Key words
return to sport - handball - test battery - knee
Introduction
Handball is a high intensity pivoting team sport that is rapidly growing in
popularity and media attention all over the world. It is characterized by intense
body contact, high running speed and quick changes of direction. Despite the growing
global interest of sports medicine and science in handball, athletes are often
affected by injuries [1]
[2]. In the last four summer Olympic Games,
handball was even one of the sports with the highest risk of injury [3]. Several surveys examined the relationship
between age and injury incidence in handball, but results are contradictory [4]
[5].
For example, Luig et al. detected the highest risk of injury for players aged
25–34 (up to 87.3% injured players during season) and the lowest
risk of injury for players<20 (56% injured players during season)
[6].
Regardless of age, the knee is the most severely injured joint (up to 35%)
and the anterior cruciate ligament (ACL) the most frequently injured structure of
the knee [7]. In addition, Muller et al.
recently found the age of the patient as one of five independent factors for the
return to preinjury sports after ACL injury [8].
Previously known risk factors of non-contact ACL injury are either determined, such
as sex, genetic predisposition, and width of intracondylar notch, or extrinsic
(sport, underground, shoes, training intensity, and level of competition) [9]
[10]
[11]
[12]. Most recently, interventional and
biomechanical studies found the knee abduction angle during landing, trunk
displacement, or power development during lift-off in a countermovement jump to be
correlated with non-contact ACL injuries and helpful in prevention [13]
[14]
[15]
[16]
[17]
[18]. In consequence,
athletes’ neuromuscular control, as an intrinsic mutable risk factor, is a
key factor to prevent such serious knee injury. Reasonable test and measures
therefore involve tasks in jumping [18],
landing [13], side-cutting [19]
[20]
and balance [21]
[22] and are summarized as “functional
knee stability” [23]. Unfortunately,
scientific studies measuring functional knee stability in an objective manner in
handball are lacking [24]
[25]. Moreover, most of the recent studies in
handball examined professional players in national teams or elite divisions of the
respective country. Since athletes in higher divisions have a higher level of
physical fitness [26]
[27], a transfer of their results to
non-professionals is only possible to a limited extent. However, the majority of
handball players are non-professional athletes and remain unrepresented so far.
The aim of the present study was to evaluate functional knee stability in non-elite
handball with respect to players’ age based on an established test battery
to identify the major risk factors of neuromuscular control as a possible link to
the high rate of knee injuries in team handball. It was assumed that knee function
differs related to age. Secondly, collected data of this study should provide the
basis for a first objective reference set to interpret the functional knee stability
in non-elite handball, used to identify athletes’ deficits to prevent
(re-)injuries and to facilitate the decision for return to sports (RTS) in an
amateur athlete.
Material And Methods
Subjects
A total of 261 non-elite handball players (female n=130; male
n=131) with a mean age of 25.1±5.8 years were recruited.
Participants had to be at least 16 years of age and free of injury for at least
six months before participation. Individuals with pain in the lower limbs were
excluded. All subjects completed a 24-item questionnaire including demographic
data as well as handball-specific characteristics (e.g., league, handball
experience, training load). After testing, participants were divided into three
groups according to their age:
-
Young athletes (AG1:16–19 years; female/male:
12/28; mean age: 18.0±0.9 years)
-
Middle-aged athletes (AG2 20–29 years; female/male:
97/68; mean age: 24.0±2.9 years)
-
Elderly athletes (AG3: over 30 years; female/male: 21/35;
mean age: 33.5±4.5 years)
Anthropometrics and handball-specific characteristics of the players in the
respective age groups are summarized in [Table
1].
Table 1 Descriptive analysis (mean and SDs) and p-value of
anthropometrics and handball-specific characteristics regarding
athlete’s age (AG1 16–19 years; AG2 20–29
years; AG3 over 30 years) for n=261. The p-values describe
significant differences between age groups. F, female; M,
male.
|
AG1 (F/M:12/28)
|
AG2 (F/M: 97/68)
|
AG3 (F/M: 21/35)
|
p-value
|
|
Body height (cm)
|
179.3±8.3
|
176.6±9.5
|
179.0±9.5
|
0.107
|
|
F169±4.6
|
F 170.1±6.3
|
F 171.0±6.8
|
0.667
|
|
M 183.8±4.9
|
M185.2±6.2
|
M 183.7±7.5
|
0.443
|
|
Body weight (kg)
|
76.8±12.1
|
78.4±14.0
|
84.0±12.3
|
0.011
|
|
F65.5±9.7
|
F 71.1±11.3
|
F 76.2±11.4
|
0.029
|
|
M 81.6±9.7
|
M88.8±10.6
|
M 88.7±10.4
|
0.006
|
|
BMI (kg/m2)
|
23.5±2.7
|
24.8±3.2
|
25.9±2.9
|
0.001
|
|
F22.7±3.4
|
F 24.3±3.2
|
F 25.7±4.0
|
0.047
|
|
M 23.8±2.3
|
M 25.4±3.2
|
M 26.0±2.1
|
0.006
|
|
Handball experience (years)
|
10.5±2.6
|
14.3±4.8
|
22.9±6.2
|
0.000
|
|
F10.0±3.2
|
F 14.2±5.0
|
F 21.2±4.6
|
0.000
|
|
M 10.7±2.4
|
M 14.5±4.6
|
M 23.8±6.9
|
0.000
|
|
Training load (hours/week)
|
7.6±3.4
|
5.9±2.1
|
5.8±2.3
|
0.000
|
|
F 7.8±4.2
|
F5.5±1.8
|
F 5.6±2.1
|
0.003
|
|
M 7.4±3.1
|
M 6.4±2.5
|
M 5.9±2.5
|
0.077
|
|
Played matches (last season)
|
24.5±10.5
|
18.6±8.5
|
17.4±9.3
|
0.000
|
|
F 25.0±10.3
|
F 19.7±7.5
|
F 17.1±7.3
|
0.021
|
|
M 24.3±10.8
|
M 17.1±9.6
|
M 17.6±10.5
|
0.007
|
|
Number of knee injuries
|
0.4±0.7
|
0.8±1.1
|
1.3±1.8
|
0.001
|
|
F 0.3±0.5
|
F 0.8±1.1
|
F 1.7±1.8
|
0.002
|
|
M 0.4±0.7
|
M 0.6±0.9
|
M 1.0±1.8
|
0.073
|
Knee injury was defined as an injury caused by handball (game or training) and
leading to a training and/or competition absence of at least one week. A
history of previous knee injury or pain was reported by 36.8% of the
subjects. Before completing the test protocol, participants or their legal
representatives provided written consent as approved by the Ethics Committee of
the local university (18–8078-BO) in accordance with the Declaration of
Helsinki. To ensure the same conditions for all subjects, data was collected
during the pre-season of all handball teams (June–August). In this
period, all athletes participated in a regular light resistance training for
upper and lower limbs twice a week.
Test battery
For assessment of functional knee stability, athletes completed the Back in
Action performance test battery (BiA) [28]
[29]. These functional tests
include:
-
stability analysis: two-legged (TL-ST) and one-legged (OL-ST),
-
countermovement jumps: two-legged (TL-CMJ) and one-legged (OL-CMJ),
-
plyometric jumps: two-legged (TL-PJ),
-
speedy jumps: one-legged (OL-SJ),
-
quick feet test (QF).
The test elements are described in detail in the following sections.
The stability analysis (TL-ST and OL-ST) was performed on a freely moveable MFT
Challenge Disc (TST Trendsport, Grosshöflein, Austria) connected to a
computer. The PC screen provided visual feedback during balancing on the disc.
For two-legged stability, athletes were asked to stand on both legs with
slightly bent knees in the center of the disc and keep their balance for
20 seconds ([Fig. 1a]). The level
of stability was measured (Balance Index Scale (BIS): 1 pt., best score;
5 pts., worst score). The one-legged stability test was performed identically to
the two-legged stability test but had to be performed one-legged ([Fig. 1b]). The one-legged stability test
was initiated with the dominant leg, followed by the non-dominant leg [28]
[29].
Fig. 1 Performing the stability tests of the Back in Action test
on the MFT disc. a) The two-legged stability test (TL-ST).
b) The one-legged stability test (OL-ST).
The jump tests were performed using the Myotest sensor (Myotest S.A., Sion,
Switzerland). The sensor was fixed to the player’s pelvis with the help
of a belt. Before performing the test, the athlete had to stand still in an
upright position with arms placed on the hips. In this initial position, the
Myotest sensor determined a baseline to calculate the maximum jump height (cm),
relative power per body weight (W/kg), and ground contact time (ms). To perform
the two-legged countermovement jumps (TL-CMJ), athletes were asked to bend their
knees and jump as high as possible. The test was performed without an arm swing,
and hands were kept on the waist throughout the jumping process ([Fig. 2a]). The one-legged test was
executed the same way as the TL-CMJ test ([Fig.
2b]). The test was initiated with the dominant leg, followed by the
non-dominant leg.
Fig. 2 Performing the jump tests of the Back in Action test.
a) The two-legged countermovement jump (TL-CMJ) test.
b) The one-legged countermovement jump (OL-CMJ) test.
For the two-legged plyometric jumps (TL-PJ), athletes were asked to perform four
consecutive jumps as high as possible. For minimal ground contact time, they
were instructed to rebound explosively between the jumps. The arms had to be
placed on the hips throughout the entire test.
To test the one-legged speedy jumps (OL-SJ), the Speedy Basic Jump Set (TST
Trendsport, Grosshöflein, Austria) was used. The athletes were
instructed to complete 16 one-legged jumps through a coordination course of red
(forward-backward-forward jumps) and blue (sideway jumps) hurdles as fast as
possible, and time was recorded ([Fig.
3a]). As in the previous one-legged measurements, the dominant and the
non-dominant legs were tested.
Fig. 3 Performing the speed and agility tests of the Back in
Action test. a) The one-legged speedy jump (OL-SJ) test.
b) The quick feet (QF) test.
For the quick feet test, the Speedy Basic Jump Set was also used. The
participants were instructed to step in and out of a box for 15 times as quickly
as possible ([Fig. 3b]). Timekeeping
started when one foot hit the center of the box and ended when both feet were
outside.
Statistical analysis
Statistical analysis was performed using SPSS (V25 for mac; IBM Corp., Armonk,
NY, USA). Mean values and standard deviations (SDs) are presented for dependent
variables. To test for normal distribution of the variables, the
Kolmogorov–Smirnov test was used. Differences between age groups were
tested either with an analysis of variance (ANOVA) or with the
Kruskal–Wallis test. The pair-wise analyses to identify differences
between the variables were made using the unpaired t-test or the
Mann–Whitney U test. After a Bonferroni correction for multiple
comparisons was applied, the level of significance was set at
p<0.05.
Results
The Back in Action test results of the 261 non-elite athletes of different age groups
revealed significant differences in functional knee stability. Related data is
presented for all, and the females (F) and the males (M) for each age group
separately ([Table 2]).
Table 2 Descriptive analysis (mean and SDs) and p-value of the
Back in Action performance tests regarding athlete’s age (AG1
16–19 years; AG2 20–29 years; AG3 over 30 years) for
n=261. The p-values describe significant differences between the
groups. F, female; M, male.
|
AG1 (F/M:12/28)
|
AG2 (F:M: 97/68)
|
AG3 (F/M: 21/35)
|
p-value
|
|
Leg Stability (score)
|
|
|
|
|
|
Two-legged
|
3.9±0.6
|
3.8±0.7
|
4.0±0.8
|
0.018
|
|
F 3.2 ± 0.4
|
F 3.4 ± 0.7
|
F 3.6 ± 0.8
|
0.247
|
|
M 4.2 ± 0.3
|
M 4.3 ± 0.4
|
M 4.3 ± 0.6
|
0.541
|
|
One-legged
|
|
Dominant leg
|
3.9±0.7
|
3.7±0.7
|
4.0±0.6
|
0.009
|
|
F 3.2±5.7
|
F 3.4±0.7
|
F 3.6±0.8
|
0.340
|
|
M 4.2±0.5
|
M 4.0±0.5
|
M 4.2±0.4
|
0.129
|
|
Non-dominant leg
|
3.7±0.7
|
3.7±0.6
|
3.9±0.6
|
0.052
|
|
F 3.1±0.6
|
F 3.4±0.6
|
F 3.4±0.6
|
0.305
|
|
M 4.0±0.5
|
M 4.0±0.5
|
M 4.2±0.4
|
0.138
|
|
Countermovement jumps
|
|
|
|
|
|
Two-legged
|
36.2±8.2
|
31.5±7.4
|
32.6±6.8
|
0.001
|
|
Height (cm)
|
F 30.2±4.3
|
F 27.4±5.4
|
F 26.3±4.7
|
0.114
|
|
M 38.7±8.2
|
M 36. 7±6.5
|
M 35.1±5.7
|
0.112
|
|
Power (W/kg)
|
46.5±6.6
|
42.6±6.0
|
43.4±5.2
|
0.003
|
|
F 41.4±4.1
|
F 39.3±4.8
|
F 38.6±4.0
|
0.237
|
|
M 48.6±6.3
|
M 46.9±4.5
|
M 45.8±3.9
|
0.079
|
|
One-legged height (cm)
|
|
|
|
|
|
Dominant leg
|
24.7±6.1
|
21.7±5.9
|
21.1±4.6
|
0.003
|
|
F 19.0±2.9
|
F 18.7±4.0
|
F 17.9±2.9
|
0.634
|
|
M 27.1±5.5
|
M 25.6±5.6
|
M 22.8±4.4
|
0.005
|
|
Non-dominant leg
|
24.5±5.5
|
21.3±5.2
|
20.1±4.1
|
0.002
|
|
F 19.8±2.3
|
F 18.9±4.0
|
F 17.8±4.8
|
0.358
|
|
M 26.5±5.2
|
M 24.2±5.3
|
M 22.5±3.1
|
0.005
|
|
One-legged power (W/kg)
|
|
|
|
|
|
Dominant leg
|
37.0±5.7
|
35.0±5.2
|
35.3±3.9
|
0.073
|
|
F 31.0±3.7
|
F 31.8±3.5
|
F 31.7±2.8
|
0.676
|
|
M 39.5±4.3
|
M 39.2±3.9
|
M 37.2±3.1
|
0.031
|
|
Non-dominant leg
|
37.1±5.0
|
34.6±4.8
|
35.0±4.9
|
0.009
|
|
F 31.7±3.2
|
F 31.9±3.8
|
F 31.6±4.7
|
0.947
|
|
M 39.3±3.8
|
M 38.2±3.6
|
M 36.6±2.7
|
0.007
|
|
Plyometric jumps
|
|
|
|
|
|
Height (cm)
|
37.2±10.9
|
32.1±9.3
|
31.9±8.2
|
0.007
|
|
F 32.1±3.3
|
F 28.4±8.3
|
F 26.2±6.2
|
0.122
|
|
M 39.5±12.2
|
M 37.0±8.0
|
M 35.1±7.5
|
0.164
|
|
Ground contact time (ms)
|
249.9±104.7
|
221.3±86.0
|
239.0±99.1
|
0.232
|
|
F 188.0±48.9
|
F 219.8±91.7
|
F 204.4±80.8
|
0.427
|
|
M 276.5±111.5
|
M 221.2±76.8
|
M 257.1±102.8
|
0.026
|
|
Speedy jumps (s)
|
|
|
|
|
|
Dominant leg
|
7.4±2.4
|
7.7±2.1
|
7.9±3.4
|
0.486
|
|
F 7.4±0.7
|
F 8.2±2.4
|
F 9.2±3.8
|
0.117
|
|
M 7.4±2.9
|
M 7.1±1.4
|
M 7.0±1.1
|
0.568
|
|
Non-dominant leg
|
7.4±3.2
|
7.7±2.0
|
7.9±3.4
|
0.162
|
|
F 7.4±1.3
|
F 8.2±2.4
|
F 9.8±5.0
|
0.066
|
|
M 7.4±3.8
|
M 7.2±1.5
|
M 7.0±1.1
|
0.760
|
|
Quick Feet (s)
|
8.8±1.1
|
9.3±1.4
|
9.3±1.3
|
0.089
|
|
F 8.6±1.2
|
F 9.5±1.2
|
F 9.9±1.4
|
0.016
|
|
M 8.8±1.0
|
M 8.9±1.5
|
M 9.1±1.3
|
0.792
|
Besides the comparable results of the speedy jumps and quick feet tests, significant
group differences were detected in all performance tests for stability and strength
measures.
With respect to stability, group differences could be detected in both the two-legged
and one-legged stability analyses. Significant differences in the TL-ST could be
shown between players of AG2 and AG3 (p=0.01) for the cohort of athletes.
Here, young players exhibited lower balance scores than elderly players, reflecting
superior balance. Significant group differences were also present in the OL-ST of
the dominant leg between AG2 and AG3. Here, young athletes presented lower balance
scores than the elderly (p=0.01). In summary, the lowest balance scores
overall were demonstrated by female athletes of AG1, while female and male players
of AG3 achieved the highest balance scores (worst balance).
Regarding strength, significant differences were shown in all jump tests ([Fig. 4]). In the two-legged countermovement
jumps, females and males of the AG1 jumped significantly higher than athletes of the
AG2 as well as AG3, and male athletes developed significantly more power than
players in the AG2 or AG3. With respect to the one-legged countermovement jump,
males of AG1 jumped significantly higher than players of AG2 or AG3 in the dominant
and the non-dominant leg, which was also significant for the entire population. In
addition, participants of AG1 reached significantly more power in the OL-CMJ of the
non-dominant leg than participants of the AG2 (p=0.01). In the jump height
of the two-legged plyometric jumps, individuals of the AG1 performed higher jumps
than individuals of the AG2 (p<0.01) and AG3 (p<0.03) with ground
contact times being comparable.
Fig. 4 Results of the jump performance of the Back in Action test with
respect to players’ age for n=261. Female and male
athletes.
Means, standard deviations (mean±SD) and significant differences of age
groups of both sexes regarding anthropometrics ([Table 1]) and functional performance tests ([Table 2]) are presented in tables.
Discussion
This cross-sectional study was designed to evaluate the functional knee stability
(jumping, landing, cutting, balance) of handball players regarding age and sex using
an established test battery giving objective metric measures. Overall, the cohort
of
athletes of AG1 performed significantly better in stability and strength compared
to
older athletes. This may lead to premature conclusions regarding the functional knee
stability and injury risk of older athletes. Nevertheless, female and male handball
players showed relevant difference in the results of the Back in Action test and
therefore, to study the effect of age alone, both sexes have to be evaluated
separately.
Surprisingly, previous studies on differences in injury incidence regarding age
groups showed contradictory results. In 2014, Monaco et al. [4] analyzed injuries of 496 elite male handball
players of different ages over five seasons. Here, no statistically significant
differences between the groups were found despite differences in age. In contrast,
Tabben et al. [5] conducted a study during the
Men’s Handball World Championship 2017 in France with 387 players and
detected a higher risk of match injuries for elderly players compared with their
younger team colleagues.
Female athletes, on the other hand, are known to be at high risk for first-time
non-contact ACL injury owing to several reasons [30], especially increased dynamic valgus and high abduction loads [17] approx. 40 milliseconds after initial
contact of landing [20], and hormonal changes
during the preovulatory phase [31]. For the
summer Olympic games in London, handball was one of the most injury-causing
disciplines (5%), with women sustaining injuries more frequently than the
men. In addition to the investigations mentioned above, differences in various
athletic skills regarding age could be detected in the current study, which are
outlined below.
Age and balance measures
Balance analysis revealed that male and female performance of different age
groups does not differ by age but by sex. Whereas males show worse balance
scores compared to women in all categories, females of AG1 present the best
results in TL-ST and OL-ST. It is worth mentioning that results of the
non-dominant leg, most often used for landing after a jump shot, were even
superior. Still, level of stability was lower when data was compared to the
reference population of both sexes published by Hildebrandt (TL-ST:
2.60±0.47) [32]. As separately
presented data for males and females does not change significantly, an effect of
age on one-legged or two-legged stability was not proven. Within this context,
training of neuromuscular stability has proven to improve dynamic balance but
not static balance in an interventional program [33]. Similar programs have been shown to reduce ACL injury risk for
female team handball players [15] and for
a population of both sexes [34]. Moreover,
a prospective study in Norway by Steffen found no association between postural
control and ACL injury in 838 cases of female handball and football players
[35]. Therefore, the significance of
postural control and its testing in handball remains a matter of debate and
needs further scientific attention.
Age and complex task measures
Speedy jump test and quick feet test results were on the same level, and
therefore a general impaired physical fitness of older athletes (AG3) in
comparison to young athletes (AG1) was not found, even when both sexes were
analyzed separately. Without reaching a significant level, male athletes trended
toward improvement in the speedy jumps, while female athletes showed slower
times with increasing age. It is noteworthy that all noted times of the complex
measures were slower than reference data of the test battery [32]. Moreover, results of female age groups
were not significantly different in stability measures but in the quick feet
test. More complex functional testing may therefore be sensitive to illustrate
differences in such settings. This is in line with studies on trunk control
[16], backward landing [36], or vertical jump kinetics [18].
Age and strength measures
Results of the strength-related test items mirror these findings: While female
athletes reflect similar measures (with only non-significant changes), male
athletes present with alterations in power (W/kg body weight) and jump
height in the one-legged test. A decrease in whole body muscle mass with age may
possibly account for these observations [37].
On the other hand, in comparison to data of 42 elite handball players reported by
Wagner et al. [38], jumping performance of
all athletes barely differed, while Wagner`s participants were
semiprofessional. In a study by Granados et al. [26], female elite (EP) and amateur players (AP) were compared by age
(EP: 23.5 y avg.; AP: 21.4 y avg.). These subjects are
comparable to female AG2 of the current study. Presented data on jump height
(EP: 34.9±5 cm and AP: 33.0±3 cm) is higher than
heights reached by females in our study. It is notable that because data between
elite and amateur players did not differ significantly, Granados went on to
conclude that the differences in the fat-free body mass alone could account for
the differences between the groups. Similarly, body weight and BMI are higher in
older age groups in this study as well, although fat-free body mass was not
measured. This trend was also noticed by Wagner, starting at the age of U15,
U17, U19 and U23 aged elite athletes in Austria [38].
The subject of leg symmetry measurements in pre-injured athletes is
controversial. This population has not shown relevant limb symmetry differences
in the CMJ or the speedy jumps, whereas elite athletes in snowboarding [39] or judo and Taekwondo were seen with up
to 25% of athletes under 90% symmetry [40]. For handball players on an elite
level, no differences in limb symmetry were found when normalized for body mass
[41] and is comparable to a
“normal population” regarding limb symmetry [42]. Rehabilitation research has even shown
significant limb differences in athletes 9 months post ACL injury [43] as well as in athletes with prior ACL
injury [44] and is consequently associated
with impaired performance in a return-to-sport testing [45].
VIn comparison to the power calculation of the CMJ, age-related measure of peak
torque (Nm) for isokinetic knee extension showed parallel results in a study on
healthy subjects by Harbo [46]. Neder et
al. .[47] evaluated 96 non-sportive
subjects between 20–80 years in an isokinetic setting with a leg
extensor. They found an inverse relationship of age and power starting at the
age of 20 in their regression model. Similarly, non-elite male athletes in this
study showed significant decreases in W/kg between AG2
(24.0±2.9 y) and AG3 (33.5±4.5 y). They
concluded that the main factors of prediction were sex and age, although only
few differences in strength exist when normalized for active muscle. Lindle et
al. [48] found similar courses of
concentric and eccentric power of the knee extensors, although their regression
model did not reveal the drop of peak torque power to become significant before
the age of 35 to 40 years. Borges [49]
controversially reported a significant decrease for males between the age of 20
and 30 years, which may partly account for the differing results of AG1 and
AG2.
In addition, the difference in training frequency of the respective age groups in
our study might be a confounder. Young athletes (16–19 years) spent
significantly more time (1.7±1.2 hours) for handball training
than older handball players. This difference in physical activity is known to
interact with age differences [50]. This
may lead to impaired physical fitness, possibly resulting in later injury.
Similar findings were recently reported by Szymski [51] for soccer players of different leagues
but also for handball players of different leagues [52].
Overall, results of male and female athletes were inferior to the initial data
set given by Hildebrandt [29], which
raises concerns regarding the physical fitness and injury risk of these amateur
handball players.
Consequently, the implementation of a prevention program is the next step, such
as the FIFA 11+, which has shown promising results in male athletes
[53]. Unfortunately, results differ
for similar programs in female populations [54]. Various freely accessible prevention programs are available,
which may be adapted depending on the specific sport discipline [23].
This study has some limitations. First, the main methodological limitation of
this study is the limited sample size. The number of 261 subjects was
constrained by the size of the handball teams. To obtain more detailed
information about functional knee stability in handball, further studies of
handball players are required. Additionally, this study was cross-sectional with
one timepoint of measurement only. Further investigations including
interventions and prospective study designs are necessary to prevent further
injuries.
Summary
In summary, the present study is first to report differences in functional
performance in relation to players’ age and sex in handball. Results show
stable levels of stability test in all three age groups and both sexes. Power
measurements revealed a clear decrease of W/kg body weight for the male
athletes as age increases, whereas female athletes maintain their level of
performance. For the quick feet test, women decrease significantly with age. These
findings demonstrate the importance of age-specific screening and prevention.
Moreover, the present data set on the basis of an established test battery can be
used as a first reference set for knee stability in non-elite handball including
sport-specific reference data for non-professional athletes. This allows coaches to
identify individual strengths and weaknesses to design training models to improve
handball-specific performance and prevent injuries. Moreover, the reference data
values of the present study may help to establish a new gold standard for
(re-)injury prevention in a return-to-sport setting. Up to now, the comparison to
the uninjured opposite leg is still common, although the uninjured knee may be
deficient itself. The objective reference values of this study can facilitate the
physicians’ decision of a safe return to sports and help to set
rehabilitation goals.
