111.952 175.935 .590 -868.940 645.036 3 -109.332 171.221 .588 -846.035 627.370 2 1 111.952 175.935 .590 -645.036 868.940 3 2.620 129.488 .986 -554.523 559.763 3 1 109.332 171.221 .588 -627.370 846.035 2 -2.620 129.488 .986 -559.763 554.523 PPRESS_Jog_Peak 1 2 244.402 195.549 .338 -596.975 1085.780 3 295.847 114.505 .123 -196.828 788.521 2 1 -244.402 195.549 .338 -1085.780 596.975 3 51.444 81.329 .592 -298.487 401.376 3 1 -295.847 114.505 .123 -788.521 196.828 2 -51.444 81.329 .592 -401.376 298.487 PPRESS_Jog_Mean 1 2 29.956 79.256 .742 -311.055 370.966 3 147.400 66.017 .155 -136.648 431.448 2 1 -29.956 79.256 .742 -370.966 311.055 3 117.444 36.084 .083 -37.814 272.703 3 1 -147.400 66.017 .155 -431.448 136.648 2 -117.444 36.084 .083 -272.703 37.814 PPRESS_Jog_PTI 1 2 9.135 19.847 .691 -76.260 94.531 3 -10.960 36.289 .791 -167.101 145.180

2 1 -9.135 19.847 .691 -94.531 76.260 3 -20.096 35.662 .630 -173.539 133.347 3 1 10.960 36.289 .791 -145.180 167.101 2 20.096 35.662 .630 -133.347 173.539 PPRESS_C_Peak 1 2 -122.683 176.245 .558 -881.004 635.637 3 -62.906 236.581 .815 -1080.833 955.022 2 1 122.683 176.245 .558 -635.637 881.004 3 59.778 61.879 .436 -206.465 326.020 3 1 62.906 236.581 .815 -955.022 1080.833 2 -59.778 61.879 .436 -326.020 206.465 PPRESS_C_Mean 1 2 -55.919 86.721 .585 -429.047 317.210 3 13.581 124.793 .923 -523.361 550.523 2 1 55.919 86.721 .585 -317.210 429.047 3 69.500 41.610 .237 -109.534 248.534 3 1 -13.581 124.793 .923 -550.523 523.361 2 -69.500 41.610 .237 -248.534 109.534 PPRESS_C_PTI 1 2 .510 32.658 .989 -140.006 141.026 3 -284.967 93.486 .093 -687.203 117.269 2 1 -.510 32.658 .989 -141.026 140.006 3 -285.477* 62.118 .044 -552.751 -18.203 3 1 284.967 93.486 .093 -117.269 687.203 2 285.477* 62.118 .044 18.203 552.751 PPRESS_Sprint_Peak 1 2 -45.668 54.262 .489 -279.137 187.801 3 -7.723 216.295 .975 -938.365 922.918 2 1 45.668 54.262 .489 -187.801 279.137 3 37.944 186.323 .857 -763.739 839.628 3 1 7.723 216.295 .975 -922.918 938.365 2 -37.944 186.323 .857 -839.628 763.739 PPRESS_Sprint_Mean 1 2 6.749 174.464 .973 -743.911 757.408 3 -80.418 165.322 .675 -791.740 630.904 2 1 -6.749 174.464 .973 -757.408 743.911 3 -87.167 49.077 .218 -298.327 123.994 3 1 80.418 165.322 .675 -630.904 791.740 2 87.167 49.077 .218 -123.994 298.327 PPRESS_Sprint_PTI 1 2 4.123 21.559 .866 -88.638 96.884 3 -4.619 32.848 .901 -145.954 136.717 2 1 -4.123 21.559 .866 -96.884 88.638

3 -8.742 11.729 .534 -59.206 41.722 3 1 4.619 32.848 .901 -136.717 145.954 2 8.742 11.729 .534 -41.722 59.206 Based on estimated marginal means *. The mean difference is significant at the .05 level. b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Appendix E

Back Matter

Future Research

 Does this observed difference in muscle activation link with difference in injury rates on

different surfaces?

 Do cleats alter plantar pressure and muscle activation?

o Does brand of cleat and type of cleat alter differently?

 Would lower leg exercises reduce risk of injury after return to play with any lower

extremity injury?

 How is muscle activation altered in unhealthy population compared to healthy

population on different playing surfaces?

 Larger sample size used

 Variation among levels of collegiate teams

o Variation among positions

o Different practice surface played on

o Different levels of collegiate football players

NATA Abstract

Effects of Playing Surface on Muscle Activation and Plantar Pressure in Collegiate Football Players Kossin E, Norte GE, Bouillon L, Glaviano NR: University of Toledo, Toledo OH

Context: Research has evaluated if there are differences in injury rates on different playing surfaces. While it is unclear why these differences are occurring, altered muscle activity and plantar pressure have been suggested. Objective: To determine if differences occur in muscle activation and plantar pressure on three different surfaces during functional activity. Design: Crossover study. Setting: Laboratory and two football fields (grass and turf). Patients or Other Participants: Nine division I football players (Age: 20.42years, Height: 185.146.4cm, Weight: 93.712.3kg). Interventions: Participants completed three functional tasks (sprint, jog, and cut) on three different surfaces (turf, grass, and lab). Main Outcome Measures: Mean muscle activation of the lower extremity (gluteus medius, gluteus maximus, biceps femoris, vastus lateralis, vastus medialis, lateral gastrocnemius, peroneal longus, and tibialis anterior) was recorded with surface electromyography (EMG). EMG activity was normalized to quiet standing. Plantar pressure (mean pressure and pressure-time integral) were collected in kPa. Participants completed the three functional tasks on three different surfaces, with simultaneous collection of both EMG and plantar pressure. A repeated measures ANOVA for each dependent variable measure between each playing surface was performed, with a priori of (p<0.05). Results: There was statistical difference in the mean EMG during the jogging task of the gluteus medius between the laboratory (115.9195.6) and the turf (53.580.3) (p=.016), and the peroneus longus between the grass (17.312.4) and the turf (27.423.9) (p=.029). During the cutting task, the peroneus longus had a greater activity on the grass (36.127.8) compared to the turf (32.226.9) (p<0.038). During the sprinting task, the biceps femoris was less activated during the grass (93.750.7) compared to the turf (101.393.9) (p=.011). There was an increase in the pressure-time integral during the cutting task on the turf (277161.0) compared to the grass (79.621.3) (p=.044). Conclusions: There were differences muscle activation in the lower extremity across all three surfaces. Greater differences were seen in the distal muscles, with the peroneus longus presenting with differences in the jogging and cutting task between turf and grass. This is consistent with previous literature that has found that increase of one lower leg muscle occurs simultaneously with other lower leg muscles. There was no significance in the mean pressure of the plantar pressure force distributions, which is different from previous tennis research which has found an increase in mean pressures on greenest when compared to clay. This new information could influence how clinicians rehabilitate lower extremity injuries and could affect when an athlete is returned to play specifically with lower leg injuries. More research will need to be done to identify if these differences observed match up with previous studies on injury rate differences. Word Count 433

NATA Poster

Appendix F

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