Course topics Muscle biomechanics Tendon biomechanics Bone biomechanics.
Biomechanics of Heading in Youth Soccer
-
Upload
nuno-franqueira -
Category
Documents
-
view
118 -
download
10
Transcript of Biomechanics of Heading in Youth Soccer
BIOMECHANICS OF HEADING IN YOUTH SOCCER
by
ERIN HANLON
DISSERTATION
Submitted to the Graduate School
of Wayne State University,
Detroit, Michigan
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
2009
MAJOR: BIOMEDICAL ENGINEERING Approved by: Advisor Date
UMI Number: 3387316
All rights reserved
INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion.
UMI 3387316
Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC 789 East Eisenhower Parkway
P.O. Box 1346 Ann Arbor, MI 48106-1346
© COPYRIGHT BY
ERIN HANLON
2009
All Rights Reserved
ii
DEDICATION
For my parents, for all of their love, support, and patience
iii
ACKNOWLEDGMENTS
This work was supported by in part by the National Operating Committee
for Standards in Athletic Equipment (NOCSAE). In addition to funding provided
by NOCSAE the author would like to acknowledge partial support from the
Anthony and Joyce Danielski Kales Scholarship. The author would like to thank
her advisor, Dr. Cynthia Bir, and her committee members, Dr. John Cavanaugh,
Dr. Pamela VandeVord, and Dr. Kenneth Podell for all of their guidance, support,
and expertise.
The author would also like to thank the Sports and Ballistics group for all
of their assistance with test preparation, data collection, and insight. Specifically,
the author would like to thank Charlene Brain, Sarah Stojsih, Demario Tucker,
and Jacob Mack for assistance with data collection and to Jonathan Beckwith for
assistance with data processing. Thank you to Nathan Dau and Donald
Sherman for input on testing methods and test setup. Thank you to Amanda
Esquivel and James Kopacz for assistance with subject recruitment. Thank you
to all athletes for taking time to participate in the study.
Finally, the author would like to thank her friends and family for their
support for the duration of this process. Thank you to my Mom and Dad, Brian,
and Alison. Thank you for your support and, most importantly, your patience.
TABLE OF CONTENTS
DEDICATION ........................................................................................................ ii
ACKNOWLEDGMENTS ...................................................................................... iii
LIST OF TABLES ................................................................................................. iv
LIST OF FIGURES ............................................................................................... v
CHAPTER 1 - INTRODUCTION ........................................................................... 1
1.1 Statement of the Problem ..................................................................... 1
1.2 Background and Significance ............................................................... 2
1.3 Specific Aims ...................................................................................... 15
CHAPTER 2 - NEISS DATABASE ...................................................................... 17
2.1 Introduction ........................................................................................ 17
2.2 Methodology ....................................................................................... 23
2.3 Results ............................................................................................... 24
2.4 Discussion .......................................................................................... 29
CHAPTER 3 - HEADING FREQUENCY IN YOUTH SOCCER .......................... 33
3.1 Introduction ........................................................................................ 33
3.2 Methodology ....................................................................................... 37
3.3 Results ............................................................................................... 40
3.4 Discussion .......................................................................................... 47
CHAPTER 4 - HEADING BIOMECHANICS IN YOUTH SOCCER ..................... 50
4.1 Introduction ........................................................................................ 50
4.2 Methodology ....................................................................................... 54
4.3 Results ............................................................................................... 60
4.4 Discussion .......................................................................................... 86
CHAPTER 5 - ACCELERATION MEASUREMENT SYSTEM VALIDATION ...... 90
5.1 Introduction ........................................................................................ 90
5.2 Methodology ....................................................................................... 93
5.3 Results ............................................................................................... 99
5.4 Discussion ........................................................................................ 110
CHAPTER 6 - ON FIELD MEASUREMENT OF HEAD ACCELERATION ........ 115
6.1 Introduction ...................................................................................... 115
6.2 Methodology ..................................................................................... 120
6.3 Results ............................................................................................. 122
6.4 Discussion ........................................................................................ 132
CHAPTER 7 - CONCLUSIONS AND FUTURE RECOMMENDATIONS .......... 137
7.1 Conclusions ...................................................................................... 137
7.2 Future Recommendations ................................................................ 141
APPENDIX A – HIC APPROVALS .................................................................... 143
ABSTRACT ....................................................................................................... 160
BIOGRAPHICAL STATEMENT ........................................................................ 162
iv
LIST OF TABLES
Table 1.1: Player Symptoms Following Soccer Heading .................................... 5
Table 2.1: Head injuries by mechanism ............................................................ 26
Table 2.2: Ball to head only injuries ................................................................... 27 Table 3.1: Number of games monitored outlined by age, gender and division of
play ..................................................................................................................... 38
Table 3.2: Maximum headers by any one player for a single game ................... 40
Table 3.3: Average total headers/game/team for male population ..................... 43 Table 3.4: Average total headers/game/team for female population .................. 44 Table 3.5: Total headers in each field position for females ................................ 45 Table 3.6: Total headers in each field position for males ................................... 46 Table 4.1: Heading Scenarios ............................................................................ 59
Table 4.2: Average angles at impact for each heading task .............................. 78 Table 4.3: Mean peak RMS EMG values for each muscle and each task ......... 82
Table 4.4: Average head acceleration on impact for each heading task ........... 85
Table 4.5: Average head flexion for each task ................................................... 87 Table 5.1: Average Peak Linear Accelerations for Ball to Head Conditions ..... 104 Table 5.2: Average Peak Angular Accelerations for Ball to Head Conditions .. 105
Table 5.3: Average Peak Linear Accelerations for Head to Head Conditions .. 106 Table 5.4: Average Peak Angular Accelerations for Head to Head Conditions 106
Table 6.1: Average results for headers by location .......................................... 125
Table 6.2: Description of non header impacts and the player that impacted .... 128
v
LIST OF FIGURES Figure 2.1: NEISS database hospitals by strata ............................................... 18
Figure 2.2: Soccer head injuries including both males and females .................. 25
Figure 2.3: Male and female ball only head injuries by age ............................... 28
Figure 2.4: Ball only head injuries by diagnosis ................................................. 29
Figure 3.1: Soccer Field Diagram ..................................................................... 39
Figure 3.2: Comparison of number of headers/minute, male versus female
across age groups .............................................................................................. 41
Figure 3.3: Number of headers/minute for the male population ......................... 42
Figure 3.4: Number of headers/minute for the female population ...................... 45
Figure 3.5: Total number of headers in each field position ............................... 47
Figure 4.1: FAB System with size scale ............................................................. 53
Figure 4.2: Example of player wearing FAB sensors ......................................... 55
Figure 4.3: Side view of head and trunk body angles ........................................ 56
Figure 4.4: Top view of head rotation ................................................................. 56
Figure 4.5: Neck musculature used for EMG testing a) sternocleidomastoid; b)
trapezius ............................................................................................................. 57
Figure 4.6: Torso flexion for all male players during task 2 ............................... 61
Figure 4.7: Head flexion for all male players during task 2 ............................... 62
Figure 4.8: Head rotation for all males during task 2 .......................................... 63
Figure 4.9: Torso flexion for all females during task 2 ........................................ 64
Figure 4.10: Head flexion for all females during task 2 ..................................... 64
Figure 4.11: Head rotation for all females during task 2 .................................... 65
vi
Figure 4.12: Example of single male participant’s torso flexion for all header
tasks that lack modifications (1, 2, 3, and 7) ...................................................... 66
Figure 4.13: Example of single male participant’s head flexion for all header tasks that lack modifications (1, 2, 3, and 7) ....................................................... 66
Figure 4.14: Example of single male participant’s head rotation for all header
tasks that lack modifications (1, 2, 3, and 7) ...................................................... 67
Figure 4.15: Example of single female participant’s torso flexion for all header
tasks that lack modifications (1, 2, 3, and 7) ...................................................... 68
Figure 4.16: Example of single female participant’s head flexion for all header
tasks that lack modifications (1, 2, 3, and 7) ...................................................... 68
Figure 4.17: Example of single female participant’s head rotation for all header
tasks that lack modifications (1, 2, 3, and 7) ...................................................... 69
Figure 4.18: Example of single male participant’s torso flexion for all passing
header tasks (2, 4, 5, and 6) ............................................................................... 70
Figure 4.19: Example of single male participant’s head flexion for all passing
header tasks (2, 4, 5, and 6) ............................................................................... 71
Figure 4.20: Example of single male participant’s head rotation for all passing
header tasks (2, 4, 5, and 6) ............................................................................... 71
Figure 4.21: Example of single female participant’s torso flexion for all passing
header tasks (2, 4, 5, and 6) ............................................................................... 72
Figure 4.22: Example of single female participant’s head flexion for all passing
header tasks (2, 4, 5, and 6) .............................................................................. 73
vii
Figure 4.23: Example of single female participant’s head rotation for all passing
header tasks (2, 4, 5, and 6) ............................................................................... 73
Figure 4.24: Example of single male participant’s torso flexion for all clearing
header tasks (7 and 8) ........................................................................................ 74
Figure 4.25: Example of single male participant’s head flexion for all clearing
header tasks (7 and 8) ....................................................................................... 75
Figure 4.26: Example of single male participant’s head rotation for all clearing
header tasks (7 and 8) ....................................................................................... 75
Figure 4.27: Example of single female participant’s torso flexion for all clearing
header tasks (7 and 8) ....................................................................................... 76
Figure 4.28: Example of single female participant’s head flexion for all clearing
header tasks (7 and 8) ....................................................................................... 76
Figure 4.29: Example of single male participant’s head rotation for all clearing
header tasks (7 and 8) ....................................................................................... 77
Figure 4.30: Peak EMG for all male players for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right
trapezius ............................................................................................................ 80
Figure 4.31: Peak EMG for all female players for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right
trapezius ............................................................................................................ 81
Figure 4.32: Sample RMS EMG for one male player for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right
trapezius ............................................................................................................ 83
viii
Figure 4.33: Sample RMS EMG for one female player for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right
trapezius ............................................................................................................ 84
Figure 5.1: HIT system ...................................................................................... 91
Figure 5.2: Back of HITS headband with circles marking accelerometer
placement .......................................................................................................... 94
Figure 5.3: Air cannon with soccer barrel ........................................................... 96
Figure 5.4: Head to head impact test setup for forehead testing ...................... 97
Figure 5.5: Linear regression of linear acceleration for HIII and HITS ball to head
conditions ......................................................................................................... 100
Figure 5.6: Linear regression of angular acceleration for HIII and HITS ball to
head conditions ................................................................................................ 100
Figure 5.7: Linear regression of linear acceleration for HIII and HITS head to
head conditions ................................................................................................ 101
Figure 5.8: Linear regression of angular acceleration for HIII and HITS head to
head conditions ................................................................................................ 102
Figure 5.9: Linear regression of linear acceleration for HIII and HITS ball to head
and head to head conditions combined ........................................................... 103
Figure 5.10: Linear regression of angular acceleration for HIII and HITS ball to
head and head to head conditions combined .................................................. 103
Figure 5.11: Linear acceleration for both HIII and HITS for one ball to head
forehead impact at the 12 m/s condition .......................................................... 107
ix
Figure 5.12: Linear acceleration for both HIII and HITS for one ball to head right
side impact at the 12 m/s condition .................................................................. 108
Figure 5.13: Linear acceleration for both HIII and HITS for one ball to head left
temple impact at the 12 m/s condition .............................................................. 108
Figure 5.14: Linear acceleration for both HIII and HITS for one head to head
forehead impact at the 4.75 m/s condition ....................................................... 109
Figure 5.15: Linear acceleration for both HIII and HITS for one head to head left
side impact at the 4.75 m/s condition ............................................................... 109
Figure 6.1: Wayne State University Tolerance Curve ...................................... 118
Figure 6.2: HITS headgear fitted to HIII headform .......................................... 121
Figure 6.3: Linear head acceleration by location for each header only impacts
.......................................................................................................................... 122
Figure 6.4: Angular head acceleration by location for each header only impacts
.......................................................................................................................... 123
Figure 6.5: HIC values for headers by location with mTBI tolerance level ...... 124
Figure 6.6: Linear head acceleration for header impacts for individual players
.......................................................................................................................... 126
Figure 6.7: Angular head acceleration for all header impacts for individual
players ............................................................................................................. 126
Figure 6.8: Linear head acceleration for all non header impacts by location ... 129
Figure 6.9: Angular head acceleration for all non header impacts by location 130
Figure 6.10: HIC for all non header impacts by location ................................. 130
x
Figure 6.11: Linear head acceleration for all non header impacts for individual
players ............................................................................................................. 131
Figure 6.12: Angular head acceleration for all non header impacts for individual
players ............................................................................................................. 132
1
CHAPTER 1
INTRODUCTION
1.1 Statement of the Problem
Soccer is one of the most popular sports throughout the world; Fédération
Internationale de Football Association (FIFA) has approximately 200 million
registered players worldwide (Dvorak and Junge, 2000). A large increase in
participation has taken place recently in the United States. This is seen clearly in
the American Youth Soccer Organization (2006) which today has 50,000 youth
soccer teams and over 650,000 players registered after starting out in 1964 with
only nine teams (2006). Unfortunately, the increase in youth players has caused
an increase in injuries (Metzl, 1999).
Soccer injuries not only cause trauma to the player, they also create a large
socioeconomic cost. Dvorak et al. (2000) reviewed relevant soccer injury data
and determined that approximately $30 billion dollars are spent annually for
treatment of soccer related injuries worldwide. While this value uses an average
injury rate, it also uses a conservative estimate of cost per injury (Dvorak and
Junge, 2000). As pointed out by Dvorak et al. (2000), this cost estimate does not
take into account lost wages or anything not directly related to primary medical
costs.
Head injuries are of particular concern due to their traumatic nature and the
lack of knowledge related to these injuries and their mechanisms, specifically
mild traumatic brain injury (mTBI). Head injuries represent up to 22% of all
soccer injuries (Ruchinskas, et al., 1997). A unique aspect of soccer is that there
2
are both intentional and unintentional head impacts. Intentional impacts, or
“heading” the ball, occur when a player purposefully uses their head to redirect
the ball. Unintentional impacts include: player to player impacts, player to
ground impacts, player to goalpost impacts, and unintentional player to ball
impacts.
The purpose of this study is to investigate the effect of the intentional head
impacts that occur during soccer play. Initial steps will be taken to determine the
frequency and severity of heading episodes in the field using both field
observation and a novel head band measurement system developed for use in
soccer play. Additionally, many laboratory studies have been performed on the
adult soccer population, but very few have focused on children. Therefore, an
analysis of the biomechanics of heading in youth soccer needs to be performed.
Comparisons will be made between youth and adult heading biomechanics to
determine if there is a difference based on age. Also, a comparison between the
youth heading biomechanics and the youth heading field data will be made. This
will provide valuable information as to whether the laboratory data collected in
previous studies is representative of actual on-field situations.
1.2 Background and Significance
Injury Epidemiology
Due to the increase in soccer participation, more injuries are occurring
(Metzl, 1999). Although the majority of soccer injuries are to the lower extremity
(Agel, et al., 2007, Arnason, et al., 2004, Backous, et al., 1988, Dvorak and
Junge, 2000, Elias, 2001, Junge and Dvorak, 2007, Keller, et al., 1987, Le Gall,
3
et al., 2008, Leininger, et al., 2007, Nielsen and Yde, 1989, Peterson, et al.,
2000, Poulsen, et al., 1991, Sandelin, et al., 1985, Schmidt-Olsen, et al., 1985),
head injuries are of specific concern due to the potential for long-term debilitating
effects. The American Academy of Pediatrics has classified soccer as a contact
sport, but many still believe that soccer is a safe alternative to American Football
(Patlak, et al., 2002). This may not be true when considering the previously
established concussion rates (Green and Jordan, 1998). When studying sports
in the National Collegiate Athletic Association (NCAA) Green et al. (1998) found
similar concussion rates in American football and men’s and women’s soccer.
Using the NCAA Injury Surveillance System (ISS) to determine the incidence of
concussion in various sports (Green and Jordan, 1998). Concussions were
measured per 1000 athlete exposures, where each exposure is the equivalent of
one practice or game. They found that women’s soccer actually has a higher
rate of concussion (.33 concussions/1000 athlete exposures) than both men’s
soccer (.31 concussions/1000 athlete exposures) and football (.31
concussions/1000 athlete exposures). This demonstrates the high risk of head
injury during soccer play.
Covassin et al. (2003) found that men’s and women’s soccer are in the
group of athletes at the highest risk for concussions (Covassin, et al., 2003).
Women’s soccer had the highest number and injury rate of concussions of the 15
NCAA women’s sports included in the study indicating that further research into
the area is necessary (Covassin, et al., 2003). Small stature, a greater ball-to-
head ratio, and potentially weaker neck muscles have been suggested as the
4
potential causes (Covassin, et al., 2003). All of these issues would also be
applicable to the youth population in addition to their lower skill level and lack of
experience.
Keller et al. (1987) found that younger players generally have a higher rate
of head and face injury (Keller, et al., 1987). They attributed this fact to a lack of
heading proficiency and the increase in ball to head weight ratio. Both of these
factors would indicate heading as a potential problem in youth soccer. This is of
significant importance because it has been indicated that younger athletes may
take longer to regain baseline neuropsychological levels following a concussion
(Field, et al., 2003).
Barnes et al. (1998) interviewed 144 elite soccer players in the 1993 US
Olympic Festival. Players were asked to estimate the number of times that they
head the ball during games and during practices. They were also asked to list
any symptoms that they had experienced as a result of heading and if they had
any previous head injuries. Sixty-five women aged 17-30 and 72 men aged 17-
22 completed the surveys. Using an odds ratio of the total concussions for men
and women it was found that men had an increased risk of concussion of 2.16
times that of women. Seventy-four concussions were reported in men and 28 in
women with 27 players reporting multiple concussions, 24 men and 3 women.
That indicates that 52% of players interviewed had experienced a concussion.
Of these concussions, 18% reported the mechanism of injury as collision with the
ball. Additionally 89% of men and 43% of women had previously had some type
of acute head injury during their soccer careers. Many of the players had
5
symptoms following heading the soccer ball (Table 1.1) with headache being the
highest reported symptom (Barnes, et al., 1998). These symptoms indicate that,
at a minimum, soccer heading creates a short-term problem.
Table 1.1: Player Symptoms Following Soccer Heading (Barnes, et al., 1998)
Symptom Men (%) Women (%)
Headaches 54.0 55.0
Dazed 31.0 49.0
Dizziness 18.1 38.5
Decreased Concentration 9.7 10.8
Blurred Vision 11.1 4.6
Lost Conciousness 1.4 0.0
Numbness/Tingling 12.8 7.7
Amnesia 0.0 3.1
In another study looking at concussion in soccer, Boden et al. (1998)
found similar concussion rates as Barnes et al. (Barnes, et al., 1998, Boden, et
al., 1998). Athletic trainers for each of the 15 Atlantic Coast Conference (ACC)
men’s and women’s soccer teams were asked to fill out a questionnaire for each
concussion occurring during the 1995 and 1996 seasons. In the 1995 season,
188 women and 162 men competed in ACC soccer, and in the 1996 season
there were 188 women and 163 men. During these two seasons 29 concussions
occurred in 26 players. This resulted in a concussion rate of 0.49
concussions/1000 athlete exposures, 0.6 concussions/1000 male athlete
exposures and 0.4 concussions/1000 female athlete exposures. Contact with the
6
ball was the second most common injury mechanism (24 %), but no injuries were
attributed to intentional heading. The concussion rates in this study were
significantly higher than those found in the NCAA data. This could be due to the
different levels of play in the NCAA with this conference being in the highest
division, Division I. One problem when comparing these studies is the use of
differing definitions of concussion.
Sandelin et al. (1985) used insurance reports to determine soccer related
injuries for 1980 in Finland. After eliminating exertion injuries, researchers
determined that 2072 soccer injuries occurred that year, with 13 % of these
located in the head and neck region. Another significant finding in this study was
the lack of differences between the genders and positions played, but there were
significantly more injuries in the two highest skill divisions (Sandelin, et al., 1985).
The results also coincide with Barnes et al. (1998) that gender was not a
significant factor in head injury occurrence (Barnes, et al., 1998). This study
included youth players, but the average age of those included was 26 for the
men and 23 for women. It was not determined whether or not age was a
significant factor in injury occurrence.
Mechanism of soccer related head injury has been studied previously in
adults (Agel, et al., 2007, Andersen, et al., 2004, Boden, et al., 1998, Dick, et al.,
2007, Dvorak, et al., 2007, Fuller, et al., 2005), but many of these studies have
significant limitations when looking strictly at injuries related to heading the
soccer ball. One significant problem is the lack of a set injury definition which is
required when comparing literature. Many studies use definitions that require a
7
loss of practice or play in order to be quantified as an injury which generally
eliminates those players that are just having post-heading symptoms. This
definition creates a problem in tabulating the total number of people that have
symptoms following heading a soccer ball.
Soccer Heading Studies
Research has been performed in the area of soccer heading and its
effects on players’ neuropsychological abilities and mental imaging scans
(Guskiewicz, 2002, Janda, et al., 2002, Tysvaer and Storli, 1981, Tysvaer and
Storli, 1989, Tysvaer, et al., 1989). These previous studies have provided very
contrasting results in which no clear understanding of what is occurring during or
following heading can be made. Without understanding what happens
biomechanically during heading events on the field, there is no way to determine
injury risk. The majority of the previous research has been performed on adults,
some of which are retired soccer players. Very little focus has been on the effect
of heading in the youth population, and there has still been no biomechanical
assessment of youth soccer players heading the soccer ball in the lab.
Additionally, previous studies have not isolated heading from other potential
deficit causes such as alcohol use and previous non-soccer related head injury.
Therefore, further research needs to be done to look at the effect heading has on
the youth population.
Although much research has been devoted to the adult population,
especially the elite players, the risks to the youth population have not been
studied in great detail. In fact, the risks to children are potentially greater. This is
8
primarily due to their size versus the force being applied by the ball (Lees and
Nolan, 1998). It has been reported that ball mass, impact velocity, and size of
the individual all contribute to the potential for injury (Lees and Nolan, 1998).
Additionally, the importance of proper technique may be especially true in the
youth population, since their skill level has not been well developed to control
their head motion when heading the ball. Therefore, the youth population could
be at an increased risk for sustaining head injuries due to rotational acceleration.
Given the increase in youth soccer participation over the past decade and
additional injury risk, it is necessary to focus research on the youth population.
Many of the earlier studies indicated that repeatedly heading the soccer
ball increased players’ risk for neuropsychological deficits and long-term
symptoms. Tysvaer et al. (Tysvaer and Storli, 1981, Tysvaer and Storli, 1989,
Tysvaer, et al., 1989) was one of the first researchers to investigate this
occurrence. In the first study, Tysvaer et al. (1981) studied 128 retired
Norwegian soccer players with an average age of 25 years. Questionnaires
were sent to each player asking about their previous soccer play. Of the 128
players, 64 had once had symptoms related to heading the soccer ball, some of
which required hospitalization. The study does not provide information on the
number of headers to which each player was subjected. Therefore, no
association between the number of headers and injury can be made.
Two additional studies were done using EEG and neuropsychological
testing to determine if active and retired soccer players displayed deficits related
to their soccer play (Tysvaer and Storli, 1989, Tysvaer, et al., 1989). Sixty-nine
9
active players were compared to controls while a parallel study of 37 retired
players used the same tests to determine long-term effects. Eighty-one percent
of the players showed a deficit in neuropsychological tests ranging from mild to
severe (Tysvaer, et al., 1989). For the study involving active players, it was
found that there was an increase in abnormal EEG findings in soccer players with
respect to controls. It was also determined that the highest abnormal findings
were in the younger players (Tysvaer and Storli, 1989).
Neuropsychological testing has been the basis for many of the previous
studies. In one such study Guskiewicz et al. (2002) studied soccer heading in
players playing in the NCAA. Six neurocognitive tests were performed on 91
soccer players, 96 other athletes, and 53 control subjects. The battery of tests
included the Trail Making Test, the Controlled Oral Word Association Test, the
Stroop Color Word Test, the Hopkins Verbal Learning Test, the Symbol Digit
Modalities Test, and the Wechsler Digit Span Test. These tests evaluated a wide
range of cognitive abilities including: orientation, concentration, visuospatial
capacity, problem-solving, verbal associations, cognitive flexibility, attention
span, verbal memory, visual tracking, incidental learning, concentration, and
immediate memory. Scholastic Aptitude Test (SAT) scores were also evaluated.
Researchers determined that there was no actual significant difference
(Guskiewicz, 2002). One test (the Hopkins Verbal Learning Test) approached
statistical significance, but once previous concussions and learning disabilities
were controlled for, significance was not found. This test was specific for
immediate memory recall which could indicate that bouts of heading can cause
10
immediate deficits, but further research is needed to determine why these deficits
may exist. Additionally, an attempt to correlate soccer exposure to test
performance was conducted. Correlations were only made in the Wechsler Digit
Span Test. This study indicated that soccer exposure did not affect
neuropsychological ability.
Similar neuropsychological testing was conducted on 53 active
professional soccer team members from The Netherlands (Matser, et al., 1998).
Both players and 27 controls were interviewed and tested using a battery of
neuropsychological examinations. The tests included in the battery differed
slightly from those used by Guskiewicz et al. (2002) as did the results. Fourteen
tests were administered including: Raven Progressive Matrices Test, Wisconsin
Card Sorting Task, Paced Auditory Serial Addition Task, Digit Symbol Test, Trail
Making Test, Stroop Test, Bourdon-Wiersman Test, Wechsler Memory Scale,
Complex Figure Test, 15-Word Learning Test, Benton’s Facial Recognition Test,
Figure Detection Test, Verbal Fluency Test, and the Puncture Test. The number
of headers experienced by each player was estimated based on position played
and number of games. Soccer players exhibited impaired performance in
memory, planning and visuoperceptual processing in comparison to the controls
with the level of impairment related to the position and number of headers as well
as concussions. The authors suggest that these data may indicate professional
soccer’s connection to neurocognitive impairment (Matser, et al., 1998).
In a similar study, Putukian et al. (2004) corroborated these results by
studying Division I male and female college athletes. Athletes were studied
11
prospectively during two practice sessions and served “as their own controls”.
Neuropsychological testing was conducted before and after each practice
session with the number of headers monitored during the session. A practice
effect was noted between the pre- and post-test scoring for attention and
concentration. However, there was no significant difference between the header
and non-header groups in either the pre- or post-test scores (Putukian, 2004).
Therefore, further tests would be required to determine the cause of the deficits.
Janda et al. (2002) performed one of the few studies involving soccer
heading in the youth population. Fifty-seven youth soccer players from five
teams having an average age of 11.5 years were studied for three seasons.
Neurocognitive testing was used to determine the effects of heading. Four
cognitive tests were included in this study: Verbal Learning, Digit Span, Symbol
Digits Modality Test, and Verbal Learning Delay. Symptoms following heading
and heading exposure were also observed throughout the duration of the study
by the coaching staffs. All impacts were included in the data sheet returned to
the researchers by the coaching staffs including those that were unintentional.
Symptoms following heading included headaches, blurred vision, nausea, and
ringing in the ears with headaches being the most common. It was also
determined that there is an inverse relationship between increased heading and
verbal learning (Janda, et al., 2002).
In a recent study, Witol and Webbe (2003) studied 60 male players at
varying levels of skill with the youngest being high school students. Using a
control of 12 males who had never played soccer, neuropsychological testing
12
was administered to evaluate a number of functions including: abstract
reasoning, general intellectual function, attention, mental flexibility, information
processing, verbal and nonverbal memory. These tests included: Shipley
Institute of Living Scale, Trail Making Test, Paced Auditory Serial Addition Test,
Test of Facial Recognition, Rey Osterreith Complex Figure Test, and Rey
Auditory Verbal Learning Test. Players were asked to self-report their heading
exposure which allowed for a cumulative heading measure to be developed to
estimate career heading. Also, players were asked in their history about
symptoms during or following games. A decrease in the scales measuring
attention, concentration, cognitive flexibility and general intellectual functioning
correlated with an increase in the number of headers (Witol and Webbe, 2003)
which supports the conclusions of Janda et al. (2002). Researchers also found
that those players described as “typical headers” had significantly more dizziness
indicating that there are short term effects caused by performing soccer headers.
Stephens et al. (2005) performed a study on 23 youth soccer players, 23
youth rugby ranging in age from 13-16 years, and age matched controls for both
the contact sports who were participants in only non-contact sports. The battery
of 13 tests included: Rey Complex Figure, WAIS-R Digit Symbol, WAIS-R Digit
Span, Trail Making, Stroop, WMS-R Logical Memory Immediate and Delayed,
the Alertness, Divided Attention, Covert Attention Shift, Flexibility and Working
Memory subtests of Test of Attention Performance, Wisconsin Card Sorting, 64-
item version, and Alternate Uses. The study showed no significant difference
between soccer players and their controls. There was also no correlation found
13
between the number of headers and neuropsychological test results (Stephens,
et al., 2005).
Imaging has also been looked at as a method to detect injuries in both
current and retired soccer players, although to a more limited extent. Tysvaer et
al. (1989), in one of the earliest soccer heading studies, used EEG to detect
abnormalities, which although it is not necessarily an imaging technique, was the
first use of brain scans in the field (Tysvaer and Storli, 1989, Tysvaer, et al.,
1989). Since the earlier studies, imaging advances have been achieved and
researchers have used MRI and computer tomography (CT) scans (Jordan, et
al., 1996, Rutherford, et al., 2003, Sortland and Tysvaer, 1989). As with the
neuropsychological testing, the imaging studies have also returned conflicting
results.
Sortland et al. (1989) performed CT scans on former international soccer
players. Thirty-three retired players were given scans with nine of these being
categorized as “typical headers”. No definition was provided by the authors, but
both the player and their teammates had categorized themselves as “typical
headers”. Scans were evaluated visually and by taking linear measurements.
When compared to normative scans, one third of the former players had
widening of the lateral ventricles. This was determined to be central cerebral
atrophy. This could also be a symptom of alcohol abuse, but this issue wasn’t
addressed. Significant differences did not occur between “typical headers” and
the other players (Rutherford, et al., 2003, Sortland and Tysvaer, 1989).
14
In one of the initial studies in the United States, Jordan et al. (1996)
reported on twenty males with an average age of 24.9 years from the US
National Soccer team. A cohort of 20 male elite-track athletes was identified.
Players completed questionnaires regarding positions played, history of head
injury, number of headers and number of years played. A scaling system was
used to estimate the number of headers for each player based on level and type
of play. All study participants were examined using magnetic resonance imaging
(MRI) to determine any neurological deficits. Based on this testing, no
differences were noted between the soccer players and the control group, but
nine of the US National Soccer players were found to have abnormal MRI results
with three of those players having multiple findings (Jordan, et al., 1996). These
results included cortical atrophy in three players, ventricular enlargement in three
players, focal atrophy in three players, cavum septum pellucidum in three
players, and cerebellar atrophy in one player.
MRI was also used in a study by Autti et al. (1997). Both soccer and
American football players were given MRI scans and were compared to the
scans of age-matched, non-athlete controls. High-signal foci were found in 11
soccer players, seven American football players, and in five of the controls. This
indicates axonal rarefaction or non-ischaemic demyelination. The majority of
these high-signal foci were not found in both T2-weighted imaging and proton
density-weighted imaging. High-signal foci were also seen on both the T2-
weighted imaging and the proton density-weighted imaging which indicates
microinfarts or ischaemic tissue damage has occurred. Of these foci found on
15
both scans, the majority were in soccer players. Autti et al. (1997) suggests that
due to the lack of helmet use in soccer, foci are being caused by slight brain
injuries taking place during play (Autti, et al., 1997).
One problem with all of the previous studies is that the biomechanics and the
physiological effects of heading is not understood completely and, therefore,
cannot be ruled out or assigned blame for injuries. As Kirkendall and Garrett
(2001) state, “it is difficult to blame purposeful heading for the reported cognitive
deficits when actual heading exposure and details of the nature of head-ball
impact are unknown” (Kirkendall and Garrett, 2001). In order to determine
whether or not heading is causing cumulative damage, it is necessary to further
understand what is taking place during actual heading events. The proposed
study will focus on studying the frequency and severity of headers as well as a
biomechanical evaluation of soccer heading in the youth soccer population. By
evaluating youth soccer players, both in the lab and on the field, assessments
can be made about differences between adult and youth heading biomechanics.
These differences, or lack thereof, will provide insight as to whether the youth
soccer population is at higher risk for injury due to soccer heading. Additionally,
by recreating previously performed laboratory tests, it will be possible to compare
lab and field data.
1.3 Specific Aims
Although previous research has been conducted to determine the effects of
repetitive heading in soccer, the results are very controversial. Conflicting results
have been observed in studies throughout the history of soccer heading
16
research. Many of these previous studies have had significant challenges within
the methodology, including the lack of controls or using improper control groups
and many have not taken into account other outside factors that could be
contributing to results. In order to determine the effects of the repetitive
subconcussive head impacts associated with heading, an in-depth analysis of the
biomechanics of heading needs to be performed. The specific aims of this
project include:
1) To determine the incidence of head injury in youth soccer related only
to head to ball impacts. This will be accomplished using the National
Electronic Injury Surveillance System (NEISS) database which was
created by the United States Consumer Product Safety Commission
(CPSC).
2) To determine the frequency of heading in youth soccer based on age,
gender, and skill level.
3) To validate a novel headband system to measure head impact
frequency during soccer play.
4) To measure the biomechanical response of youth soccer players
during heading events using the Functional Assessment of Biomechanics
motion capture system and compare them to adults.
5) To measure head impact frequency and severity using the Head
Impact Telemetry System (HITS) (Simbex, Lebanon, NH). By using a
wireless acceleration measurement system, actual field data may be
collected.
17
CHAPTER 2
NEISS DATABASE
2.1 Introduction
The United States Consumer Product Safety Commission (CPSC)
established a sample of hospitals which gather information on each emergency
room patient who has an injury related to a consumer product. Using this sample
data, estimations can be made for the entire population. The collective database
is called the National Electronic Injury Surveillance System (NEISS).
The NEISS database was designed in its original form in 1970 using a
sample of 119 hospitals. Updates have taken place throughout the duration of its
existence in order to maintain a statistically applicable hospital sample and
estimation technique. The current database collects data from 100 hospitals
nationwide using 5 strata, or divisions based on size. Hospital size is determined
by the number of visits that the emergency department handles yearly. The
current strata represent hospitals of four different sizes as well as children’s
hospitals from across the United States (Figure 2.1).
18
Figure 2.1: NEISS database hospitals by strata (Consumer Product Safety Commission 2000)
The database can be searched using different variables to eliminate
unwanted cases. Variables which can be looked at include: date, product, sex,
age, diagnosis, disposition, locale, and body part. Additionally, short notes are
often available along with the case information providing a more detailed
description of the injury and related circumstances. Product codes for each
consumer product are available and any injury that was related to the use of the
specific product is reported and can be searched for based on that product code.
Statistical weight is calculated for each hospital for each month and the
estimates are calculated using these values. The calculation of the statistical
weights takes into account any non-response of hospitals, merging hospitals, and
any additional alterations made within the sampling frame (CPSC 2001).
Statistical weights are calculated using the following equation (CPSC 2001):
19
������� � �� �′� �� ��
Where:
Nh = Number of hospitals in the 1995 sampling frame for sampling for stratum h
nh = Number of hospitals selected for the NEISS sample for stratum h
n’h = Number of in-scope hospitals in the NEISS sample for stratum h
rh = Number of NEISS hospitals participating in stratum h for the given month
Rh = Ratio adjustment for combined stratum h
Using the weights for each hospital, the estimates can then be calculated.
The following equation is used to calculate the estimates for each hospital
(2001):
�������� � � � ����′� ���
���
�� !�
Where:
m = Number of strata in the NEISS sample during the given time period
Nh = Number of hospitals in the NEISS sampling frame for sampling for stratum h
nh = Number of hospitals selected for the NEISS sample for stratum h
n’h = Number of in-scope hospitals in the NEISS sample for stratum h
rh = Number of NEISS hospitals participating in stratum h for the given month
Rh* = Ratio adjustment for combined stratum h
xhi = Number of cases for a specified product or type of injury reported by
hospital i in stratum h for the given month
20
Since sporting equipment is considered a consumer product, injuries
related to equipment are a part of the NEISS database. The NEISS database
has been used in previous studies related to sports injury (Conn, et al., 2006,
Hostetler, et al., 2005, Hostetler, et al., 2004, Sosin, et al., 1996, Xiang, et al.,
2005, Yard and Comstock, 2006, 2006, Yard, et al., 2007), some of which are
specific to soccer (Adams and Schiff, 2006, Delaney, 2004, Leininger, et al.,
2007), to determine the injuries relating to the sport that have presented to the
Emergency Departments over a specified time period. In addition to soccer,
sports which have been researched using the NEISS database include martial
arts, ice hockey, lacrosse, field hockey, water skiing, wakeboarding, snow skiing,
snowboarding, cycling, skateboarding, and rugby (Adams and Schiff, 2006,
Hostetler, et al., 2005, Hostetler, et al., 2004, Leininger, et al., 2007, Pickett, et
al., 2005, Sosin, et al., 1996, Xiang, et al., 2005, Yard and Comstock, 2006,
2006, Yard, et al., 2007). The majority of these studies include all injuries
reported during the specified activity, but there are also those that focus on a
specific variable such as the region of the body or level of injury. For example,
one study focused specifically on head injuries which led to fatalities during
bicycling (Sosin, et al., 1996).
Previous studies using the NEISS database to research the frequency of
soccer injuries in the pediatric population have been performed. These studies
have looked at the overall injuries sustained (Adams and Schiff, 2006, Leininger,
et al., 2007) and total head injuries in multiple sports (Delaney, 2004). Injuries
were investigated by examining any factor taking place during a soccer match
21
and the details of those injuries. Although these studies discuss body region and
injury cause, further investigating head injuries, specifically those caused by
impact with the ball, can provide specific information related to the injuries
associated with soccer heading.
Although previous research has been conducted to determine whether
soccer heading is injurious, the results are varied. Conflicting results have been
observed in studies throughout the history of soccer heading research
(Guskiewicz, 2002, Jordan, et al., 1996, Matser, et al., 1998, Putukian, 2004,
Tysvaer and Storli, 1981, Tysvaer and Lochen, 1991, Tysvaer and Storli, 1989,
Witol and Webbe, 2003). Therefore, it has yet to be determined if injury occurs
strictly from head contact with the ball. The purpose of the current study is to
gain insights into the occurrence of head injuries from contact with the ball only.
By reviewing hospital emergency room data coded through the NEISS data it can
be determined if ball only impacts can be a mechanism of injury in soccer.
Gender differences have been hypothesized as being an issue in soccer
heading related issues due to the ball to weight ratio differences (Barnes, et al.,
1998, Covassin, et al., 2003). Previous studies of the NEISS database have
found that the majority of players injured while playing soccer were boys (Adams
and Schiff, 2006, Leininger, et al., 2007). Leinenger et al. (2007) found that 58.6
% of total injuries were to boys, and they were also more likely to have head and
neck injuries. While the number of injuries was higher in boys than girls, the
concussion rates remained similar (Leininger, et al., 2007). Similarly, Adams and
Schiff (2007) found that boys had a higher number of total injuries (55.5 %).
22
While both of these studies found that boys have a higher incidence of injury, it is
necessary to determine if girls have a higher incidence of ball to head related
injuries. It has been hypothesized previously that girls have a higher rate of head
injuries, specifically concussions, due to their smaller stature, greater ball to head
ratio, and potentially weaker neck muscles (Covassin, et al., 2003).
Player age is also considered a possible factor for increased risk of soccer
head injuries specifically related to heading the ball. Previous studies have found
conflicting results as to which age group has a higher likelihood of experiencing
head injuries during soccer play (Adams and Schiff, 2006, Leininger, et al.,
2007). Leinenger et al. (2007) found that younger players were more likely to
have head/neck/face injuries, but Adams and Schiff (2006) found that head and
face injuries occurred more frequently in the oldest age group (15-19 years old).
This is most likely due to the fact that Leinenger et al. (2007) included 2-4 year
old players in their study, and they were not included in the other previous
studies. Although these studies looked at head injuries, the mechanism of injury
was not determined. This is necessary to determine if younger children are more
likely to suffer from ball to head injuries related to soccer heading.
Delaney (2004) studied head injuries in ice hockey, soccer, and football.
Using the NEISS database, head injuries to anyone participating in any of the
three sports during a ten year span, 1990 – 1999, were calculated. Results were
not limited by gender or age. The inclusion criteria were limited to sport being
played and body region injured. Total head injuries for the ten year period were
found to be the highest in football (204,802), followed by soccer (86,697) and ice
23
hockey (17,008). The total number of concussions during the ten year span also
followed a similar pattern with football (68,860) being the highest, followed by
soccer (21,714) and ice hockey (4,820) respectively. Although the total number
of injuries was higher for football and soccer than hockey, the injury rate was
found to be similar. Injury rate was determined by dividing the number of injured
athletes by the total number of athletes. Although this study investigates head
injuries during soccer play, injury mechanism, gender, and age were all
neglected as pure between sport comparisons were made (Delaney, 2004).
The previous studies performed a broad soccer injury analysis using the
NEISS database. In-depth analyses have not been completed on the types of
head injuries or the cause of these injuries. The current study will focus on head
injuries caused by intentional or unintentional head contact with the ball and their
diagnoses. This will represent the occurrence of injuries caused purely by ball
contact.
2.2 Methodology
Data were collected using the United States Consumer Product Safety
Commission (CPSC) National Electronic Injury Surveillance System (NEISS).
The database was queried using for soccer injuries that occurred to the head
using product code 1267. Injuries considered were limited based on age, body
region injured, and year taking place. The injuries that were included were head
injuries in children ranging in age from 5 – 18 years that occurred from 2002 –
2007. Using statistical weights collected as part of the data set for each case,
national estimates were made.
24
Included cases are both game and practice injuries. In order to focus on
ball-to-head injury, the injury mechanism for each case was investigated. Using
the narrative description provided with each case, the estimated number of head
injuries resulting from impact with the ball only, the ground, collision with another
player, the goal post, and unknown mechanism were calculated. Additionally,
age, gender, and diagnosis were assessed for injuries that occurred from impact
with the ball only. Age groups were broken down into 5-9 years old, 10-14 years
old, and 15-18 years old. Diagnoses included all of those that were seen after
limited other variables and include: concussion, contusion, laceration, internal
organ injury, and other. Internal organ injuries included such things as closed
head injuries.
Statistical analysis was performed using the Kruskal-Wallis test for testing
more than 2 groups in a hypothesis. Whenever this was significant, Mann-
Whitney tests were performed. It was determined by the Institutional Review
Board that this study did not require approval for the study of human subjects.
2.3 Results
A total of 62,022 soccer head injuries were estimated to take place from
2002 to 2007 in the United States. Following 2003, a significant increase in the
number of head injuries was seen in 2004. This increased from 9022 head
injuries in 2003 to 11,762 head injuries in 2004 (Figure 2.2). The increase held
relatively steady through 2007 which had a total of 10,122 head injuries.
Throughout the six years included in the study, ball to head injuries remained
25
consistent. The year 2004 was statistically higher than all other years, except
2005, which it was statistically lower than.
Figure 2.2: Soccer head injuries including both males and females
The majority of soccer head injuries from 2002 to 2007 were caused by a
collision with another player (38 %). Injuries caused by impact with the ball only
represent 16 % of soccer head injuries (Table 2.1). Additional injuries with an
unknown mechanism could cause an under prediction of the four main
mechanisms investigated, but was required due to a lack of description for these
injuries. Also, goalpost injuries comprised 5.75 % of total injuries and included
impact with the wall in indoor soccer.
26
Table 2.1: Head injuries by mechanism Selected Characteristics Actual, n Weighted
N %
Injury Mechanism n = 2081 N = 62024 100.00
Ball Only Head Injuries 309 9861 15.90
Unknown 471 12720 20.51
Ground 424 12471 20.11
Goalpost 117 3569 5.75
Collision with Player 760 23404 37.73
Although males made up the majority of soccer head injuries (53.99%),
they did not have the highest percentage of ball only head injuries. Females had
59.64% of those injuries caused by impact with the ball only (Table 2.2). The
only age group in which males had a higher percentage of ball only injuries was
the 5-9 year olds where females only consisted of 19.68% of the injuries. The
gender difference had the largest increase of females in comparison to males in
the 15-18 year old age group where males only made up 29.87% of the ball to
head injuries. Total head injuries were significantly different between genders
(p=.015), but ball to head injuries were not. Hospitalization following ball to head
injury was not common, with 99.67 % of players being treated and released.
27
Table 2.2: Ball to head only injuries Selected Characteristics Actual, n Weighted
N %
Gender n = 309 N = 9861 100.00
Male 134 3980 40.36
Female 175 5881 59.64
Age n = 309 N = 9861 100.00
5-9 46 1232 12.49
10-14 130 3789 38.42
15-18 133 4840 49.08
Diagnosis n = 309 N = 9861 100.00
Concussion 100 3526 35.76
Contusion 36 1793 18.18
Laceration 3 99 1.00
Internal Organ 163 4317 43.78
Other 7 126 1.28
The lowest number of injuries took place in the 5-9 year old age group
(1231.66) and the highest was in the 15-18 year old age group (4840.38). The
15-18 year old age group had significantly higher ball to head injuries than both
other age groups (Figure 2.3). There was no statistical difference between the
two younger age groups. Although the females had an increase in injuries from
the younger age group to the older, the males did not. Males had an increase in
injury from the 5-9 year olds to the 10-14 year olds.
28
Figure 2.3: Male and female ball only head injuries by age. *p < 0.05
Both male and female soccer players had similar patterns of injury
diagnosis (Figure 2.4). Internal organ injuries represent the highest percentage
of injuries with 44 % for each gender. These injuries occurred significantly more
frequently than both concussions and contusions. The second most frequent
diagnosis for males and females was concussion with 38.27% and 34.06%
respectively. Additionally, concussions occurred significantly more than
contusions which accounted for only 18% of ball to head injuries. Lacerations
were not frequent injuries, but were seen more frequently in males versus
females, 2.18% and 0.21% respectively.
29
Figure 2.4: Ball only head injuries by diagnosis
2.4 Discussion
Although many studies have used the NEISS database to study the
incidence of soccer injuries (Adams and Schiff, 2006, Delaney, 2004, Leininger,
et al., 2007), this is the first to focus specifically on youth head injuries. Previous
studies have looked at the total incidence of soccer injury presenting to the
emergency departments, specific body areas injured, and the adult population,
but studying the incidence of head injury in the youth population is novel along
with investigating mechanism of injury specific to ball to head only head impacts.
This makes direct comparisons with previous studies challenging due to different
inclusion criteria.
During the study period, high school soccer participation steadily
increased from 339,101 boys participants and 295,265 girls participants in the
2001-2002 school year to 377,999 boys participants and 337,632 girls
participants in the 2006-2007 school year. Due to this increase in player
participation it was expected that an increase in ball to head injuries would occur
30
steadily as well. This steady increase did not occur demonstrating a non-linear
relationship between the number of participants and the number of head injuries.
It is, however, challenging to determine a total number of participants due to the
inclusion of organized and non-organized soccer injuries. Additionally, the
previous comparison was made for high school age players and did not include
of the entire study age population.
Ball only injuries comprised 15.9% of total head injuries. These injuries
were not necessarily caused by heading the soccer ball, as some of the impacts
were due to unintentional ball to head impact, but many of the cases were
described as heading related. It was challenging to delineate the mechanisms
further due to the limited case descriptions. However, these data indicate that
heading alone can result in injuries severe enough to require medical attention.
Injury severity for these types of injuries is also cause for concern. While
contusions are limited to skin bruising, lacerations and concussions are more
serious. Lacerations are skin tears that are generally caused by blunt trauma,
i.e. impact, and can require suturing. The most serious diagnosis, internal organ
injuries, was also found to be the most frequent diagnosis in the ball only head
injuries for both males and females, followed by concussion. There are a limited
number of possible diagnoses that can be input into the NEISS database, which
is why many of the injuries listed are internal organ injuries. This is an unspecific
diagnosis that includes closed head injuries, cerebral bleeding, and brain
contusion (Delaney, 2004). This is relevant in that it shows that impact with the
31
ball only can cause significant injury level. This diagnosis was given to 43.84%
of female injuries, and 43.68% of male injuries.
Concussions are serious injuries, also representing injuries to the brain, as
opposed to contusions and lacerations which are skin injuries. They were the
second most frequent diagnosis for both males and females. A total of 100
concussions were reported at the participating hospitals during the study period.
This correlates to almost 36% of the ball only impacts being treated. There is no
way to determine the severity of these concussions; therefore, they can range
from mild to a more severe injury. However, several narratives describe
scenarios where the child would complain of a headache after heading the ball
during a practice or game without any other etiology.
There are limitations when examining these severe injuries in terms of
percentage of total injuries. The more serious injuries most likely are over
represented when using the NEISS data alone due to the fact that the injuries
included in this study are only those which have reported to the emergency
department. Therefore, it is less likely that less severe injuries are taken to the
emergency department. Players could be experiencing symptoms related to ball
to head impacts, but if they did not go to the emergency department, these
injuries were not included. The current study most likely underestimates both
total injuries and ball to head injuries because many less severe injuries would
not be included. This is an inherent problem with using the NEISS database to
estimate injuries. Therefore, the current study represents the more severe
cases, and is potentially an underestimate of the total injury occurrences.
32
More ball to head only injuries occurred in the older age groups, which
contradicts Leininger et al. (2007) who found that the majority of head injuries
occurred to the youngest players. Leininger et al. (2007) included all head
injuries, not just those specific to ball impacts, and included a younger set of
players. This could be due to the introduction of heading in those two age
groups. Additionally the majority of those two age groups use the adult ball size,
a standard size 5 soccer ball weighing 450 g with a 22 cm diameter, as opposed
to the smaller size 4 soccer ball which weighs approximately 390 g and has a
diameter of 21 cm. Also, they would presumably have higher kick velocities.
Therefore, it was expected that an increase in ball to head only injuries occurred.
Although much research has been devoted to the adult population with
respect to soccer heading, especially the elite players, the risks to the youth
population have not been studied in great detail. The risks to children are
potentially greater due to their size versus the force being applied by the ball
(Lees and Nolan, 1998). It has been reported that ball mass, impact velocity,
and size of the individual all contribute to the potential for injury (Lees and Nolan,
1998). The importance of proper technique may be especially true in the youth
population, since their skill level has not been well developed to control their
head motion when heading the ball. The current study demonstrates that injuries
can occur from ball to head impact only. It is recognized that all of these injuries
may not be the result of purposeful heading, but it is challenging to differentiate
them from the unintentional ball to head impacts. The data do establish the
possibility of being injured based on an impact with the ball alone.
33
CHAPTER 3
HEADING FREQUENCY IN YOUTH SOCCER
3.1 Introduction
Soccer, one of the most popular team sports worldwide, has recently
shown considerable growth among the United States youth population. This is
seen clearly in the American Youth Soccer Organization (AYSO) which today has
50,000 youth soccer teams and over 650,000 players registered after starting out
in 1964 with only nine teams (2006). With this increase in the number of players,
an increase in injuries has also occurred. Dvorak et al. (2000) determined that
approximately 30 billion dollars are spent annually worldwide on the treatment of
soccer injuries. It has been estimated that up to 22% of all injuries in soccer are
to the head (Ruchinskas, et al., 1997). These injuries can occur from
unintentional or intentional impacts. Intentional impacts, or “heading” the soccer
ball, are a standard practice within the game that is taught at the youth level.
Heading is a technique where a player intentionally uses their head to redirect
the soccer ball. Generally, players are instructed to start with their feet
approximately shoulder width apart and knees bent in a slightly staggered
stance, body squared to the ball with their torso in an extended position. While
keeping their eyes on the ball, players move their torso into a flexed position and
impact the ball at the hairline on their forehead. Following impact, players are
instructed continue through the ball with follow-through and to decelerate their
motions following impact (Shewchenko, et al., 2005). There are still conflicting
results as to whether repetitive sub-concussive forces associated with heading
34
are a cause of long term health problems (Tysvaer and Lochen, 1991).
Several studies have been conducted to look at these effects, but one of
the main limitations has been the estimation of exposure incident. One of the
first steps in delineating the effects of repetitive heading should be to determine
an accurate exposure incidence. In most studies, the incidence of heading
exposure is reported by the players themselves; often times as broad estimates.
There are several limitations related to having players self report their exposure
rate especially in a retrospective manner.
In one of the first studies conducted (Tysvaer and Storli, 1989), EEG
results of current Norwegian First Division League Clubs players were reviewed
with results showing the highest abnormal results occurred in the youngest
players. Another study conducted in 1991 by Tysvaer and Løchen (1991) of
retired players showed neuropsychological deficits in 81% of the 37 participants.
In the two Tysvaer et al. (1989, 1991) studies, players were also asked if they
were typical headers however a definition was not provided as to the criteria for
being a “typical header”. 14% of the 69 active players interviewed and 27% of
the 37 retired players interviewed reported that they were typical headers,
however a quantitative value was not reported (Tysvaer and Lochen, 1991,
Tysvaer and Storli, 1989).
Matser et al. (1998) estimated the number of headers that each of the 53
active professional soccer team members from The Netherlands who participated
based on their position and number of games (Matser, et al., 1998). A
classification system was used and categorized midfielders and goalkeepers as
35
“non-headers” with forwards and defensive players as “headers”. Matser et al.
(1998) also reported the number of headers per match and in a season. The
number of headers per match and the number of matches were obtained through
player interviews. Players reported a range of 1 to 42 headers during games with
16 being the median number of headers in a single game. These numbers along
with the total number of games were then used to calculate headers per season.
A range of 50 to 2100 headers in a season were calculated which resulted in a
median of 800 headers in a season. The number of average headers/per game
by an individual player was then stratified into three groups: 0-10 (47%), 11-20
(36%), or >21 (17%) (Matser, et al., 1998).
In a recent study, Witol and Webbe (2003) studied 60 male players at
varying levels of skill. The number of headers each player experienced was
determined based on player reporting in an interview. Players were asked if they
considered themselves a header and how many headers they experience in a
typical game. Players were then placed in one of four categories: control (no
heading), low (0-4 headers/game), moderate (5-8 headers/game), or high (>9
headers/game). The results showed that 12 players were in the control group,
19 players were in the low group, 20 players were in the moderate group, and 21
players were in the high headers/game group (Witol and Webbe, 2003).
These studies demonstrate a reliance on estimations and player memory
with a lack of data on the actual exposure rate. As reported in Chapter 1, soccer
players may have deficits in the area of memory (Matser, et al., 1998) which
could provide for inaccurate recollections. Very few studies actually observe
36
players to determine their frequency of heading. Two studies have reported
results based on this type of observation. The first by Tysvaer et al. (1981)
reported results after following 20 games. These games broke down as follows:
10 First Division games, 6 English games, and 4 International games. Tysvaer et
al. (1981) reported average headers per game which were 117, 124, and 94
respectively, but the number of headers per specific player was not reported
(Tysvaer and Storli, 1981).
The second study which observed headers is also one of the few studies
conducted in the youth population (Janda, et al., 2002). A total of 57 players
participated with an average age of 11.5 years. All players were followed for at
least three seasons, with 18 players followed for two years. The number of
headers per player was monitored by the individual coaches. Over the period of
one year, players heading the ball an average of 185.9 times with the maximum
number for one player being 450 times/year one. For the second year, the 18
players monitored had an average of 129.6 headers with a maximum of 344 for
all three seasons (Janda, et al., 2002).
Although these studies provide some information regarding the number
of headers sustained during the play of soccer, they are focused on the specific
age and skill level studied, with only one study focusing on the youth population
(Janda, et al., 2002). The frequent use of athlete reporting in studies relating
heading frequency to findings is unreliable and a better method of estimation is
required. Therefore, the purpose of this study is to explore the frequency of
soccer heading in the youth population across age groups, skill levels and
37
gender. These data are essential to conduct the controlled laboratory
experiments needed to ultimately determine the effects of repetitive heading.
Given the increase in youth soccer participation over the past decade and the
continued expected growth, the current research focused on the youth
population.
3.2 Methodology
Males’ and females’ teams ranging in ages from U12 (under 12 years old)
to U18 (under 18 years old) were observed during the 2006 Canton Cup Soccer
Tournament, a weekend long tournament in Canton, MI. The tournament
director, along with the Wayne State University Human Investigations
Committee, granted approval prior to the event. Only teams participating in the
top two divisions of their age bracket were included in the study. The highest
division was given the designation by the tournament of blue and the next
highest level was red. These designations were maintained throughout the
study. A total of 158 games were observed throughout the tournament. Table
3.1 outlines the breakdown in terms of number of teams and number of games
monitored at each division, age and gender group. It should be noted that due to
the fact that soccer is a spring sport for high school aged females in Michigan,
the highest age division for the tournament was U14.
38
Table 3.1: Number of games monitored outlined by age, gender and division of play
Age/Gender Female Male
U12 U13 U14 U12 U13 U14 U15 U16 U17 U18
Number of
Teams
Blue 6 8 6 5 8 8 8 8 8 8
Red 6 8 7 8 6 8 8 N/A N/A N/A
Number of
Games
Blue 8 11 8 6 11 10 7 10 9 10
Red 8 10 12 9 8 10 11 N/A N/A N/A
Games were monitored by 22 individuals with a minimum of five years of
experience as a soccer player. At each game, a stopwatch was started at the
kick-off. This was then allowed to run throughout the entire game, including
halftime. Each header that took place during the game was recorded on a data
sheet that contained a grid outline of the soccer field (Figure 3.1). Both the
player number and time of occurrence were noted within the specific area of the
grid where the header took place. Player position was not noted, but defensive
and offensive position was defined by which half of the field the header took
place on. No personal identifiers were recorded. Different colors were used to
denote different teams to allow for a better representation of defensive versus
offensive position on the field.
39
Figure 3.1: Soccer Field Diagram
Data were normalized using time due to the differences in game length for
varying age divisions. This was done by dividing the total number of headers per
team by the total number of minutes that each team was monitored throughout
the tournament. This resulted in a value of headers/minute for each team for the
tournament. The data were then analyzed using a repeated measures ANOVA
using gender, age group, division, game day, game number within the day, and
game number within the tournament as the independent variables and
headers/minute as the independent variable. All analyses were stratified by
gender in order to account for different age groups within gender.
Total headers were also determined for each game by team, half, and field
area (12 total areas). The total number of headers was analyzed using the
generalized estimating equation (GEE). Poisson regression analysis was then
40
used to determine if there was a significant association between the number of
headers and age, division, offensive field placement, defensive field placement,
game day, game number within the day, game number within the tournament,
and position on the field.
3.3 Results
Maximum headers
Maximum headers in one game by a single player were monitored to
determine the highest exposure incidence. The maximum number of headers in
a single game by a player was 13 headers. This was observed in a U14 male
blue division game. The range of maximum headers in one game by one player
was from 4 to 13 headers (Table 3.2).
Table 3.2: Maximum headers by any one player for a single game.
Age/Gender Female Male
U12 U13 U14 U12 U13 U14 U15 U16 U17 U18
Maximum
Headers
Blue 7 5 7 11 8 13 9 7 7 9
Red 4 4 4 5 5 7 7 N/A N/A N/A
Gender effects
Significant differences were reported between the male and female
populations following adjustment for age and division (p<0.0001). The male
populations were observed to have a higher header/minute ratio than their
female counterparts with a mean of 0.135 ± 0.079 headers/minute for females
and 0.283 ± 0.122 for males (Figure 3.2). The total number of headers was also
significantly different between males and females. Therefore, all further analyses
41
were stratified by gender.
Figure 3.2: Comparison of number of headers/minute, male versus female
across age groups.
Males
Age (p<.0001), division (p<.0001), and game number (p=.015) all
demonstrated significant findings with regard to headers/minute within the male
population. Age groups U14 and higher were found to have significantly more
headers/minute than the U12 and U13 age groups. Also, a consistent positive
increase regression was noted in the male blue division from U12 to U15,
however from U15 to U18 there was no significant increase with age (Figure 3.3).
The number of headers/minute was also significantly higher in the blue division
when compared with the red and white divisions. Also of interest,
headers/minute was significantly higher for game 1 of the tournament when
compared to both games 2 and 3.
42
Figure 3.3: Number of headers/minute for the male population.
The total number of headers (Table 3.3) was significantly lower for both
the red and white division when compared to the blue division (p<.0001), but
there was no difference between the red and white divisions. The number of
headers increased with increasing age group, with the exception of U17/18 which
was slightly lower than U15/16. All age groups 13 or higher had significantly
higher number of headers compared to U12 (p<.0001). U13 players had lower
adjusted mean number of headers compared to all older age groups (p<.01), and
U14 players were lower than U15/16 and U17/18 (p<.001). No significant
difference was observed between U15/16 and U17/18 (p=0.307). Players on
defense had a higher adjusted mean number of headers (0.62 ± 0.02) compared
to players on offense (0.47 ± 0.02) (p<.0001). The adjusted mean number of
headers was lower on Saturday and Sunday compared to Friday (p<.01) with no
difference between Saturday and Sunday (p=0.056).
43
Table 3.3: Average total headers/game/team for male population Age Division Average Headers/Game/Team
12 R 9.8
B 11.8
13 R 9.9
B 15.6
14 R 16.3
B 16.8
15 R 19.7
B 23.3
15/16 W 17.0
16 B 23.7
17 B 23.8
17/18 R 17.1
18 B 23.4
Females
The average value of headers/minute was highest in the U14 girls (0.153
headers/minute). Headers/minute showed no significant associations between
headers/minute and any of the dependent variables. However, the total number
of headers (Table 3.4) had several parameters which were significant predictors.
Within the female population, division (p=.0009), length field position (p<.0001),
width field position (p=.0002), and age group (p=.036) were found to be
predictors of the number of headers.
44
It was determined that the blue division players had more headers than
those in the red division. Differences were also seen within age groups.
Significantly more headers took place in the U14 age group when compared with
both U12 and U13, but no differences were found between U12 and U13.
Table 3.4: Average total headers/game/team for female population
Age Division
Average
Headers/Game/Team
U 12 R 5.9
B 6.5
U 13 R 5.5
B 8.7
U 14 R 7.0
B 10.8
A positive increase in headers/minute with age trend similar to that
observed in the male population was also noted in the blue division of the female
population. Due to the lack of data in the older age groups, the stabilization effect
with age was unable to be assessed (Figure 3.4).
45
Figure 3.4: Number of headers/minute for the female population.
Field Position
Table 3.5: Total headers in each field position for females Field Position
Age 1A 2A 3A 4A 1B 2B 3B 4B 1C 2C 3C 4C Totals
U12 1 7 2 5 5 11 12 6 11 9 14 2 85
U13 2 11 16 1 9 18 19 9 4 9 13 4 115
U14 5 15 16 6 13 22 22 4 8 15 13 6 145
Totals 8 33 34 12 27 51 53 19 23 33 40 12 345
The total number of headers in each portion of the field is shown in Figure
3.5. The majority of the headers, for both male and female, took place in the
middle of the field. The occurrence of headers in the four corner regions was
significantly less than all other regions on the field. Females had fewer total
headers in each field position when compared with males of the same age group
(Table 3.5, 3.6).
46
Table 3.6: Total headers in each field position for males Field Position
Age 1A 2A 3A 4A 1B 2B 3B 4B 1C 2C 3C 4C Totals
U12 6 15 21 2 9 18 18 9 3 14 13 5 133
U13 6 19 15 2 12 37 24 19 10 23 25 6 198
U14 14 23 18 9 30 50 39 28 6 22 24 7 270
U15 6 31 32 10 52 77 57 36 15 45 33 13 407
U15/16 3 7 10 4 12 20 24 8 5 5 8 2 108
U16 5 16 18 4 28 33 33 21 3 14 18 6 199
U17 3 22 18 2 23 37 36 24 3 14 13 2 197
U17/18 0 17 11 5 9 13 17 11 4 12 15 2 116
U18 7 11 13 2 27 45 42 29 7 16 20 10 229
Totals 50 161 156 40 202 330 290 185 56 165 169 53 1857
47
Figure 3.5: Total number of headers in each field position
3.4 Discussion
Putukian et al. (2004) followed college soccer players for a season to
perform neuropsychological testing (Putukian, 2004). Heading contacts and
minutes played were also counted for all home games by team trainers and
physicians, and it was found that male college age players had an average of
0.783 headers/minute during a single season and females had an average of
0.753 headers/minute. Both of these values are much higher than those found in
the current study. This is most likely due to the fact that actual minutes played by
the player were used as opposed to game length. The average headers/minute
of for U15/16 males was the highest at 0.303 headers/minute, and for the
females 0.153 headers/minute for the U14 age group. It is expected that players
48
at the college level would have more headers/minute than those playing at the
lower skill and age level observed in the current study. Also, following teams
within the younger age group for an entire season could provide a more detailed
look at the differences noted between these two skill levels.
Matser et al. (1998) found that 47% of the players that were studied
headed the ball between 0 and 10 times per game. Looking at the maximum
number of headers by a single player in a game, only two players fell outside of
that range throughout the tournament. The first, a male, blue division player in
the U12 age bracket had 11 headers in a single game, and the second, also a
male, blue division player in the U14 age group had 13 headers in a single game.
These findings are similar to Matser et al. (1998) because the majority of players
were found to remain in the lower header per game category.
The current study is limited by the fact that data was collected over a
weekend long tournament and that a maximum of three games were observed
for each team as opposed to following teams for an entire season. Additionally, it
would have been an improvement if more female age groups had been available
for analysis, but due to high school scheduling that was not possible. Even with
these limitations, important trends were evident.
One of these trends was noted in the positive correlation between age and
headers/minute within the higher division in both the males and females. This
occurs up until the age division of U15 in the male population at which point a
plateau occurs. Although there is currently no data available for the higher age
groups within the female population, it is expected that there would be a similar
49
trend based on the data available for the U12-U14 age groups. This indicates
that there is an increase in the amount of headers players experience during the
years in which they are learning the skill, but that in the upper skill levels they
experience essentially the same amount throughout.
It was also noted that the vast majority of heading occurred in the middle
of the field. This is most likely related to this being the area where long kicks,
from both the goalie and players trying to cross the field, are targeted allowing for
players to align themselves for headers. The regions directly in front of the goal
(1B and 4B in Figure 3.5) lend themselves to areas of heading due to corner
kicks being directed to those regions and the desire to redirect the ball into the
goal. However, this can also be the most dangerous position due to the number
of players near the goal during a corner kick and proximity of the goal posts.
Game number within the tournament was also determined to be a factor
in the number of headers/minute in the male population. This is of interest
because the number of headers/minute seems to decrease after the first game.
This could be due to symptoms felt following the initial game. Further research
needs to be conducted to understand the cause of this reduction following the
initial game in the tournament.
50
CHAPTER 4
HEADING BIOMECHANICS IN YOUTH SOCCER
4.1 Introduction
The overall goal of heading is redirection of the ball. Depending on the
approach of the player and the intent of the redirection, the player may move
his/her head in particular manner. Alignment of the head, neck and torso can
vary and are often dependent on the intent of the redirection i.e. clearing,
passing, or controlling (Shewchenko, et al., 2005, 2005). All of these scenarios
require a specific skill level to accomplish the intent of the redirection through the
use of correct techniques.
Proper techniques and skill level often come with age. Younger players
who are learning good techniques may not always perform the skill as taught.
This may be to a variety of reasons including improper ball size for child size, not
eliciting neck muscles, and using top of head instead of forehead. All of these
factors change the biomechanics of heading the ball. Size differences between
the player and the ball have been a recognized concern not just for heading but
for the development of foot skills as well, therefore age recommended sizes have
been developed (Lees and Nolan, 1998). The recruitment of neck muscles plays
a big part in proper heading techniques. Incorporation of entire body mass
allows for the mass of the ball to be negligible in comparison. And, finally, ball
placement affects the impact vector through the head itself.
One of the first studies to look at the biomechanics of soccer heading,
specifically head and neck motion, was performed by Ludwig (1999). Twenty-
51
four college age female soccer players performed 10 standard headers served to
them at 8 m/s. Standard camcorders were used at 120 frames per second to
capture trunk motion and various acceleration measurements during each
header. It was determined that frequent headers and non-frequent headers, as
self-described by the players, used different technique when heading the ball and
that trunk range of motion and neck motion played a part in this difference. This
indicates that players with less heading experience, i.e. non-frequent headers,
use a different technique which could be similar to the less practiced youth
population. The study did not investigate differences specific to soccer
experience level or gender. Neck muscle activation was also not investigated as
a possible contributing factor to changes between the groups.
EMG has also been studied previously during soccer heading (Bauer, et
al., 2001). Again, this study focused solely on female college soccer players.
Players were asked to perform a series of soccer headers, all at the same ball
speed, 6.8 m/s, while instrumented with EMG sensors on both their left and right
sternocleidomastoid and trapezius muscles. Three types of headers were
performed by each participant in an effort to represent the various headers seen
in the field, clearing, passing, and shooting. Each of these three types of
headers were performed while standing and also while jumping to get a more
diverse representation. It was determined that muscle activation was not
significantly different for the various header scenarios studied.
One of the more comprehensive analyses of common scenarios was
recently conducted using both head and neck motion analysis techniques and
52
EMG (Shewchenko, et al., 2005). Seven adult soccer players ranging in age
from 20 to 23 years old were asked to perform various soccer headers. Subjects
were attempting to head the ball to a target with a pre-determined level of neck
muscle activation in the sternocleidomastoid and trapezius: normal, pre-tensed,
or relaxed. Soccer balls were presented to players at two different speeds in
order to elicit a wider range of response. The authors also reported a wide
variability between subjects when looking at head angle, back angle, relative
head to back angle, and neck muscle activity. In addition, it was suggested that
additional scenarios would produce additional results (Shewchenko, et al., 2005).
This variation of biomechanical scenarios of soccer heading will only be
increased in the youth population due to the varying skill levels.
The current study aims to determine if there is a difference between adult
soccer players and youth soccer players with respect to soccer heading
technique. In order to accomplish this aim, heading scenarios that have been
previously used (Shewchenko, et al., 2005) in adults were recreated in the youth
population. Using the Functional Assessment of Biomechanics (FAB) system, a
novel motion analysis system, various head and torso body angles were
measured to provide comparisons to previous studies and to participants within
the current study.
The FAB system (Figure 4.1) uses a combination of accelerometers,
gyroscopes, and magnetometers to provide real-time kinematic and kinetic data.
Angle, force, torque, velocity, acceleration, power, and foot sole weight and
pressure are all calculated for each body segment (Biosyn Systems). The
system has a maximum data collection of 100 Hz and a battery life up to 12
hours. The current study will use the system for measuring head a
angles with an accuracy of
without the limitations of using a traditional camera system
limitations and marker occlusion
traditional motion analysis system, are both eliminated.
Figure 4.1
In addition to comparing head and torso angles between youth players
and adult players, the current study
technique between genders
will also be examined to establish their level of involvement during various
53
system has a maximum data collection of 100 Hz and a battery life up to 12
The current study will use the system for measuring head a
angles with an accuracy of ± 2 degrees. The system allows for motion analysis
without the limitations of using a traditional camera system. For example, space
limitations and marker occlusion, which are common issues with using a
motion analysis system, are both eliminated.
Figure 4.1: FAB System with size scale
In addition to comparing head and torso angles between youth players
and adult players, the current study will investigate differences soccer heading
technique between genders in the youth population. Neck muscle activity levels
will also be examined to establish their level of involvement during various
system has a maximum data collection of 100 Hz and a battery life up to 12
The current study will use the system for measuring head and torso
The system allows for motion analysis
or example, space
, which are common issues with using a
In addition to comparing head and torso angles between youth players
will investigate differences soccer heading
. Neck muscle activity levels
will also be examined to establish their level of involvement during various
54
heading scenarios in the youth population. This will be done using traditional
EMG techniques and measuring the neck muscle activity of the
sternocleidomastoid and the trapezius muscles during heading events. These
muscles are both superficial and have been used previously to determine neck
muscle activation during soccer heading (Bauer, et al., 2001, Shewchenko, et al.,
2005).
4.2 Methodology
Fifteen youth soccer players, 9 females and 6 males, participated in the
current study to investigate the biomechanics of soccer heading. Players ranged
in age from 14 to 16 years old, with an average age of 15 for the females and 16
for the males. Players performed a sequence of headers while wearing
instrumentation to assess muscle activation and body position during heading
events.
Prior to any instrumentation, anthropometric measurements were taken to
provide an accurate representation in the FAB measurements. Height, weight,
trunk length, upper arm length, forearm length, thigh length, and calf length were
measured and recorded for each participant. Additionally, player age, gender,
and skill level were recorded.
Following initial measurements, players were instrumented for data
collection. Players were fitted with the FAB (Biosyn Systems, Surrey BC,
Canada). The system uses 13 wireless sensors each of which is approximately
4 cm x 7 cm x 2 cm and is attached to the subject using an elastric strap with
velcro attachment. One sensor was placed on the head, one on the chest, one
55
around the waist, one on each upper arm, one on each wrist, one on each thigh,
one below each knee, and one at each ankle (Figure 4.2).
Figure 4.2: Example of player wearing FAB sensors
Data were sampled at 100 Hz and recorded for post-processing. Head
and trunk body angles will be collected including: cervical flexion and extension,
cervical lateral flexion, trunk flexion and extension, and trunk lateral flexion
(Figures 4.3, 4.4). Prior to data collection using the FAB, a player calibration was
performed. The player was instructed to stand facing forward with arms to the
side and feet shoulder width apart while the system performed calibration
procedures. Zero degree measurements are taken from the initial calibration
position, and the trunk and head angles measured represent a change from that
initial position.
56
Figure 4.3: Side view of head and trunk body angles a) torso flexion (+ α) and extension (- α); b) head flexion (+ β) and extension (- β)
Figure 4.4: Top view of head rotation left (+ θ) and right (- θ)
a) b)
0° 0°
Low Back Sensor
Upper Back Sensor
Head Sensor
Upper Back Sensor
Head Sensor
0°
Head Sensor
57
Players were also be instrumented with electromyography (EMG) sensors.
Surface EMG data was collected at 256 samples/second using the BioCapture
physiological monitoring system (Cleveland Medical Devices Inc., Cleveland,
OH). Sensors were placed bilaterally on the sternocleidomastoid and the
trapezius muscles (Figure 4.5) with a reference sensor behind the left elbow.
After preparing the skin using an alcohol swab, Ag/AgCl electrodes were placed
over the muscle belly approximately 2 cm apart. Tape was placed over each
sensor to maintain solid contact during all movements. Data were collected
continuously for the duration of the tests for each participant. Impact times were
marked for each heading scenario within the data collection files.
Figure 4.5: Neck musculature used for EMG testing a) sternocleidomastoid; b) trapezius (Gray, et al., 1995)
a) b)
58
Following instrumentation, players performed 8 heading scenarios (Table
4.1). The headers were performed at two speeds, 6 m/s (low) and 8 m/s (high).
Balls were launched at subjects from 6 m away, providing ample reaction time at
both test speeds. Three types of headers were evaluated, passing, clearing, and
controlling. A passing header is when the player attempts to redirect the ball to
another player at a medium distance away, a clearing header is when the player
attempts to redirect the ball as far downfield as possible, and a controlling header
is when the player attempts to head the ball a very short distance down and in
front of them self.
Additionally, three neck muscle activity levels were explored. Prior to
heading the ball players were instructed to either pre-tense their neck muscles, to
perform headers with their muscles activated as they would normally, or to have
their neck muscles completely relaxed. Along with neck muscle activation levels,
prior to impact, subjects were also instructed to try to head the ball at a certain
target. This was done to try to represent the majority of heading scenarios that
players would see in the field as well as to provide comparisons to previous adult
data (Shewchenko, et al., 2005).
59
Table 4.1: Heading Scenarios (Shewchenko, et al., 2005) Task Heading
Scenario
Ball Speed Modification Ball Target
1 Controlling Low None Front, down, 2.5 m from
player
2 Passing Low None Front, down, 5.5 m from
player
3 Clearing Low None Up and away, as far as
possible
4 Passing Low Neck tensing Front, down, 5.5 m from
player
5 Passing Low Follow through Front, down, 5.5 m from
player
6 Passing Low Torso
alignment
Front, down, 5.5 m from
player
7 Clearing High None Up and away, as far as
possible
8 Clearing High Neck tensing Up and away, as far as
possible
EMG data was processed using MyoResearch XP Master Edition 1.07
(Noroxan, Inc., Scottsdale, AZ). Data were filtered using a notch filter to remove
the noise at 60 Hz prior to any additional processing. Following filtering, EMG
data were full-wave rectified and then the RMS was calculated using a 50 ms
60
moving average. The point of impact is designated as time 0 s for all
descriptions. Generally, all header related motion took place during the 50 ms
pre and post impact.
Torso angle, head angle, and relative head to torso angle were recorded
for each impact. Each angle was measured using the FAB from the original
neutral position that the player stood in during calibration. Therefore, the 0
degree position is the original calibration stance (Figure 4.2). Additionally,
positive designation was given to flexion and a negative designation was
assigned to extension for both head and torso motion. For the twist motions,
positive was assigned to left twist and negative to right twist. Motion data were
analyzed to determine the differences between the various heading scenarios
within each subject. Additionally, each heading scenario was analyzed
individually to determine subject variability.
Head acceleration was also recorded for each impact using the FAB.
These data were not taken at the center of gravity of the head will not be used to
determine injury risk. The data will be used strictly as a comparative between
tasks.
4.3 Results
Torso flexion, head flexion, and head rotation were measured for each
task. Focus was placed on the tasks that required no modification, tasks 1, 2, 3,
and 7. Task 2 was used for the standard heading case. Extensive subject
variability occurred for all tasks. Head rotation was the most consistent between
61
subjects because it at seemed to have similar patterns although actual angles
varied.
In task 2, used as the typical header case, male torso flexion ranged from
-14.81 to 27.13 degrees (41.94 degree span) at the point of impact (Figure 4.6)
with an average of 14.90 ± 15.49 degrees (Table 4.2). Peak torso flexion took
place following impact in 83 % (5 subjects) of the male players. In the remaining
17 % (1 subject), the entire task was performed in negative flexion, or torso
extension, with the minimum extension occurring just prior to impact. Peak torso
flexion ranged from -10.13 to 30.52 degrees with an average of 21.96 ± 17.06 for
the males during task 2.
Figure 4.6: Torso flexion for all male players during standard heading
scenario (task 2)
Subject variability was also seen in head flexion. Male head flexion for
task 2 ranged from -22.54 to 34.61 degrees on impact (Figure 4.7). The average
head flexion on impact was 0.91 ± 19.56 degrees with an average peak head
flexion of 8.44 ± 21.94 degrees. Although all male participants started in head
-40
-30
-20
-10
0
10
20
30
40
50
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Torso Flexion
Player 1
Player 2
Player 3
Player 4
Player 5
Player 6
62
extension for task 2, half of the participants had positive head flexion during task
2 and half did not. Therefore 3 of the participants’ peak head flexion, which
ranged from -22.54 to 34.69 degrees, was their minimum head extension.
Figure 4.7: Head flexion for all male players standard heading scenario
(task 2)
Head rotation for the male population remained relatively consistent
throughout task 2 (Figure 4.8). Each subject had a stable head rotation
throughout the task, but variability still existed between subjects. Head rotation
ranged from -22.54 to 34.61 degrees on impact with an average of 9.81 ± 25.90
degrees. The maximum values for head rotation ranged from -28.06 to 43.33
degrees with an average maximum of 19.27 ± 25.91 degrees.
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Flexion
Player 1
Player 2
Player 3
Player 4
Player 5
Player 6
63
Figure 4.8: Head rotation for all males during standard heading scenario
(task 2)
Female torso flexion for task 2 was similar to the males with a lack of
consistency between players (Figure 4.9). A large range occurred for the impact
torso flexion of 0.10 to 35.03 degrees. All players impacted the ball with their
torso in flexion with only 1 player starting with their torso extended. This pattern
is quite similar to the males. The average torso flexion on impact was 15.96 ±
10.77 degrees. The peak torso flexion ranged from 4.78 to 37.38 degrees with
an average of 24.07 ± 9.68. Although there is no clear pattern between subjects,
67 % of participants had a peak torso flexion in the between 24.40 and 29.51.
-80
-60
-40
-20
0
20
40
60
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Rotation
Player 1
Player 2
Player 3
Player 4
Player 5
Player 6
64
Figure 4.9: Torso flexion for all females during standard heading scenario
(task 2)
Female head flexion ranged from -26.91 to 10.31 degrees upon impact
(Figure 4.10) with an average impact flexion of 1.09 ± 12.99 degrees. Maximum
head flexion ranged from -17.56 to 17.10 degrees averaging 4.58 ± 12.29
degrees. The peak head flexion generally occurred just prior to impact.
Figure 4.10: Head flexion for all females during standard heading scenario
(task 2)
-20
-10
0
10
20
30
40
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Torso Flexion
Player 7
Player 8
Player 9
Player 10
Player 11
Player 12
Player 13
Player 14
Player 15
-60.00
-50.00
-40.00
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Flexion
Player 7
Player 8
Player 9
Player 10
Player 11
Player 12
Player 13
Player 14
Player 15
65
Female head rotation is similar to the males in that it stays relatively
consistent for each participant across the entire task (Figure 4.11). Impact head
rotation ranged from -20.84 to 44.69 degrees with an average of 2.81 ± 20.57
degrees.
Figure 4.11: Head rotation for all females during standard heading
scenario (task 2)
In general, the within subject header tasks seemed to develop a more
consistent pattern. Although the subjects did not start or finish in the same
orientation for each task, the peaks and valleys of the torso and head flexion
generally occurred at similar time points with relationship to the impact. When
this varied, it generally did so during the high speed task (task 7). This pattern is
evident in Figures 4.10 – 4.12.
The example male participant’s torso flexion ranged from 11.96 to 18.14
degrees for the four different heading tasks investigated that did not require
modification (Figure 4.12). This is a much smaller range than that seen when
comparing between participants when they were performing the same task.
-40
-30
-20
-10
0
10
20
30
40
50
60
-100 -50 0 50 100An
gle
(D
eg
ree
s)
Time (ms)
Female Head Rotation
Player 7
Player 8
Player 9
Player 10
Player 11
Player 12
Player 13
Player 14
Player 15
66
Figure 4.12: Example of single male participant’s torso flexion for all
header tasks that lack modifications (1, 2, 3, and 7)
The example male participant’s head flexion showed a very distinct
pattern (Figure 4.13). Although the flexion ranged from -22.70 to 3.93 degrees,
the maximum extension took place at the same time for all four tasks, -60 ms.
The maximum flexion also took place at the same time, -10 ms, for all four tasks.
Figure 4.13: Example of single male participant’s head flexion for all
header tasks that lack modifications (1, 2, 3, and 7)
-15
-10
-5
0
5
10
15
20
25
30
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Torso Flexion
Task 1
Task 2
Task 3
Task 7
-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Flexion
Task 1
Task 2
Task 3
Task 7
67
The example male participant’s head rotation has similar angles for the
first three tasks, but the high speed header (task 7) falls further out (Figure 4.14).
The range of head rotation on impact for the first three tasks is -8.73 to 8.18
degrees while the head rotation angle for task 7 was -27.88 degrees. This
indicates a change in technique when the ball is moving at a higher speed.
Figure 4.14: Example of single male participant’s head rotation for all
header tasks that lack modifications (1, 2, 3, and 7)
The female example participant’s torso flexion, head flexion, and head
rotation are very consistent for the first 3 tasks and then they do not follow similar
patterns for task 7 (Figures 4.15 - 4.17). This shows that the female patterns are
similar to the males. It is also further evidence of a technique change for higher
speed impacts. Female torso flexion on impact ranges from 14.7 to 22.79
degrees for tasks 1 – 3, but has a torso flexion of 70.48 degrees on impact for
task 7 (Figure 4.15). Similar patterns were found in head flexion with an impact
angle range of -15.5 – 12.91 degrees for the first three tasks and an impact angle
of -59.13 degrees for task 7 (Figure 4.16). Differences between the first three
-60
-40
-20
0
20
40
60
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Rotation
Task 1
Task 2
Task 3
Task 7
68
tasks and task 7 were very apparent in the head rotation during the pre-impact
stage (Figure 4.17). The impact head rotation ranged from 1.25 – 7.21 degrees
for the first three tasks with -9.84 degrees for task 7. Similar patterns were seen
throughout the male and female participants.
Figure 4.15: Example of single female participant’s torso flexion for all
header tasks that lack modifications (1, 2, 3, and 7)
Figure 4.16: Example of single female participant’s head flexion for all
header tasks that lack modifications (1, 2, 3, and 7)
0
10
20
30
40
50
60
70
80
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Torso Flexion
Task 1
Task 2
Task 3
Task 7
-120
-100
-80
-60
-40
-20
0
20
40
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Flexion
Task 1
Task 2
Task 3
Task 7
69
Figure 4.17: Example of single female participant’s head rotation for all
header tasks that lack modifications (1, 2, 3, and 7)
In addition to comparing all of the un-modified tasks, comparable tasks
were also compared. The four passing tasks were compared (tasks 2, 4, 5, and
6) and the two clearing tasks were also compared (tasks 7 and 8). These tasks
were compared for both males and females. All figures are representative of an
example participant.
The first tasks that were compared were the passing tasks. Male torso
flexion upon impact for the passing was relatively consistent within participants.
For the male example participant, torso flexion ranged from 16.5 to 22.55
degrees (Figure 4.18). This within subject consistency was expected, especially
since all four tasks were at the low ball impact speed.
-100
-80
-60
-40
-20
0
20
40
60
80
100
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Rotation
Task 1
Task 2
Task 3
Task 7
70
Figure 4.18: Example of single male participant’s torso flexion for all
passing header tasks (2, 4, 5, and 6)
The male example participant’s head flexion for the passing tasks was not
as consistent on impact, but did show a similar pattern of flexion over the
duration of the task. The head flexion ranged from -22.54 to 5.53 degrees
(Figure 4.19). Two of the impacts, tasks 2 and 5, were performed with the head
in extension, while tasks 4 and 6 were performed with the head in flexion. The
maximum extension took place at very similar time points for the four tasks.
0
5
10
15
20
25
30
35
40
45
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Torso Flexion
Task 2
Task 4
Task 5
Task 6
71
Figure 4.19: Example of single male participant’s head flexion for all
passing header tasks (2, 4, 5, and 6)
During the passing tasks, similarly to the non modified tasks, the head
rotated very little during the activity. For the male example participant, head
rotation upon impact ranged from -4.18 to 7.78 degrees (Figure 4.20). This a
very small range, indicating that head rotation was not altered between tasks.
Figure 4.20: Example of single male participant’s head rotation for all
passing header tasks (2, 4, 5, and 6)
-70
-60
-50
-40
-30
-20
-10
0
10
20
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Flexion
Task 2
Task 4
Task 5
Task 6
-30
-20
-10
0
10
20
30
40
-100 -50 0 50 100An
gle
(D
eg
ree
s)
Time (ms)
Male Head Rotation
Task 2
Task 4
Task 5
Task 6
72
The female example participant showed similar consistency between the
four passing tasks. When inconsistencies arose, they were with task 5, which
was modified to include extended follow through. Therefore, the high torso
flexion for task 5 can be attributed to the follow through. The female example
participant had a torso flexion on impact that ranged from 14.24 to 15.99 degrees
for task 2, 4, and 6. Task 5, however, had a torso flexion on impact of 47.23
degrees (Figure 4.21).
Figure 4.21: Example of single female participant’s torso flexion for all
passing header tasks (2, 4, 5, and 6) Female head flexion showed consistency with the pattern of flexion, the
minimum flexion took place at approximately the same time point for each task,
as did the maximum flexion (Figure 4.22). For the female example participant,
head flexion ranged from -6.76 to 11.86 degrees with three of the four tasks
taking place with the head in extension. Only task 4 took place with the head in
the flexion position.
0
10
20
30
40
50
60
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Torso Flexion
Task 2
Task 4
Task 5
Task 6
73
Figure 4.22: Example of single female participant’s head flexion for all
passing header tasks (2, 4, 5, and 6)
Female head rotation was similar for tasks 2, 4, and 6. Task 5, the task
with the follow through modification, was performed with the head rotated right,
but to a similar degree as the other three tasks (Figure 4.23). The three tasks
with a leftward rotation ranged from 5.25 to 7.63 degrees while the task with
rotation to the right was rotated -7.35 degrees on impact.
Figure 4.23: Example of single female participant’s head rotation for all
passing header tasks (2, 4, 5, and 6)
-60
-50
-40
-30
-20
-10
0
10
20
30
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Flexion
Task 2
Task 4
Task 5
Task 6
-20
-15
-10
-5
0
5
10
15
20
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Rotation
Task 2
Task 4
Task 5
Task 6
74
Tasks 7 and 8 showed slightly more inconsistency within participants than
the others previously discussed. This is mostly attributed to some abnormalities
within task 7 for both male and female participants. The differences are noted in
both the example male and female torso flexion and head rotation. The oddities
were at similar time points for both participants and took place prior to impact.
This is most likely due to task 7 being the first high speed task performed, and
the participants had yet to get used to the new ball speed.
Figure 4.24: Example of single male participant’s torso flexion for all
clearing header tasks (7 and 8)
Male torso flexion upon impact was 15.9 degrees for task 8 and 19.36 for
task 9. Although some differences did occur, they were at the very beginning of
the task and flexion upon impact remained very similar (Figure 4.24). For head
flexion, a very similar pattern occurred with minimum flexion and maximum
flexion occurring at approximately the same time for tasks 7 and 8 (Figure 4.25).
Head rotation for the males was -2.8 degrees for task 7 and -27.88 degrees for
task 8 (Figure 4.26).
-15
-10
-5
0
5
10
15
20
25
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Torso Flexion
Task 7
Task 8
75
Figure 4.25: Example of single male participant’s head flexion for all
clearing header tasks (7 and 8)
Figure 4.26: Example of single male participant’s head rotation for all
clearing header tasks (7 and 8) Females had similar patterns to the males for the clearing tasks. Torso
flexion was 70.48 degrees for task 7 and 23.69 degrees for task 8 upon impact
(Figure 4.27). Head flexion was also like the male head flexion in that the impact
angles were not alike, but the pattern of flexion over the event remained similar
(Figure 4.28). Head rotation was also much like the males for the clearing tasks
-60
-50
-40
-30
-20
-10
0
10
20
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Flexion
Task 7
Task 8
-60
-40
-20
0
20
40
60
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Male Head Rotation
Task 7
Task 8
76
with differences between the tasks occurring in the early stages of the event.
The impact head rotation for task 7 was -9.84 degrees and 21.83 degrees for
task 8 (Figure 4.29).
Figure 4.27: Example of single female participant’s torso flexion for all
clearing header tasks (7 and 8)
Figure 4.28: Example of single female participant’s head flexion for all
clearing header tasks (7 and 8)
0
10
20
30
40
50
60
70
80
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Torso Flexion
Task 7
Task 8
-120
-100
-80
-60
-40
-20
0
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Flexion
Task 7
Task 8
77
Figure 4.29: Example of single male participant’s head rotation for all
clearing header tasks (7 and 8)
Averages for each task for head flexion, head rotation, and torso flexion
are presented in Table 4.2. The large standard deviations indicate the sizeable
subject variability. Averages were not statistically compared between males and
females because of the large subject variability within their own populations.
-100
-80
-60
-40
-20
0
20
40
60
80
100
-100 -50 0 50 100
An
gle
(D
eg
ree
s)
Time (ms)
Female Head Rotation
Task 7
Task 8
78
Table 4.2: Average angles at impact for each heading task
Heading
Task
Head Flexion
(degrees)
Head Rotation
(degrees)
Torso Flexion
(degrees)
Male Female Male Female Male Female
1
4.99 ±
14.29
12.99 ±
13.74
14.94 ±
24.23
5.06 ±
24.80
18.55 ±
11.88
14.14 ±
8.11
2
0.91 ±
19.56
1.09 ±
12.99
9.81 ±
25.90
2.81 ±
20.57
14.90 ±
15.49
15.96 ±
10.77
3
-12.18 ±
22.21
-3.78 ±
13.42
17.91 ±
29.34
11.48 ±
22.34
8.53 ±
6.67
13.60 ±
10.22
4
-0.09 ±
15.92
7.83 ±
15.16
3.44 ±
27.01
6.89 ±
22.52
14.10 ±
14.35
10.97 ±
12.22
5
7.99 ±
20.90
-5.94 ±
21.56
10.54 ±
25.16
6.96 ±
26.44
17.50 ±
12.21
13.74 ±
17.67
6
12.78 ±
20.40
9.58 ±
16.33
0.95 ±
13.77
6.60 ±
22.97
11.21 ±
16.59
15.48 ±
13.69
7
-4.60 ±
15.56
-15.02 ±
19.88
11.28 ±
34.74
5.02 ±
20.15
9.12 ±
8.02
23.66 ±
19.48
8
3.88 ±
14.01
-6.05 ±
11.81
7.51 ±
35.76
4.59 ±
19.99
8.99 ±
10.02
12.60 ±
10.72
EMG values provided similarly variable results as the body position
angles. For the males, this variability was more noticeable in the trapezius
muscles (Figure 4.30). The females had a more overall inconsistency as the
variation was not limited to a specific muscle group (Figure 4.31). There was
also considerable variation within the subjects when comparing tasks. However,
79
it was not limited to a specific task as it was in the body position data. The mean
peak RMS EMG, used to provide a clearer view of muscle activation, values
(Table 4.3) indicate a much more consistent appearance of the peaks when
comparing players than the overall values provide (Figure 4.30, 4.31).
80
Figure 4.30: Peak EMG for all male players for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right trapezius
81
Figure 4.31: Peak EMG for all female players for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right trapezius
82
Table 4.3: Mean peak RMS EMG values for each muscle and each task
Muscle Gender
Task 1
Mean Peak
(mV)
Task 2
Mean Peak
(mV)
Task 3
Mean Peak
(mV)
Task 7
Mean Peak
(mV)
Left
Sternocleidomastoid
M 1.17 ± 0.46 1.63 ± 0.77 1.89 ± 0.53 2.29 ± 1.38
F 1.26 ± 1.17 1.18 ± 0.77 1.45 ± 1.25 1.33 ± 0.85
Right
Sternocleidomastoid
M 1.68 ± 1.02 1.15 ± 0.31 1.36 ± 0.91 1.48 ± 0.70
F 1.07 ± 0.71 1.32 ± 1.25 1.68 ± 1.75 1.06 ± 0.52
Left Trapezius M 1.86 ± 2.18 1.46 ± 1.28 1.95 ± 1.51 1.45 ± 0.34
F 1.15 ± 1.35 0.97 ± 0.56 1.68 ± 1.32 1.39 ± 1.51
Right Trapezius M 2.01 ± 1.43 2.40 ± 2.18 2.23 ± 1.61 2.60 ± 1.66
F 1.23 ± 1.07 1.29 ± 1.02 1.08 ± 0.71 1.13 ± 0.57
The majority of the muscle activation takes place prior to the impact event.
This is true for both the male and female participants (Figure 4.32, 4.33). Muscle
activation prior to and at impact being higher than that post-impact was seen
throughout the participants. Subjects varied as to how even their contractions
were. As seen in Figure 4.32, the male example participant had relatively
consistent contraction on both the left and right sides. In contrast, the female
example participant had much less contraction in the right trapezius versus the
left (Figure 4.33). These data also indicate subject variability.
83
Figure 4.32: Sample RMS EMG for one male player for each muscle a) left
sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right trapezius
84
Figure 4.33: Sample RMS EMG for one female player for each muscle a) left sternocleidomastoid, b) right sternocleidomastoid, c) left trapezius, d) right
trapezius
85
Head acceleration was evaluated for each task for both males and
females. When looking at average head acceleration for each task, the head
acceleration appears much less variable than the biomechanics and the EMG
between tasks. There is still a wide range between players in each task which is
evident by the standard deviations (Table 4.4). Tasks 7 and 8 do not have higher
head acceleration than the low speed header tasks. Also the modifications do
not appear to have caused a change in head acceleration. The males do,
however, have higher head acceleration for each task. When evaluating
individual players for each task, there was a consistency in the head acceleration
between tasks for each player, generally with one to two tasks being lower than
the rest. These tasks did not show a pattern between subjects.
Table 4.4 : Average angular head acceleration on impact for each heading task
Heading
Task
Head Acceleration (radians/s2)
Male Female
1 89.47 ± 33.20 60.09 ± 23.08
2 100.33 ± 18.47 60.90 ± 20.05
3 97.92 ± 39.07 79.62 ± 30.30
4 95.47 ± 18.25 63.44 ± 16.25
5 95.61 ± 48.61 49.50 ± 22.19
6 75.00 ± 20.99 48.93 ± 14.07
7 109.73 ± 42.58 75.04 ± 25.60
8 85.46 ± 42.58 81.05 ± 28.47
86
4.4 Discussion
Head and back angles were found to have similar tendencies to previous
studies (Shewchenko, et al., 2005). Shewchenko et al. (2005) found that some
players tend to flex their head during contact while others extend. The current
study found this to occur as well. Overall, the torso angle was in flexion for the
majority of players at ball contact which contrasted with the previous findings
where players seemed to remain in a relatively neutral position upon impact and
move into flexion during follow through (Shewchenko, et al., 2005). Players were
found to continue with torso flexion in the follow through period in the youth
population as well. It appears that they arrive at this state earlier than the adult
players. This trend was found in all scenarios, both un-modified and modified.
Table 4.5 describes the average head flexion for the previous study
(Shewchenko, et al., 2005) in comparison to the current study.
87
Table 4.5: Average head flexion for each task
Heading
Task
Current Study
Shewchenko et al.
(2005)
Male (n = 6) Female (n = 9) Male (n = 7)
1 5 13 33
2 1 1 18
3 -12 -4 9
4 0 8 18
5 8 -6 14
6 13 10 15
7 -5 -15 4
8 4 -6 16
Due to the overall variation in player body position, it is expected that
these results would vary from previous results found in adult players. The player
variation is consistent with previous findings (Shewchenko, et al., 2005). This is
most likely due to the many possible heading scenarios. Therefore, players do
not use muscle memory to perform the task in the same manor every time, but
instead learn to adjust to whatever scenario occurs. This provides unlimited
possibilities for heading scenarios and would most likely result in additional
variation in results. The current study only looked at redirection directly at the
source of the ball launch. If additional scenarios were introduced to provide
redirection in other ways, additional distinctions would be made particularly in
head rotation and EMG activity.
88
EMG activity provided insight into the activation of the neck muscles
during the phases of heading. The majority of neck muscle activity during soccer
head was found to take place prior to impact and during impact. Overall, the
peak values were similar, but otherwise there was extensive subject variability.
In addition to inconsistency between subjects, there were also differences from
one task to another within the same subject. Although this was less noticeable in
the EMG results as in the body position results.
Angular head acceleration was compared between tasks to determine if
any of the modifications that were instituted in the study provided a decrease in
head acceleration which could lead to suggested alterations to current heading
methods. It was found that none of the modifications decreased the average
linear head acceleration for the males or the females. When looking at individual
players, decreases were seen between tasks, but no pattern was visible from
one player to the next. This just reiterates that there is extensive player
variability and what works at reducing injury risk for one player may not work for
another.
Comparisons between genders or between tasks were challenging to
make. Based on the results of the current study, it indicates that differences are
not related these variables, but that the differences occur between each player.
This also made making a comparison with the adult players unproductive as any
difference would most likely have nothing to do with age, but with the players
being different and the scenarios being slightly different.
89
One of the main limitations of the current study was the method of head
acceleration measurement. This limited the overall usefulness of the study since
we lacked the ability to make comparisons with previous studies. Also, due to
the method of head acceleration measurement, no injury criteria could be
evaluated. Initial trials were performed with a head acceleration measurement
system in place, but due to system interference the system that allowed for
angular head acceleration measurement and linear head acceleration
measurement at the center of gravity of the head was not possible. It would be
of interest in future studies to measure head acceleration in the youth population
during heading events to determine if any modifications or technique changes
can reduce linear or angular head acceleration.
90
CHAPTER 5
ACCELERATION MEASUREMENT SYSTEM VALIDATION
5.1 Introduction
Previous studies of soccer heading have lacked the ability to measure
real-time game impacts (Naunheim, et al., 2003, Naunheim, et al., 2000,
Shewchenko, et al., 2005, 2005, 2005). These previous head acceleration
measurements were done using re-creations in a laboratory or restricted setting.
By using a novel head acceleration measurement system, the Head Impact
Telemetry System (HITS) (Simbex, Lebanon, NH), linear and angular head
acceleration can be measured during actual games. The system has been
implemented and validated in both football helmets and boxing headgear
(Beckwith, et al., 2007, Duma, et al., 2005, Manoogian, et al., 2006).
Previously HITS (Figure 5.1) was implemented in football helmets (Duma,
et al., 2005), and is now commercially available for use (Duma, et al., 2005). The
data processing algorithm, previously developed by Crisco et al. (2004), allows
for calculation of both linear and angular head acceleration (Crisco, et al., 2004).
System and algorithm validation was performed using a HIII dummy
instrumented with a 3-2-2-2 accelerometer setup for both football and boxing
(Crisco, et al., 2004, Duma, et al., 2005). Correlations were found to be strong
with an R2 = .97 (Duma, et al., 2005).
This system uses six wireless accelerometers which are placed inside a
football helmet along with a wireless transceiver, data acquisition, and on-board
memory (Duma, et al., 2005). The accelerometers are spring-mounted so that
91
they are closely coupled to the head (Duma, et al., 2005). This ensures that
head acceleration is measured as opposed to helmet acceleration. Recording of
impact data occurs when any accelerometer registers above the threshold of 10
g. When the threshold is reached, 40 ms of data are recorded. This information
is then time stamped and downloaded to the sideline computer for later
processing using the algorithm (Crisco, et al., 2004).
Figure 5.1: HIT system
The HIT system has recently been modified for use in boxing headgear
(Beckwith, et al., 2007). The system is much like the football system, but the
boxing headgear has a total of twelve accelerometers as opposed to six.
Additionally, accelerometer placement was more of a concern because the
headgear has no outer shell and the accelerometers had to be placed where it
92
was least likely for impacts to occur. Therefore, the accelerometers were placed
toward the back of the headgear. The battery pack and transmitter were placed
in the back panel. This system was validated using a Hybrid III (HIII) head and
neck (Beckwith, et al., 2007). Using a 3-2-2-2 accelerometer array mounted in
the HIII head, linear and angular accelerations were calculated to compare to
those calculated using the HIT system. Facial, forehead, side, and left chin
impacts were performed using a pendulum impactor at 3 m/s, 5 m/s, and 7 m/s.
Four impacts were performed at each location and speed, with the exception of
forehead impacts not being performed at 7 m/s.
Linear head acceleration, angular head acceleration, impact location, GSI,
and HIC were calculated for both systems. High correlations, r2 = .91, for both
linear and angular head acceleration were found. Estimations made by the HITS
headgear were slightly low (2%) for linear acceleration and high (8%) for angular
acceleration. RMS error was calculated over the time series and was an average
of 5.9 ± 2.6 g for linear acceleration and 595 ± 405 rad/s2. Additionally,
correlations between the two systems were calculated for HIC and GSI. Again,
high correlations were found, r2 = .88 and r2 = .89, for HIC and GSI respectively.
It was found that a limitation of the headgear development was the need to place
accelerometers in the back of the headgear. While this potentially creates error,
it is necessary for avoiding direct accelerometer impact. This could also be a
problem during development of the soccer headgear because impacts will take
place in the forehead region and there will be no padding. Therefore,
accelerometers will need to be placed in the rear portion of the headgear.
93
Implementation of this device into a headband system that can be worn
during normal soccer play would allow for collection of real-time game head
accelerations without restricting player movement. The headband system will be
modeled after a commercially available headgear, but will provide none of the
protective effects to the players. The limitation of this system is that it does
require the player to wear some type of headband to allow for player
instrumentation to take place. The system has previously been used in sports
that require helmets or headgear of some type, but in soccer this is not the case.
Although headgear is available for soccer players, it is not a required piece of
equipment.
HITS Validation
Prior to any field testing, laboratory validation of the HITS headgear was
executed. This was done by testing various possible scenarios that could occur
during soccer games. These scenarios included head to head impacts and head
to ball impacts. These impacts were done using a modified 50th percentile HIII
head (Denton ATD, Milan, OH) instrumented with a 3-2-2-2 accelerometer setup.
The HIII head was then fitted with a HITS headgear. The scenarios were tested
using an air cannon (head to ball) and a linear impactor (head to head).
Comparisons will then be made between the HIII accelerations and the HITS
accelerations. The level of error obtained from HITS will be analyzed.
5.2 Methodology
A soccer headband HITS (Simbex Inc., Lebanon, NH), similar to those
commercially available, was instrumented with 6 (± 250 G) single-axis linear
94
accelerometers (Analog Devices, Inc.) (Figure 5.2). In order to measure forces
normally seen during the play of soccer, no padding was placed in the headband.
All accelerometers were placed in the back of the headband in order to avoid ball
contact during heading events. The battery pack, placed in the back of the
headband, is a rechargeable Nickel Metal-Hydride battery which allows for
extended use, 1 – 2 weeks depending on use, and minimal additional weight,
with the entire headband system weighing 147 g. The headband has a threshold
level of 10 g, meaning that when any accelerometer registers a reading of 10 g or
greater, the impact will be downloaded. Once an impact above the threshold is
recognized, 8 ms prior to the impact and 32 ms post impact will be recorded.
Data are downloaded to the sideline computer as long as players remain within
range, approximately 200 yards. If players are out of range, up to 100 impacts
can be stored within the headgear itself until the player returns within range.
Figure 5.2: Back of HITS headband with circles marking accelerometer
placement
95
The 50th percentile male Hybrid III (HIII) head was used as the standard of
comparison for the linear and angular head accelerations for the HITS headband.
The HIII head was instrumented with nine linear accelerometers (Endevco 7264C
and 7264D) in the 3-2-2-2 setup which was mounted inside the modified HIII
head (Padgaonkar, et al., 1975) with a tri-axial linear block placed at the center of
gravity (CG) of the head form. HIII head acceleration data were collected at
20,000 Hz while HITS acceleration data were collected at 1,000 Hz. The
headband was placed on the HIII head and Velcro straps were tightened to the
manufacturer’s specifications allowing all accelerometers to make firm contact
with the head form. Impacts occurred at the forehead, side, and temple of the
HIII head using an air cannon and a linear impactor. No impacts were performed
with an impact direction going directly through the center of gravity of the head as
this would be highly unlikely in an on-field data collection scenario. Both ball to
head and head to head contacts were simulated.
Ball to head impacts were performed using an air cannon with a barrel
fitted to accommodate a soccer ball (Figure 5.3). A standard size 5 soccer ball
with a mass of 450 g, a diameter of 22 cm, and an inflation pressure of 10 psi
was used for all testing. The ball was shot through a three screen chronograph
in order to obtain velocity readings. In order to obtain velocities representative of
soccer impacts, Helium was used in the air cannon. Impacts were performed at
8 m/s, 10 m/s, and 12 m/s based on previous research performed (Withnall, et
al., 2005). Ten impacts were performed at each velocity to the forehead (n=30),
right side (n=30), and left temple of the head (n=30). These locations were
96
chosen to represent various impacts seen during soccer play, as well as to
provide a variety of impact locations possible in soccer games while not
impacting accelerometers directly.
Figure 5.3: Air cannon with soccer barrel
Head to head impacts were conducted by mounting one HIII head and
neck face down, to a linear impactor and placing another on a trolley in front of
the impactor (Figure 5.4). By placing the head and neck on the impactor, some
flex was possible allowing for an impact more closely representative of an on-
field situation. The head on the trolley was instrumented as described above and
the HITS headgear was placed on it. Tests were run at three velocities: 2.5 m/s,
3.5 m/s, and 4.75 m/s based on previous research on head to head field impacts
(Withnall, et al., 2005). Ten impacts were performed at each of these velocities
at two locations: the forehead (n=30), and to the right side (n=30). These
97
locations represent standard impacts seen during soccer play, and provide a
more severe impact condition than the ball to head impacts.
Figure 5.4: Head to head impact test setup for forehead testing
Data analysis was conducted to determine the agreement between the
HIII and the HITS headgear. Linear head acceleration and angular head
acceleration were calculated for both systems. The HITS system data was
processed using an algorithm previously described in detail by Chu et al. (2006)
which calculates both linear and angular head acceleration based on the 6
accelerometer measurements (Chu, et al., 2006). Linear regression was used to
compare the systems for the ball to head impacts, the head to head impacts, and
then all impacts together. This was done for both linear and angular head
accelerations.
Additionally, root mean square (RMS) error was calculated using the
98
equation below for the duration of the impacts for linear head acceleration. This
will provide information about specific portions of the curve and how closely they
match up in value. Cross correlation was also calculated for linear head
acceleration. These values provide insight into how strongly the variables are
related. Cross correlation values were assessed using a scale to determine
correlation strength: >0.95 was considered “excellent”, >0.85 was considered
“good”, and >0.75 was considered “acceptable”. Due to the fact that the HIT
System has built-in data acquisition and wireless communication, the HIII and
HITS could not be linked. In order to compare HITS data and HIII data during
post-processing, data was synchronized at the point of minimum RMS error. The
two resultants were synchronized by shifting the HIII data incrementally until a 40
ms span of the HIII gave the lowest cross correlation factor.
Due to the fact that data were collected at different frequencies, HITS data
must be time matched to HIII data. In order to do this, the HIT System data was
first up sampled to match the sampling frequency of the HIII so that no HIII data
was lost and the overall numbers from the HIT System output was unaffected.
The two resultants were then synchronized by shifting the HIII data incrementally
until a 40 ms span of the HIII gave the lowest cross correlation factor.
"� ���#� � $∑ �!�,� ' !(,��()��� �
Where:
x1 = HITS measurement at single time point
x2 = HIII measurement at the same time point
99
5.3 Results
Linear regressions were performed for the ball to head impacts, head to
head impacts, and all impacts combined. All impact locations are combined in
the linear regressions. Regressions for both linear and angular accelerations are
shown below with each impact location denoted by a different shape (Figures 5.5
– 5.10).
Ball to head comparisons provided minimal correlation for both linear and
angular acceleration, R2 = 0.3403 and R2 = 0.5716 respectively (Figure 5.5, 5.6).
Correlations were also investigated for each location separately. For ball to head
testing, the forehead had the highest correlation for linear acceleration (R2 =
0.4419), followed by the right side (R2 = 0.3975) and the left temple (R2 =
0.2446). Angular acceleration had the highest correlation in the right side
impacts (R2 = 0.8022), followed by the left temple (R2 = 0.2832) and the forehead
(R2 = 0.1600). The minimal correlation is most likely due to the fact that although
various impact velocities were tested, a range of linear and angular head
accelerations were not obtained. Output accelerations for both the HIII and HITS
systems were very limited in range when impacted over the three ball to head
impact velocities. Although the correlations are not ideal due to the lack of
acceleration range, the average difference between the two acceleration
measurements is minimal. This is especially true for the linear head acceleration
which has an average difference of 2.25 g. The angular head acceleration has
an average difference of 100.58 rad/s2.
100
Figure 5.5: Linear regression of linear acceleration for HIII and HITS ball to
head conditions
Figure 5.6: Linear regression of angular acceleration for HIII and HITS ball
to head conditions
Head to head comparisons provided strong correlations for both linear and
angular acceleration, R2 = 0.8940 and R2 = 0.8998 respectively (Figure 5.7, 5.8).
The forehead location had a higher correlation in both linear acceleration (R2 =
0.9653) and angular acceleration (R2 = 0.9799) than the left side which had an
R2 = 0.8411 for linear acceleration and R2 = 0.8979 for angular acceleration. A
101
much wider range of output velocities was provided from the three impact
velocities used in the head to head impact conditions providing a much stronger
dataset for linear regression. This demonstrates that the HITS is a very good
system for measuring higher velocity impacts. The average differences between
the two systems are -2.01 g for the linear acceleration measurements and -
1721.05 rad/s2 for the angular acceleration measurements. These differences
are calculated over all three impact velocities.
Figure 5.7: Linear regression of linear acceleration for HIII and HITS head
to head conditions
102
Figure 5.8: Linear regression of angular acceleration for HIII and HITS head
to head conditions
Linear regressions were also performed for all head impacts, ball to head
and head to head, combined. This was done to determine the overall accuracy
of the system for the range of velocities over which it will be used. Very strong
correlations were found over all of the impact conditions, R2 = 0.9437 and R2 =
0.9194 for linear acceleration and angular acceleration respectively (Figure 5.9,
5.10). This provides a very strong basis for using the system for future soccer
research.
103
Figure 5.9: Linear regression of linear acceleration for HIII and HITS ball to
head and head to head conditions combined
Figure 5.10: Linear regression of angular acceleration for HIII and HITS ball
to head and head to head conditions combined
Peak linear and rotational acceleration was also calculated for each of the
impacts. These values for both the HITS and HIII systems are shown in Tables
5.1 - 5.4. All values shown are the average for the impact condition listed.
104
The HITS system slightly over predicts linear head acceleration in the ball
to head impacts. This can be seen in all impact conditions, except the forehead
condition at 12 m/s. At this condition the HITS provides a slight under prediction
(Table 5.1).
Table 5.1: Average Peak Linear Accelerations for Ball to Head Conditions
8 m/s 10 m/s 12 m/s
Impact
Location HITS (g) HIII (g) HITS (g) HIII (g) HITS (g) HIII (g)
Forehead
15.20 ±
0.49
12.14 ±
0.64
18.23 ±
1.51
16.57 ±
0.70
18.70 ±
2.54
21.09 ±
1.35
Right Side
18.03 ±
4.85
13.31 ±
0.76
19.93 ±
3.20
17.67 ±
0.87
22.98 ±
4.02
21.13 ±
0.93
Left Temple
18.03 ±
3.53
13.39 ±
0.46
18.72 ±
3.26
18.08 ±
0.46
21.64 ±
4.33
21.45 ±
1.11
Angular head acceleration for the ball to head impacts provides a different
pattern for the peak values. The HITS system over predicts angular head
acceleration in the ball to head impacts for all conditions except the forehead
impacts. Forehead impacts at all three velocities have the HITS under predicting
angular acceleration (Table 5.2).
105
Table 5.2: Average Peak Angular Accelerations for Ball to Head Conditions
8 m/s 10 m/s 12 m/s
Impact
Location
HITS
(rad/s2)
HIII
(rad/s2)
HITS
(rad/s2)
HIII
(rad/s2)
HITS
(rad/s2)
HIII
(rad/s2)
Forehead
822.07 ±
244.4
833.12 ±
74.40
857.74 ±
259.28
954.86 ±
95.32
949.57 ±
362.40
1450.81 ±
252.08
Right
Side
1416.29 ±
94.57
1343.23 ±
197.30
1828.96 ±
334.27
1509.16 ±
152.15
1882.67 ±
318.00
1715.39 ±
378.91
Left
Temple
1958.79 ±
425.65
1321.29 ±
179.61
1453.86 ±
535.28
1281.71 ±
282.16
1917.18 ±
348.35
1695.22 ±
347.64
Linear head acceleration for the head to head impacts shows a general
over prediction by the HITS for the two lower impact velocities. At the 4.75 m/s
impact condition, the HITS under predicts linear head acceleration for both the
forehead and left side conditions. Although a general over prediction occurs,
values are very similar as shown by the strong correlations between the two
systems (Table 5.3).
106
Table 5.3: Average Peak Linear Accelerations for Head to Head Conditions
2.5 m/s 3.5 m/s 4.75 m/s
Impact
Location HITS (g) HIII (g) HITS (g) HIII (g) HITS (g) HIII (g)
Forehead
33.68 ±
0.55
32.54 ±
0.69
74.26 ±
4.10
66.93 ±
1.85
115.45 ±
8.02
125.14 ±
2.80
Left Side
37.14 ±
3.46
30.58 ±
2.06
69.58 ±
10.12
63.18 ±
3.31
95.82 ±
13.02
119.65 ±
4.94
Angular head acceleration for the head to head impacts shows that the
HITS under predicts the angular head acceleration slightly for nearly all the
impact conditions. The HITS system only over predicts angular head
acceleration in the head to head impacts for one impact condition, the left side at
2.5 m/s. This is shown in Table 5.4 below.
Table 5.4: Average Peak Angular Accelerations for Head to Head Conditions
2.5 m/s 3.5 m/s 4.75 m/s
Impact
Location
HITS
(rad/s2)
HIII
(rad/s2)
HITS
(rad/s2)
HIII
(rad/s2)
HITS
(rad/s2)
HIII
(rad/s2)
Forehead
1245.63 ±
109.99
1544.50 ±
32.89
2013.79 ±
176.65
3177.79 ±
205.66
6930.20 ±
530.95
9414.49 ±
218.99
Left Side
2795.55 ±
179.72
2366.77 ±
456.59
5629.58 ±
550.05
6020.31 ±
365.15
9801.20 ±
1413.74
16218.41 ±
855.65
Root mean square (RMS) error was calculated for each impact on a point
by point basis. An example of the waveforms being compared is shown in
107
Figures 5.11 – 5.15. An average was then calculated for each impact and each
impact condition. The average RMS error of the linear accelerations for ball to
head impacts at the 8 m/s condition was 2.04 ± 0.25 for forehead impacts, 3.55 ±
0.44 for right side, and 2.20 ± 0.74 for left temple impacts. Similarly RMS error
for the 10 m/s condition was 2.35 ± 0.27 for forehead impacts, 3.86 ± 0.62 for
right side, and 1.97 ± 0.54 for left temple impacts. The 12 m/s conditions had
RMS errors of 2.55 ± 0.77 for forehead impacts, 3.08 ± 0.94 for right side, and
3.89 ± 1.21 for left temple impacts.
Figure 5.11: Linear acceleration for both HIII and HITS for one ball to head
forehead impact at the 12 m/s condition
108
Figure 5.12: Linear acceleration for both HIII and HITS for one ball to head
right side impact at the 12 m/s condition
Figure 5.13: Linear acceleration for both HIII and HITS for one ball to head
left temple impact at the 12 m/s condition
RMS errors were also calculated for the head to head conditions (Figure
5.14, 5.15). Head to head impacts RMS errors were 2.69 ± 0.32 for the forehead
and 5.83 ± 0.67 for the left side at the 2.5 m/s condition, 5.82 ± 0.65 for the
forehead and 15.19 ± 1.30 for the left side at the 3.5 m/s condition, and 9.47 ±
0.57 for the forehead and 21.89 ± 2.62 for the left side at the 4.75 m/s condition.
109
These values are higher due to the higher accelerations provided from the head
to head impact conditions.
Figure 5.14: Linear acceleration for both HIII and HITS for one head to head
forehead impact at the 4.75 m/s condition
Figure 5.15: Linear acceleration for both HIII and HITS for one head to head
left side impact at the 4.75 m/s condition
Cross correlations were performed and demonstrate a strong relationship
between the two systems. Average cross correlation (r) values were 0.95 ± 0.01
for forehead impacts, 0.88 ± 0.05 for right side, and 0.95 ± 0.04 for left temple
110
impacts for the 8 m/s condition, 0.94 ± 0.01 for forehead impacts, 0.87 ± 0.04 for
right side, and 0.96 ± 0.02 for left temple impacts for the 10 m/s condition, and
0.95 ± 0.03 for forehead impacts, 0.96 ± 0.02 for right side, and 0.92 ± 0.04 for
left temple impacts for the 12 m/s condition. Head to head impacts had cross
correlation values of 0.97 ± 0.01 for the forehead and 0.94 ± 0.01 for the left side
at the 2.5 m/s condition, 0.98 ± 0.00 for the forehead and 0.88 ± 0.04 for the left
side at the 3.5 m/s condition, and 0.94 ± 0.00 for the forehead and 0.83 ± 0.02 for
the left side at the 4.75 m/s condition. All ball to head conditions fell either
within the good or excellent range when looking at cross correlation values. Of
the nine ball to head conditions, five of them were above the 0.95 value required
for an excellent rating. Head to head conditions also provided very strong cross
correlation values, with five of six conditions falling into either the good or
excellent categories.
5.4 Discussion
Although attempts have been made to determine head acceleration during
soccer heading events, a system for on field data collection had not been
previously available. A system has now been created for research purposes,
however validation was necessary before the system could be used to collect
data during a game or scrimmage situation. The results show that the new
soccer HITS system correlates well with the standard measurement system of
the HIII 3-2-2-2 accelerometer system. Locations and impact velocities were
chosen to simulate events that take place in normal soccer play.
111
Good cross correlation values were found for the linear accelerations for
all conditions with two of the conditions having excellent correlation. The lowest
correlation value of 0.83 ± 0.02 was for the left side during head to head impact
and is not a location that is expected to be frequently impacted during soccer
play. Even as the lowest correlation, it still shows an acceptable level of
agreement. Additionally, all other linear acceleration cross correlation values
exceeds the 0.85 value and shows a very well matched system.
Strong correlations were found between the systems for both linear and
angular head acceleration for the head to head condition, 0.8940 and 0.8998
respectively. Additionally, very strong correlation was found for overall use of the
system. This was shown by performing linear regression over all conditions with
results of 0.9437 for linear acceleration and 0.9194 for angular acceleration. The
ball to head condition did not have a strong correlation, but this is due to the lack
of velocity distribution as all of the impacts were at a very low magnitude.
Although these impacts have low R2 values, they did have a very small absolute
difference, 2.25 g for linear head acceleration and 100.58 rad/s2 for angular head
acceleration. Also, all average peak values for linear acceleration were well
below 66 g which has been previously established as a 25 % risk of injury
(Zhang, et al., 2004). This indicates that a difference of ± 2.25 g would not be
clinically significant.
RMS error values showed a consistency between the two waveforms for
each of the conditions (Figures 5.11 – 5.15). Slightly higher average RMS error
values were found in some waveforms, but upon further inspection it seems as
112
though the discrepancy in the waveforms took place in the tail of the impact or in
a small secondary impact but not in the peak. Therefore, even in the impacts
with a slightly higher average RMS error the peaks were still similar.
One limitation of this system is that it requires the player to wear some
type of headband to allow for player instrumentation to take place. The system
has previously been used in sports that require helmets or headgear of some
type, but in soccer this is not the case. Although headgear is available for soccer
players, it is not a required piece of equipment. Therefore, it could be more
challenging to find players willing to wear the system during play. An additional
concern with the system is movement during play (Beckwith, et al., 2007). This
was not a problem during validation testing, but when used in the field it may be
an issue due to player hair as opposed to the HIII skin. Although this is a
concern, movement would most likely just alter the accuracy of the impact
location (Beckwith, et al., 2007). Headband slippage was not a problem in
laboratory tests, and impact location is not a primary interest for use with this
system. Therefore, headband slippage is not considered a major concern but
on-field research is warranted to assess these concerns.
During soccer play, many impact scenarios exist, and although the current
study made every effort to recreate scenarios typically seen during soccer play, it
was impossible to include all scenarios. One limitation is the limited impact
locations and velocities included. Soccer has a wide range of impact locations
and although they are not all included, the range included provided sufficient
simulation of events to validate the system. An additional limitation is the
113
simulation of on-field scenarios accurately. Using two HIII heads in head to head
impacts as opposed to just an impactor ram provided some give due to the neck
flexion on the impactor, but may not be an exact replication of on-field impacts.
The current study was not designed to recreate exact scenarios, but to provide
reasonable recreations in order to determine a correlation between the
acceleration measurement systems. Therefore, the system needs to be used in
on-field situations to determine fully its ability to accurately measure soccer head
impacts.
Rotational acceleration for left side, head-to-head impacts had the largest
difference measurement for any test method. For these impacts, the HITS under
predicted the HIII by 6417.21 rad/s2. While this discrepancy is of concern, it is
unclear how translatable these results will be to in vivo data collection. These
test conditions are intended to be representative of on-field events, however, the
complex biomechanical interactions that take place during live impacts may not
be completely captured by our simulated event as it was impossible to recreate
every possible impact scenario. Due to the high correlation found for all other
test combinations, we suggest the HITS is a viable method for recording impacts
during competition, however, while linear acceleration measures appear
acceptable, caution should be taken when evaluating rotational acceleration for
impacts similar to our head-to-head condition. As part of this ongoing work,
future studies will address this concern by identifying head to head impacts
through video analysis and comparing on-field measures with those recorded
here.
114
In conclusion, this system provides a much needed method to measure
head acceleration in soccer players during normal play. It allows for accurate
measurements to be taken which could potentially lead to an injury threshold
specific to certain soccer impacts. Additionally, this system will allow a
comparison between different types of impacts that occur during soccer play.
115
CHAPTER 6
ON FIELD MEASUREMENT OF HEAD ACCELERATION
6.1 Introduction
Head acceleration has been successfully measured during sporting events
previously (Duma, et al., 2005, Stojsih, et al., 2008), but it has been a challenge
for researchers to successfully do this during soccer games or scrimmages due
to the lack of headgear (Naunheim, et al., 2003, Naunheim, et al., 2000,
Shewchenko, et al., 2005). Attempts have been made to instrument helmets
from other sports in order to collect data from soccer heading events (Naunheim,
et al., 2000). Although these efforts provide a starting point, data do not
represent actual on-field events as they are recreations and by adding a hard
shell helmet the impact event is altered.
Naunheim et al. (2000, 2003) previously studied head acceleration in
soccer. In the first of these studies (Naunheim, et al., 2000), researchers used a
football helmet with a tri-axial accelerometer mounted on the helmet’s vertex to
measure head accelerations of high school soccer, football and hockey players.
Football and hockey impact acceleration data were collected during games, but
the soccer impacts were done in a simulated game situation. The soccer players
headed a ball kicked 30 yards while wearing the instrumented football helmet.
Significant neurological damage was not anticipated from any single impact
based on standard threshold values for Gadd Severity Index, Head Injury
Criterion, and peak linear acceleration. Soccer players did see higher values for
the three reported results than the football and hockey players. The data,
116
however, does not accurately represent the accelerations that soccer players
would see when heading the ball in a game. This is due to the fact that soccer
players would not be wearing a protective helmet, but they would instead be
heading the ball with no head protection at all.
In order to address the issues with the first study, Naunheim et al. (2003)
instrumented players with an instrumented headpiece, designed specifically for
the study, and a mouthpiece to measure linear and angular accelerations.
Subjects were asked to head a ball which was launched from a distance of six
meters. Linear accelerations of up to 199 + 27 m/s2 were measured. Angular
accelerations were reported to be 1.46 + .297 krad/s2. Although the study
provided some basiline data, the ability to measure on-field data was lacking
(Naunheim, et al., 2003).
Shewchenko et al. (2005) also performed a study measuring head
acceleration during soccer heading. This study used seven current soccer
players to measure kinematics, head acceleration, and muscle activity in the
neck. The subjects were asked to recreate heading scenarios in a laboratory
while wearing reflective targets, EMG electrodes, and a bite plate instrumented
with linear and angular accelerometers. Ten scenarios were performed using
two ball speeds, 6 m/s and 8 m/s, while high speed video, acceleration, and EMG
data were recorded. Results showed that the average peak linear acceleration
did not exceed 194 ± 40 m/s2 for any of the 10 scenarios. Average peak angular
accelerations were also calculated and did not exceed 2.41 ± 1.81 krad/s2 for the
117
scenarios recorded. Although the study is comprehensive, it still did not provide
actual field data representing what occurs in a soccer game.
Laboratory recreations have provided insight into heading impact events
(Naunheim, et al., 2003, Shewchenko, et al., 2005). These recreations provide
valuable kinematic and muscle activation data, but are only representative of the
least severe cases for head acceleration. During these recreations, players were
wearing reflective targets, EMG electrodes, and an instrumented bite plate
(Shewchenko, et al., 2005). All of this instrumentation changes the ability of the
player to move freely and, therefore, alters the dynamic of the impact. Although,
linear acceleration results from two laboratory recreation studies are similar,
there is no way to know that this is what takes place during actual game play
(Naunheim, et al., 2003, Shewchenko, et al., 2005). Ultimately, a wireless
acceleration measurement system which does not inhibit movement and
provides no head protection is needed to determine the linear and angular head
accelerations during actual soccer play.
The exact contribution of linear versus angular acceleration for a given
impact when heading the ball is related to several factors including how quickly
the neck muscles are recruited and the overall intent of the redirection
(Naunheim, et al., 2003, Shewchenko, et al., 2005, 2005). Players may
purposely rotate their head in an effort to redirect the ball towards a particular
target i.e. the goal. There is much debate as to whether linear or angular
acceleration should be investigated when studying mTBI since both have been
shown to predict injury (Gurdjian, et al., 1966, Ommaya and Hirsch, 1971). For
118
the current study, each will be evaluated independently along with various other
head injury criteria which have established thresholds for mTBI.
Two major injury criteria can be assessed along with linear and angular
head acceleration. These are the Head Injury Criterion (HIC) and the Gadd
Severity Index (GSI). HIC and GSI were developed primarily for automotive
impact. Both criteria take into account acceleration over a period of time (Gadd,
1966, Newman, et al., 2000). Each of these criteria is useful only to assess one
individual impact. There are still no suggested head acceleration limits for
multiple subconcussive impact events.
Figure 6.1: Wayne State University Tolerance Curve (Cory, et al., 2001)
The Wayne State Tolerance curve (Figure 6.1) provided the basis for both
GSI and HIC (Gurdjian, et al., 1966). Gadd (1966) developed the Severity Index
using the Wayne State Tolerance Curve plotted on a log-log scale. The slope of
119
the resulting curve was -2.5 which provided the power which is used in the
calculation. It has been suggested that life threatening injuries are increasingly
likely when GSI values are greater than 1000.
*�� � +����(.-.�/
Where:
a(t) = CG resultant translational acceleration
T = duration of acceleration
HIC is the most widely used within automotive testing. This criterion is
based on the GSI calculation and the Wayne State Tolerance curve. HIC is an
optimization of the GSI formula (Versace, 1971). As opposed to using the total
impact duration, a time interval providing the maximum value is used. Various
recommended HIC limits exist, with 1000 being the original limit for automotive
testing. This limit was based on the probability of life-threatening injury, and
represented a 16 % risk of serious brain injury or skull fracture (Prasad and
Mertz, 1985), and has now been reduced to 700 which represents a 5 % risk for
automotive impacts. Pellman et al. (2003) recommended a mTBI HIC limitation
value of 250 based on American football concussions (Pellman, et al., 2003).
0�1 � 2 1��( ' ��� + ����.��4
�56
(.-��( ' ���
Where:
t1, t2 = time points which provide maximum HIC value (generally 15 ms interval is
used)
a(t) = CG resultant translational acceleration
120
In addition to HIC and GSI, other head injury measurement values have
been previously established (Newman, et al., 2000, Ommaya, et al., 2002,
Zhang, et al., 2004) for both linear and angular head acceleration which are
described below. Zhang et al. (2004) used data from football head impact
recreations (Newman, et al., 2000) and finite element modeling to determine an
injury threshold for mTBI. Using data from these recreations as input values, the
Wayne State Brain Injury Model was used to determine probability of injury
(Zhang, et al., 2004). The model was used to calculate the brain’s mechanical
responses which were then related to an injury severity and the resultant
probability of injury. For linear head acceleration, a 25 % risk of mTBI was found
at 66 G, a 50 % risk at 82 G, and an 80 % risk at 106 G. Angular head
acceleration thresholds were also determined and were 25 % at 4600 rad/s2, 50
% at 5900 rad/s2, and 80 % at 7900 rad/s2. One limitation of using these mTBI
threshold levels in the current study is that they were developed based on
impacts that occurred between helmeted individuals and are valid for single
impact events only. The values are, however, based directly on sports injury
data and are not scaled from an animal model which provides a solid basis for
use when evaluating sports injury data.
6.2 Methodology
A total of 24 girls youth soccer players in the U14 age group agreed to
participate in the study. Prior to any testing, approval from Wayne State
University’s Human Investigation Committee was obtained. All players were
fitted with the Head Impact Telemetry System (HITS) headgear (Figure 6.2),
121
described in detail in Chapter 6, and then asked to participate in a scrimmage.
Some participants were involved in more than one scrimmage and were
processed as a new player for each. Players wore the headgear for the duration
of the scrimmages which lasted 30 to 65 minutes. Data were collected at
1000 Hz and downloaded to the sideline computer for later analysis. Games
were videotaped for later analysis in determining what type of impacts occurred
at each downloaded time point.
Figure 6.2: HITS headgear fitted to HIII headform
Following field data collection, data analysis was performed using a
validated algorithm provided by Simbex (Chu, et al., 2006, Crisco, et al., 2004).
Each individual impact was analyzed using this algorithm. Information obtained
included several parameters including HIC and resultant linear and angular
acceleration. Along with all of this information, number of headers, location of
impact, and incidence of other impact events (player collisions with other players,
player falls, collisions with goalposts, and unintentional collisions with the ball)
were also determined from the video.
122
6.3 Results
The majority of header impacts took place to the front location (n = 17)
and ranged in peak linear acceleration from 4.5 g to 34.3 g with an average peak
linear acceleration of 17.4 g (Figure 6.3). Additionally, peak angular
accelerations ranged from 493.3 rad/s2 to 3649.7 rad/s2 with an average of
1657.5 rad/s2. None of these values exceed tolerance levels previously
established.
Figure 6.3: Linear head acceleration by location for each header only
impacts
The second highest number of header impacts by location took place at
the top of the head (n = 9) and ranged in peak linear acceleration from 11.1 g to
44.4 g with an average peak linear acceleration of 19.5 g. Additionally, peak
angular accelerations ranged from 598.0 rad/s2 to 3637.2 rad/s2 with an average
of 1851.8 rad/s2 (Figure 6.4). Again, none of these values exceed tolerance
values for mTBI.
123
Figure 6.4: Angular head acceleration by location for each header only
impacts
The left and right sides had the next highest number of header impacts
with 7 and 8 respectively. The right side impacts ranged in peak linear
acceleration from 5.5 g to 62.9 g with an average peak linear acceleration of 17.4
g. Additionally, peak angular accelerations for the right side ranged from 523.7
rad/s2 to 8869.1 rad/s2 with an average of 3003.4 rad/s2. For the left side, peak
linear accelerations ranged from 11.4 g to 49.4 g with an average of 27.2 g.
Peak angular accelerations ranged from 762.1 rad/s2 to 4509.8 rad/s2 with an
average of 2586.6 rad/s2 for the left side. The back of the head had the fewest
header impacts with 6. These impacts were also low in linear acceleration with a
range of 4.9 g to 19.0 g averaging 11.9 g. Angular acceleration values were also
low with a range of 444.8 rad/s2 to 927.0 rad/s2 and an average of 723.2 rad/s2.
124
Figure 6.5: HIC values for headers by location with mTBI tolerance level
HIC values for the header impacts were generally low for all locations.
The right side had the highest HIC values with an average of 38.1 (Table 6.1)
and a peak value of 154.1 (Figure 6.5). The left side followed with an average
HIC of 27.36 and a peak of 79.40. Although the front location had the most
impacts, the HIC values remained low with an average of 7.7 and a peak of 24.0.
The top and back of the head had very low HIC values with averages of 9.5 and
2.6 respectively (Table 6.1).
125
Table 6.1: Average results for headers by location
General
Location
Peak Linear
Acceleration
(g)
Peak Angular
Acceleration
(rad/s2) HIC 15
GSI
L Side 27.2 2586.6 27.4 36.7
R Side 28.1 3003.4 38.1 51.2
Top 19.5 1851.8 9.5 15.5
Front 17.4 1657.5 7.7 13.6
Back 11.9 723.2 2.6 5.1
The impacts that each individual player saw during each scrimmage was
also investigated. The maximum number of header impacts a single player
experienced in a scrimmage was four with players 3 and 18 both having that total
(Figures 6.6, 6.7). In addition to these header impacts, player 18 also had a non
header impact, a collision with the goal post, which was above the angular
acceleration 25 % threshold for injury (Figure 6.12). Players 5 and 19 were the
only two players that had header impacts with angular accelerations exceeding
head injury tolerance limits (Zhang, et al., 2004). Both of these players had
multiple impacts during their scrimmages, not just the single tolerance exceeding
blow (Figures 6.6 – 6.7).
126
Figure 6.6: Linear head acceleration for all header impacts for individual
players
Figure 6.7: Angular head acceleration for all header impacts for individual
players
In addition to the header impacts, there were also various other impacts.
There were 21 non-header impacts recorded (Table 6.2). These included
collision with the goal, player collisions, player falls, and unintentional ball to
head impacts (Table 6.2). The majority of players participating had only one non-
header event occurring during their scrimmage and many had no occurrences.
127
Three players, however, had multiple instances. These were players 12, 13, and
29 (Table 6.2). Player 12 also had multiple header events during their
scrimmage (Figures 6.6, 6.7). All impacts to player 12 were well under the
current tolerance limits for mTBI.
128
Table 6.2: Description of non header impacts and the player that impacted
Player Description
Peak Linear
Acceleration
(g)
Peak Angular
Acceleration
(rad/s2)
HIC
15
GSI
5 Player fell 23.7 3739.3 13.7 17.9
7 Unintentional ball to head 20.4 1749.9 5.4 11.4
9 Player fell 15.1 1332.3 6.4 8.1
12 Player fell 7.7 628.5 0.5 1.2
12 Player fell 11.1 881.8 1.0 1.6
13 Player collision 10.7 811.3 1.7 2.2
13 Player collision 12 828.4 1.9 2.8
14 Player hit ground 18 823.6 4.1 7.2
15 Ball hit back of head 32.2 1090.7 25.4 31.6
17 Player collision 11.6 1247.1 4.0 7.5
19 Player fell 16 1315 6.7 10.9
21 Player collision 24 2831.8 15.4 31.7
22 Collision with goalpost 27.1 5179.5 16.5 20.4
24 Player collision 25.7 2847 17.3 20.8
26 Player fell 18.5 753.6 8.7 10.3
27 Player hit ground 5 497.5 0.2 0.3
28 Player collision 56.7 2910.3 90.8 114.7
29 Player collision 18.9 987 2.9 5.4
29 Player collision 18.9 1982.3 8.7 10.3
29 Unintentional ball to head 15 899.9 1.9 2.7
129
The majority of the non-header impacts took place to the front and to the
top of the head, with six impacts each (Figures 6.8, 6.9). None of the impacts at
either of these locations reached any of the threshold levels for either linear or
angular acceleration. Following these two locations, five impacts occurred to the
left side. Of these five impacts, one had an angular head acceleration (5179.5
rad/s2) that exceeded the 25 % risk of injury tolerance level. This was the only
non-header impact to exceed any of the previously established tolerance levels.
The right side of the head and the back of the head had limited non-header
impacts with two and one respectively.
Figure 6.8: Linear head acceleration for all non header impacts by location
130
Figure 6.9: Angular head acceleration for all non header impacts by
location
HIC was also evaluated by location of the impact for the non-header
impacts (Figure 6.10). The highest HIC value for non-header events was 90.8
and occurred to the top of the head. This value is not considered to be at an
injurious level as it is well under the tolerance level of 250 established for mTBI.
Figure 6.10: HIC for all non header impacts by location
The highest peak linear acceleration of 56.7 g, to player 28, was found to
131
occur from a player collision (Figure 6.11). This was the only impact that player
28 experienced that was high enough to trigger the HIT system. With a
corresponding angular acceleration of 2910.3 rad/s2, it is unlikely that injury
would occur as none of these values reach any of the injury tolerances currently
established for mTBI.
A separate incident, a collision with the goal, resulted in the peak angular
acceleration of 5179.5 rad/s2 which occurred to player 22 (Figure 6.12). In
addition to this non-header impact, player 22 had four header impacts during
their scrimmage. Although none of the header impacts exceeded tolerance
values for linear head acceleration, one of the headers had an angular
acceleration of 4509.1 rad/s2. This nearly exceeds the tolerance level proposed
by Zhang et al. (2004) and could increase the risk of additional impacts.
Figure 6.11: Linear head acceleration for all non header impacts for
individual players
132
Figure 6.12: Angular head acceleration for all non header impacts for
individual players 6.4 Discussion
There have not been any previous studies recording on-field
measurements of head acceleration during soccer play. Therefore, the current
study cannot be compared to previous on-field data collections. It can, however,
be compared to previous laboratory studies. The maximum linear and angular
accelerations found in the current study greatly exceed those found by Naunheim
et al. (2003). In the laboratory study, Naunheim et al. (2003) did not have linear
or angular accelerations exceeding 199 m/s2 and 1.46 krad/s2 in contrast to a
maximum linear acceleration of 617 m/s2 and a maximum angular acceleration of
8.87 krad/s2 determined in the on-field study. This is a 418 m/s2 difference in the
maximum linear accelerations seen between the two studies and a 7.41 krad/s2
difference in the angular acceleration measurements.
The current study also exceeds the acceleration measurements
determined by Shewchenko et al. (2005). Shewchenko et al. (2005) determined
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
An
gu
lar
Acc
ele
rati
on
(ra
d/
s2)
Player
Angular Head Acceleration
p = 0.50
p = 0.25
p = 0.80
133
that the average peak linear acceleration over the different heading scenarios
participants performed to not exceed 194 m/s2 which was found to be much
lower than the peak average linear acceleration of 276 m/s2 from the current
study. A peak average angular acceleration of 2.41 krad/s2 was found for the
laboratory study in contrast to the 3.00 krad/s2 found in the current on-field study.
These values are much more similar to the on-field collections than the study
performed by Naunheim et al. (2003). This is due to the use of averages as
opposed to maximums and it does not take into account worst case scenarios.
Additionally the laboratory studies looked only at a redirection directly back
towards where the ball came from. This reduces the amount of angular
acceleration that participants will experience and only represents a small portion
of the type of headers players experienced during actual play.
Data were compared to mTBI head injury tolerance values proposed
previously (Ommaya, et al., 2002, Pellman, et al., 2003, Zhang, et al., 2004).
None of the impacts, heading events or otherwise, exceeded the 66 g threshold
which was the 25% risk of injury tolerance level (Zhang, et al., 2004) or the HIC
value of 250 (Pellman, et al., 2003). Based on these observations, it seems as
though the linear acceleration contribution of heading is not causing head injury
based on a single impacts.
Angular accelerations, however, did exceed the suggested limits. Three
angular acceleration measurements for heading events (4509.8 rad/s2, 5298.3
rad/s2, 8869.1 rad/s2) exceeded the 4500 rad/s2 limit which has been suggested
as the limit required to produce concussion in adults (Ommaya, et al., 2002). Of
134
these three impacts, one also exceeded the 25% risk of injury threshold of 4600
rad/s2 and one exceeded the 7900 rad/s2 limit which correlates to an 80% risk of
head injury (Zhang, et al., 2004). In addition to the three heading events
exceeding the 4500 rad/s2 concussion injury tolerance level, an impact caused
by a collision with the goal resulted in an angular acceleration of 5179 rad/s2
which also exceeds the 25 % risk of injury value proposed by Zhang et al. (2004).
Although single impacts exceeded the suggested mTBI tolerance levels,
there was no stoppage of play during any of the scrimmages due to injury. Funk
et al. (2007) suggest a 10 % risk of injury with a linear acceleration of 165 g, a
HIC of 400, and a peak angular acceleration of 9000 rad/s2 to produce mTBI.
These values are much higher and indicate a lower risk of injury at the values
seen in the current study. Tolerance values required to induce mTBI could be
higher than Zhang et al. (2004) as suggested (Funk, et al., 2007), or the lack of
stoppage of play could be due to other causes. This could be due to the fact that
they fell within the percentage of the population that would not be injured at the
suggested tolerances, or it is possible that these tolerance levels are not
representative of the types of impacts that occur during soccer heading. It is also
possible that angular acceleration alone is not the best predictor of injury in
soccer heading impacts. Since these were the only values that exceeding injury
tolerances, it is possible that these values alone are not representative of injury
causation.
The high levels observed could be due to a limitation of the
instrumentation itself, but it when looking at the validation of the HITS higher
135
level impacts had a much better correlation than the lower level impacts. This
indicates that the system responds accurately at the impact level at which injury
is presumed to occur. However, data seem to indicate that angular acceleration
during heading events could potentially pose a problem; especially as single
impacts are exceeding 80% risk of injury tolerances. Additionally, all heading
impacts with angular accelerations exceeding suggested limits took place to
either the right or left side of the head. This indicates an unusual heading
method or inaccurate technique. When impacts took place to the front or top of
the head, no limits were exceeded. This further emphasizes the importance of
teaching proper technique.
All of the suggested tolerance levels which current data were compared to
were developed for single impact events. Many of the players in the current
study experienced multiple impacts, not all of which were header impacts. All of
the players who experienced tolerance exceeding impacts had other impacts as
well. Player 22 actually experienced two of the four impacts that had angular
accelerations above the recommended tolerance levels, one a header and one a
collision with a goalpost. In addition to those two impacts, player 22 also had
three other headers that fell below recommendations. Based on standard
tolerance levels it is challenging to determine if the combination of all of those
impacts in a single scrimmage increases the likelihood of injury. Further
research is necessary to determine if the level of multiple impacts has an effect
on mTBI probability. Additionally, further research is needed to determine if
136
symptoms are occurring after scrimmages where head accelerations are known.
This could provide a possible correlation.
Some limitations of the current study include the limited study population.
The study was limited to a single age group and to female soccer players.
Further research would be necessary to determine if differences exist in head
acceleration based on age or gender. Additionally, the current study investigated
soccer scrimmages and not actual games. This is due to the challenge of getting
players to wear equipment that is not required during competition. Higher head
accelerations may be seen during a more competitive, and likely more
aggressive, game scenario.
137
CHAPTER 7
CONCLUSIONS AND FUTURE RECOMMENDATIONS
7.1 Conclusions
Soccer is one of the most popular sports throughout the world. With a recent
increase in youth players in the United States, an increase in injuries has also
been reported. In addition to the unintentional head impacts that occur during
soccer play, similar to those of other sporting activities, soccer players also
intentional use their head to redirect the soccer ball, an act known as “heading”.
The effect of these intentional impacts has been studied, the majority of research
being conducted on adults, with conflicting outcomes. The risks to children are
potentially greater due to their size versus the force being applied by the ball. It
has been reported that ball mass, impact velocity, and size of the individual all
contribute to the potential for injury. The importance of proper technique may be
especially true in the youth population, since their skill level has not been well
developed to control their head motion when heading the ball.
Although previous research has been conducted to determine the effects of
repetitive heading in soccer, the results are very controversial. Conflicting results
have been observed in studies throughout the history of soccer heading
research. Many of these previous studies have had significant challenges within
the methodology, including the lack of controls or using improper control groups
and many have not taken into account other outside factors that could be
contributing to results. In order to determine the effects of the repetitive
138
subconcussive head impacts associated with heading, an in-depth analysis of the
biomechanics of heading needed to be performed.
The current study investigated the effect of the intentional head impacts that
occur during soccer play. Initial steps were taken to determine the frequency and
severity of heading episodes in the field using both field observation and to
determine the possibility of head injury caused by impact with the ball only. The
NEISS database was used to determine injury occurrence from ball to head only
impacts. Ball only injuries comprised 15.9% of total head injuries. These injuries
were not necessarily caused by heading the soccer ball, as some of the impacts
were due to unintentional ball to head impact, but many of the cases were
described as heading related. It is, however, challenging to determine a total
number of participants due to the inclusion of organized and non-organized
soccer injuries. These data indicate that heading alone can result in injuries
severe enough to require medical attention. However, the current study most
likely underestimates both total injuries and ball to head injuries because many
less severe injuries would not be included. This is an inherent problem with
using the NEISS database to estimate injuries. Therefore, the current study
represents the more severe cases, and is potentially an underestimate of the
total injury occurrences.
After establishing that youth soccer players have reported to the Emergency
Department complaining of injuries that occurred from impact with the ball only,
an analysis of the biomechanics of heading in youth soccer needed to be
performed. In order to assess the biomechanics of heading in youth soccer, a
139
laboratory study was performed to determine head and back angles and neck
muscle activation during a variety of heading tasks. Heading tasks were also
included that had players modify there traditional technique in order to compare
linear head acceleration and to see if any of the tasks were effective in reducing
head acceleration. It was found that heading techniques are quite variable
between players. An overall inconsistency was found for head flexion, torso
flexion, and head rotation. It was also noted that this variability existed in EMG
data as well. This is very similar to what has been previously observed in adult
players. This is most likely due to the many possible heading scenarios.
Therefore, players do not use muscle memory to perform the task in the same
manor every time, but instead learn to adjust to whatever scenario occurs. This
provides unlimited possibilities for heading scenarios and would most likely result
in additional variation in results. The current study only looked at redirection
directly at the source of the ball launch. If additional scenarios were introduced
to provide redirection in other ways, additional distinctions would be made.
Comparisons between genders or between heading tasks were
challenging to make. Based on the results of the current study, it indicates that
differences are not related these variables, but that the differences occur
between each player. This also made making a comparison with the adult
players unproductive as any difference would most likely have nothing to do with
age, but with the players being different and the scenarios being slightly different.
Although attempts have been made to determine head acceleration during
soccer heading events, a system for on field data collection had not been
140
previously available. This system was created for use in soccer research, but
had to be validated prior to use. A validation was conducted using various
scenarios that typically occur in soccer play. The results show that the new
soccer HITS system correlates well with the standard measurement system of
the HIII 3-2-2-2 accelerometer system. Although the system requires the use of
a headband, it provides a much needed method to measure head acceleration in
soccer players during normal play. It allows for accurate measurements to be
taken which could potentially lead to an injury threshold specific to certain soccer
impacts. Additionally, this system will allow a comparison between different
types of impacts that occur during soccer play.
Once validation occurred of the HITS head acceleration system, this
system was implemented in soccer scrimmages to determine actual on-field
head acceleration. The current study found both linear and angular head
accelerations that exceeded the acceleration measurements determined
previously in laboratory studies. This could be due to the fact that laboratory
studies using players have to use a ball impact speed that is on the low end of
what would be seen in the field. Additionally the laboratory studies looked only at
a redirection directly back towards where the ball came from. This reduces the
amount of angular acceleration that participants will experience and only
represents a small portion of the type of headers players experienced during
actual play.
Data were compared to mTBI head injury tolerance values proposed
previously, and none of the impacts exceeded the injury tolerance levels for
141
linear head acceleration or HIC. Based on these observations, it seems as
though the linear acceleration contribution of heading is not causing head injury
based on a single impacts. There were, however, impacts that exceeded
suggested values for angular head acceleration. Although single impacts
exceeded the suggested mTBI tolerance levels, there was no stoppage of
scrimmage play due to injury. This could be due to the fact that they fell within
the percentage of the population that would not be injured at the suggested
tolerances, or it is possible that these tolerance levels are not representative of
the types of impacts that occur during soccer heading. It is also possible that
angular acceleration alone is not the best predictor of injury in soccer heading
impacts. Additionally, all heading impacts with angular accelerations exceeding
suggested limits took place to either the right or left side of the head. This
indicates an unusual heading method or inaccurate technique. When impacts
took place to the front or top of the head, no limits were exceeded. This further
emphasizes the importance of teaching proper technique.
7.2 Future Recommendations
One of the major challenges in the current study was comparing head
acceleration measurements to injury tolerance levels that were established for
single impacts of a different nature than those that occur during soccer play. It
would be of interest in future research to determine a threshold for multiple
impacts. This would be of specific interest in the sporting community where
many players are at risk for multiple low level impacts.
142
One of the first steps to determining this threshold occurred in the current
study, where a greater knowledge of what occurs during soccer heading events
was gained. It is, however, necessary to further investigate the head
acceleration values that are obtained in other populations. The current study
lacks any on-field data collection during soccer scrimmages involving male
players. Additionally, different age groups should be investigated to determine if
there is an difference as players age. Although it would be of interest to
determine head acceleration in younger players, it is unlikely that there would be
enough heading events to warrant this investigation.
Soccer scrimmages and not actual games were investigated due to the
challenge of getting players to wear equipment that is not required during
competition. Higher head accelerations may be seen during a more competitive,
and likely more aggressive, game scenario. Future studies would be required to
determine if these differences actually occur.
In addition to these on-field measurements, further research should be
performed in the area of soccer biomechanics within the laboratory setting. The
current study, along with previous studies, only looked at redirection directly at
the source of the ball launch. If additional scenarios were introduced to provide
redirection in other ways, additional variation would most likely be noted. It
would be of interest to determine if head acceleration changes with the addition
of alternate redirection scenarios.
143
APPENDIX A – HIC APPROVALS
144
145
146
147
148
REFERENCES AYSO, 2006, www.soccer.org.
ADAMS, A. L., AND SCHIFF, M. A., 2006. CHILDHOOD SOCCER INJURIES
TREATED IN U.S. EMERGENCY DEPARTMENTS. ACAD EMERG MED
13(5), PP. 571-574.
AGEL, J., ET AL., 2007. DESCRIPTIVE EPIDEMIOLOGY OF COLLEGIATE
MEN'S SOCCER INJURIES: NATIONAL COLLEGIATE ATHLETIC
ASSOCIATION INJURY SURVEILLANCE SYSTEM, 1988-1989
THROUGH 2002-2003. J ATHL TRAIN 42(2), PP. 270-277.
ANDERSEN, T. E., ET AL., 2004. MECHANISMS OF HEAD INJURIES IN ELITE
FOOTBALL. BR J SPORTS MED 38(6), PP. 690-696.
ARNASON, A., ET AL., 2004. A PROSPECTIVE VIDEO-BASED ANALYSIS OF
INJURY SITUATIONS IN ELITE MALE FOOTBALL: FOOTBALL
INCIDENT ANALYSIS. AM J SPORTS MED 32(6), PP. 1459-1465.
NATIONAL FEDERATION OF STATE HIGH SCHOOL ASSOCIATIONS,
http://www.nfhs.org/custom/participation_figures/default.aspx.
AUTTI, T., ET AL., 1997. BRAIN LESIONS IN PLAYERS OF CONTACT
SPORTS. LANCET 349(9059), P. 1144.
BACKOUS, D. D., ET AL., 1988. SOCCER INJURIES AND THEIR RELATION
TO PHYSICAL MATURITY. AM J DIS CHILD 142(8), PP. 839-842.
BARNES, B. C., ET AL., 1998. CONCUSSION HISTORY IN ELITE MALE AND
FEMALE SOCCER PLAYERS. AM J SPORTS MED 26(3), PP. 433-438.
149
BAUER, J. A., ET AL., 2001. IMPACT FORCES AND NECK MUSCLE ACTIVITY
IN HEADING BY COLLEGIATE FEMALE SOCCER PLAYERS. J
SPORTS SCI 19(3), PP. 171-179.
BECKWITH, J. G., ET AL., 2007. VALIDATION OF A NONINVASIVE SYSTEM
FOR MEASURING HEAD ACCELERATION FOR USE DURING BOXING
COMPETITION. J APPL BIOMECH 23(3), PP. 238-244.
BODEN, B. P., ET AL., 1998. CONCUSSION INCIDENCE IN ELITE COLLEGE
SOCCER PLAYERS. AM J SPORTS MED 26(2), PP. 238-241.
CHU, J. J., ET AL., 2006, A NOVEL ALGORITHM TO MEASURE LINEAR AND
ROTATIONAL HEAD ACCELERATION USING SINGLE-AXIS
ACCELEROMETERS, 5TH WORLD CONGRESS OF
BIOMECHANICSMUNICH, GERMANY.
US CONSUMER PRODUCT SAFETY COMMISSION, 2000, NEISS THE
NATIONAL ELECTRONIC INJURY SURVEILLANCE SYSTEM: A TOOL
FOR RESEARCHERS, http://www.cpsc.gov/neiss/2000d015.pdf.
US CONSUMER PRODUCT SAFETY COMMISSION, 2001, THE NEISS
SAMPLE (DESIGN AND IMPLEMENTATION) 1997 TO PRESENT,
http://www.cpsc.gov/neiss/2001d011-6b6.pdf.
CONN, J. M., ET AL., 2006. NON-FATAL SPORTS AND RECREATIONAL
VIOLENT INJURIES AMONG CHILDREN AND TEENAGERS, UNITED
STATES, 2001-2003. J SCI MED SPORT 9(6), PP. 479-489.
CORY, C. Z., ET AL., 2001. THE POTENTIAL AND LIMITATIONS OF
UTILISING HEAD IMPACT INJURY MODELS TO ASSESS THE
150
LIKELIHOOD OF SIGNIFICANT HEAD INJURY IN INFANTS AFTER A
FALL. FORENSIC SCI INT 123(2-3), PP. 89-106.
COVASSIN, T., ET AL., 2003. EPIDEMIOLOGICAL CONSIDERATIONS OF
CONCUSSIONS AMONG INTERCOLLEGIATE ATHLETES. APPL
NEUROPSYCHOL 10(1), PP. 12-22.
COVASSIN, T., ET AL., 2003. SEX DIFFERENCES AND THE INCIDENCE OF
CONCUSSIONS AMONG COLLEGIATE ATHLETES. J ATHL TRAIN
38(3), PP. 238-244.
CRISCO, J. J., ET AL., 2004. AN ALGORITHM FOR ESTIMATING
ACCELERATION MAGNITUDE AND IMPACT LOCATION USING
MULTIPLE NONORTHOGONAL SINGLE-AXIS ACCELEROMETERS. J
BIOMECH ENG 126(6), PP. 849-854.
DELANEY, J. S., 2004. HEAD INJURIES PRESENTING TO EMERGENCY
DEPARTMENTS IN THE UNITED STATES FROM 1990 TO 1999 FOR
ICE HOCKEY, SOCCER, AND FOOTBALL. CLIN J SPORT MED 14(2),
PP. 80-87.
DICK, R., ET AL., 2007. DESCRIPTIVE EPIDEMIOLOGY OF COLLEGIATE
WOMEN'S SOCCER INJURIES: NATIONAL COLLEGIATE ATHLETIC
ASSOCIATION INJURY SURVEILLANCE SYSTEM, 1988-1989
THROUGH 2002-2003. J ATHL TRAIN 42(2), PP. 278-285.
DUMA, S. M., ET AL., 2005. ANALYSIS OF REAL-TIME HEAD
ACCELERATIONS IN COLLEGIATE FOOTBALL PLAYERS. CLIN J
SPORT MED 15(1), PP. 3-8.
151
DVORAK, J., AND JUNGE, A., 2000. FOOTBALL INJURIES AND PHYSICAL
SYMPTOMS. A REVIEW OF THE LITERATURE. AM J SPORTS MED
28(5 SUPPL), PP. S3-9.
DVORAK, J., ET AL., 2007. HEAD INJURIES IN THE FEMALE FOOTBALL
PLAYER: INCIDENCE, MECHANISMS, RISK FACTORS AND
MANAGEMENT. BR J SPORTS MED 41 SUPPL 1, PP. I44-46.
ELIAS, S. R., 2001. 10-YEAR TREND IN USA CUP SOCCER INJURIES: 1988-
1997. MED SCI SPORTS EXERC 33(3), PP. 359-367.
FIELD, M., ET AL., 2003. DOES AGE PLAY A ROLE IN RECOVERY FROM
SPORTS-RELATED CONCUSSION? A COMPARISON OF HIGH
SCHOOL AND COLLEGIATE ATHLETES. J PEDIATR 142(5), PP. 546-
553.
FULLER, C. W., ET AL., 2005. A SIX YEAR PROSPECTIVE STUDY OF THE
INCIDENCE AND CAUSES OF HEAD AND NECK INJURIES IN
INTERNATIONAL FOOTBALL. BR J SPORTS MED 39 SUPPL 1, PP. I3-
9.
FUNK, J. R., ET AL., 2007. BIOMECHANICAL RISK ESTIMATES FOR MILD
TRAUMATIC BRAIN INJURY. ANNU PROC ASSOC ADV AUTOMOT
MED 51, PP. 343-361.
GADD, C. W., 1966, "USE OF WEIGHTED-IMPULSE CRITERION FOR
ESTIMATING INJURY HAZARD," TENTH STAPP CAR CRASH
CONFERENCE, SOCIETY OF AUTOMOTIVE ENGINEERS, INC., NEW
YORK.
152
GRAY, H., ET AL., 1995. GRAY'S ANATOMY. CHURCHILL LIVINGSTONE,
EDINBURGH ; NEW YORK, P.^PP. PAGES
GREEN, G. A., AND JORDAN, S. E., 1998. ARE BRAIN INJURIES A
SIGNIFICANT PROBLEM IN SOCCER? CLIN SPORTS MED 17(4), PP.
795-809, VIII.
GURDJIAN, E. S., ET AL., 1966. TOLERANCE CURVES OF ACCELERATION
AND INTRACRANIAL PRESSURE AND PROTECTIVE INDEX IN
EXPERIMENTAL HEAD INJURY. J TRAUMA 6(5), PP. 600-604.
GUSKIEWICZ, K. M., 2002. NO EVIDENCE OF IMPAIRED NEUROCOGNITIVE
PERFORMANCE IN COLLEGIATE SOCCER PLAYERS. AM J SPORTS
MED 30(4), P. 630.
HOSTETLER, S. G., ET AL., 2005. CHARACTERISTICS OF WATER SKIING-
RELATED AND WAKEBOARDING-RELATED INJURIES TREATED IN
EMERGENCY DEPARTMENTS IN THE UNITED STATES, 2001-2003.
AM J SPORTS MED 33(7), PP. 1065-1070.
HOSTETLER, S. G., ET AL., 2004. CHARACTERISTICS OF ICE HOCKEY-
RELATED INJURIES TREATED IN US EMERGENCY DEPARTMENTS,
2001-2002. PEDIATRICS 114(6), PP. E661-666.
JANDA, D. H., ET AL., 2002. AN EVALUATION OF THE CUMULATIVE
CONCUSSIVE EFFECT OF SOCCER HEADING IN THE YOUTH
POPULATION. INJ CONTROL SAF PROMOT 9(1), PP. 25-31.
153
JORDAN, S. E., ET AL., 1996. ACUTE AND CHRONIC BRAIN INJURY IN
UNITED STATES NATIONAL TEAM SOCCER PLAYERS. AM J SPORTS
MED 24(2), PP. 205-210.
JUNGE, A., AND DVORAK, J., 2007. INJURIES IN FEMALE FOOTBALL
PLAYERS IN TOP-LEVEL INTERNATIONAL TOURNAMENTS. BR J
SPORTS MED 41 SUPPL 1, PP. I3-7.
KELLER, C. S., ET AL., 1987. THE MEDICAL ASPECTS OF SOCCER INJURY
EPIDEMIOLOGY. AM J SPORTS MED 15(3), PP. 230-237.
KIRKENDALL, D. T., AND GARRETT, W. E., JR., 2001. HEADING IN SOCCER:
INTEGRAL SKILL OR GROUNDS FOR COGNITIVE DYSFUNCTION? J
ATHL TRAIN 36(3), PP. 328-333.
LE GALL, F., ET AL., 2008. INJURIES IN YOUNG ELITE FEMALE SOCCER
PLAYERS: AN 8-SEASON PROSPECTIVE STUDY. AM J SPORTS MED
36(2), PP. 276-284.
LEES, A., AND NOLAN, L., 1998. THE BIOMECHANICS OF SOCCER: A
REVIEW. J SPORTS SCI 16(3), PP. 211-234.
LEININGER, R. E., ET AL., 2007. EPIDEMIOLOGY OF 1.6 MILLION
PEDIATRIC SOCCER-RELATED INJURIES PRESENTING TO US
EMERGENCY DEPARTMENTS FROM 1990 TO 2003. AM J SPORTS
MED 35(2), PP. 288-293.
MANOOGIAN, S., ET AL., 2006. HEAD ACCELERATION IS LESS THAN 10
PERCENT OF HELMET ACCELERATION IN FOOTBALL IMPACTS.
BIOMED SCI INSTRUM 42, PP. 383-388.
154
MATSER, J. T., ET AL., 1998. CHRONIC TRAUMATIC BRAIN INJURY IN
PROFESSIONAL SOCCER PLAYERS. NEUROLOGY 51(3), PP. 791-
796.
METZL, J. D., 1999. SPORTS-SPECIFIC CONCERNS IN THE YOUNG
ATHLETE: SOCCER. PEDIATR EMERG CARE 15(2), PP. 130-134.
NAUNHEIM, R. S., ET AL., 2003. LINEAR AND ANGULAR HEAD
ACCELERATIONS DURING HEADING OF A SOCCER BALL. MED SCI
SPORTS EXERC 35(8), PP. 1406-1412.
NAUNHEIM, R. S., ET AL., 2000. COMPARISON OF IMPACT DATA IN
HOCKEY, FOOTBALL, AND SOCCER. J TRAUMA 48(5), PP. 938-941.
NEWMAN, J. A., ET AL., 2000. A PROPOSED NEW BIOMECHANICAL HEAD
INJURY ASSESSMENT FUNCTION - THE MAXIMUM POWER INDEX.
STAPP CAR CRASH J 44, PP. 215-247.
NIELSEN, A. B., AND YDE, J., 1989. EPIDEMIOLOGY AND TRAUMATOLOGY
OF INJURIES IN SOCCER. AM J SPORTS MED 17(6), PP. 803-807.
OMMAYA, A. K., ET AL., 2002. BIOMECHANICS AND NEUROPATHOLOGY
OF ADULT AND PAEDIATRIC HEAD INJURY. BR J NEUROSURG 16(3),
PP. 220-242.
OMMAYA, A. K., AND HIRSCH, A. E., 1971. TOLERANCES FOR CEREBRAL
CONCUSSION FROM HEAD IMPACT AND WHIPLASH IN PRIMATES. J
BIOMECH 4(1), PP. 13-21.
155
PADGAONKAR, A., ET AL., 1975. MEASUREMENT OF ANGULAR
ACCELERATION OF A RIGID BODY USING LINEAR
ACCELEROMETERS. ASME J. APPL. MECH. 42, PP. 552-558.
PATLAK, M., ET AL., 2002, "IS SOCCER BAD FOR CHILDREN'S HEADS?
SUMMARY OF THE IOM WORKSHOP ON NEUROPSYCHOLOGICAL
CONSEQUENCES OF HEAD IMPACT IN YOUTH SOCCER," NATIONAL
ACADEMY PRESS, WASHINGTON, D.C., P. 26 P.
PELLMAN, E. J., ET AL., 2003. CONCUSSION IN PROFESSIONAL
FOOTBALL: RECONSTRUCTION OF GAME IMPACTS AND INJURIES.
NEUROSURGERY 53(4), PP. 799-812; DISCUSSION 812-794.
PETERSON, L., ET AL., 2000. INCIDENCE OF FOOTBALL INJURIES AND
COMPLAINTS IN DIFFERENT AGE GROUPS AND SKILL-LEVEL
GROUPS. AM J SPORTS MED 28(5 SUPPL), PP. S51-57.
PICKETT, W., ET AL., 2005. HEAD INJURIES IN YOUTH SOCCER PLAYERS
PRESENTING TO THE EMERGENCY DEPARTMENT. BR J SPORTS
MED 39(4), PP. 226-231; DISCUSSION 226-231.
POULSEN, T. D., ET AL., 1991. INJURIES IN HIGH-SKILLED AND LOW-
SKILLED SOCCER: A PROSPECTIVE STUDY. BR J SPORTS MED
25(3), PP. 151-153.
PRASAD, P., AND MERTZ, H. J., 1985. THE POSITION OF THE UNITED
STATES DELEGATES TO THE ISO WORKING GROUP 6 ON THE USE
OF HIC IN THE AUTOMOTIVE ENVIRONMENT. SOCIETY OF
AUTOMOTIVE ENGINEERS PAPER 851246.
156
PUTUKIAN, M., 2004. HEADING IN SOCCER: IS IT SAFE? CURR SPORTS
MED REP 3(1), PP. 9-14.
RUCHINSKAS, R. A., ET AL., 1997. MILD HEAD INJURY IN SPORTS. APPL
NEUROPSYCHOL 4(1), PP. 43-49.
RUTHERFORD, A., ET AL., 2003. THE NEUROPSYCHOLOGY OF HEADING
AND HEAD TRAUMA IN ASSOCIATION FOOTBALL (SOCCER): A
REVIEW. NEUROPSYCHOL REV 13(3), PP. 153-179.
SANDELIN, J., ET AL., 1985. ACUTE SOCCER INJURIES IN FINLAND IN 1980.
BR J SPORTS MED 19(1), PP. 30-33.
SCHMIDT-OLSEN, S., ET AL., 1985. SOCCER INJURIES OF YOUTH. BR J
SPORTS MED 19(3), PP. 161-164.
SHEWCHENKO, N., ET AL., 2005. HEADING IN FOOTBALL. PART 1:
DEVELOPMENT OF BIOMECHANICAL METHODS TO INVESTIGATE
HEAD RESPONSE. BR J SPORTS MED 39 SUPPL 1, PP. I10-25.
SHEWCHENKO, N., ET AL., 2005. HEADING IN FOOTBALL. PART 2:
BIOMECHANICS OF BALL HEADING AND HEAD RESPONSE. BR J
SPORTS MED 39 SUPPL 1, PP. I26-32.
SHEWCHENKO, N., ET AL., 2005. HEADING IN FOOTBALL. PART 3: EFFECT
OF BALL PROPERTIES ON HEAD RESPONSE. BR J SPORTS MED 39
SUPPL 1, PP. I33-39.
SORTLAND, O., AND TYSVAER, A. T., 1989. BRAIN DAMAGE IN FORMER
ASSOCIATION FOOTBALL PLAYERS. AN EVALUATION BY
157
CEREBRAL COMPUTED TOMOGRAPHY. NEURORADIOLOGY 31(1),
PP. 44-48.
SOSIN, D. M., ET AL., 1996. PEDIATRIC HEAD INJURIES AND DEATHS
FROM BICYCLING IN THE UNITED STATES. PEDIATRICS 98(5), PP.
868-870.
STEPHENS, R., ET AL., 2005. NEUROPSYCHOLOGICAL IMPAIRMENT AS A
CONSEQUENCE OF FOOTBALL (SOCCER) PLAY AND FOOTBALL
HEADING: A PRELIMINARY ANALYSIS AND REPORT ON SCHOOL
STUDENTS (13-16 YEARS). CHILD NEUROPSYCHOL 11(6), PP. 513-
526.
STOJSIH, S., ET AL., 2008. A PROSPECTIVE STUDY OF PUNCH
BIOMECHANICS AND COGNITIVE FUNCTION FOR AMATEUR
BOXERS. BR J SPORTS MED.
BIOSYN SYSTEMS, 3D FULL BODY PORTABLE KINEMATIC SYSTEM,
http://www.noraxon.com/downloads/documents/partners/Biosyn%20FAB
%20System.pdf.
TYSVAER, A., AND STORLI, O., 1981. ASSOCIATION FOOTBALL INJURIES
TO THE BRAIN. A PRELIMINARY REPORT. BR J SPORTS MED 15(3),
PP. 163-166.
TYSVAER, A. T., AND LOCHEN, E. A., 1991. SOCCER INJURIES TO THE
BRAIN. A NEUROPSYCHOLOGIC STUDY OF FORMER SOCCER
PLAYERS. AM J SPORTS MED 19(1), PP. 56-60.
158
TYSVAER, A. T., AND STORLI, O. V., 1989. SOCCER INJURIES TO THE
BRAIN. A NEUROLOGIC AND ELECTROENCEPHALOGRAPHIC
STUDY OF ACTIVE FOOTBALL PLAYERS. AM J SPORTS MED 17(4),
PP. 573-578.
TYSVAER, A. T., ET AL., 1989. SOCCER INJURIES TO THE BRAIN. A
NEUROLOGIC AND ELECTROENCEPHALOGRAPHIC STUDY OF
FORMER PLAYERS. ACTA NEUROL SCAND 80(2), PP. 151-156.
VERSACE, J., 1971. A REVIEW OF THE SEVERITY INDEX. SOCIETY OF
AUTOMOTIVE ENGINEERS PAPER 710881.
WITHNALL, C., ET AL., 2005. EFFECTIVENESS OF HEADGEAR IN
FOOTBALL. BR J SPORTS MED 39 SUPPL 1, PP. I40-48; DISCUSSION
I48.
WITOL, A. D., AND WEBBE, F. M., 2003. SOCCER HEADING FREQUENCY
PREDICTS NEUROPSYCHOLOGICAL DEFICITS. ARCH CLIN
NEUROPSYCHOL 18(4), PP. 397-417.
XIANG, H., ET AL., 2005. SKIING- AND SNOWBOARDING-RELATED
INJURIES TREATED IN U.S. EMERGENCY DEPARTMENTS, 2002. J
TRAUMA 58(1), PP. 112-118.
YARD, E. E., AND COMSTOCK, R. D., 2006. INJURIES SUSTAINED BY
PEDIATRIC ICE HOCKEY, LACROSSE, AND FIELD HOCKEY
ATHLETES PRESENTING TO UNITED STATES EMERGENCY
DEPARTMENTS, 1990-2003. J ATHL TRAIN 41(4), PP. 441-449.
159
YARD, E. E., AND COMSTOCK, R. D., 2006. INJURIES SUSTAINED BY
RUGBY PLAYERS PRESENTING TO UNITED STATES EMERGENCY
DEPARTMENTS, 1978 THROUGH 2004. J ATHL TRAIN 41(3), PP. 325-
331.
YARD, E. E., ET AL., 2007. PEDIATRIC MARTIAL ARTS INJURIES
PRESENTING TO EMERGENCY DEPARTMENTS, UNITED STATES
1990-2003. J SCI MED SPORT 10(4), PP. 219-226.
ZHANG, L., ET AL., 2004. A PROPOSED INJURY THRESHOLD FOR MILD
TRAUMATIC BRAIN INJURY. J BIOMECH ENG 126(2), PP. 226-236.
160
ABSTRACT
EVALUATION OF REPETITIVE HEADING IN YOUTH SOCCER
BY
ERIN HANLON
December 2009
Advisor: Cynthia Bir, Ph.D.
Major: Biomedical Engineering
Degree: Doctor of Philosophy
The specific aims of this project were: 1) to determine the incidence of
head injury in youth soccer related only to head to ball impacts using the National
Electronic Injury Surveillance System database; 2) determine the frequency of
heading in youth soccer based on age, gender, and skill level; 3) validate a novel
headband system to measure head impact frequency during soccer play; 4)
measure the biomechanical response of youth soccer players during heading
events using the Functional Assessment of Biomechanics motion capture system
and; 5) measure head impact frequency and severity using the Head Impact
Telemetry System (HITS). A total of 62,021.55 soccer head injuries were
estimated to occur from 2002 to 2007 in the United States with 15.9 % of these
injuries being caused by impact with the ball only. When observing the
frequency of heading occurrences, males had significantly more headers/minute
and total headers than females, but females incurred 59.64% of the injuries that
were caused by impact with the ball only. A novel head acceleration
measurement system, HITS, was found to provide a much needed method to
161
measure head acceleration in soccer players during normal play. It allows for
accurate measurements to be taken which could potentially lead to an injury
threshold specific to certain soccer impacts. The system was used in during
soccer scrimmages and measured head acceleration during impact events that
took place during these scrimmages. None of the impacts, heading or otherwise,
exceeded the tolerance levels for mild traumatic brain injury (mTBI) that were
previously established for linear head acceleration. Angular accelerations for
three heading events and one non heading even, however, did exceed the
suggested limits. Based on these observations, it seems as though the linear
acceleration contribution of heading is not causing head injury based on a single
impacts. Angular acceleration has potential problem and should be investigated
further in conjunction with determining tolerance values for multiple impacts.
162
BIOGRAPHICAL STATEMENT
ERIN HANLON
PLACE OF BIRTH: Georgetown, OH, USA EDUCATION: 2009 Ph.D Biomedical Engineering Wayne State University 2006 MS Biomedical Engineering Wayne State University 2004 BS Biomedical Engineering Wright State University ACADEMIC EXPERIENCE: 2005 to date Graduate Research Assistant Wayne State University 2007 to 2009 Mentor, Freshman Engineering Wayne State University 2005 Graduate Student Assistant Wayne State University SELECTED PUBLICATIONS: Hanlon, E., Bir, C. (2009). Validation of a Wireless Head Acceleration Measurement System for Use in Soccer Play. Journal of Applied Biomechanics. Accepted with Revisions. Hanlon, E., Bir, C. (2009). Real Time Measurement of Head Acceleration During Youth Soccer Play. Poster Presentation at the Summer Bioengineering Conference, June 2009, Lake Tahoe, California. Hanlon, E., Bir, C. (2008). A Model to Determine the Effect of Multiple Subconcussive Impacts in the Rat. Podium Presentation at the American Society of Biomechanics Annual Conference, August 2008, Ann Arbor, Michigan. Hanlon, E., Bir, C. (2007). The Determination of Heading Frequency in Youth Soccer. Podium Presentation at the American Society of Biomechanics Annual Conference, August 2007, Palo Alto, California.