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Unanticipated fake-and-cut maneuvers do not increase knee abduction moments in sport-specific tasks: Implication for ACL injury prevention and risk screening

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Non-contact anterior cruciate ligament injuries typically occur during cutting maneuvers and are associated with high peak knee abduction moments (KAM) within early stance. To screen athletes for injury risk or quantify the efficacy of prevention programs, it may be necessary to design tasks that mimic game situations. Thus, this study compared KAMs and ranking consistency of female handball players in three sport-specific fake-and-cut tasks of increasing complexity. The biomechanics of female handball players ( n = 51, mean ± SD: 66.9 ± 7.8 kg, 1.74 ± 0.06 m, 19.2 ± 3.4 years) were recorded with a 3D motion capture system and force plates during three standardized fake-and-cut tasks. Task 1 was designed as a simple pre-planned cut, task 2 included catching a ball before a pre-planned cut in front of a static defender, and task 3 was designed as an unanticipated cut with three dynamic defenders involved. Inverse dynamics were used to calculate peak KAM within the first 100 ms of stance. KAM was decomposed into the frontal plane knee joint moment arm and resultant ground reaction force. RANOVAs (α ≤ 0.05) were used to reveal differences in the KAM magnitudes, moment arm, and resultant ground reaction force for the three tasks. Spearman's rank correlations were calculated to test the ranking consistency of the athletes' KAMs. There was a significant task main effect on KAM ( p = 0.02; η p 2 = 0.13). The KAM in the two complex tasks was significantly higher (task 2: 1.73 Nm/kg; task 3: 1.64 Nm/kg) than the KAM in the simplest task (task 1: 1.52 Nm/kg). The ranking of the peak KAM was consistent regardless of the task complexity. Comparing tasks 1 and 2, an increase in KAM resulted from an increased frontal plane moment arm. Comparing tasks 1 and 3, higher KAM in task 3 resulted from an interplay between both moment arm and the resultant ground reaction force. In contrast to previous studies, unanticipated cutting maneuvers did not produce the highest KAMs. These findings indicate that the players have developed an automated sport-specific cutting technique that is utilized in both pre-planned and unanticipated fake-and-cut tasks.
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TYPE Original Research
PUBLISHED 10 November 2022
DOI 10.3389/fspor.2022.983888
OPEN ACCESS
EDITED BY
Alan Godfrey,
Northumbria University,
United Kingdom
REVIEWED BY
Paul J. Byrne,
South East Technological
University, Ireland
Rachel Mason,
Northumbria University,
United Kingdom
*CORRESPONDENCE
Patrick Mai
patrick.mai@hs-oenburg.de
These authors have contributed
equally to this work and share first
authorship
SPECIALTY SECTION
This article was submitted to
Sports Science, Technology and
Engineering,
a section of the journal
Frontiers in Sports and Active Living
RECEIVED 01 July 2022
ACCEPTED 28 October 2022
PUBLISHED 10 November 2022
CITATION
Mai P, Bill K, Glöckler K,
Claramunt-Molet M, Bartsch J,
Eggerud M, Tidemann Pedersen A,
Sæland F, Bergh Moss R, Mausehund L,
Willwacher S, Kersting UG, Eriksrud O
and Krosshaug T (2022) Unanticipated
fake-and-cut maneuvers do not
increase knee abduction moments in
sport-specific tasks: Implication for
ACL injury prevention and risk
screening.
Front. Sports Act. Living 4:983888.
doi: 10.3389/fspor.2022.983888
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©2022 Mai, Bill, Glöckler,
Claramunt-Molet, Bartsch, Eggerud,
Tidemann Pedersen, Sæland, Bergh
Moss, Mausehund, Willwacher,
Kersting, Eriksrud and Krosshaug. This
is an open-access article distributed
under the terms of the Creative
Commons Attribution License (CC BY).
The use, distribution or reproduction
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does not comply with these terms.
Unanticipated fake-and-cut
maneuvers do not increase knee
abduction moments in
sport-specific tasks: Implication
for ACL injury prevention and
risk screening
Patrick Mai1,2*, Kevin Bill1†, Katharina Glöckler1,
Mireia Claramunt-Molet3,4, Julia Bartsch5,6 , Mathias Eggerud5,
Anniken Tidemann Pedersen7, Fredrik Sæland5,
Reidar Bergh Moss7, Lasse Mausehund5, Steen Willwacher1,2,
Uwe G. Kersting1, Ola Eriksrud7and Tron Krosshaug5
1Institute of Biomechanics and Orthopaedics, German Sport University Cologne, Cologne,
Germany, 2Department of Mechanical and Process Engineering, Oenburg University, Oenburg,
Germany, 3Digital Health Unit, Eurecat Centre Tecnològic de Catalunya, Barcelona, Spain,
4Biomechanical Engineering Lab, Universitat Politècnica de Catalunya, Barcelona, Spain,
5Department of Sports Medicine, Oslo Sports Trauma Research Center, Norwegian School of Sport
Sciences, Oslo, Norway, 6Department of Sport Science, University of Konstanz, Konstanz, Germany,
7Department of Physical Performance, Norwegian School of Sports Sciences, Oslo, Norway
Non-contact anterior cruciate ligament injuries typically occur during cutting
maneuvers and are associated with high peak knee abduction moments (KAM)
within early stance. To screen athletes for injury risk or quantify the ecacy
of prevention programs, it may be necessary to design tasks that mimic
game situations. Thus, this study compared KAMs and ranking consistency of
female handball players in three sport-specific fake-and-cut tasks of increasing
complexity. The biomechanics of female handball players (n=51, mean ±SD:
66.9 ±7.8 kg, 1.74 ±0.06 m, 19.2 ±3.4 years) were recorded with a 3D motion
capture system and force plates during three standardized fake-and-cut tasks.
Task 1 was designed as a simple pre-planned cut, task 2 included catching
a ball before a pre-planned cut in front of a static defender, and task 3 was
designed as an unanticipated cut with three dynamic defenders involved.
Inverse dynamics were used to calculate peak KAM within the first 100 ms
of stance. KAM was decomposed into the frontal plane knee joint moment
arm and resultant ground reaction force. RANOVAs (α0.05) were used
to reveal dierences in the KAM magnitudes, moment arm, and resultant
ground reaction force for the three tasks. Spearman’s rank correlations were
calculated to test the ranking consistency of the athletes’ KAMs. There was
a significant task main eect on KAM (p=0.02; η2
p=0.13). The KAM in the
two complex tasks was significantly higher (task 2: 1.73 Nm/kg; task 3: 1.64
Nm/kg) than the KAM in the simplest task (task 1: 1.52 Nm/kg). The ranking of
the peak KAM was consistent regardless of the task complexity. Comparing
tasks 1 and 2, an increase in KAM resulted from an increased frontal plane
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Mai et al. 10.3389/fspor.2022.983888
moment arm. Comparing tasks 1 and 3, higher KAM in task 3 resulted from
an interplay between both moment arm and the resultant ground reaction
force. In contrast to previous studies, unanticipated cutting maneuvers did
not produce the highest KAMs. These findings indicate that the players have
developed an automated sport-specific cutting technique that is utilized in
both pre-planned and unanticipated fake-and-cut tasks.
KEYWORDS
anterior cruciate ligament, joint loading, sports medicine, inverse dynamics, change
of direction, cutting, anticipated, handball
Introduction
In team sports, the majority of ACL injuries are non-contact
in nature (1,2), with a subset of these occurring during cutting
maneuvers (2). Young female handball players are at greater
risk than their male counterparts (1,3,4).Athletes participating
in ball sports are constantly challenged by interacting with
teammates and reacting to opposing players while performing
highly dynamic cutting maneuvers, both with and without
handling a ball. In handball, video analysis suggests ACL
injuries frequently occur during fake-and-cut situations (5,6).
Biomechanical analysis from injury video sequences is limited to
a joint kinematic description of injury situations (2,6). However,
joint moments correspond more directly to ligament loading
(7), which is currently only measurable in the biomechanics
laboratory. Simple game-unspecific lab screening tasks have
been suggested for determining the athlete’s risk profile, but with
poor success (8).
From a biomechanical perspective, the external knee
abduction moment (KAM) is likely to contribute to the
ACL injury mechanism (2,6,9). Therefore, it is essential to
reduce KAM through injury prevention training, specifically by
targeting fake-and-cut situations. However, it is unclear how
different game elements influence knee joint loading. Previous
studies have suggested that introducing static defenders, a ball,
and an unanticipated change of direction could substantially
increase KAM (10). However, an isolated view of such game-
specific elements might not reflect the complexity of game
scenarios. Therefore, designing laboratory-based tasks that
closely mimic game situations more realistically seems necessary
to understand joint loading mechanisms (11). Understanding
the complex interplay between players and their environment
might improve injury risk screening. Hence, the identification of
athletes being at high risk for ACL injuries might be significantly
improved by implementing game specificity. Yet, it remains
unknown if game-specificity increases knee joint loading and if
these changes are systematic across players.
Therefore, the purpose of the study was to compare KAM
and ranking consistency in female handball players in three
sport-specific fake-and-cut tasks of increasing complexity. We
hypothesized that the KAM’s magnitude increases when athletes
face more complex tasks.
Materials and methods
Participants
We recruited fifty-one female handball players (mean ±
SD: 67.0 ±7.7 kg, 1.70 ±0.06 m, 19.2 ±3.4 years) from
various Norwegian handball divisions (Premier, 1st, 2nd, or
3rd division) through personal meetings with coaches and
announcements on the Norwegian Handball Association’s
website. Thirty-five of these 51 players competed in the three
highest Norwegian handball divisions. All athletes were at least
16 years of age and played either the back or the wing position.
Players in these positions typically perform frequent sidestep
cuts during matches. All athletes were pain-free at the time of
testing. Seven of the 51 players had recovered from a previous
ACL injury. All procedures were followed by the Declaration of
Helsinki. The Regional Ethics Committee approved the study
before data collection. Informed consent was obtained from
all players.
Experimental setup and protocol
Eighty-two retro-reflective markers of a full-body marker set
were attached to the athletes’ skin. In detail, lower extremity
markers were attached to the following anatomical landmarks:
left and right anterior superior iliac spines and posterior superior
iliac spines; medial and lateral femoral condyles; medial and
lateral malleoli. Furthermore, tracking clusters attached to a
rigid shell consisting of four markers were attached to the lateral
aspect of the thigh and the shank. Rearfoot markers were placed
on the athletes’ shoes at the calcaneus’ most medial, lateral,
and posterior aspects. Forefoot markers were attached to the
shoe upper at the head of the first and fifth metatarsal and the
head of the first distal phalanx. Following marker attachment, a
standardized warm-up routine was carried out, including 5 min
of cycling, ten jump squats, seven squats, and seven calf raises.
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A three-dimensional marker-based tracking system (24
cameras, Qualisys, Gothenburg, Sweden, 200 Hz) and two floor-
embedded force plates (AMTI, Watertown, Massachusetts, USA,
1,000 Hz, 1,200 ×600 mm) sampled the marker trajectories and
the ground reaction forces (GRFs) of the athletes during three
standardized cutting tasks of different complexities.
For all tasks, the players accelerated for 6 meters and arrived
at an angle of 35to the long axis of the runway. Athletes
performed all tasks at self-selected speeds but were instructed
to match game intensity.
For task 1, the athletes were instructed to perform a pre-
planned fake-and-cut maneuver, similar to what they would do
during active gameplay (Figure 1A). No ball or defender was
included in this task. Task 2 was performed the same way as
task 1, but the athletes would additionally catch a ball passed
by a teammate one step before initiating a pre-planned fake and
cut in front of a static defender (Figure 1B) (12). For task 3,
two defenders were added to either side of the static defender
of task 2. The middle defender and one additional randomly
selected defender moved toward the athlete at the catch, forcing
the athlete to cut away from the moving defenders. This scenario
resulted in an unanticipated cut (Figure 1C).
The order of the three tasks was randomized. The players
were allowed to familiarize themselves with each task. A
minimum of five valid cuts per task was recorded. A cut was
considered valid if the foot landed clearly within the boundaries
of one of the force plates. For task 2 and task 3, we also tracked
the defenders’ positions using a marker attached to their back.
The cutting leg for tasks 1 and 2 was determined based on
playing position and throwing arm, while task 3 was performed
on both the left and right leg. However, only the leg determined
for tasks 1 and 2 was analyzed for the current study.
Data analysis
We applied a recursive 4th order low-pass Butterworth filter
with a 20 Hz cut-off frequency to the kinematic and kinetic
data (13,14). Knee and ankle joint centers were defined as
the midpoints between medial and lateral femoral condyles
and malleoli markers. We defined the hip joint centers and
pelvis coordinate systems according to Bell et al. and Seidel
et al. (15,16). Segment inertial properties were calculated on
anthropometric data from de Leva (17). We determined lower
extremity resultant external joint moments with the explicit
expression provided by Hof (18) using a rigid body model of
the lower extremities, including forefoot, rearfoot, shank, thigh,
and pelvis segments. Peak external KAM was normalized to
body mass and extracted for the first 100 ms after initial ground
contact (IC). IC and toe-off (TO) were defined as time points
where the unfiltered vertical GRF component exceeded or fell
below 30 N. To test whether changes in the peak KAM are
caused by the knee’s frontal plane moment arm or the magnitude
of the resultant GRF, we analyzed both variables separately.
All model calculations were performed using a custom-made
Matlab script (R2021a, The Mathworks, Natick, USA). Details of
these calculations can be found in previous publications (1921).
Additionally, we analyzed the horizontal CoM velocity
(medio-lateral and anterior-posterior velocity) at initial contact,
the distance between attacker and defender at initial contact, and
reaction times. We suspected that these variables might explain
possible differences in KAMs between the three tasks. For task
3, the athlete’s time to decide on a cutting direction and plan
the cutting was calculated as the time difference between IC and
initiation of the block by the defenders. Initiation of the block
by the defenders was defined as the instance when the individual
defender’s marker velocity reached 0.5 m/s.
Statistics
To test whether the external peak KAM within the first
100 ms of stance changes with different task complexities, we
used a repeated-measures ANOVA. We quantified the effect size
using partial eta squared (ηp2). Repeated-measures ANOVAs
were additionally carried out for the resultant GRF and the GRF’s
frontal plane moment arm to the knee joint at the time point of
the peak KAM. Post-hoc (pposthoc) analysis using Bonferroni-
corrected alpha levels was used to identify differences within the
three task complexities. Cohen’s d effect size for paired samples
was calculated to indicate the strength of statistically significant
post-hoc results. Cohen’s d was calculated as the mean differences
between two tasks, x1and x2, divided by the pooled standard
deviation Spooled (Equation 1). Effect sizes were interpreted
as trivial (d =0–0.19), small (d =0.20–0.49) medium (d =
0.50–0.79) and large (d 0.8).
d=(x1x2)
spooled
(1)
Additionally, we calculated Spearman’s rank correlation
coefficient (rs) for the KAM to assess the athlete’s ranking
consistency across the three tasks. To test if the timing of the
KAM was affected by the task complexity, we used statistical
parametric mapping on the time-normalized KAM curves for
the first 100 ms after IC (22). All statistical tests were performed
using Matlab (The MathWorks, Inc. MATLAB 2021a). The
significance level was set to α0.05.
Results
Peak knee abduction moment, frontal
plane moment arm, and resultant ground
reaction force
The repeated-measures ANOVA revealed a statistically
significant (p=0.02) task effect on the peak KAM within
the first 100 ms after IC (Figures 2A–C). Partial eta squared
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Mai et al. 10.3389/fspor.2022.983888
FIGURE 1
(A) Illustration of task 1. Players approached the force plate and performed a pre-planned fake-and-cut maneuver. (B) Illustration of task 2.
Players caught a ball passed by a teammate while approaching the force plate and performed a pre-planned fake-and-cut in front of a static
defender. (C) Illustration of task 3. Players caught the ball passed by a teammate while two of the three defenders moved toward the athlete to
block one side. This scenario forced the athlete to cut to the unblocked side, resulting in an unanticipated cut.
indicated a medium effect size (ηp2=0.13). The peak KAM
was significantly (pposthoc =0.002) higher (+14%) when
comparing task 2 (1.73 ±0.61 Nm/kg) to task 1 (1.52 ±0.54
Nm/kg), with Cohen’s dindicating a medium effect size (d=
0.50). Comparing the least and most complex tasks, the peak
KAM in task 3 (1.64 ±0.56 Nm/kg) was, on average, 8% higher
than in task 1 (pposthoc =0.02). Effect size indicate a small
effect on KAM magnitudes between the two tasks (d=0.22).
On average, the peak KAM was 5% higher in task 2 than in task
3, but no statistical difference could be detected (p>0.05).
Decomposing the peak KAM into the frontal plane moment
arm and GRF components, the repeated-measures ANOVA
revealed a statistically significant task effect on the moment arm
(p=0.002, ηp2=0.11) and resultant GRF (p=0.014, ηp2
=0.08) with both showing medium effect sizes using partial
eta squared. The frontal plane moment arm at peak KAM
(Figures 2D–F) in task 2 (4.52 ±1.50 cm) was significantly
(pposthoc =0.004) longer (+17%) than the moment arm in task
1 (3.87 ±1.51 cm). Effe ct size indicates a small effect (d=0.47)
on the frontal plane moment arm between task 1 and task 2. In
task 3, the moment arm at peak KAM (4.16 ±1.32 cm) was 7%
longer than in task 1 and 8% shorter compared to task 2, without
any statistical significance (p>0.05). Pairwise comparing the
resultant GRF at peak KAM (Figures 2G–I), we found similar
magnitudes for task 1 (31.68 ±6.17 N/kg) and task 2 (32.74
±6.38 N/kg). Post-hoc tests revealed a statistically significant
(pposthoc =0.01) 6% lower resultant GRF at peak KAM in task
3 (30.88 ±6.66 N/kg) than in task 2. A small effect size (d=0.43)
on the resultant GRF was observed between the two tasks.
Pairwise comparisons of the peak KAM ranking revealed a
very strong (rs=0.80; p<0.001) relationship between task 1
and task 3. On average, the rank change of the athlete’s peak
KAM was six positions, and the maximal observed rank change
was 27. With nine ranks on average and 29 ranks as the most
extreme observed rank change, a strong (rs=0.65; p<0.001)
relationship in the KAM ranking between task 1 and task 2 was
found. When comparing task 2 and task 3, a strong relationship
(rs=0.65; p<0.001) was observed. On average, a player
changed nine ranks, and 36 ranks were observed as the most
extreme rank change.
Functional knee abduction moment
analysis within the first 100 ms of stance
Statistical parametric mapping using a repeated-measures
ANOVA showed significant differences between the three
tasks (Figure 3A). The statistical parametric mapping pairwise
comparison revealed significantly (pposthoc =0.004) higher
KAM for task 2 than for task 1 at 20–45 ms of stance
(Figure 3B). When comparing task 2 and task 3, the KAM
curves were statistically (pposthoc <0.001) different within 60–
80 ms of stance, with higher KAMs in task 3 (Figure 3C). When
comparing the two most extreme tasks, the KAM was elevated in
task 3 compared to task 1 (pposthoc <0.001) within 50–100 ms
after IC (Figure 3D). During the first 100 ms of st ance, the KAM
in task 3 was, on average, higher than in any other task.
Center of mass kinematics, reaction time,
and attacker-defender dynamics
Since identical movement instructions for the three tasks
led to different results in KAMs, we investigated additional
variables that might potentially shed light on the causes for
these differences. Center of mass (CoM) kinematics of the
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Mai et al. 10.3389/fspor.2022.983888
FIGURE 2
Distribution of the peak knee abduction moment [Nm/kg] within the first 100ms after initial contact for task 1 (A), task 2 (B), and task 3 (C). Bold
lines within the distribution curves represent the average peak knee abduction moment, and dashed lines the standard deviations. The diameters
of the dots in the scatters are scaled to the individual standard deviation. The player’s number in each scatter helps track the individual rank in
each task. (D–F) Distribution of the frontal plane moment arms of the knee joint for the three task complexities; (G–I) Resultant ground reaction
forces and peak knee abduction moments for the three task complexities.
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FIGURE 3
Average external knee abduction moment curve s within the first 100 ms of stance. (A) Statistical parametric mapping results using
repeated-measures ANOVA. The gray lines above the graph indicate significant dierences over the respective time interval. (B–D) Pairwise
comparison of the knee abduction moment curves for each possible task combination, gray lines highlighting significant dierences over time
using Bonferroni-corrected alpha levels.
dynamic test situations were found to be modulated by the task
complexity (Figures 4A–C). When comparing the horizontal
CoM velocity at IC, the repeated-measures ANOVA revealed a
statistically significant (p<0.001; ηp2=0.31) effect on the task
complexity. Post-hoc analysis showed that athletes in task 2 (3.15
±0.34 m/s) approached the force plate with a greater horizontal
velocity compared to task 1 (2.91 ±0.35 m/s; pposthoc <0.001;
d=0.32) and task 3 (2.96 ±0.35 m/s; pposthoc <0.001; d=
0.30). CoM horizontal velocity was not different between task 1
and 3 (p=0.64).
Next, we computed the time the athlete had to react to
the defenders in task 3 based on the velocity of the back
marker of the dynamic defenders. On average, the athletes had
0.94 ±0.15 s to plan their motion and initiate the cut to the
unblocked side.
Lastly, the repeated-measures ANOVA showed a significant
main effect of the task complexity on the cutting angle
(p<0.001; ηp2=0.27). Post-hoc analysis revealed that
task 3 (61.5 ±14.0) resulted in significantly smaller
(p<0.001) cutting angles compared to task 1 (70.8
±14.0). Cohen’s dindicated a medium effect size (d
=0.76) on the cutting angle between the two tasks.
Statistically significant differences in the cutting angle were
additionally observed between task 3 and task 2 (69.2 ±
14.9), with a medium effect size (d=0.63). No significant
differences were found when comparing the cutting angles in
tasks 1 and 2.
Discussion
The study aimed to investigate the effect of match-specific
cutting maneuvers with varying task complexities on the peak
KAM in female handball players. All three tasks produced
substantially higher KAMs than previously reported cutting
tasks lacking game specificity (2325). However, our results
are in accordance with results by Kristianslund et al. (12) who
assessed KAMs using task 2. In contrast to previous studies
(26,27), the unanticipated cutting maneuver did not generate
the highest KAM magnitudes. This finding, combined with the
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FIGURE 4
Bird’s eye view of the dynamic testing situations. (A) Kinematics of the center of mass (CoM) and the point of force application (PoFA) for each
cut at initial contact (IC) and toe-o (TO) for task 1. (B) Kinematics of the CoM for each cut at IC and at TO, and the position of the static defender
for Task 2. (C) Kinematics of the CoM for each cut at IC and TO for task 3. Turquoise dots visualize the position of each defender before initiating
the block, blue dots represent the defenders’ position at the instance of the athletes’ IC, and black dots represent the defenders’ positions at the
athletes’ TO. Dashed lines represent the average distance to the respective regions. Ellipses around regions visualize standard deviations.
FIGURE 5
(A) Time continuum of the frontal plane moment arm of the knee joint from initial ground contact to 100 ms after ground contact for the three
task complexities. (B) Time continuum of the resultant ground reaction force for the three task complexities. The gray lines above the graph
indicate significant dierences over the respective time interval.
fact that the athlete’s KAM ranking was consistent regardless of
the task complexity, indicates that the players have developed
an automated motor program throughout their careers which
is utilized in both pre-planned and unanticipated fake-and-
cut tasks. Further, a single complexity might be sufficient
for screening purposes. Our findings suggest that their fake-
cut technique, including the knee joint loading, is a unique
“fingerprint” of each player. Therefore, screening focusing on
knee joint loading and cutting technique in faking maneuvers
may be an important tool to identify players with increased ACL
injury risk.
The potential to modify the fake-cut technique and, hence,
reduce knee joint loading is likely higher for young athletes
who might not have developed an automated motor program
yet (28). Still, previous studies have shown that joint moments
are modifiable in teenagers and young adults, e.g., by muscular
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strength training (29) or altering cutting technique (28) the
potential to change the risk profile in all players with high KAM
may be substantial.
In a large-scale cohort study, Kristianslund et al. reported
an average peak KAM of 1.64 ±0.66 Nm/kg when 123 female
handball players were screened in a testing scenario equal to task
2 (12). Across all three tasks, the peak KAMs and the timing of
the KAM are in agreement with the previously published data (2,
6). Motivated by the experimental design of Kristianslund et al.
(12), task 3 in the present study aimed to include the effect of
reduced anticipation time on knee joint loading. Kristianslund
et al. found that lower knee abduction loads during sidestep
cutting result from cutting technique variables, e.g., small cutting
angles. A general observation of our data indicates that the
athletes performed cuts with smaller cutting angles in task 3
than in tasks 1 and 2. The relationship between knee abduction
loading and cutting technique variables might explain why the
joint moment in task 3 was not increased compared to task
2. Although the frontal plane moment arm of the knee joint
was similar for tasks 2 and 3 at the instance of peak KAM
(Figure 5A), the resultant ground reaction force was lower in
task 3 than in task 2 (Figure 5B). However, in the present study,
it appeared difficult to tightly control for cutting technique
variables, e.g., cutting angle and approach speed, due to high
variability in solving the given cutting tasks across the athletes.
To not interfere with an individual athlete’s cutting technique,
subjects were not instructed to hit the force plate while cutting.
Instead, slight shifts to the starting positions of the dynamic
defenders were made to direct the athletes to hit the force plates.
Due to the high number of cuts for the three task
complexities, we only analyzed one leg which was selected based
on the player’s position and the throwing arm. 2 months
after the biomechanical assessment, one athlete sustained a non-
contact ACL injury to the left knee (72.3 kg; 1.66 m; 25 yrs, coded
as 41 in Figures 2A–C). Even though we only analyzed KAMs
for this athlete’s right knee joint, the KAM was remarkably
high across all three task complexities (task order: 1 3 2).
Moreover, the athletes ranking was consistent regardless of the
task complexity and ranged between the three highest KAMs for
the tasks (Figures 2A–C, coded as 41).
Several limitations must be considered when interpreting
our results. The first limitation is the uncertainty regarding
the lack of anticipation. On average, 0.94 ±0.15 s elapsed
between the dynamic defenders’ block initiation and the athletes’
contact with the ground in task 3. It is unclear if this time
was sufficient to provoke game-like unanticipated cuts. Previous
studies (27,30) using light signals in single-leg landing tasks
reported 0.35–0.65 s between light signal and ground contact.
It must be noted that in these studies, subjects could focus on
the light signal without any other perturbations. In contrast,
subjects in the present study had to react to the defenders and
simultaneously look at a teammate passing a ball and catching
it before initiating the cut. This might have taken away time for
the athletes to react to the defenders, resulting in lower net times
to plan their cuts. In the present study, we observed high inter-
individual variances in the number of invalid trials due to cuts in
the wrong direction, indicating high variance in the time needed
to plan and execute the cutting maneuver. We aimed to account
for individuality by adjusting the timing of the block initiation
to the individual athlete, starting with lower times to react and
progressively slightly increasing the time for the athletes until
wrong decisions were still present but happened sporadically. It
is possible that for some players, the task was too challenging,
possibly leading to the slower approach speeds in task 3 relative
to task 2 and, in turn, lower KAMs. However, since the defender
behavior was adjusted such that the vast majority of cuts were
performed to the predetermined side, we believe the task was
adequately challenging.
Second, we showed that cutting task complexity affects the
KAM. However, it remains unclear which individual automated
control strategies regulate the effect of task complexity. For
example, subject 1 could keep a constant KAM across the tasks,
while other subjects, e.g., subject 40, gradually increased their
KAM with increasing task complexity (Figures 2A–C). Besier
and co-workers have identified different neuromuscular control
strategies for unanticipated compared to anticipated cuts (26).
The authors were able to show that in a pre-planned cut,
medial leg muscles are activated to support against the externally
applied abduction moment. In contrast, a more general co-
contraction strategy is adopted in unplanned cuts. While no
statistically significant difference in KAM was observed between
tasks 2 and 3 in the present study, the muscle activation patterns
of the athletes might reveal differences in muscle control
strategies. It is unclear how these control strategies might be
affected by fatigue, so future studies should investigate the effect
of game-specific elements on muscle activation parameters.
Further limitations in this study aiming at mimicking game
situations might be the lack of spectators, noise, and the
psychological pressure associated with these factors.
Lastly, we included athletes with previous ACL injuries. We
compared knee kinematics and kinetics of the athletes with a
history of ACL injury to the athletes who never sustained an ACL
injury. However, we observed no abnormalities in knee joint
biomechanics. Moreover, the KAM of the previously injured
athletes fell within one standard deviation of the cohort.
Despite these limitations, we could show that a pre-planned
fake-and-cut maneuver with additional visual obstacles (i.e.,
catching a ball and faking a static defender) resulted in similar
knee joint loading as a more complex unanticipated fake-and-
cut task. Further, the strong to very strong consistencies in the
rankings of the athletes’ KAMs indicate that these athletes have
developed a stable motor program that is used regardless of
the task complexity or anticipation level. Therefore, training
to internalize cutting technique adaptations such as adopting a
forefoot landing or minimizing knee valgus to reduce KAM (12)
in a simple pre-planned yet game-specific task might potentially
Frontiers in Sports and Active Living 08 frontiersin.org
Mai et al. 10.3389/fspor.2022.983888
produce carry-over to more complex and unanticipated tasks.
These findings have practical implications for researchers and
coaches alike, as more time-efficient and less complex screening
protocols and training interventions might suffice to identify
athletes at risk of injury and to produce adaptations to their
neuromuscular control strategies used across sidestep cuts of
different complexities.
Conclusion
In conclusion, we found that unanticipated fake-and-cut
maneuvers do not generate higher KAMs than a complex yet
pre-planned game-specific task. Interestingly, the correlations
between tasks were strong to very strong, indicating that
cutting technique and joint loading are “fingerprints” for
each player’s individual motor program These findings have
important clinical implications, suggesting that a sport-specific
fake-and-cut maneuver can identify players with high-risk
cutting technique.
Data availability statement
The original contributions presented in the study are
included in the article/supplementary material, further inquiries
can be directed to the corresponding author.
Ethics statement
The studies involving human participants were reviewed
and approved by the Regional Ethics Committee of the
Norwegian School of Sports Science. Written informed consent
to participate in this study was provided by the participants’ legal
guardian/next of kin.
Author contributions
PM, KB, KG, MC-M, JB, ME, AT, FS, RB, and LM
contributed to data acquisition and processing. PM, KB, and SW
contributed to the data analysis. TK, UK, and OE contributed
to project planning and study design. PM and KB contributed
to the draft of the manuscript. UK, SW, OE, and TK helped
finalize the manuscript. All authors contributed to the article and
approved the submitted version.
Funding
The Oslo Sports Trauma Research Center has been
established at the Norwegian School of Sport Sciences through
generous grants from the Royal Norwegian Ministry of
Culture, the South-Eastern Norway Regional Health Authority,
the International Olympic Committee, the Norwegian
Olympic Committee & Confederation of Sport, and Norsk
Tipping AS.
Acknowledgments
We thank Muscle Animations for providing
the graphical representation of the tasks
(Figures 1A–C).
Conflict of interest
The authors declare that the research was conducted in
the absence of any commercial or financial relationships
that could be construed as a potential conflict
of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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