Keywords
knee function - functional test battery - return to sport criteria - field hockey - reference data
Introduction
Field hockey is a physically demanding Olympic sport played by both men and women at
recreational and professional levels. In Germany, the sport has gained more
attention thanks to the menʼs team winning the 2023 World Cup. All studies conducted
to date indicate that field hockey is an intermittent, high-intensity sport [1]
[2]
[3]
[4]
[5]
[6]
[7]. Elite male players cover an average
total distance of 9.8 km during a field hockey game, while elite female players
cover an average of 6.6 km [6]. Differences
in total distance and highest maximum speed were found comparing genders as well as
playersʼ position on the field [1]
[7]. Compared to other team sports, field
hockey shows the highest running volume besides soccer [8]. Additionally, the posture required in
field hockey is special, with players often adopting a crouched position and flexing
their trunks and knees due to the ball being played predominantly on the ground
[9]
[10].
Barboza et al.ʼs review highlights the limited research on field hockey injuries, but
current evidence suggests a significant problem [11]. Injuries are more prevalent at the professional level, resulting in
higher rates than at the recreational level. The frequency of injuries ranges from
20.8 to 90.9 injuries per 1000 player hours at the professional level. The knee
accounts for up to 32% of lower limb injuries [12]. Women’s field hockey has shown the highest increase in anterior
cruciate ligament (ACL) injuries in recent years compared to other team sports [13].
Several intrinsic risk factors for knee injuries, particularly ACL injuries, have
been identified from investigations in other sports. These risk factors include leg
asymmetries, valgus loading, sudden changes in direction, hard surfaces, muscular
fatigue, or the menstrual phase for female athletes [14]
[15]
[16].
Functional knee stability is not only important for injury prevention but also for
the return to sport decision-making and secondary injury prevention. For this
purpose, balance, and mobility measurements such as the Y-balance test and the
single-leg jump test have been proposed as predictors of safe return [17]
[18]
[19]
[20]. However, despite their widespread use,
these tests have been criticized for lacking objectivity and being inadequate as
predictors of safe return [21]
[22]. The high re-injury rate of up to 20%
for ACL injuries in athletes underscores the significance of addressing this issue
[23]. Therefore, various test batteries
have now been developed and tested [24]
[25]
[26]. One of these standardized test
batteries is the Back in Action (BiA) test system (CoRehab, Trento, Italy). The BiA
test comprises seven functional assessments, providing data on balance, speed,
agility, and strength. Normative data from 434 healthy individuals of similar age
and gender are utilized for comparison via a software program. The test was
developed in two phases: initially gathering data from participants without previous
knee, hip, or ankle injuries, then applying the test battery clinically to 69
patients with unilateral ACL reconstruction. Test-retest reliability was determined
using intraclass correlation [24]
[25].
Several other studies have already used this test battery on healthy athletes in
various sports [27]
[28]
[29].
However, existing studies have not explored sport-specific risk factors for field
hockey athletes, nor is there sufficient objective data on functional testing in
this population, particularly among healthy, uninjured players. Since there are
differences in both performance parameters and injury rates in the different playing
classes, it is hypothesized that knee function would also vary between the playing
classes. In addition, the influence of anthropometry on physical performance was
investigated [11]
[12].
Therefore, this pilot study aimed to evaluate functional knee stability in elite
field hockey players and establish sport-specific reference data for a safe return
to sport with the help of the Back in Action (BiA) test system.
Methods
Athletes
A total of 72 field hockey players, consisting of 42 females and 30 males with an
average age of 19.82±3.74 years, participated in this study. Only athletes of
the three highest playing classes in Germany, i. e., elite field hockey, were
taken into account. Before participating in the study, players had to be
injury-free for at least six months. Athletes were divided into two groups based
on their playing class: High Playing Class (HPC; n=30) consisted of Germanyʼs
“1. Bundesliga,” while Moderate Playing Class (MPC; n=42) included Germanyʼs “2.
Bundesliga” and “Regionalliga West.” [Table
1] summarizes the playersʼ demographic data and field hockey-specific
information based on their playing class and gender. The study was approved by
the local ethics committee in compliance with the Declaration of Helsinki, and
each player and legal guardian for minors gave informed consent.
Table 1 Descriptive analysis (mean and SDs) and p-value
of the anthropometric characteristics and field hockey-specific data
regarding athletes’ playing class for n=72. The p-values describe
significant differences between the playing classes. HPC: high
playing class; MPC: moderate playing class.
|
HPC m (n=12)
|
HPC f (n=18)
|
MPC m (n=18)
|
MPC f (n=24)
|
p-value
|
|
Age (years)
|
20.17±3.01
|
19.67±2.97
|
20.67±5.09
|
19.13±3.46
|
0.599
|
|
Height (cm)
|
183.67±7.10
|
169.86±6.04
|
185.61±4.84
|
170.13±4.34
|
<0.001
|
|
Weight (kg)
|
76.50±6.92
|
61.56±5.03
|
76.06±7.99
|
65.13±7.44
|
<0.001
|
|
BMI (kg/m²)
|
22.66±1.39
|
21.34±1.30
|
22.07±1.92
|
22.49±2.44
|
0.129
|
|
Field hockey experience (years)
|
15.92±3.23
|
14.33±2.40
|
16.39±4.39
|
14.25±3.64
|
0.386
|
|
Training load (hours/week)
|
10.25±2.99
|
9.50±2.55
|
5.578±1.00
|
6.88±2.29
|
<0.001
|
|
Injury rate (absolute number)
|
6.42±4.68
|
3.11±2.06
|
4.22±4.11
|
3.83±2.60
|
0.134
|
Procedure
A questionnaire was used to collect demographic data and sport-specific
characteristics (playing class, field hockey experience, and hourly training
load). Additionally, body weight and height were measured using a digital scale
(Body+, Withings France SA, Issy-les-Moulineuax, France).
The functional knee stability was evaluated with the Back in Action (BiA) test
system (CoRehab) to assess objective measures.
The tests were always performed in the same gym with a stable, level floor to
ensure accurate measurements and comparability of data. All one-legged tests
were initiated with the dominant leg. All subjects were instructed and tested by
the same team using standardized test instructions. Prior to testing, they were
familiarized with a video on how to perform the tests. The test battery was
conducted after an individual warm-up program to ensure optimal conditions for
all athletes depending on their individual demands. The warm-up programs did not
exceed 10 minutes and consisted mainly of sub-maximal running, individual
dynamic stretching, and jumping exercises.
A separate description for the BiA test battery has been published elsewhere,
test elements are briefly described below [24]
[25]
[27].
Postural control/balance (TL-ST, OL-ST)
All balance tests were performed with an MFT Challenge Disc (TST Trendsport,
Großhöfflein, Austria). The disc was connected to a PC and provided visual
feedback on the athletesʼ position while balancing. Athletes were instructed to
hold the center of the disc two-legged (TL-ST) and then one-legged (OL-ST) for
20 s ([Fig. 1]). The test parameter
was the level of stability (1=best; 5=worst).
Fig. 1 Performing the one- (OL-ST) and two-legged stability test
(TL-ST) of the Back in Action test battery on the MFT disc.
Jump tests (TL-CMJ, OL-CMJ, TL-PJ)
The jump tests were performed by using the Myotest sensor (Myotest S.A., Sion,
Switzerland) [30]. The sensor was
placed on the athletes´ waist with a belt. All jump tests had to be performed
without arm swinging and jump height (cm) was recorded. In addition, the power
output was calculated in watts according to the athleteʼs weight (W/kg). For the
two-legged plyometric jumps (TL-PJ), participants had to perform four
consecutive jumps as high as possible with minimal ground contact. Jump height
(cm) and ground contact time (ms) were recorded.
Speed and agility (OL-SJ, QF)
To perform the speed and agility tests, the Speedy Basic Jump Set (TST
Trendsport, Grosshöfelein, Austria) was used. For the one-legged speedy jump
test (OL-SJ), athletes were asked to jump one-footed through a coordination
course of red (forward-backward-forward jumps) and blue (sideway jumps) hurdles
as fast as possible. The Quick Feet test (QF) consisted of stepping in and out
of a rectangle 15 times as fast as possible. For both tests, time (s) was
recorded.
Statistical analysis
All statistical analyses were performed using SPSS (IBM SPSS Statistics for
Macintosh, V27.0; IBM Corp., Armonk, NY, USA). Findings are shown as means with
standard deviations (SDs) and 95% confidence interval (95% CI). Normal distribution
was tested using the Shapiro-Wilk test. Normally distributed data were tested with
parametric t-tests. In case of violation of the assumption for parametric tests
(i. e., normality and homogeneity of variances), the Mann–Whitney U test was used
instead. The Kruskal–Wallis test was used if more than two groups were compared.
G*Power was calculated post hoc for t-test comparisons between playing classes (Power
0.66) and between playing class and gender (HPC: Power 0.37, MPC: Power 0.42).
A Spearman’s correlation analysis was used to assess the correlations. Effect sizes
(r=Spearman–Rho) were categorized as negligible (0.00 to 0.30), low (0.30 to 0.50),
moderate (0.50 to 0.70), high positive (0.70 to 0.90), and very high positive (0.90
to 1.00) [31]. For all analyses, the level
of significance was set at p≤0.05.
Results
All athletes filled in the questionnaire and completed the test battery. No athlete
had to be excluded and no injuries occurred. Means, standard deviations (mean±SD),
and significance levels of anthropometric and field hockey-specific data as well as
the performance test results are presented below ([Table 1]
[2]
[3]
[4]).
Table 2 Descriptive analysis (mean and SDs) and p-value of
the Back in Action test results regarding athletes’ playing class for
n=72. The p-values describe significant differences between the playing
classes. HPC: high playing class; MPC: moderate playing
class.
|
HPC (n=30)
|
MPC (n=42)
|
p-value
|
Significant differences between playing classes
|
|
Leg stability (points)
|
|
Two-legged
|
3.06±0.81
|
3.26±0.65
|
0.181
|
|
|
One-legged
|
|
|
|
|
|
Dominant leg
|
2.89±0.72
|
3.17±0.60
|
0.095
|
|
|
Non-dominant leg
|
2.77±0.57
|
3.19±0.65
|
0.008
|
HPC>MPC
|
|
Countermovement jumps
|
|
Two-legged height (cm)
|
38.82±8.24
|
34.55±7.93
|
0.024
|
HPC>MPC
|
|
Two-legged power (W/kg)
|
48.83±6.82
|
44.98±6.56
|
0.021
|
HPC>MPC
|
|
One-legged height (cm)
|
|
|
|
|
|
Dominant leg
|
27.10±7.35
|
22.21±4.98
|
0.008
|
HPC>MPC
|
|
Non-dominant leg
|
25.46±6.12
|
22.44±5.31
|
0.059
|
|
|
One-legged power (W/kg)
|
|
|
|
|
|
Dominant leg
|
38.33±6.74
|
34.38±4.94
|
0.019
|
HPC>MPC
|
|
Non-dominant leg
|
36.97±5.56
|
34.43±5.34
|
0.090
|
|
|
Plyometric jumps
|
|
Height (cm)
|
35.17±7.73
|
31.41±7.70
|
0.072
|
|
|
Ground contact time (ms)
|
189.73±32.01
|
213.31±56.16
|
0.071
|
|
|
Speedy jump test (s)
|
|
Dominant leg
|
6.55±0.68
|
7.72±1.78
|
<0.001
|
HPC>MPC
|
|
Non-dominant leg
|
6.73±0.68
|
7.82±1.51
|
<0.001
|
HPC>MPC
|
|
Quick feet (s)
|
7.95±0.94
|
9.03±1.42
|
0.001
|
HPC>MPC
|
Table 3 Descriptive analysis (mean and SDs) and p-value of
the Back in Action test results regarding gender in high playing class.
The p-values describe significant differences between the playing
classes. HPC m: high playing class male players; HPC f: high playing
class female players.
|
HPC m (n=12)
|
HPC f (n=18)
|
p-value
|
Significant differences between genders
|
|
Leg stability (points)
|
|
Two-legged
|
3.72±0.63
|
2.62±0.59
|
<0.001
|
HPC f>HPC m
|
|
One-legged
|
|
|
|
|
|
Dominant leg
|
3.38±0.56
|
2.56±0.62
|
0.001
|
HPC f>HPC m
|
|
Non-dominant leg
|
3.17±0.53
|
2.51±0.45
|
0.001
|
HPC f>HPC m
|
|
Countermovement jumps
|
|
Two-legged height (cm)
|
46.21±6.17
|
33.89±5.20
|
<0.001
|
HPC m>HPC f
|
|
Two-legged power (W/kg)
|
54.50±5.04
|
45.06±5.02
|
<0.001
|
HPC m>HPC f
|
|
One-legged height (cm)
|
|
|
|
|
|
Dominant leg
|
31.80±6.81
|
23.97±6.01
|
0.006
|
HPC m>HPC f
|
|
Non-dominant leg
|
29.08±6.03
|
23.04±4.98
|
0.028
|
HPC m>HPC f
|
|
One-legged power (W/kg)
|
|
|
|
|
|
Dominant leg
|
43.17±5.31
|
35.11±5.63
|
0.001
|
HPC m>HPC f
|
|
Non-dominant leg
|
41.00±4.90
|
34.28±4.25
|
0.001
|
HPC m>HPC f
|
|
Plyometric jumps
|
|
Height (cm)
|
41.23±6.87
|
31.13±5.31
|
<0.001
|
HPC m>HPC f
|
|
Ground contact time (ms)
|
169.92±25.38
|
184.94±35.64
|
0.104
|
|
|
Speedy jump test (s)
|
|
Dominant leg
|
6.758±0.77
|
6.42±0.59
|
0.134
|
|
|
Non-dominant leg
|
6.83±0.56
|
6.66±0.75
|
0.368
|
|
|
Quick feet (s)
|
8.19±1.06
|
7.79±0.85
|
0.232
|
|
Table 4 Descriptive analysis (mean and SDs) and p-value of
the Back in Action test results regarding gender in moderate playing
class. The p-values describe significant differences between the playing
classes. MPC m: moderate playing class male players; MPC f: moderate
playing class female players.
|
MPC m (n=18)
|
MPC f (n=24)
|
p-value
|
Significant differences between genders
|
|
Leg stability (points)
|
|
Two-legged
|
3.68±0.64
|
2.95±0.47
|
<0.001
|
MPC f>MPC m
|
|
One-legged
|
|
|
|
|
|
Dominant leg
|
3.52±0.55
|
2.90±0.49
|
0.001
|
MPC f>MPC m
|
|
Non-dominant leg
|
3.46±0.64
|
2.99±0.59
|
0.014
|
MPC f>MPC m
|
|
Countermovement jumps
|
|
Two-legged height (cm)
|
40.90±7.01
|
29.79±4.54
|
<0.001
|
MPC m>MPC f
|
|
Two-legged power (W/kg)
|
50.22±5.01
|
41.04±4.37
|
<0.001
|
MPC m>MPC f
|
|
One-legged height (cm)
|
|
|
|
|
|
Dominant leg
|
24.82±4.21
|
20.26±4.67
|
0.004
|
MPC m>MPC f
|
|
Non-dominant leg
|
26.07±3.21
|
19.71±4.95
|
<0.001
|
MPC m>MPC f
|
|
One-legged power (W/kg)
|
|
|
|
|
|
Dominant leg
|
37.39±3.91
|
32.12±4.45
|
<0.001
|
MPC m>MPC f
|
|
Non-dominant leg
|
38.39±2.79
|
31.46±4.85
|
<0.001
|
MPC m>MPC f
|
|
Plyometric jumps
|
|
Height (cm)
|
36.17±6.74
|
27.84±6.40
|
<0.001
|
MPC m>MPC f
|
|
Ground contact time (ms)
|
205.22±34.06
|
219.38±68.34
|
0.959
|
|
|
Speedy jump test (s)
|
|
Dominant leg
|
7.14±0.96
|
8.15±2.12
|
0.050
|
MPC m>MPC f
|
|
Non-dominant leg
|
7.22±0.64
|
8.26±1.81
|
0.035
|
MPC m>MPC f
|
|
Quick feet (s)
|
8.96±1.45
|
9.08±1.42
|
0.638
|
|
[Table 1] presents the anthropometric data
of the athletes, which did not differ across playing classes. However, if an
additional subdivision by gender was made, it was seen that in both playing classes,
there were significant differences in weight and height in favor of the men
(p<0.001). The training load was significantly higher in the high
playing class (p<0.001). Significant differences regarding injury rates
were found in the high playing class, where men had more injuries than women
(p=0.019).
[Table 2] shows the test results of all
athletes, divided by playing class. Except for the plyometric jump tests,
significantly better results were found in favor of HPC in all test categories. In
the balance tests, HPC athletes were significantly superior to MPC athletes only in
the stability of the non-dominant leg. In the two-legged stability and the
one-legged stability of the dominant leg, HPC athletes also achieved better results
than MPC athletes, but this difference was not significant.
[Table 3] depicts the differences in
performance between genders in the high playing class in detail. Female HPC athletes
reached significantly higher results than male HPC athletes in balance tests for
both two-legged and one-legged tests on both dominant and non-dominant legs.
For counter-movement jumps, male HPC athletes achieved better results in terms of
height and power for both two-legged and one-legged tests on both dominant and
non-dominant legs.
In the plyometric jumps, male HPC athletes again performed better in terms of height,
but there was no significant difference in ground contact time between male and
female HPC athletes as indicated by a p-value of 0.104. For the Speedy Basic Jump
test and Quick Feet test, there were no significant differences between the two
groups with p-values greater than 0.05.
[Table 4] shows the differences in moderate
playing class between genders. Again, the female athletes performed significantly
higher than the male athletes in the balance tests on both the two-legged and
one-legged tests on the dominant and non-dominant leg. In all other subtests, the
male subjects achieved significantly better results. Only in the Quick Feet test
were no differences detectable.
In addition, significant correlations were found between body characteristics and
physical performance ([Table 5]). In all
balance tests, body height was positively correlated with leg stability. Taller
athletes scored higher than shorter ones and therefore showed poorer results.
Table 5 Correlations (r=Spearman–Rho) between anthropometrics
and performance tests of the Back in Action test battery of 72 elite
field hockey players.
|
Body height
|
Body height
|
|
r
|
p-value
|
r
|
p-value
|
|
Leg stability (points)
|
|
Two-legged
|
0.607
|
<0.001
|
0.621
|
<0.001
|
|
One-legged
|
|
|
|
|
|
Dominant leg
|
0.572
|
<0.001
|
0.550
|
<0.001
|
|
Non-dominant leg
|
0.513
|
<0.001
|
0.592
|
<0.001
|
|
Countermovement jumps
|
|
Two-legged height (cm)
|
0.432
|
<0.001
|
0.390
|
<0.001
|
|
Two-legged power (W/kg)
|
0.399
|
<0.001
|
0.365
|
0.002
|
|
One-legged height (cm)
|
|
|
|
|
|
Dominant leg
|
0.254
|
0.031
|
0.185
|
0.121
|
|
Non-dominant leg
|
0.337
|
<0.001
|
0.248
|
0.035
|
|
One-legged power (W/kg)
|
|
|
|
|
|
Dominant leg
|
0.408
|
<0.001
|
0.379
|
<0.001
|
|
Non-dominant leg
|
0.494
|
<0.001
|
0.458
|
<0.001
|
|
Plyometric jumps
|
|
Height (cm)
|
0.460
|
<0.001
|
0.257
|
0.030
|
|
Ground contact time (ms)
|
0.052
|
0.667
|
0.226
|
0.057
|
|
Speedy jump test (s)
|
|
Dominant leg
|
0.008
|
0.947
|
0.146
|
0.221
|
|
Non-dominant leg
|
0.015
|
0.901
|
0.127
|
0.287
|
|
Quick feet (s)
|
0.008
|
0.946
|
0.002
|
0.988
|
Discussion
The purpose of this study was to evaluate functional knee stability in elite field
hockey players and to determine sport-specific reference data. Secondly, we
investigated the differences in knee function in relation to playing class and
gender.
By examining 72 elite field hockey players, a reference dataset for functional knee
stability in field hockey players was established. As assumed, the test results
revealed differences in balance, strength, speed, and agility between the genders
and playing classes. Overall, better performance was observed in higher playing
classes, while gender differences were noticed in some characteristics. As the BiA
test has already been used in other team sports with uninjured athletes, it was also
possible to identify sport-specific characteristics.
Regarding gender, males performed better than females in all parts of the test
battery, except for all stability tests. Here, female athletes performed
significantly better in both one-leg and two-leg stability.
Taking into account the biological differences between the genders, men in our study
have a greater body height, and there is already a consensus in the literature that
a larger body height worsens results of balance test on wobble boards. The higher
center of gravity due to the longer mechanical lever arm leads to impaired balance
[33]. This present study also found a
moderate correlation between body height and one- or two-legged stability ([Table 5]).
However, male HPC athletes had a significantly higher injury rate compared to female
HPC athletes, as shown in [Fig. 2], where
the injury rates and the one-leg stability of the non-dominant leg are displayed for
both genders ([Fig. 2]). This is in line
with previous studies, which underline balance as an associated factor for lower
limb injury [11]
[34]. Surprisingly, there was a moderate
correlation between the stability of the non-dominant leg and injury frequency, with
r=0.623 (p=0.031) within this group. Nevertheless, our results are also
consistent with previous studies on team sports [1]
[35] that associate higher
intensity of play with a higher risk of injury.
Fig. 2 Comparison of one leg stability of the non-dominant leg and
injury frequency in different participant groups.
The frequency of stability training was assessed by questionnaire (frequency of
stability exercises during training session 0: never; 4: always, mean 2.8±1.13
pts.), but no significant correlation was found between the weekly training load or
frequency of stability training and stability measured. Remarkably, all athletes
appeared to incorporate stability training regularly into their training. As
described above, there were gender differences in measured stability, although males
and females in both playing classes did not differ in the frequency of stability
training.
Barboza et al. [36] designed a warm-up
program for young field hockey players that included stability exercises to prevent
lower limb injuries. They were able to show that the injury rate was lower in the
intervention group, but the difference was not statistically significant.
Additionally, they were unable to verify a reduction in the severity of injuries.
Therefore, field hockey still lacks injury prevention programs and studies proving
their effectiveness, as other sports already have developed injury prevention
programs (such as the FIFA 11+) with significant reductions in injury incidence
[37]
[38].
Looking at ACL injuries throughout other sports in isolation, previous literature has
found that females have a 4–6 times higher injury rate [13]. Increased dynamic valgus and high
abduction load have been identified as possible causes [39]. Postural stability is assumed to be a
protective factor for ACL injuries in other sports [34]. Our study shows that women perform
better in all stability tests ([Fig. 2]).
Therefore, it is imperative to collect statistics on ACL injuries in field hockey to
conclude if women still have a higher occurrence rate, although wobble board
performance is superior to men.
When using the BiA test battery to make return-to-sport decisions after injury, a
limb symmetry index (LSI) above 90%, i. e., a deviation of<10% between the
injured and uninjured side, is required. A deviation of>10% is considered
incomplete rehabilitation and therefore a risk factor for re-injury [40]
[41]. However, in this sample, 42 of 72 uninjured athletes had a side
difference of>10% in at least one test category. These side-to-side deviations
could also be found in healthy athletes of other sports, for example, judo and
Taekwondo [42]. This was particularly
noticeable in the strength tests, where the average LSI across the entire sample was
well below 90% at 85.81±9.382%.
These results indicate that uninjured athletes in field hockey tend to have lateral
differences in knee functionality, possibly due to the asymmetric movement profile
of the sport. Such sport-specific side differences must be considered when assessing
functional knee stability, as they may interfere with the return-to-sport clearance
after injury. Ronden et al. [43] showed
that 9 months after bone-patellar tendon-bone anterior cruciate ligament
reconstruction, only 17.5% of athletes passed the BiA test. Studies that do not
include the LSI as a criterion for a safe return show higher passing rates [44].
Therefore, screening regularly may identify pre-existing asymmetries and weaknesses.
BiA test results are already compared with normative data from healthy, gender- and
age-matched controls.
Compared to other team sports such as handball, by Ruehlemann et al. [27], it is striking that the HPC field
hockey players achieved better results in all categories. This was especially
surprising for the jump tests, as jumping plays a major role in handball [32], but Ruehlemann et al. collected only
data on “non-elite” athletes. Expectedly, field hockey players were faster in the
Speedy Basic Jump test and Quick Feet test, as field hockey involves quick movement
of feet and dribbling rather than in handball [1]
[2].
However, sport-specific characteristics in knee stability, such as seen in this
study, should be taken into consideration when interpreting individual test
results.
Unfortunately, this study is limited by the small number of athletes, which reduces
the power of analysis, and the single time point of measurement. Standardizing
warm-up protocols before testing could also be a consideration for future studies to
minimize potential influences on test execution quality.
Despite its limitations, the test battery utilized in this study proved effective in
highlighting disparities among athletes and pinpointing specific weaknesses,
including imbalances exceeding 10% or deficits in balance, within the field test
setting. The authors of the study propose that this test battery represents a
valuable resource for screening and overseeing elite field hockey athletes, not only
for establishing when they can return to sport after an injury but also for
identifying weaknesses throughout the season and tracking progress throughout.
Further studies with larger groups of participants could provide additional insight
into the relationship between performance, knee function, and injury rates in field
hockey players.
Conclusion
This study is the first to investigate functional knee stability of elite field
hockey players, considering their gender and playing class. The results indicate
that there are significant differences in functional knee stability between male and
female players as well as among athletes of different playing classes. The study
also highlights the importance of balance and stability in preventing lower limb
injuries and emphasizes the potential impact of sport-specific factors on functional
knee stability. The study underscores the importance of considering pre-existing
side deviations or other deficits in functional knee stability when assessing
athletes for return to sport decisions. These findings can benefit sports coaches
and physicians in improving athletic performance, identifying individual strengths
and weaknesses, preventing injuries or re-injuries, and facilitating the return to
sport after an injury.
Bibliographical Record
Lucie Hiepen, Niklas Bosserhoff, Florian Schaudig, Falko Heitzer, Marcus Jäger, Constantin Mayer. Functional Knee Stability in Elite Field Hockey Depends on Playing
Class and Gender. Sports Med Int Open 2025; 09: a24172488.
DOI: 10.1055/a-2417-2488
