Abstract
Background: Anterior cruciate ligament (ACL) injuries in youth athletes are multifactorial, and the relative contributions of neuromuscular and neurocognitive variables remain inadequately comprehended. Methods: In this prospective cohort study, 220 young handball players (104 girls and 116 boys; mean age 16.3 ± 1.4 years) participated in functional testing with the Back in Action system and baseline neurocognitive evaluation with the ImPACT battery. During the 24-month follow-up period, orthopedic specialists identified ACL damage, which was confirmed by magnetic resonance imaging (MRI). Univariable logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to evaluate predictive capability. Results: During the 24-month follow-up, 26 athletes sustained an ACL injury. Prolonged plyometric ground contact time was significantly associated with ACL injury occurrence in logistic regression analysis (p = 0.019) and demonstrated fair discriminatory ability (AUC = 0.63) (OR = 0.98 per ms; 0.98 95% CI: 0.964–0.997). Female sex emerged as a profound and independent risk factor (OR = 5.74). Conclusions: Neuromuscular performance, specifically plyometric ground contact time and female sex, has predictive ability for ACL damage in youth handball players, while separate cognition assessments failed to independently differentiate injury risk. These findings support the use of objective neuromuscular evaluation in comprehensive injury prevention strategies in youth sport.
Keywords: neurocognition, ground contact time, handball, ImPACT, ACL
1. Introduction
Prior research has shown that, besides executing their own sport-specific motions, team players must also visually perceive the actions of their opponents and teammates [1,2,3,4]. Furthermore, team athletes have to react to stimuli as quickly as possible which can play a crucial role in the development of non-contact ACL injuries [5,6]. Delays in decision-making, attention, reaction time, or coordination due to time pressure can increase the risk of injury [7,8,9,10]. Consequently, it has been mentioned multiple times to not only assess physical performance but also to conduct sport-specific neurocognitive evaluations [10,11,12,13,14]. Bittencourt et al. were among the first to say that the etiology of injuries can only be properly examined with a complex approach; it is not enough to examine functional performance [2]. Neurological, biomechanical, neuromuscular, functional, and sport-specific factors contribute to a player’s injury [2]. Bahr and Krosshaug supplemented this by analyzing specific interactions between the athlete and their opponents [1]. Furthermore, physiological fatigue must be considered a critical component of this complex etiology, as it can significantly impair both cognitive processing and neuromuscular control [2,15,16,17,18,19]. Understanding the interplay between baseline neurocognitive skills and functional capacity is therefore essential to establish a criterion for injury predisposition before fatigue-induced performance degradation occurs. For example, in team sports such as handball, perception–action is key, meaning an athlete must be able to assess their own, their teammate’s, and the opponent’s actions in a certain situation under time pressure. The athlete also has to choose the best solution, as quickly as possible, and then implement it in a coordinated manner [5,6,15,16,17,18]. A possible solution can be a screening process which focuses on neurocognitive skills, such as visual–motor speed, reaction time, multitasking skills, visual memory, verbal memory, error rate, and the ability to make decisions [11,13,14,20,21,22,23,24].
Recent research suggests that injury etiology is a complex, multifactorial phenomenon where neurological, biomechanical, and neuromuscular factors converge [2]. Consequently, assessing functional performance in isolation may be insufficient to capture the full spectrum of injury risk; a comprehensive approach integrating neurocognitive evaluation is increasingly advocated [10,11,12,13,14]. One of the most validated tools for this purpose is ImPACT® (Immediate Post-Concussion Assessment and Cognitive Testing) software, Version 4.0 (ImPACT Applications, Inc., Coralville, IA, USA), a computer-based system designed to measure domains such as visual–motor speed, reaction time, and memory. For instance, Herman et al. [8] found that athletes who subsequently sustained ACL injuries performed significantly worse in all ImPACT domains compared to healthy controls. This suggests that the neurocognitive deficits captured by ImPACT may manifest as a “cognitive–motor interference,” where the brain’s processing demands detract from the neuromuscular control required for joint stability.
To fully understand this interference, however, neurocognitive data must be integrated with objective measures of physical execution. While the ImPACT system assesses the “central” processing speed, functional deficits in strength, neuromuscular control, and dynamic balance—particularly lower-limb asymmetries—represent the “peripheral” risk factors that increase knee valgus loads [23,24]. To bridge this gap, the Back in Action (BIA) test battery (Corehab S.r.l., Trento, Italy) has been developed as a comprehensive functional screening tool. The BIA battery assesses agility, balance, and plyometric control through a series of increasingly complex tasks [25]. By utilizing both the ImPACT and BIA systems, it is possible to screen for a broader spectrum of risks, from delayed decision-making to the subsequent failure in neuromuscular compensation during high-demand sports like handball.
Despite the clinical relevance of these tools, there is currently no unified framework to analyze how position-specific neurocognitive and functional profiles correlate with ACL injury incidence. This study aims to address this gap by employing the ImPACT system for neurocognitive profiling and the Corehab BIA system for functional assessment.
The primary objective was to detect both the neurocognitive and functional profiles of elite players by gender and position, and to examine the correlation of these multidimensional profiles with ACL injury incidence. We hypothesized that: (1) suboptimal neuromuscular outcomes and functional asymmetries identified by the BIA test battery would correlate with a higher incidence of ACL injuries; and (2) diminished neurocognitive performance, specifically prolonged reaction time and reduced visual–motor speed measured with ImPACT, would be associated with increased ACL injury incidence.
2. Materials and Methods
2.1. Setting
This prospective cohort study included a baseline cross-sectional performance assessment followed by injury surveillance. A total of 220 youth handball players competing in the Hungarian National First League (U15–U20) were recruited between September and October 2023. Inclusion criteria were active roster status and full participation in team training. Athletes with an ACL injury within the previous 12 months were excluded. All participants and their legal guardians provided signed informed consent. The study was approved by the institutional ethics committee. The research license number from the Department of Clinical Research of the National Public Health and Pharmaceutical Center was NNGYK/03180-6/2025. Athletes were recruited from National First League youth teams. The measurements took place in Budapest, with a total of 220 people 16.33 ± 1.38 (104 girls 16.32 ± 1.39 and 116 16.34 ± 1.39 boys). Functional testing was done with the Back in Action (Corehab) system, while neurocognitive performance was tested with the ImPACT system. The athletes were then followed up for 24 months to record the ACL injury incidence. The injuries were reported by a doctor working alongside the teams.
Handball players who had an ACL injury in the last 1 year were excluded, and during the examined period the players who were medically diagnosed with an ACL injury were considered. ACL injury was defined as a complete or partial rupture confirmed by clinical examination and magnetic resonance imaging (MRI) and diagnosed by an orthopedic specialist.
2.2. Neuromuscular Performance—Back in Action System
Functional lower-limb performance was evaluated using the Back in Action (BIA) test battery (Corehab S.r.l., Trento, Italy), assessing postural stability, plyometric performance, agility, speed, and limb symmetry indices. The BIA testing battery utilizes its own proprietary stabilometer. The ‘Pass/Fail’ designation is determined by a proprietary algorithm that benchmarks the athlete’s performance against an internal database, adjusted for individual anthropometric variables (age, height, body mass, and BMI). This automated categorization eliminates potential subjective bias, as the investigator cannot manually influence or override the system-generated outcomes.
Table 1 shows the functional skills and their description. The test reliability is ICCs = 0.688–0.921 (depending on the subtest) [25]. The detailed description and photos of the subtests can be found in the Supplementary Materials.
Table 1.
Functional skills (BIA) and their detailed description.
| Skill | Description |
|---|---|
| Stability | The test series begins with a two-leg stability test on a stabilometer with a trial attempt and three examinations. Then, there is an assessment of one-legged stability, also with a trial and two measurements. The players had to keep the ball (which represents the center of pressure) in the center of the target board marked on the laptop screen for 20 s. |
| Plyometrics | Four consecutive jumps were performed, during which the players have to jump as high as possible, with the least amount of time spent on the ground. Similar to stability tests, the plyometric jump is tested with one trial and two measurements, where the system evaluates both the height of the jumps and the ground contact time. |
| Speed | The speed was tested with the quick-feet test, where the players had to perform 15 stepping movements as quickly as possible on the stepladder manufactured for this purpose. There was a trial following with two measurements. |
| Agility | The Parkour (obstacle course) system examines knee joint stability in different directions, and dynamic balance. The player at the red step has to jump forward–backward–forward as fast as possible while standing on one leg, and at each blue step jump sideways. Here, after one trial, there are two measurements for both the dominant and non-dominant side. |
2.3. Neurocognitive Performance—ImPACT System
Neurocognitive performance was assessed using the ImPACT® (Immediate Post-Concussion Assessment and Cognitive Testing) software, Version 4.0 (ImPACT Applications, Inc., Coralville, IA, USA), which evaluates verbal memory, visual memory, visual–motor speed, reaction time, and dismiss. Testing was performed under standardized environmental conditions [10]. Regarding the ImPACT protocol: Participants did not perform warm-up trials to avoid practice effects, as per the standard clinical administration guidelines. Each specific task within the ImPACT battery consisted of three automated trials, with randomized stimuli to ensure data validity. To clarify the criteria for a ‘Pass’ result, we followed the international ImPACT standardization protocol. A ‘Pass’ was defined as achieving performance levels that met or exceeded the normative mean for the respective age group across the test modules. Specifically, a success rate of over 80% within the specific cognitive domains was required to satisfy the criteria.
The test reliability depends on the subtest components. Verbal memory ICC = 0.61; visual memory ICC = 0.68; reaction time ICC = 0.59; visual–motor speed ICC = 0.84. The composite scores and measurement details are included in the Supplementary Materials. Table 2 shows the subtests of each composite skill.
Table 2.
Neurocognitive skills (ImPACT) and their detailed description.
| Skill | Description |
|---|---|
| Verbal memory | Evaluates attentional processes, learning, and memory within the verbal domain. This composite score represents the average performance on:
|
| Visual memory | Evaluates visual attention and scanning, learning, and memory. This score in its current form comprises the average of:
|
| Visual–motor speed | Evaluates visual processing, learning and memory, and visual–motor response speed. This score comprises the average of the following scores:
|
| Reaction time | Evaluates average response speed. This score comprises the average of the following scores:
|
| Dismiss | Provides a measure of mistakes during each test module. This score is obtained by adding:
|
2.4. Testing
The neurocognitive test part was completed by the athletes on a weekend day at FTC Handball Academy in calm conditions. The regulations were complied with the cooperation and help of the parents. The test started at 10 am. Participants were instructed to refrain from electronic device use after 10:00 pm the night before testing and to get at least 8 h of sleep. To mitigate potential learning effects and task adaptation, participants were prohibited from performing any warm-up or practice sessions prior to the administration of the test.
The neuromuscular testing was held in the gym section of the academy, where the players arrived in teams, each of them on separate days. In 6 days, 6 teams were assessed, arriving from 10 am each day. On the day before the measurement, they were not allowed to participate in match situation training. On the day of the measurement, they were not allowed to participate in any kind of training.
2.5. Statistical Analysis
Statistical analyses were performed using IBM SPSS Statistics version 24.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean ± standard deviation or median and interquartile range, depending on distribution) were used to characterize the study population. Data normality was assessed using the Shapiro–Wilk test. To identify potential risk factors for ACL injury, univariable logistic regression analyses were first conducted for each neuromuscular and neurocognitive variable.
To address potential confounding factors as suggested during the peer-review process, sex was included as a covariate in our predictive models. The association between baseline neuromuscular, neurocognitive variables (e.g., ImPACT composite scores, 1-leg stability), and ACL injury occurrence was evaluated using multivariable logistic regression analysis. Results are reported as regression coefficients (ß), odds ratios (ORs), and 95% Confidence Intervals (CIs).
The discriminatory ability of the significant predictors was determined through receiver operating characteristic (ROC) curve analysis. The Area Under the Curve (AUC) was calculated to evaluate the overall model performance, and the optimal diagnostic threshold was identified using the Youden Index. Statistical significance was set at p ≤ 0.05 for all analyses.
3. Results
The mean age of the male participants (n = 116) was 16.34 ± 1.39 years, with an average body weight of 74.21 ± 12.45 kg, height of 182.39 ± 10.32 cm, and BMI of 22.40 kg/m2. Female participants (n = 104) had a mean age of 16.32 ± 1.40 years, body weight of 63.62 ± 10.27 kg, height of 170.85 ± 7.43 cm, and BMI of 21.75 kg/m2.
3.1. Neurocognitive Performance
Regarding neurocognitive screening, 26.9% of girls and 24.1% of boys passed the baseline tests while the majority (73.1% of girls and 75.9% of boys) did not reach the expected performance level.
Table 3 shows the results for verbal memory:
Table 3.
Verbal memory results per position.
| Average | Passed | Failed | Success % | |
|---|---|---|---|---|
| Wingers | 84.8 ± 10.1% | 46 people | 18 people | 71.9% |
| Pivots | 84.0 ± 9.0% | 20 people | 10 people | 66.7% |
| Back players | 83.7 ± 10.2% | 38 people | 22 people | 63.3% |
| Goalkeepers | 81.4 ± 8.9% | 18 people | 14 people | 56.3% |
| Center backs | 79.3 ± 13.9% | 20 people | 14 people | 68.8% |
Table 4 shows the results for visual memory:
Table 4.
Visual memory results per position.
| Average | Passed | Failed | Success % | |
|---|---|---|---|---|
| Wingers | 75.2 ± 9.9% | 29 people | 3 people | 90.6% |
| Pivots | 73.6 ± 10.4% | 24 people | 6 people | 80.0% |
| Back players | 72.0 ± 14.3% | 10 people | 5 people | 66.7% |
| Goalkeepers | 71.7 ± 14.8% | 12 people | 4 people | 75.0% |
| Center backs | 70.6 ± 8.5% | 14 people | 3 people | 82.4% |
Table 5 shows the results for visual–motor speed:
Table 5.
Visual–motor speed results per position.
| Average | Passed | Failed | Success % | |
|---|---|---|---|---|
| Center backs | 37.8 ± 4.7 points | 14 people | 20 people | 41.2% |
| Wingers | 36.5 ± 4.6 points | 30 people | 34 people | 46.9% |
| Back players | 37.4 ± 7.5 points | 28 people | 32 people | 46.7% |
| Pivots | 36.8 ± 4.7 points | 12 people | 18 people | 40.0% |
| Goalkeepers | 36.7 ± 7.4 points | 12 people | 20 people | 37.5% |
Table 6 shows the results for reaction time:
Table 6.
Reaction time results per position.
| Average | Passed | Failed | Success % | |
|---|---|---|---|---|
| Goalkeepers | 0.6 ± 0.1 s | 30 people | 2 people | 93.8% |
| Center backs | 0.6 ± 0.1 s | 28 people | 6 people | 82.4% |
| Wingers | 0.6 ± 0.1 s | 48 people | 16 people | 75.0% |
| Back players | 0.7 ± 0.1 s | 40 people | 20 people | 66.7% |
| Pivots | 0.7 ± 0.1 s | 18 people | 12 people | 60.0% |
Table 7 shows the results for dismiss:
Table 7.
Dismiss results per position.
| Average | Passed | Failed | Success % | |
|---|---|---|---|---|
| Pivots | 4.9 ± 3.0 points | 28 people | 2 people | 93.3% |
| Wingers | 5.2 ± 2.4 points | 64 people | 0 people | 100.0% |
| Goalkeepers | 5.4 ± 3.2 points | 32 people | 0 people | 100.0% |
| Back players | 5.8 ± 4.0 points | 54 people | 6 people | 90.0% |
| Center backs | 6.4 ± 3.4 points | 32 people | 2 people | 94.1% |
Comparison of the performance in neurocognitive skills by gender and position is summarized in Table 8.
Table 8.
Neurocognitive performance per position and sex.
| N = 220 People | Women (N = 104 People) |
Men (N = 116 People) |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Competency | Position | Average | Passed | Failed | % | Average | Passed | Failed | % |
| Verbal memory | Wingers | 86.0 ± 9.1% |
26 people | 4 people | 86.7% | 83.8 ± 11.1% |
20 people | 14 people | 58.8% |
| Back players | 85.6 ± 9.7% |
24 people | 6 people | 80.0% | 81.9 ± 10.8% |
14 people | 16 people | 46.7% | |
| Pivots | 84.0 ± 10.2% |
10 people | 6 people | 62.5% | 84.0 ± 8.3% |
10 people | 4 people | 71.4% | |
| Center backs | 73.6 ± 15.8% |
6 people | 8 people | 42.9% | 83.3 ± 11.5% |
14 people | 6 people | 70.0% | |
| Goalkeepers | 82.9 ± 8.2% |
8 people | 6 people | 57.1% | 80.3 ± 9.8% |
10 people | 8 people | 55.6% | |
| Visual memory | Wingers | 75.7 ± 10.9% |
26 people | 4 people | 86.7% | 74.8 ± 9.3% |
32 people | 2 people | 94.1% |
| Back players | 74.6 ± 11.9% | 24 people | 6 people | 80.0% | 72.6 ± 8.9% |
24 people | 6 people | 80.0% | |
| Pivots | 71.8 ± 17.3% |
10 people | 6 people | 62.5% | 72.3 ± 11.2% |
10 people | 4 people | 71.4% | |
| Center backs | 70.6 ± 9.6% |
12 people | 2 people | 85.7% | 70.6 ± 8.2% |
16 people | 4 people | 80.0% | |
| Goalkeepers | 73.0 ± 11.2% |
12 people | 2 people | 85.7% | 70.7 ± 17.7% |
12 people | 6 people | 66.7% | |
| Visual–motor speed | Wingers | 36.2 ± 4.8 p |
14 people | 16 people | 46.7% | 36.9 ± 4.8 p |
16 people | 18 people | 47.1% |
| Back players | 36.3 ± 6.7 p |
14 people | 16 people | 46.7% | 38.1 ± 7.6 p |
14 people | 16 people | 46.7% | |
| Pivots | 37.6 ± 5.5 p |
8 people | 8 people | 50.0% | 35.9 ± 3.9 p |
4 people | 10 people | 28.6% | |
| Center backs | 38.1 ± 6.4 p |
6 people | 8 people | 42.9% | 37.7 ± 3.4 p |
8 people | 12 people | 40.0% | |
| Goalkeepers | 37.6 ± 8.7 p |
6 people | 8 people | 42.9% | 35.9 ± 6.6 p |
6 people | 6 people | 33.3% | |
| Reaction time | Wingers | 0.6 ± 0.1 s |
22 people | 8 people | 73.3% | 0.6 ± 0.1 s |
26 people | 8 people | 76.5% |
| Back players | 0.7 ± 0.1 s |
18 people | 12 people | 60.0% | 0.6 ± 0.1 s |
22 people | 8 people | 73.3% | |
| Pivots | 0.7 ± 0.1 s |
8 people | 8 people | 50.0% | 0.6 ± 0.1 s |
10 people | 4 people | 71.4% | |
| Center backs | 0.6 ± 0.1 s |
12 people | 2 people | 85.7% | 0.6 ± 0.1 s |
16 people | 4 people | 80.0% | |
| Goalkeepers | 0.6 ± 0.1 s |
12 people | 2 people | 85.7% | 0.6 ± 0.1 s |
18 people | 0 people | 100.0% | |
| Dismiss | Wingers | 5.3 ± 2.3 p |
30 people | 0 people | 100.0% | 5.0 ± 2.6 p |
34 people | 0 people | 100.0% |
| Back players | 5.8 ± 3.7 p |
28 people | 2 people | 93.3% | 5.7 ± 4.5 p |
26 people | 4 people | 86.7% | |
| Pivots | 5.6 ± 3.6 p |
14 people | 2 people | 87.5% | 4.0 ± 2.2 p |
14 people | 0 people | 100.0% | |
| Center backs | 5.0 ± 2.8 p |
14 people | 0 people | 100.0% | 7.4 ± 3.6 p |
18 people | 2 people | 90.0% | |
| Goalkeepers | 4.4 ± 2.9 p |
14 people | 0 people | 100.0% | 6.2 ± 3.4 p |
18 people | 0 people | 100.0% | |
3.2. Functional Performance
In the functional tests, 80.8% of girls and 37.9% of boys met the passing criteria.
Table 9 shows the results for stability:
Table 9.
Stability test results per position.
| Two-Leg Stability | One-Leg Stability D | One-Leg Stability ND | Symmetry Index | |
|---|---|---|---|---|
| Wingers | 2.4 ± 0.8 point | 2.4 ± 0.7 point | 2.4 ± 0.7 point | 86.8 ± 9.2% |
| Back players | 2.5 ± 0.7 point | 2.7 ± 0.8 point | 2.5 ± 0.8 point | 88.2 ± 8.2% |
| Pivots | 2.6 ± 1.0 point | 2.8 ± 0.8 point | 2.5 ± 0.7 point | 87.1 ± 10.7% |
| Goalkeepers | 2.7 ± 0.9 point | 3.0 ± 0.9 point | 2.7 ± 0.7 point | 85.5 ± 14.9% |
| Center backs | 2.8 ± 0.6 point | 2.6 ± 0.6 point | 2.7 ± 0.6 point | 88.7 ± 8.3% |
Table 10 shows the results for plyometrics and speed:
Table 10.
Plyometrics and speed results per position.
| Plyometric Jump Height | Plyometric Jump Ground Contact Time | Quick Feet | |
|---|---|---|---|
| Wingers | 30.8 ± 7.7 cm | 163.8 ± 30.6 ms | 8.0 ± 0.8 s |
| Back players | 30.6 ± 7.0 cm | 170.1 ± 29.5 ms | 8.1 ± 1.3 s |
| Pivots | 30.5 ± 9.8 cm | 174.1 ± 32.0 ms | 7.7 ± 0.7 s |
| Center backs | 31.4 ± 7.6 cm | 186.4 ± 32.4 ms | 8.0 ± 0.8 s |
| Goalkeepers | 28.5 ± 6.0 cm | 183.3 ± 30.2 ms | 8.1 ± 0.8 s |
Table 11 shows the results for agility and obstacle course (Parkour):
Table 11.
Agility and Parkour system results per position.
| Parkour D | Parkour ND | Symmetry Index | |
|---|---|---|---|
| Wingers | 6.7 ± 0.6 s | 6.6 ± 0.5 s | 94.9 ± 3.9% |
| Center backs | 6.7 ± 0.8 s | 6.6 ± 0.9 s | 93.7 ± 5.4% |
| Back players | 6.8 ± 1.2 s | 6.8 ± 1.6 s | 94.7 ± 4.9% |
| Pivots | 6.9 ± 0.6 s | 6.7 ± 0.7 s | 95.1 ± 5.4% |
| Goalkeepers | 7.3 ± 1.1 s | 7.2 ± 1.2 s | 93.6 ± 6.0% |
Comparison of the neuromuscular performance by gender and position is summarized in Table 12.
Table 12.
Neuromuscular performance per position and sex.
| N = 220 People | Women (N = 104 People) |
Men (N = 116 People) |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Competency | Position | Average | Passed | Failed | % | Average | Passed | Failed | % |
| Two-leg stability | Wingers | 1.8 ± 0.6 points | 30 people | 0 person | 100% | 2.9 ± 0.5 points | 34 people | 0 person | 100% |
| Back players | 2.1 ± 0.5 points | 30 people | 0 person | 100% | 2.9 ± 0.7 points | 26 people | 4 people | 86.7% | |
| Pivots | 2.0 ± 0.7 points | 16 people | 0 person | 100% | 3.3 ± 0.9 points | 10 people | 4 people | 71.4% | |
| Center backs | 2.5 ± 0.5 points | 12 people | 2 person | 85.7% | 2.9 ± 0.5 points | 18 people | 2 person | 90% | |
| Goalkeepers | 2.1 ± 0.5 points | 14 people | 0 person | 100% | 3.2 ± 0.9 points | 14 people | 4 people | 77.8% | |
| One-leg stability D | Wingers | 1.9 ± 0.5 points | 30 people | 0 person | 100% | 2.7 ± 0.6 points | 32 people | 2 person | 94.1% |
| Back players | 2.2 ± 0.5 points | 26 people | 4 people | 86.7% | 3.0 ± 0.9 points | 26 people | 4 people | 86.7% | |
| Pivots | 2.3 ± 0.6 points | 14 people | 2 person | 87.5% | 3.3 ± 0.7 points | 10 people | 4 people | 71.4% | |
| Center backs | 2.3 ± 0.5 points | 12 people | 2 person | 85.7% | 2.8 ± 0.7 points | 16 people | 4 people | 80% | |
| Goalkeepers | 2.5 ± 1.0 points | 12 people | 2 person | 85.7% | 3.3 ± 0.7 points | 14 people | 4 people | 77.8% | |
| One-leg stability ND | Wingers | 2.0 ± 0.5 points | 30 people | 0 person | 100% | 2.8 ± 0.6 points | 30 people | 4 people | 88.2% |
| Back players | 2.3 ± 0.5 points | 26 people | 4 people | 86.7% | 3.1 ± 0.8 points | 28 people | 2 person | 93.3% | |
| Pivots | 2.1 ± 0.5 points | 16 people | 0 person | 100% | 3.1± 0.5 points | 10 people | 4 people | 71.4% | |
| Center backs | 2.5 ± 0.4 points | 12 people | 2 person | 85.7% | 2.9 ± 0.6 points | 18 people | 2 person | 90% | |
| Goalkeepers | 2.2 ± 0.5 points | 14 people | 0 person | 100% | 3.0 ± 0.8 points | 16 people | 2 person | 88.9% | |
| Plyometric height | Wingers | 27.0 ± 4.0 cm | 30 people | 0 person | 100% | 34.1 ± 8.7 cm | 34 people | 0 person | 100% |
| Back players | 28.6 ± 6.8 cm | 26 people | 4 people | 86.7% | 32.6 ± 6.9 cm | 26 people | 4 people | 86.7% | |
| Pivots | 26.6 ± 6.6 cm | 16 people | 0 person | 100% | 34.9 ± 11.4 cm | 14 people | 0 person | 100% | |
| Center backs | 28.6 ± 5.7 cm | 14 people | 0 person | 100% | 32.6 ± 8.6 cm | 16 people | 4 people | 80% | |
| Goalkeepers | 26.8 ± 7.1 cm | 10 people | 4 people | 71.4% | 29.8 ± 5.0 cm | 14 people | 4 people | 77.8% | |
| Plyometric ground contact time | Wingers | 153.4 ± 18.3 ms | 30 people | 0 person | 100% | 173.1 ± 36.5 ms | 34 people | 0 person | 100% |
| Back players | 158.3 ± 19.2 ms | 26 people | 4 people | 86.7% | 181.9 ± 33.7 ms | 26 people | 4 people | 86.7% | |
| Pivots | 164.9 ± 23.9 ms | 16 people | 0 person | 100% | 184.6 ± 38.1 ms | 14 people | 0 person | 100% | |
| Center backs | 153.9 ± 23.9 ms | 14 people | 0 person | 100% | 196.7 ± 34.7 ms | 16 people | 4 people | 80% | |
| Goalkeepers | 179.7 ± 22.3 ms | 10 people | 4 people | 71.4% | 186.0 ± 36.3 ms | 14 people | 4 people | 77.8% | |
| Parkour D | Wingers | 7.0 ± 0.5 s | 30 people | 0 person | 100% | 6.5 ± 0.4 s | 34 people | 8 people | 76.5% |
| Back players | 6.6 ± 0.6 s | 30 people | 0 person | 100% | 6.9 ± 1.7 s | 22 people | 8 people | 73.3% | |
| Pivots | 6.9 ± 0.4 s | 16 people | 0 person | 100% | 6.9 ± 0.7 s | 8 people | 6 people | 57.1% | |
| Center backs | 6.9 ± 0.5 s | 14 people | 0 person | 100% | 6.5 ± 0.8 s | 14 people | 6 people | 70% | |
| Goalkeepers | 8.1 ± 1.0 s | 12 people | 2 person | 85.7% | 6.8 ± 0.8 s | 8 people | 10 people | 44.4% | |
| Parkour ND | Wingers | 6.7 ± 0.6 s | 30 people | 0 person | 100% | 6.5 ± 0.6 s | 34 people | 8 people | 76.5% |
| Back players | 6.8 ± 0.5 s | 30 people | 0 person | 100% | 7.0 ± 2.3 s | 18 people | 12 people | 60% | |
| Pivots | 6.9 ± 0.8 s | 16 people | 0 person | 100% | 6.5 ± 0.5 s | 12 people | 2 person | 85.7% | |
| Center backs | 7.0 ± 0.5 s | 14 people | 0 person | 100% | 6.5 ± 1.0 s | 16 people | 4 people | 80% | |
| Goalkeepers | 7.7 ± 0.6 s | 14 people | 0 people | 100% | 6.8 ± 1.3 s | 12 people | 6 people | 55.6% | |
| Quick feet | Wingers | 8.1 ± 0.9 s | 30 people | 0 person | 100% | 7.9 ± 0.7 s | 34 people | 0 person | 100% |
| Back players | 8.0 ± 1.0 s | 30 people | 0 person | 100% | 8.2 ± 1.6 s | 28 people | 2 person | 93.3% | |
| Pivots | 8.1 ± 0.9 s | 16 people | 0 person | 100% | 8.0 ± 0.8 s | 14 people | 0 person | 100% | |
| Center backs | 7.4 ± 0.5 s | 14 people | 0 person | 100% | 7.8 ± 0.7 s | 20 people | 0 person | 100% | |
| Goalkeepers | 8.4 ± 0.4 s | 14 people | 0 people | 100% | 8.0 ± 1.0 s | 18 people | 0 person | 100% | |
3.3. Correlations with ACL Injury Incidence
In the examined period, there was a total of 26 ACL injuries. Exploratory correlation analyses were performed to screen potential associations between baseline variables and subsequent ACL injury occurrence. Moderate correlations were observed for reaction time and visual memory; however, these associations were not retained in logistic regression models. In contrast, plyometric ground contact time demonstrated consistent association patterns and was therefore further evaluated using predictive modeling.
3.4. Injury Prediction
Univariable Logistic Regression
Neurocognitive Predictors: The univariable models revealed that none of the baseline neurocognitive composite scores were significant independent predictors of subsequent ACL injury. Verbal memory (p > 0.05, AUC = 0.52) and reaction time (p > 0.05, AUC = 0.55) showed no significant association with injury risk. Visual memory performance similarly did not reach the level of statistical significance (p > 0.05, AUC = 0.54). Visual–motor speed and dismiss scores also demonstrated poor discriminatory capacity, with all AUC values falling between 0.50 and 0.55.
Neuromuscular Predictors: In contrast to the neurocognitive measures, plyometric performance showed a significant relationship with injury incidence. Plyometric ground contact time was a significant predictor of ACL injury (ß = −0.019, p = 0.019). The calculated odds ratio (OR) was 0.98 per millisecond (95% CI: 0.964–0.997), indicating that shorter ground contact times during plyometric tasks are associated with an increased probability of ACL injury. Other neuromuscular variables, such as quick-feet performance and stability scores, did not show significant independent predictive value (p > 0.05). Static Stability: Neither bilateral nor unilateral (dominant and non-dominant) stability scores were significantly associated with ACL injury occurrence (all p > 0.05). Two-leg stability showed poor discriminatory ability (AUC = 0.51). For unilateral stability, neither the dominant side (p = 0.42, AUC = 0.52) nor the non-dominant side (p = 0.38, AUC = 0.54) reached statistical significance. Symmetry index was not a significant independent predictor of injury (p > 0.05, AUC = 0.50). Quick-feet performance also failed to show a significant predictive relationship with subsequent injury (all p > 0.05).
The final univariable model, predicting ACL injury from plyometric ground contact time, showed fair discriminatory capacity with an AUC of 0.63 (Figure 1). The optimal threshold determined by the Youden index was 168 ms, yielding a sensitivity of 77% and a specificity of 55%.
Figure 1.
Plyometric contact time predicting ACL injury. The blue line represents the sensitivity and 1-specificity of the plyometric ground contact time model (AUC = 0.63). This value indicates a fair discriminatory capacity for identifying athletes at risk of ACL injury. The orange diagonal line serves as the reference line, representing chance-level prediction with an AUC of 0.50. Based on the Youden index, the optimal diagnostic threshold was identified at 168 ms. At this cutoff point, the model demonstrated a sensitivity of 77% and a specificity of 55% in predicting injury occurrence.
3.5. Multivariable Logistic Regression
To evaluate the independent predictive value of the examined parameters and to control for potential confounding, sex was included as a covariate in the multivariable models. In the combined model including both sex and plyometric ground contact time, sex emerged as the only significant independent predictor of ACL injury (p = 0.003, OR = 5.74, 95% CI: 1.78–18.48). This indicates that female athletes in this cohort were more than five times as likely to sustain an ACL injury compared to their male counterparts, regardless of other baseline measures.
Notably, when adjusted for sex, the independent effect of plyometric ground contact time was reduced and did not reach statistical significance (p = 0.35, OR = 0.99, 95% CI: 0.97–1.01). This suggests that the relationship between contact time and injury risk observed in the univariable model is significantly influenced by sex-specific differences in jump and landing mechanics. The overall discriminatory performance of the multivariable model yielded an AUC of 0.73.
4. Discussion
The primary finding of this prospective cohort study is that neuromuscular performance—particularly shortened plyometric ground contact time—was associated with subsequent ACL injury occurrence and demonstrated fair discriminatory capacity. Our findings challenge the independent predictive capacity of baseline neurocognitive screening in this specific cohort, while highlighting the critical interplay between plyometric landing mechanics and the athlete’s sex.
Ground contact time during plyometric tasks likely reflects impaired reactive force production and delayed neuromuscular activation strategies. Prior biomechanical studies show that non-contact ACL injuries generally occur within the initial 40 ms following ground contact, a timeframe marked by rapid force absorption and the necessity for joint stabilization [15,24]. Insufficient neuromuscular stiffness regulation and delayed eccentric control during this critical window may increase anterior tibial translation and valgus loading, thereby elevating ACL strain.
4.1. The Role of Neurocognitive and Stabilometric Performance
Contrary to our initial hypothesis and some existing literature [10,12], baseline neurocognitive scores (verbal/visual memory, reaction time, and processing speed) did not independently predict subsequent ACL injuries. Similarly, static and unilateral stabilometric measures failed to show significant predictive capacity.
A possible explanation is that while cognitive processing delays might contribute to injury mechanisms in chaotic, open-skill game situations, isolated baseline computer-based neurocognitive tests may lack the ecological validity to capture the dynamic, sport-specific decision-making processes required on the field. Furthermore, the lack of predictive value in static stabilometry suggests that dynamic, high-force tasks are more representative of the actual injury mechanism than static equilibrium.
4.2. Plyometric Ground Contact Time Landing Mechanics
In the univariable analysis, shorter plyometric ground contact time emerged as a significant predictor of ACL injury. Specifically, every 1 ms decrease in contact time increased the injury odds by approximately 2% (OR = 0.98).
From a biomechanical perspective, a shorter ground contact time during plyometric tasks often indicates a “stiff landing” strategy. Athletes adopting this strategy tend to absorb kinetic energy over a shorter duration, which typically involves reduced knee and hip flexion angles upon impact [14,18]. This limited energy attenuation through the lower extremity musculature exponentially increases the ground reaction forces and the sheer stress transmitted directly to the passive restraints of the knee, primarily the ACL.
4.3. The Confounding Influence of Sex
When adjusted for sex in the multivariable model, the independent predictive value of plyometric contact time diminished, while the female sex emerged as a profound and independent risk factor (OR = 5.74).
This shift implies a strong interaction between sex and neuromuscular landing strategies. Female athletes inherently demonstrate distinct anatomical (e.g., wider pelvis, increased Q-angle) and hormonal profiles, but crucially, they also tend to exhibit different neuromuscular control strategies, often landing with greater knee valgus and stiffer mechanics compared to males [22,25,26,27,28,29]. The multivariable model confirms that the high injury rate associated with shorter plyometric contact times is largely driven by these sex-specific biomechanical disparities.
4.4. Clinical and Rehabilitation Implications
For sports physiotherapists and rehabilitation specialists, these findings emphasize that injury prevention programs must prioritize dynamic neuromuscular training over static balance or purely cognitive tasks.
Since sex is a non-modifiable risk factor, clinical interventions must target the modifiable biomechanical components observed in female athletes. Training protocols should heavily emphasize the elongation of ground contact phases during deceleration and landing.
4.5. Limitations
Numerous limitations must be acknowledged. Initially, while the prospective design enhances causal inference, the limited number of ACL injury cases (n = 26) constrains the intricacy of multivariable modeling and may have reduced statistical power for identifying lower effect sizes. Secondly, neurocognitive performance was evaluated by standardized laboratory testing, which may not adequately reflect the dynamic perceptual–motor requirements seen in high-velocity, sport-specific contexts. Third, only baseline tests were incorporated, and any alterations in neuromuscular or cognitive performance during the follow-up period were not observed.
Additionally, external load exposure (such as training amount and match participation) and prior injury history were not accounted for, potentially affecting injury risk. Consequently, the current data must be understood within the framework of a multifactorial damage model rather than as proof of single-variable causation.
Future studies incorporating longitudinal neuromuscular monitoring and exposure-adjusted injury incidence may further clarify the interaction between cognitive processing and mechanical load in ACL injury mechanisms.
5. Conclusions
In this prospective cohort of youth handball players, neuromuscular performance—specifically plyometric ground contact time—demonstrated fair capacity to discriminate athletes at increased ACL injury risk, whereas isolated neurocognitive measures did not independently predict injury occurrence.
These findings reinforce the concept that ACL injury risk emerges from complex neuromechanical interactions and underscore the value of integrating objective neuromuscular assessment into comprehensive injury risk management strategies in youth sport.
Future Directions
Future research should extend these findings to adult elite handball players to determine whether neuromuscular and neurocognitive profiles differ across developmental stages and competitive levels. Longitudinal monitoring over multiple seasons, combined with exposure-adjusted injury incidence, may provide a more precise understanding of temporal changes in neuromuscular performance and their relationship with ACL injury mechanisms.
Notably, most prior investigations have focused on adult elite populations; therefore, the present study extends the existing literature by providing prospective neuromuscular profiling data in youth handball athletes. This may facilitate age-appropriate screening and tailored development tactics inside talent development systems. Accordingly, neuromuscular assessment should be interpreted as a complementary component within a multifactorial injury prevention framework rather than as a standalone predictive tool.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/sports14050185/s1, Figure S1: Stabilometer of BIA system (own picture); Figure S2: Center of Pressure displayed on the screen (own picture); Figure S3: Parkour subtest (own picture); Figure S4: Quick–feet subtest (own picture); Figure S5: The composite scores and their subtests.
Author Contributions
G.C.S.: theoretical background, planning, pilot, practical implications, measurement, data collection, data handling, statistical analysis, writing. A.P. (Attila Pavlik): supervision of practical implications, commenting, entrustment. Á.M.: theoretical background, planning, supervision of practical implications, commenting, entrustment. A.P. (András Pavlik): theoretical background, planning, commenting. D.F.: statistical analysis. A.T.: supervision of practical implications, commenting, entrustment. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to ethical and privacy restrictions but are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
Project no. TKP2021-NKTA-46 was funded by the National Research, Development and Innovation Fund, financed under the TKP2021-NKTA grant scheme, with support from the Ministry of Innovation and Technology of Hungary.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
References
- 1.Bahr R., Krosshaug T. Understanding injury mechanisms: A key component of preventing injuries in sport. Br. J. Sports Med. 2005;39:324–329. doi: 10.1136/bjsm.2005.018341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Bittencourt N.F.N., Meeuwisse W.H., Mendonça L.D., Nettel-Aguirre A., Ocarino J.M., Fonseca S.T. Complex systems approach for sports injuries: Moving from risk factor identification to injury pattern recognition-narrative review and new concept. Br. J. Sports Med. 2016;50:1309–1314. doi: 10.1136/bjsports-2015-095850. [DOI] [PubMed] [Google Scholar]
- 3.Broadbent D.P., Causer J., Williams A.M., Ford P.R. Perceptual-cognitive skill training and its transfer to expert performance in the field: Future research directions. Eur. J. Sport Sci. 2015;15:322–331. doi: 10.1080/17461391.2014.957727. [DOI] [PubMed] [Google Scholar]
- 4.Della Villa F., Buckthorpe M., Grassi A., Nabiuzzi A., Tosarelli F., Zaffagnini S., Della Villa S. Systematic video analysis of ACL injuries in professional male football (soccer): Injury mechanisms, situational patterns and biomechanics study on 134 consecutive cases. Br. J. Sports Med. 2020;54:1423–1432. doi: 10.1136/bjsports-2019-101247. [DOI] [PubMed] [Google Scholar]
- 5.Van der Kamp J., Rivas F., Van Doorn H., Savelsbergh G.J.P. Ventral and dorsal system contributions to visual anticipation in fast ball sports. Int. J. Sport Psychol. 2008;39:100–130. [Google Scholar]
- 6.Vogel L., Schack T. Cognitive representations of handball tactic actions in athletes-The function of expertise and age. PLoS ONE. 2023;18:e0284941. doi: 10.1371/journal.pone.0284941. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Grooms D.R., Page S.J., Onate J.A. Brain Activation for Knee Movement Measured Days Before Second Anterior Cruciate Ligament Injury: Neuroimaging in Musculoskeletal Medicine. J. Athl. Train. 2015;50:1005–1010. doi: 10.4085/1062-6050-50.10.02. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Herman D.C., Zaremski J.L., Vincent H.K., Vincent K.R. Effect of Neurocognition and Concussion on Musculoskeletal Injury Risk. Curr. Sports Med. Rep. 2015;14:194–199. doi: 10.1249/JSR.0000000000000157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Jurado M.B., Rosselli M. The elusive nature of executive functions: A review of our current understanding. Neuropsychol. Rev. 2007;17:213–233. doi: 10.1007/s11065-007-9040-z. [DOI] [PubMed] [Google Scholar]
- 10.Swanik C.B. Brains and Sprains: The brain’s role in noncontact anterior cruciate ligament injuries. J. Athl. Train. 2015;50:1100–1102. doi: 10.4085/1062-6050-50.10.08. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Gokeler A., Benjaminse A., Della Villa F., Tosarelli F., Verhagen E., Baumeister J. Anterior cruciate ligament injury mechanisms through a neurocognition lens: Implications for injury screening. BMJ Open Sport Exerc. Med. 2021;7:e001091. doi: 10.1136/bmjsem-2021-001091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Swanik C.B., Covassin T., Stearne D.J., Schatz P. The relationship between neurocognitive function and noncontact anterior cruciate ligament injuries. Am. J. Sports Med. 2007;35:943–948. doi: 10.1177/0363546507299532. [DOI] [PubMed] [Google Scholar]
- 13.Herman D.C., Barth J.T. Drop-Jump landing varies with baseline Neurocognition: Implications for anterior cruciate ligament injury risk and prevention. Am. J. Sports Med. 2016;44:2347–2353. doi: 10.1177/0363546516657338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Hewett T.E., Myer G.D., Ford K.R., Heidt R.S., Jr., Colosimo A.J., McLean S.G., Van Den Bogert A.J., Paterno M.V., Succop P. Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: A prospective study. Am. J. Sports Med. 2005;33:492–501. doi: 10.1177/0363546504269591. [DOI] [PubMed] [Google Scholar]
- 15.Koga H., Nakamae A., Shima Y., Iwasa J., Myklebust G., Engebretsen L., Bahr R., Krosshaug T. Mechanisms for noncontact anterior cruciate ligament injuries: Knee joint kinematics in 10 injury situations from female team handball and basketball. Am. J. Sports Med. 2010;38:2218–2225. doi: 10.1177/0363546510373570. [DOI] [PubMed] [Google Scholar]
- 16.Krosshaug T., Nakamae A., Boden B.P., Engebretsen L., Smith G., Slauterbeck J.R., Hewett T.E., Bahr R. Mechanisms of anterior cruciate ligament injury in basketball: Video analysis of 39 cases. Am. J. Sports Med. 2007;35:359–367. doi: 10.1177/0363546506293899. [DOI] [PubMed] [Google Scholar]
- 17.Lee M.J.C., Lloyd D.G., Lay B.S., Bourke P.D., Alderson J.A. Effects of different visual stimuli on postures and knee moments during sidestepping. Med. Sci. Sports Exerc. 2013;45:1740–1748. doi: 10.1249/MSS.0b013e318290c28a. [DOI] [PubMed] [Google Scholar]
- 18.Padua D.A., Marshall S.W., Boling M.C., Thigpen C.A., Garrett W.E., Jr., Beutler A.I. The landing error scoring system (less) is a valid and reliable clinical assessment tool of jumplanding biomechanics: The JUMP-ACL study. Am. J. Sports Med. 2009;37:1996–2002. doi: 10.1177/0363546509343200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Pinder R.A., Davids K., Renshaw I., Araujo D. Representative learning design and functionality of research and practice in sport. J. Sport Exerc. Psychol. 2011;33:146–155. doi: 10.1123/jsep.33.1.146. [DOI] [PubMed] [Google Scholar]
- 20.Wilke J., Groneberg D.A. Neurocognitive function and musculoskeletal injury risk in sports: A systematic review. J. Sci. Med. Sport. 2022;25:41–45. doi: 10.1016/j.jsams.2021.07.002. [DOI] [PubMed] [Google Scholar]
- 21.Wilkerson G.B. Neurocognitive reaction time predicts lower extremity sprains and strains. Int. J. Athl. Ther. Train. 2012;17:4–9. doi: 10.1123/ijatt.17.6.4. [DOI] [Google Scholar]
- 22.Giesche F., Wilke J., Engeroff T., Niederer D., Hohmann H., Vogt L., Banzer W. Are biomechanical stability deficits during unplanned single-leg landings related to specific markers of cognitive function? J. Sci. Med. Sport. 2020;23:82–88. doi: 10.1016/j.jsams.2019.09.003. [DOI] [PubMed] [Google Scholar]
- 23.Williams A.M., Ward P., Smeeton N.J., Allen D. Developing Anticipation Skills in Tennis Using On-Court Instruction: Perception versus Perception and Action. J. Appl. Sport Psychol. 2004;16:350–360. doi: 10.1080/10413200490518002. [DOI] [Google Scholar]
- 24.Waldén M., Krosshaug T., Bjørneboe J., Andersen T.E., Faul O., Hägglund M. Three distinct mechanisms predominate in non-contact anterior cruciate ligament injuries in male professional football players: A systematic video analysis of 39 cases. Br. J. Sports Med. 2015;49:1452–1460. doi: 10.1136/bjsports-2014-094573. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Herbst E., Hoser C., Hildebrandt C., Raschner C., Hepperger C., Pointner H., Fink C. Clinical application of a new test battery: Functional assessments for decision-making regarding return to sports following ACL reconstruction, Part II. Knee Surg. Sports Traumatol. Arthrosc. 2015;23:1283–1291. doi: 10.1007/s00167-015-3546-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ebert J.R., Edwards P., Currie J., Smith A., Joss B., Ackland T., Buelow J.-U., Hewitt B. Comparison of the ‘Back in Action’ test battery to standard hop tests and isokinetic knee dynamometry in patients following anterior cruciate ligament reconstruction. Int. J. Sports Phys. Ther. 2018;13:389–400. doi: 10.26603/ijspt20180389. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Michalsik L.B., Aagaard P. Physical demands in elite team handball: Comparisons between male and female players. J. Sports Med. Phys. Fit. 2015;55:878–891. [PubMed] [Google Scholar]
- 28.Hermassi S., Laudner K., Schwesig R. Playing Level and Position Differences in Body Characteristics and Physical Fitness Performance Among Male Team Handball Players. Front. Bioeng. Biotechnol. 2019;7:149. doi: 10.3389/fbioe.2019.00149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Wagner H., Fuchs P., Fusco A., Fuchs P., Bell J.W., von Duvillard S.P. Physical Performance in Elite Male and Female Team-Handball Players. Int. J. Sports Physiol. Perform. 2019;14:60–67. doi: 10.1123/ijspp.2018-0014. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to ethical and privacy restrictions but are available from the corresponding author on reasonable request.