the link between agility and injuries in...
TRANSCRIPT
THE LINK BETWEEN AGILITY AND
INJURIES IN HOCKEY PLAYERS
A research towards possible risk factors in agility characteristics
concerning non-contact injury events in amateur hockey players
Yosheng Liu, Hatem Sassi, Stef Thierie
Supervisor: Prof. Dr. Damien Van Tiggelen.
A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of
Master of Science in Rehabilitation Sciences and Physiotherapy
Academic year: 2016 – 2017
THE LINK BETWEEN AGILITY AND
INJURIES IN HOCKEY PLAYERS
A research towards possible risk factors in agility characteristics
concerning non-contact injury events in amateur hockey players
Yosheng Liu, Hatem Sassi, Stef Thierie
Supervisor: Prof. Dr. Damien Van Tiggelen
A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of
Master of Science in Rehabilitation Sciences and Physiotherapy
Academic year: 2016 – 2017
Dankwoord
Veel mensen hebben bijgedragen aan de totstandkoming van deze masterproef. Langs deze weg willen
wij graag volgende personen in het bijzonder bedanken.
Eerst en vooral een woord van dank aan onze promotor, Prof. Dr. Damien Van Tiggelen, omdat hij het
project zowel logistiek als inhoudelijk in goede banen heeft geleid.
Ten tweede bedanken we ook graag de coaches Joffrey Jablonski, Pascal Kina, Khan Naeem, Maurice
Dubois en de fysieke coaches Pascal Bleys en Damien Van Tiggelen van hockey clubs A.R.A. La Gantoise
H.C. en T.H.C. Indiana. Zonder hun engagement en enthousiasme zou deze masterproef niet mogelijk
geweest zijn.
Bedankt aan alle spelers die meegewerkt hebben en aan de Universiteit Gent die het gehele project
omkaderd heeft.
Hartelijk dank
5
TABLE OF CONTENTS
List of figures and tables ----------------------------------------------------------------------------------------------p.6
List of abbreviations ----------------------------------------------------------------------------------------------p.7
Abstract in English ----------------------------------------------------------------------------------------------p.8
Abstract in Dutch ----------------------------------------------------------------------------------------------p.9
Introduction ---------------------------------------------------------------------------------------------p.10
Methods ---------------------------------------------------------------------------------------------p.15
Results ---------------------------------------------------------------------------------------------p.19
Discussion ---------------------------------------------------------------------------------------------p.22
Conclusion ---------------------------------------------------------------------------------------------p.28
References ---------------------------------------------------------------------------------------------p.29
Abstract for laymen ---------------------------------------------------------------------------------------------p.34
Ethical Committee approval ---------------------------------------------------------------------------------------------p.35
Attachments ---------------------------------------------------------------------------------------------p.36
Attachment 1: L AND T-drill
Attachment 2: Questionnaire
Attachment 3: Qualitative scoring form T- and L-test
Attachment 4: Chi²- results
6
LIST OF FIGURES AND TABLES
FIGURES:
Figure 1: Risk factors of an athlete
Figure 2: Agility in invasion sports
Figure 3: Universal agility components
Figure 4: ROC curve for the variable ‘average time T-run’
TABLES:
Table 1: Descriptive statistics participants
Table 2: Coefficients and standard errors logistic regression
Table 3: Classification table logistic regression
Table 4: Area under the ROC curve for the variable ‘average time T-run’
Table 5: Optimal criteria for the average time T-run
7
LIST OF ABBREVIATIONS
ACL Anterior cruciate ligament
FADI Foot & Ankle Disability Index
SAQ Speed, agility and quickness
COD Change of direction
CES Cognitive Element of Surprise
COG Center of gravity
TPR True positive rate
FPR False positive rate
AUC Area under the curve
ROC Receiver operating characteristic
CODS Change of direction speed
GCT Ground contact time
MTt Modified T-test
TTt Traditional T-test
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ABSTRACT ENGLISH
Background: Numerous risk factors for injuries in sports have been reported. In order to prevent athletes from non-
contact injuries, many researchers attempted to establish an adequate definition for agility, injury mechanisms and
the physical needs of the individual athlete. However, results still remain unclear.
Purpose: To determine if there is a link between agility and injuries in hockey, to create a screening tool for athletes.
Hypothesis: Agility and its characteristics play an important role concerning non-contact injuries in field hockey
athletes.
Study design: prospective cohort study; level of evidence, 2.
Methods: 50 amateur field hockey players from four teams performed a T-test and L-run. The T-test was adjusted
with the use of a reactive component in order to implement a cognitive factor to the test. Based on 13 qualitative
and quantitative parameters a score was assigned to each athlete. Athletes were followed for a period of 7 months.
The correlation between these parameters and their link with injury was measured.
Results: 25 athletes sustained a relevant non-contact injury. Two parameters tended to be eligible to apply in a
logistic regression model. The mean time on the T-test appeared to be a significant predictor (Coef. = 0.70552; P =
0.1258) concerning injuries during the follow-up period. The predictive value of this model can be slightly
augmented if a general impression on the L-run is taken into consideration (Coef. = 0.98034; P = 0.1451). The AUC of
the ROC-curve marginally rises from 0,706 (mean time T-run) to 0,726 (mean time T-run + general impression L-run).
Addition of a reactive component in the T-test did not alter the predicting value for injuries during the period of
follow-up (P= 0,0433).
Conclusion: The use of mean time on a T-test to measure an athlete’s agility skills may be a useful screening tool to
indicate potential future injuries. Notwithstanding the time on this test could be indicative for susceptible athletes, it
is not predictive for inciting injury events. A merger with a general impression on the L-run could slightly augment
the indicative value regarding injury. Nonetheless, further research regarding agility and its link with injury is
necessary.
Keywords: agility; injury; (field) hockey; screening; prevention
9
ABSTRACT NEDERLANDS
Achtergrond: Verschillende risicofactoren inzake sportletsels werden reeds gerapporteerd. Meerdere onderzoeken
hebben getracht om blessure incidentie bij atleten te reduceren door het omschrijven van risicofactoren, definiëren
van verschillende letselmechanismen en de fysieke, sportspecifieke vereisten van de individuele atleet. Over agility
als risicofactor voor letsels is tot op heden weinig beschreven in de huidige literatuur.
Doel: Aan de hand van agility-testen bepalen of er een link is tussen agility en non-contact letsels bij
veldhockeyspelers om op deze manier een screeningstool te creëren dat kan gebruikt worden bij de voorbereiding
van het seizoen.
Hypothese: Agility, met zijn verschillende componenten, heeft een invloed op non-contact blessures bij
veldhockeyspelers.
Design van de studie: prospectieve cohortstudie; niveau van evidentie, 2.
Methode: 50 veldhockey spelers uit vier verschillende teams op amateurniveau ondergingen een T en L-Test. De T-
test werd gemodificeerd door toevoeging van een reactieve prikkel, om zo een cognitieve component van agility te
implementeren in de traditionele T-test. Aan de hand van een scorelijst met 13 kwalitatieve en kwantitatieve criteria
kreeg elke atleet een individuele score toegewezen. De atleten werden opgevolgd over een periode van 7 maand. Er
werd onderzocht welke criteria gelinkt kunnen worden aan blessures.
Resultaten: 25 atleten kregen gedurende de follow-up periode een relevante non-contact blessure. Twee
paramaters bleken voldoende significant om in een logistisch regressiemodel te integreren. De gemiddelde tijd op
de T-test bleek een significante indicator (Coef. = 0.70552; P = 0.1258) te zijn voor het ontwikkelen van blessures
tijdens de follow-up periode. Een algemene impressie op de L-test bleek ook een significant indicatieve parameter
maar voegt slechts een weinig toe aan de voorspellende kracht (Coef. = 0.98034; P = 0.1451). De verklarende
integraal onder de ROC-curve stijgt van 0,706 voor de tijd op de T-test naar 0,726 na toevoeging van de score op
algemene impressie bij de L-run. De invoering van een cognitieve component bij de T-test bleek in dit onderzoek
geen toegevoegde waarde te hebben betreffende non-contact letsels (P= 0,0433).
Conclusie: Het gebruik van de gemeten tijd op de T-test zou een handig screeningsmiddel kunnen zijn om te
onderzoeken of een hockeyspeler een hoger risico loopt om een non-contact blessure op te lopen. Indien de tijd op
de T-test gecombineerd wordt met een algemene impressie van de L-test (goed of slecht), kan het voorspellend
vermogen van de testbatterij verhoogd worden. Verder onderzoek naar het verband tussen agility en blessures is
nodig.
Trefwoorden: agility; blessure; (veld)hockey; screening; preventie
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INTRODUCTION
Injuries have been a common problem in sports. Researchers tried to establish and describe risk factors to
prevent athletes for different kind of injuries. In 2007 Meeuiwisse et al.1 drafted a multifactorial model to
elucidate the interaction of multiple risk factors resulting in an injury-inciting event. Intrinsic risk factors,
such as neuromuscular control, previous injury, strength, age etc., alter risk and etiology of injury within a
context of sports. Added exposure to extrinsic risk factors (e.g. equipment, environment, etc.) converts
the predisposed athlete to a susceptible athlete and can, potentially, cause injurious situations with
inciting events as result (Figure 1)1. A dynamic interaction of these factors culminates in a model to
describe risk factors for injury in repeated participation of contact sports. Nowadays, a lot of
biomechanical risk factors have been theorized, with some unambiguous and some contradicting results2
3 4 5 6 7 8 9 10 11. A more unknown, possible risk factor for injuries is the absence of appropriate agility. A
lack of studies regarding the correlation between agility and injuries led to conflicting results.
Figure 1: Risk factors of an athlete
Determining risk factors in sports is highly relevant concerning prevention of injuries. The avoidance of
injuries to occur includes two different kinds of prevention. Primary prevention attempts to prevent the
onset of specific injuries via risk reduction, e.g. every athlete of a hockey team wears ankles braces to
avoid ankle distorsions12. Secondary prevention uses screening tools that detect and prevent injury
mechanisms to occur a second time. For example The Foot & Ankle Disability Index (FADI) score13
can be
utilized to trace remarkable changes that need intervention. As prevention has been a lifelong challenge is
sports, this study tried to investigate the role of agility in this story of primary and secondary prevention.
11
In a vast majority of sports speed, agility and quickness (SAQ) are determining factors and separate low
and high-levelled players. The in-game skill to change direction, accelerate or decelerate, tended to be
more crucial than straight-line sprint speed in most sports14. In recent literature, agility is defined as “a
rapid whole-body movement with change of velocity or direction in response to a stimulus” 15 and is
susceptible to technical, physical and cognitive components in sports (Figure 2)16. Technical aspects are
specific for every sport and accordingly trainable by executing sport specific exercises. Physical aspects
are general bodily characteristics such as muscle qualities, strength and power, core-stability and straight
sprint speed. These two aspects are the two major parts on which athletes attempt to improve to
enhance their performance. A third, frequently neglected, component is the cognitive component which
is fundamental regarding decision-making time and accuracy 17. Response to an unpredicted external
stimulus is determined by earlier learning experiences and knowledge of the situation. When an
opponent hockey player, who is well-known for his strong backhand, is thriving with the ball towards a
defender, the defender is probably going to prevent the attacker from shooting with his backhand. While
scanning the behavior and movements, the defender recognizes the pattern and anticipates to the
possible following situation. Hence, the way and time to react adequately when a sudden stimulus
appears is depending on these technical, physical and cognitive factors.17
Figure 2: Agility in invasion sports
12
In 2006, Sheppard and Young15 attempted to describe factors involved in agility performance and
designed a diagram for universal agility components (Figure 3). The diagram was modified from Young et
al.18 and served to classify agility in running sports. The classification was not only made in order to
describe the components of agility, but also for movements with no unpredicted, external stimuli15. In a
vast part of the so-called “agility tests” or “agility exercises” a pre-planned track with change of direction
(COD) is outlined with cones (e.g. T-test, Illinois test, pro-agility test, etc.)19. It is crucial to acknowledge
the weaknesses in present agility tests such as the T-test, Illinois agility test, arrowhead agility test and the
pro-agility test19. These tests are referred to as agility tests. Nevertheless, these tests are dissimilar with
the multi-faceted agility, including a reactive component, as established in the recent literature. Sheppard
et al.20 described COD-speed (CODS) as a separated part of the overarching term ‘agility’. Combined with
perceptual and decision-making factors, these factors are defined as the two major components of agility
(Figure 2).
Figure 3: Universal agility components
13
This study attempted to make an adapted T-test with the implementation of these perceptual and
decision-making factors 20. The test contained an additional external stimulus, which indicated the starting
direction to pursue. This unpredicted stimulus differs this test from other T-tests, since it is a test aiming
for agility and not for COD. In 1976, Chelladurai et al.21 proposed a classification of agility with four levels
based on the type of stimuli, this model was modified by Sheppard et al.15 A stimulus can be unpredicted
due to temporal uncertainty, spatial uncertainty, both or no uncertainty. Regarding spatial uncertainty,
the timing of the movement is pre-planned, e.g. a defending hockey player knows when a free kick is
coming but not where. Concerning temporal uncertainty, the movement is pre-planned, however the
timing of the stimulus is unpredictable. This study used a spatial uncertainty as the athlete cannot
anticipate on the COD, though he knows when he is going to receive the determining external stimulus.
Remarkably, there is no significant difference concerning COD-tests between high-level athletes and low-
level athletes 16. Despite this similarity on CODS, high-level athletes are tended to perform better on
agility tests 22 20 23 24 23 suggesting agility to be one of the major differences between high and low-skilled
athletes. Nonetheless these results, COD can still be useful to examine athletes during performance tests
and detect shortcomings in the specific kinetic chain of an athlete.
Multiple studies described altered kinematics in sports contributing to injury mechanisms25. In 2012
Cheng-Feng Lin et al.26 described the biomechanical link between kinetics and kinematics in non-contact
ACL-injuries. Previous studies confirmed that small knee flexion angles, greater peak impact posterior
ground forces (PGRF) and knee valgus moments augment strain on the ACL26. Boden et al.25 suggested
that the augmentation of this strain increases the likelihood of ACL-injuries and predisposes athletes for
lower limb injuries. Hence, beside the quantitative scores in this survey, a qualitative scoring system was
developed to determine possible risk factors of the investigated athletes.
Although many possible risk factors are described in the literature, the link between agility and injury is a
more unprecedented topic. Limited insights how agility may manifest within the injury mechanisms
restrict investigators to counter its debilitative effects. In game situations athletes are subjected to a lot of
unpredicted or unanticipated stimuli. Athletes must have the ability to react quickly and adequately to
prevent a potential injurious situation. While reacting, mal-alignment can unwillingly follow this event and
cause injuries. If a hockey player lands on an opponent’s foot, he has to react rapidly to avoid a possible
ankle distortion. Although abilities to react properly are improved with the use of unpredicted stimuli in
14
training (e.g. different game simulating exercises), too many trainers focus on straight-line sprinting and
therefore improve just a limited part of SAQ. Many researches tried to determine which traits or abilities
are crucial in these situations. Nevertheless, different contradicting epidemiological studies were made in
the past with no homogeneous conclusions.
The trainability of agility performance remains unclear in both low-skilled and high-skilled athletes. As too
many trainers focus on pure speed, they measure speed over a certain distance to estimate how fast an
athlete can move. However, this measurement of sprint time is not valid parameter to describe whether
an athlete is agile or whether he is not. In a vast part of the sports distances of sprints are short and not
linear, except for sprint tracks. Accelerations and COD are repetitive actions affected by a cognitive,
reactive component and used in most sports, as maximum straight-line sprint speed is rarely reached.
This study was performed on field hockey players. As in former times the game was played on natural turf
(grass), nowadays the pitch consists of water-based artificial turf. This conversion of surface took along
many technical, tactical and physiological adaptations on all levels of the game27. Altered mechanisms and
prevalence in injuries are suggested in diverse studies. Nevertheless, no firm ambiguous conclusions could
be found28. Field hockey requires the combination of a good level of aerobic and anaerobic endurance and
a large power output with even reported VO₂ values of 2.26 L/min 27. Various somatotypes are noticeable
on different levels of hockey players, varying from mesomorph to ectomorph. Beside appropriate eye-
hand coordination, it is important to consider the size of the round, spherical ball and the length of the
composite stick.
The purpose of this research was to examine the link between agility and non-contact injury mechanisms.
Findings towards results could be helpful concerning individual prevention programs for hockey players or
other athletes. Furthermore, this study attempted to make a quantitative and qualitative scoring form to
predict potential non-contact injuries based on whether an athlete is agile or whether he is not. The
hypothesis that agility is linked with non-contact injury events was plotted in this study.
15
METHODS
Study design
In this prospective cohort study, trials were conducted in August and September 2016. Participants were
recruited from two field hockey clubs in Ghent, Belgium. Each subject completed a pre-season testing
procedure consisting of a T-test, an L-drill and a short questionnaire in which sports-related information
and medical history was collected (Attachment 1). Non-contact injuries were followed up and registered
until the end of March 2017.
The testing procedure and injury follow up was conducted by H.S., S.T. and Y.L., three second grade
master students Physiotherapy and Rehabilitation Sciences at Ghent University. The researchers were
supervised by Prof Dr. Damien Van Tiggelen (DVT).
Study population
Two different field hockey clubs situated in Ghent participated in this study. Two teams of A.R.A. La
Gantoise H.C. and two teams of Indiana T.H.C. were investigated in this research. Players were amateurs
playing competitively. The number of training sessions per week varied between two and three times,
depending on the level of competition. Meetings with the coaches in which information concerning the
concept and purpose of this research were given, were organized by the researchers. Ethical Committee
approval was obtained for this study.
In total, the population contained 50 male hockey players (height x̅: 182 cm; mass x̅: 76kg, Table 1)
including 14 strikers, 14 midfielders, 16 defenders and 6 players with no fixed position. Participants
suffering from lower or upper extremity injuries during the testing were excluded. Goalkeepers were also
excluded from this study.
There were 22 drop outs throughout the season, three participants because of inconsistent playing or
quitting their career, one because of the withdrawal of his visa and others (18) due to absence or injury.
16
Range Mean Std. Deviation
Age 17-32 23,5 4,22
Length (cm) 165-194 182,1 6,57
Weight (kg) 59-102 76,4 8,52
Table 1: Descriptive statistics participants (n=50)
Testing procedure
Before being submitted to the testing procedure, each subject completed a short questionnaire
(Attachment 1) in which administrative data, (previous) hockey clubs, field position and medical history
was enquired. After questioning, athletes performed a T-test and L-drill (Attachment 2, Figure 1 and 2)
while both procedures were captured by two GoPro Hero4 cameras at 60 frames per second. The GoPro
cameras were placed on a tripod with the height of 1,20 meter.
For the L-drill (Attachment 2, Figure 1), time of execution was not recorded. This drill was executed only
once after one rehearsal.
Regarding the T-test (Attachment 2, Figure 2), time of execution was recorded using an iPhone 6 at the
starting point. Furthermore, a modification, compared to the conventional method of testing, was
implemented in two out of four runs. An arrow, placed behind a wooden board at the center of the ‘T’,
indicated the direction to pursue. The board was applied in order to ensure that the subject saw the
indicated direction only after arriving at the center. In this way, a Cognitive Element of Surprise (CES) was
added and was applied in two out of four trials. Incorporating this reactive component made it possible to
conduct a decision time.
Both tests were assessed on a water-based artificial turf field and the athletes wore hockey shoes. Hockey
sticks were not incorporated in the testing procedures.
17
The resulting administrative information and data derived from the tests was implemented in one Excel-
file. The file contained all the data of the four teams including: name, length, weight, age, team, previous
injuries, injuries current season, data pre-season T-test and data pre-season L-test.
Data analysis
After assembling all gathered data, qualitative and quantitative analysis were applied, resulting in
individual scores. Each item on the scoring form (Attachment 3) was scored with a number from 0 to 1 or
2, which led to a total score on 13. This study suggested that athletes with a higher total score, were more
likely to become injured.
The quantitative measurements of the T-test included time of execution (with and without CES) and
decision time (mean of execution with CES subtracted with the mean of execution without CES). These
quantitative measurements were compared with normative data (Attachment 3, criteria 1 and 2), defined
by the investigators. The qualitative score of the T-test consisted of eight criteria on motion analysis
including: push-off with the outer (0) or inner (1) leg and its knee-foot alignment, the distance (extension
position: 1) from the center of gravity (COG) to the ground, the presence (1) of a cross-step while
progressing sideways and the presence of trunk rotation (1) on the lower limbs or not.
Concerning the L-drill, three criteria on motion analysis were implemented in the scoring form. The width
of the turn at 90 and 180 degrees was scored by 0 or 1 depending on the subjective perception of the
researchers. General impression on the L-drill was scored by 0 or 1 depending on running style, turning
style, arm and trunk movement, lower limb alignment, limberness and effort. All criteria were established
by the researchers in dialogue with DVT (Attachment 3). No quantitative analysis was used on the L-drill.
Kinematic criteria were separately evaluated by three individual researchers. After scoring, the three
observers came together to deliberate the resulting scores. Considering the fact that there were three
testers, there was always a majority. Disagreements according the score were resolved by discussion to
come to a consensus. The intertester reliability between the researchers for the scoring was 97%, which
induced minimal risk of interrater bias.
18
The purpose of this scoring form was to compose a screening tool for agility to detect athletes who are
susceptible for injuries. This tool contained both qualitative and quantitative parameters, resulting in a
total score on 13. Objectives to make a cut-off value towards prediction of injuries were depending on the
received outcomes.
Injury definition
Injury was considered as relevant when a physical complaint, obtained during a hockey training or match
in a non-contact situation, hindered the athlete to participate in hockey for a certain amount of time or
required any medical attention.
Statistical analysis
The correlation between agility characteristics and injuries was investigated in four hockey teams from
different levels. Characteristics of participants who sustained an injury during the follow-up were
analyzed to determine relevant risk factors for non-contact injuries. The statistical analysis was executed
with the use of Medcalc – version 13.1.2. Thirteen quantitative and qualitative parameters were
subjected to a chi-square test and were conducted into a logistic regression model. The binary logistic
regression model was used to determine the probability of injury incidence based on the predicting
variables concerning agility. This study attempted to establish risk factors that determine the likelihood of
an athlete to become injured.
Sensitivity and specificity were obtained using a receiver operating characteristic curve (ROC-curve). This
curve exposed the true positive rate (TPR) and the false positive rate (FPR) as operating characteristics
(TPR and FPR) regarding injuries. Useful cut-off values were attempted to be established based on this
ROC-curve.
19
RESULTS
During the period of follow-up, 25 amateur field hockey players in the sample size of 50 were diagnosed
with relevant non-contact injuries, with a consequential injury incidence of 50 percent. Thirteen predictor
variables were tested by means of the chi-square test (Attachment 4). The ‘general impression L-run’ was
found significant (p = 0,0433) to apply in a logistic regression model. After the execution of an
independent samples t-test on ‘mean time T-run’, this variable was found eligible as well for incorporation
in the model (p = 0.053).
Results of the logistic regression model analysis are shown in Table 2 and 3. The effect of the predictor
‘mean time T-run’ on injury incidence has been found significant (Coef. = 0.70552; P = 0.1258). A change
in predictor ‘general impression L-run’ makes the event of a non-contact injury significantly more likely
(Coef. = 0.98034; P = 0.1451). With a standard cut-off value of P = 0.5, 70 percent of the subjects has been
correctly classified by the model. This is shown in the classification table. The area under the curve (AUC)
after ROC-curve analysis is 0.726.
The ROC-curve analysis of predictor ‘mean time T-run’, with a significant (P = 0.0062) AUC of 0.706, is
displayed in Table 4. The cut-off value of this predictor is 10.45 seconds (Table 4). In case of a longer
‘mean time T-run’, an amateur hockey player has a 64 percent chance to develop a non-contact injury
during the following season (sensitivity = 64.00). When the athlete achieves a ‘mean time T-run’ within
10.45 seconds, there is a 76 percent chance that he will not develop a non-contact injury in the following
season (specificity = 76.00) (Table 5). The ROC curve is represented in Figure 4.
Table 2: Coefficients and standard errors logistic regression
20
Table 3: Classification table logistic regression The event injury: 0 = no injury 1 = non-contact injury
Table 4: Area under the ROC curve for the variable ‘average time T-run’
Table 5: Optimal criteria for the average time T-run
21
Figure 4: ROC curve for the variable ‘average time T-run’
22
DISCUSSION
Statement of general findings
Based on the independent variables ‘mean time T-run’ and ‘general impression L-run’, pre-season
screening of non-contact injuries in amateur hockey players can be made with an accuracy of 72.6
percent, using the T-test and L-drill. However, when only grounding on predictor ‘mean time T-run’, a
similar prediction can be attained with an accuracy of 70.6 percent. The optimal cut-off criterion of the
latter predictor is 10.45 seconds, accompanied by a sensitivity of 64.00 and specificity of 76.00.
The added value of the ‘general impression L-run’ as a predictor can be discussed. One can conclude that,
when comparing both prediction models, predictor ‘general impression L-run’ adds only two percent to
the prediction model. In addition, it must be said that this general impression is a subjective assessment
criterion. Qualitative assessment was executed by three physiotherapy students, experienced in human
body motion analysis. Interpretation of this criterion differs not only from one another, but as well, and
more importantly, from standard coaches and physical trainers. Assessing the quality of a movement calls
for additional education and training to increase standardization. Next to this given fact, the screening
procedure will take longer when including the L-run.
In order to perform CODS, which is defined as “the ability to decelerate, reverse or change movement
direction and accelerate again”29, several components have been suggested as influential. Among them
are running technique (body lean and posture, foot placement, stride adjustment), straight line sprint
speed, and lower limb power qualities (strength, power, rate of force development, and reactive
strength)15 18. When decelerating in a certain direction and accelerating in another, sufficient lower limb
strength, consisting out of eccentric (braking phase), isometric (plant phase) and concentric strength
(propulsive phase), is required while maneuvering in sports 30. Anthropometrics of the athlete in
combination with the type and angle of the change of direction, are of importance regarding
biomechanical elements (kinetic and kinematic)31 32 33 34. Since in this study performance on time of the T-
test is a significant predictor value for injuries, it is important to know in what way faster COD-performing
athletes differ from slower. A faster CODS performance is associated with a shorter ground contact
time(GRT), greater horizontal propulsive forces, greater horizontal braking forces, greater horizontal
breaking forces in the penultimate foot contact and lower vertical impact forces during final foot
23
contact30. Taking these components into account, one can suggest that there is a difference in
biomechanical conditions between faster and slower COD-performing athletes. An increased breaking
force during the braking phase 35 36, implies a more extensive eccentric lengthening of the muscle,
resulting in an enlarged storage and potential use of elastic energy in the muscle37 36. With this increased
storage of elastic energy, greater concentric strength and thus greater propulsive forces could be
attained. This results ultimately in reduced GCTs, increased exit velocities and faster CODS performance38
36 30. Knowing what distinguishes faster COD-performing athletes from slower, could be indicative for
further risk factor screening procedures regarding athletes at risk.
Milanović et al.39 stated that a 12 week SAQ-program enhanced sprinting time for short distances of 5 and
10 m, but not for distances larger than 20 m. This could implicate that SAQ-training improves quickness,
but not the maximal speed needed for a 20 m sprint39. It is likely to assume that running technique plays a
major role in these shorter distances, more than in straight-line sprint speed. Thomas Little et al. 41
mentioned that acceleration, maximum speed and agility are relatively independent characteristics that
need to be trained separately and that training one of these characteristics does not influence the others.
Young et al.40 stated that there was little to no transfer possible between different elements of the agility
diagram (figure 2). This suggests that shortcomings in these characteristics should be managed
independently, e.g. a weak muscle group should be treated with specific strengthening exercises and bad
technique requires technical adjustments to optimize movement patterns. Wensing et al.30 postulated the
importance of these independent variables associated with agile turning performances, due to the quick
application of large loads of propulsive and braking forces in brief GCTs. These brief GCTs tended to
improve CODS performances. However, the major correlating criterion with potential future injuries in
this study emerged to be the measured time on the T-test. This T-test includes multiple facets and
characteristics of agility, hence could be an argument to incorporate different aspects of agility in one
training or exercise. In this research, separate qualitative criteria appeared to be less correlated with non-
contact injuries and could thus be a counter-argument on Tomas Little et al.41 and Young et al.40 to train
agility characteristics isolated. In summary this implicates that future training programs should focus on
every single component of agility (Figure 2), with distinguished attention when shortcomings are
presented in individual athletes. Nevertheless, regarding injury prevention, training programs should
incorporate exercises with a merger of multiple agility components.
24
In this study, incorporating a reactive component to the standard T-test did not improve the predicting
value for injury events. A possible reason could be the type of external stimulus that was implemented in
the modified T-test. This research utilized a spatial stimulus as CES but the temporal component was
constant. Athletes could not anticipate on the COD, though athletes knew when they were going to
receive the indicating stimulus of the arrow. A possible reason for the weak indicative values of the
modified T-test is the lack of the temporal uncertainty21. Future studies should try to incorporate
unanticipated temporal stimuli additional to the spatial uncertainty in the testing procedures, e.g. by the
use of FitLights or other such.
Limitations
Firstly, a larger sample size (n > 50) would possibly strengthen the logistic regression model and allow
researchers to add variables to the regression model. Furthermore, a bigger sample size could increase
the chance that other variables meet the significance criteria as well 42.
Secondly, the assessed qualitative criteria were susceptible to subjectivity and discussion. Out of thirteen
predictors, two were quantitative and eleven were qualitative. This study attempted to describe clear
bounds for qualitative parameters to diminish subjectivity. Nevertheless, examiners were encountered
with unavoidable differences in opinion during their evaluation. For example, the general impression was
defined by running style, turning style30, arm and trunk movement6, lower limb alignment43 3 4, limberness
and effort. The criteria of general impression tended to be highly subjective according to the individual
observer and his experiences and knowledge of movement patterns. Results will differ depending on the
person evaluating the athletes, e.g. the interpretation of a physiotherapist will vary from a hockey coach
his interpretation. Knowledge of human biomechanical risk factors could be a benefit for subjective
parameters concerning movement patterns. Studies must acknowledge the difficulties regarding
subjective parameters and its interpretation. Therefore, future studies should focus on using more
objective criteria that are not susceptible for tester’s subjectivity.
Another downside is the homogeneity of the study population. Two of the four teams compete on a
considerably higher level. This implies significant differences in training frequency and intensity (higher
chronic load), match intensity and attendance on training sessions. These differences in chronic load
implicate a diverse exposure and susceptibility to non-contact injuries. Competing on a higher level
25
implicates a better ‘injury awareness’. This awareness can be explained as a ‘lower threshold to seek
medical care’, even in the case of feeling minorly impaired.
Further remarks on this screening procedure, is the fact that once screened as athlete at risk for
developing injuries, additional questions emerge. Certain questions concerning specific risk factors and
needed strategies must be ascertained in order to prevent injuries from happening. However, these
additional problems transcend the scope of this study. The procedure used in this study could imply a first
screening link, in a large chain of measurements regarding injury prevention in hockey.
Since agility includes a cognitive, technical and a physical component (figure 2), not all aspects of agility
were taken into account when conducting an individual score for the athletes15 16. Certain elements of
agility make it hard to provide an overview and thus compare individual subjects. Especially cognitive
decision-making speed and accuracy (visual scanning, anticipation, pattern recognition and knowledge of
situation) are seemingly impossible to objectify. Various physical and technical parameters were
neglected in this study because of the redundancy to quantify or qualify these parts of agility. Researchers
must acknowledge the incompleteness of the used agility characteristics in this study and concede the
difficulties to describe its link with injuries. Agility demands further investigations towards the different
characteristics and accordingly with the implication on injury events. Seen the difficulties of these
parameters, a certain insight of the coach is required to recognize the malfunctioning component
responsible for the ‘bad’ outcome on the screening test and on which level the intervention should focus.
Strengths
A first strength in this study is the emphasis on a clear differentiation between the concepts ‘agility’ and
‘change of direction speed’ (CODS), both in description and testing procedure. Regarding the description,
agility can be defined as ‘a rapid whole-body movement, with change of velocity or direction in response
to a stimulus’ 15, as presented earlier in the introduction. Agility is susceptible to technical, physical and
cognitive components in sports (figure 2). Sheppard et al.15 described preceding first two components as
CODS (figure 3). This is thoroughly tested in the traditional agility tests (e.g. T-test, pro-agility test, etc.),
wherein athletes must complete a pre-planned course of directional changes as quickly as possible16. The
cognitive component, although fundamental regarding decision making time and accuracy 17, seems to
stay ignored in the conventional way of testing. Consequential to the absence of a cognitive element in
26
the traditional tests, these should be labeled as ‘COD tests’17 20. Misconception regarding these concepts
should be avoided in future research by stressing on the use of accurate terminology. For this reason, it
was necessary to take this differentiation into account for the testing procedure. A modified T-test (MTt)
was implemented in this study alongside the traditional T-test (TTt). Placing an arrow behind a wooden
board provided a ‘cognitive element of surprise’ (CES), as described in methods. This CES presented the
opportunity to differentiate between an anticipated and an unanticipated route. By subtracting the mean
time of the MTt (unanticipated route) with the mean time of the TTt (anticipated route), the exact
decision time of every athlete could be recorded. This measurement directly refers to the, until now
missing, cognitive aspect in traditional agility tests. With this modification, it was possible to test agility
under its current definition. Future research should define accurately whether they intend to test agility
rather than CODS or vice versa.
A second strength in this study is the fact that the commonly used L-drill, also known as the ‘cone drill’, is
an important test in the NFL Scouting Combine. This is held every year before the NFL Draft, to measure
athletic abilities and football skills of college football players44. To our knowledge, this test has only been
used for performance purposes only, and not for an injury screening procedure. Our findings concerning
the predictive value of the ‘general impression L-run’ on non-contact injuries, could contribute a new
dimension to this test.
Another benefit of this study is the usability and low-cost of this screening procedure. When only using
performance on time of the T-test, coaches and personal trainers can rely on the prediction model and its
cut-off value of 10,45 seconds. An adequate (AUC = 0.706) and reliable (sensitivity = 64.00; specificity =
76.00) preseason screening is made possible by this simple, objective and low-cost tool. Necessities are a
stopwatch, and four cones. It is up to the coach to decide whether it is worth the effort to add the
‘general impression of the L-run’ predictor to the screening, making the procedure slightly more accurate.
Once the team has been screened for injuries, the group at risk could be recommended to undergo
specialized screening procedures so as to find specific risk factors. These susceptible athletes could be
subjected to an injury prevention program set up by a physical coach.
27
Future research
Since ‘general time T-test’ is a strong predictor in this study, we suggest that in future research ‘general
time of the L-drill’ should be investigated as an independent predictor variable. Comparable skills are
required for the performance on time in both L and T-tests. This means also that independence of the
variable should be investigated in order to be able to include these variables in the same prediction
model. Skills like CODS and its associated subdivisions are fundamental for agility according to Sheppard
and Young15. In theory, one can assume that a fast performing athlete on the T-test would perform fast on
the L-test as well. Nonetheless, this hypothesis should first be investigated. If ‘general time of the L-drill’
would be a predictive variable, the screening procedure setup becomes possibly more efficient and less
time consuming, because a setup of the L-drill only would suffice. Since the L-drill is part of the NFL
combined, more insights on this drill would be useful.
Future studies should alter the type of unpredicted external stimuli and incorporate temporal
uncertainty21. A merger of spatial and temporal uncertainty tends to be more specific with regard to in-
game situations and thus agility characteristics. This broader perspective towards agility could alter the
degree of significance concerning non-injury events. Because previously mentioned stimuli were visual, it
could be interesting to reflect on using auditory stimuli, since these also reflect in-game situations in case
of team sports.
A bigger sample size is recommended. In this way, the possibility that more predictor values could be
significant, increases. Consequently, more variables could be included in the logistic regression model.
In order to avoid future misconception, the use of accurate terminology regarding agility and COD, is
recommended. Since traditional agility tests are in fact COD tests, a clear statement concerning what the
aim of the testing procedure is, should be presented. When future research intends to test agility, a
similar approach, as conducted in this study (CES and MTt) would be advisable. Furthermore, taking up
more extensive testing procedures (Illinois test, shuttle run, etc.), will provide further information
concerning this subject.
28
CONCLUSION
Notwithstanding the lacking aspects of agility in this study, the mean time on the T-test could be
indicative for susceptible athletes. The performance time on T-test combined with a general impression
on the L-run could slightly augment the indicative value regarding injury. The accuracy of this pre-season
screening test in predicting athletes at risk is acceptable in terms of specificity and sensitivity but could be
improved if other variables would be taken into consideration. Examples of these variables could be
quantitative or qualitative. However, in this study quantitative parameters tended to be superior towards
injury-inciting events. Further research towards various agility characteristics and the link with injury is
necessary.
29
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34
ABSTRACT FOR LAYMEN
Achtergrond: In de literatuur is veel geweten over risicofactoren in de sport die blessures kunnen
veroorzaken. Heel wat onderzoekers hebben bijgevolg getracht om atleten te beschermen tegen
dergelijke blessures door in te grijpen op deze risicofactoren. Over behendigheid als risicofactor is echter
weinig gekend.
Hypothese: Behendigheid met zijn verschillende componenten speelt een rol in blessures bij hockey
spelers.
Methode: 50 veldhockey spelers voerden een looptest in een T en L-vorm uit. Aan de hand van een
scorelijst kreeg elke atleet een individuele score toegewezen. De atleten werden opgevolgd over een
periode van 7 maand. Er werd onderzocht welke criteria van de scorelijst gelinkt konden worden aan
blessures en of ze deze konden voorspellen.
Resultaten: 25 atleten liepen gedurende het seizoen een niet-contact blessure op. De gemiddelde tijd op
de T-test bleek een goede predictor te zijn om potentiële blessures te voorspellen. Deze voorspelling
werd accurater door de atleten ook te scoren op een algemene indruk tijdens de L-test.
Conclusie: Het gebruik van de tijd op de T-test en een algemene indruk op de L-test is mogelijks een
handige indicator om te voorspellen of een hockeyspeler een hoger risico loopt om een blessure op te
lopen of niet.
35
ETHICAL COMMITTEE APPROVAL
36
ATTACHEMENTS
Attachment 1: Questionnaire
Name:
Date of birth:
Length:
Weight:
Profession:
Years of hockey experience:
Position:
Previous club(s):
Previous injuries (Which ones and when):
37
ATTACHMENT 2: L-drill AND T-test
L-drill (Figure 1):
Three cones are set up in an L-shape with 9 meters between each cone. The participant was instructed to
run as fast as possible from cone one to cone two, making a turn of 90 degrees to the third cone. Arriving
at the third cone participants were asked to turn 180 degrees, starting the turn from the right. After this
turn, one had to run back to the first cone, making an L shape by passing exterior to the second cone.
Participants were instructed to execute the drill as fast as possible. A Go-Pro camera Hero (60 frames per
second) on a tripod with a height of 1,20 meter, was placed on 4,5 meters from the last cone on, filming
the L drill with emphasis on the 180 degrees turning manoeuver. Each participant had the opportunity to
practice the L-drill one time before the final execution.
Figure 1: L-drill
38
T-test (Figure 2):
Participants started in front of three cones that were set up in a T-shape. The distance between cone one
and two was 9 meter, the distance between cones two and three and two and four was 4 meters. A
camera (iPhone 6) was placed on a tripod next to the starting cone on order to time the duration of one
execution. Another camera (GoPro Hero4) was placed 3,50 meter behind the second cone on a height of
1,20 meters. Starting at the first cone, participants were asked to run as fast as possible to the second
cone. When arriving, they were instructed to run sideways to cone three and subsequently run sideways
to cone four. Arriving at cone four, participants had to run back sideways to cone two and backwards to
the first cone
This test was performed four times with 15 seconds rest in between each execution. During the third and
fourth execution, decision-making was incorporated into the test by an unprecedented indication of
direction at cone 2. An arrow was placed on 40 cm behind a vertical board (90x30x1cm) behind the
second cone. The arrow was placed at random pointing left or right and indicated the direction to pursue.
The pointing direction of the arrow changed completely at random during the third and fourth
performance by the tester.
Figure 2: T-test
Legend figures:
39
Attachment 3: Qualitative and quantitative scoring form T and L-test
SCORING SHEET
T test L test
1. Total time :
- <10 sec (0)
- 10-11 sec (1)
- >11 sec (2)
1. Width of first turn (90°)/turning cycle:
- Short (0)
- Wide (1)
2. Decision time:
- <0.5 sec (0)
- 0.5-1 sec (1)
- >1 sec (2)
2. Width of second (180°)/turning cycle:
- Short (0)
- Wide (1)
3. Push-off with outer leg left cone:
- Yes (0)
- No (1)
3. General impression (running style, turning…):
- Good (0)
- Bad (1)
4. Push-off with outer leg right cone:
- Yes (0)
- No(1)
5A. Correct alignment knee-foot of inner leg:
- Alignment foot-knee (0)
- Malalignment foot-knee (1)
5B. Correct alignment knee-foot of outer leg:
- Alignment foot-knee (0)
- Malalignment foot-knee (1)
6. Criteria 5A + 5B:
- 0/1 = 0 points
- 2 = 1 point
7. Distance COG to ground:
- Flexion position (0)
- Extension position (1)
8. Cross step (one foot before the other) while
progressing sideways:
- No (0)
- Yes (1)
9. Trunk rotation on hip/lower limbs:
- No (0)
- Yes (1)
40
ATTACHMENT 4: Chi2-results
Variables Signifcance level
KS L-test 1 P= 1,0000
KS L-test 2 P= 0,5704
KS L-test 3 P= 0,0433
KS T-test 1 P= 0,0646
KS T-test 2 P= 0,3442
KS T-test 3 P= 0,4705
KS T-test 4 P= 0,4705
KS T-test 5A P= 0,7405
KS T-test 5B P= 1,0000
KS T-test 6 P= 0,7405
KS T-test 7 P= 0,3447
KS T-test 8 P= 0,7528
KS T-test 9 P= 0,252
41