Introduction

The anterior cruciate ligament (ACL) injury risk reduction is a current open question for the sports medicine community. The investigation of situational patterns that lead to ACL injury has gained importance in the light of primary and secondary injury prevention [13, 18, 19, 29]. Recent ACL injury video analysis studies [6, 11, 22] identified pressing/tackling as the most common situational patterns for ACL injury in football. The pressing pattern has a common frontal sprint with sudden deceleration close to the opponent. Although such movement task can be easily resembled in laboratory contexts, it has been mainly used for sprint performance purposes.

Few studies conducted a biomechanical investigation of the deceleration task [15, 25]. A recent prospective study by Dix et al. [15] found significant differences in lower limb biomechanics between injured and uninjured female basketball players, advocating the deceleration task as a valuable screening task for ACL injury.

To facilitate the screening for ACL injury risk in a user-friendly fashion, 2D scoring systems are emerging as a valuable option for complex movement assessment during rehabilitation after ACL injury and reconstruction [5, 12, 16, 23, 26, 28]. These scoring systems have been mainly focused on jump landings [5, 23, 26] and change of direction [12, 16, 28] tasks. To the best of the authors’ knowledge, no 2D assessment method of the deceleration task evaluation has been proposed. Given the intrinsic speed difference reached in a single frontal sprint with deceleration compared to a multidirectional task, the investigation of such a discussed pattern might have a significant role in primary and secondary ACL injury risk patients’ stratification and in the development of personalized preventative strategies.

Therefore, the aim of the present study was to describe a 2D scoring system for the qualitative assessment of a deceleration task and to look for potential associations with the knee abduction moment (KAM) evaluated through the gold standard 3D motion capture. The hypothesis was that lower 2D scores would be associated with higher KAM. A qualitative scoring system that accurately detects high knee joint loadings may be clinically relevant and useful to target additional training in the ACL injury.

Materials and methods

The research study was approved by the Institutional Review Board (IRB approval: 555/2018/Sper/IOR of 12/09/2018) of Area Vasta Emilia Romagna Centro (AVEC, Bologna, Italy) and registered on ClinicalTrials.gov (Identifier: NCT03840551). All the subjects signed informed consent before starting the acquisition protocol. The rights of the subjects were protected.

The analysis was conducted in the Education and Research Department of the Isokinetic Medical Group in Bologna (Italy). Overall, 34 recreational and elite footballers were recruited for the study (Table 1). Inclusion criteria were age between 18 and 50 years old and Tegner activity level at least 7. Exclusion criteria were: (1) evidence of musculoskeletal disorders or functional impairment; (2) body mass index (BMI) > 35; (3) previous surgery to lower limbs; (4) cardiopulmonary or cardiovascular disorders; (5) inability to perform the required tasks.

Table 1 Demographics data
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Deceleration acquisition protocol

As part of a multi-movement assessment, each footballer performed a deceleration task. The athletes were asked to perform a 10 m frontal sprint at maximum speed possible followed by deceleration and a single support strike on a force platform, and a last backward sprint. Full foot contact on the force platform was required to consider a trial valid. Before the test, the athletes performed a 10 min dynamic warm-up and few repetitions of the movement to get confident with the environment and the motor task. A sport and exercise medicine physician specialized in sports biomechanics (F.D.V.) instructed each subject on the movements to perform and verified the validity of each trial. All athletes performed six valid repetitions (three right and three left strikes).

3D motion analysis was recorded in a specialized laboratory equipped with artificial turf [12, 14]. A set of ten stereophotogrammetric cameras, a force platform embedded in the floor (AMTI 400 × 600, Watertown, MA USA), and three high-speed cameras placed frontally and bilaterally towards movement direction (VICON Nexus, Vicon Motion Systems Ltd, Oxford, UK) were used. The sampling frequency of the cameras and the force platform was 120 Hz, while the sampling frequency of the high-speed cameras was 100 Hz. The systems were synchronized for direct data comparison.

The system calibration was performed at the beginning of the acquisition and repeated periodically during the session. A total of 42 retroreflective markers were placed on each subject according to the full-body Plug-in Gait protocol. The same expert user conducted the entire marker positioning process. After marker positioning, subjects’ model calibration was performed before each acquisition.

Data processing—3D analysis

Joint kinematics, kinetics, and GRF were quantified through VICON Nexus software. Marker trajectories were collected through the stereophotogrammetric cameras and interpolated through a spline-based algorithm; GRFs were collected through the force platform. The KAM was quantified using the standard “bottom-up” inverse dynamics approach of the Plug-in Gait protocol. The entire phase of foot contact on the force platform was considered in the analysis. The peak KAM value was extracted for each trial and normalized by the subject’s body weight (BW).

Data processing—2D analysis

A 2D scoring system was adopted based on the qualitative movement evaluation of frontal and sagittal plane joint kinematics. Such a scoring system is included in a clinical multiple movements evaluation for RTS decision-making after ACL reconstruction [7, 8, 12]. The test is aimed to identify biomechanical and neuromuscular control deficits providing an intuitive and quick response to the patient. The evaluation is performed in a specific VICON software environment through the recordings of the three high-speed cameras and the resultant GRF vector of the force platform. Joint kinematics are evaluated at the frame of maximal knee flexion angle after the foot contact with the force platform.

Each deceleration trial was evaluated through five scoring criteria [12], and a score from 0 to 2 was attributed to the movements for each scoring criteria.

Statistical analysis

The intraclass correlation coefficient (ICC) was used to calculate intra-rater and inter-rater reliability for each criterion and the total score. The reliability ICC values lower than 0.50, between 0.50 and 0.75, between 0.75 and 0.90, and greater than 0.90 were considered poor, moderate, good, and excellent, respectively [21].

The peak KAM extracted from each trial was grouped according to the results of the 2D evaluation. The normal distribution of the data was verified through the Shapiro–Wilk test. The categorical variables were presented as a percentage over the total, while the continuous variables were presented as mean ± standard deviation (95% confidence interval–CI).

The association between the peak KAM and the two components of the limb stability (LS) criteria (i.e., the frontal plane knee projection angle–FPKPA and the GRF vector score [12]) was assessed. The peak KAM was decomposed in quartiles: the players with peak KAM higher than the third quartile were assigned to “high KAM” and the players with peak KAM lower than the first quartile were assigned to “low KAM”. The presence of outliers in the data was checked. Three groups were generated for the 2D parameters: LS 0, 1, 2; GRF vector score 0, 1, 2. The FPKPA was also divided into three groups based on the terciles: lower than the first tercile, between the first and the third tercile, higher than the third tercile. The rate of 0/2, 1/2, and 2/2 scores obtained by the players was computed according to the KAM quartiles. The Cochran’s Q test was used to assess the rate differences.

Furthermore, the peak KAM was grouped according to the LS, the FPKPA, the GRF vector score, and the Total score. The one-way ANOVA was used to investigate the statistical differences among the three groups, and the Student’s t test with the Bonferroni correction for multiple comparisons was used to investigate the differences between the single groups.

Spearman’s coefficient ρ was used to investigate the rank correlation between the 2D Total Score and the peak KAM.

Differences were considered statistically significant for p < 0.05. All the statistical analyses were performed in SPSS (v26, IBM, Armonk, NY, US).

An a-priori power-analysis was performed in G*Power (v3.1, Brunsbüttel, Germany) based on the results of a previous similar study analyzing the deceleration task [25]. Considering a standard deviation of 0.7 N/BW (Newton/Body Weight) and a mean group difference of 1 N/BW (Effect size 1.43), at least 20 subjects were required to have a power of 0.9 and a type I error of 0.05.

Results

Overall, 181 valid trials were included in the analysis. The peak KAM was 1.4 ± 1.0 N/BW and 1.6 ± 0.8 N/BW for male and female athletes, respectively (n.s.). Both the intra-rater and inter-rater reliability showed an ICC between 0.94 and 1.00 for all the 2D criteria and total score. The first and the third terciles of the FPKPA were 21° and 33°, respectively. No outliers were present in the peak KAM data distribution.

Peak KAM according to the 2D scores

Peak KAM significantly differed among the three groups based on the LS, the FPKPA, the GRF vector, and the Total score (p < 0.001) (Table 2, Appendix Fig. 3). The players with LS 0/2, FPKPA > 33°, GRF vector score 0/2, and Total score 0–4 showed significantly higher peak KAM values (Table 3). A significant negative rank correlation was found between the total score and the peak KAM (ρ = − 0.25, p < 0.001).

Table 2 Peak knee abduction moment based on the 2D scoring system
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Table 3 Multiple comparisons of the knee abduction moment based on the 2D evaluations
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Rate of 2D score assigned to high- and low-peak KAM

The low KAM values ranged from − 0.4 N/BW to 0.8 N/BW (minimum-Q1) and the high KAM values ranged from 2.1 N/BW to 4.7 N/BW (Q3-maximum). The low KAM values corresponded to the better 2D scores, i.e., a higher percentage of FPKPA < 21° (45%, p < 0.001) and of GRF vector score 2/2 (68%, p < 0.001). The high KAM values corresponded to the worst 2D scores, i.e., a higher percentage of 0/2 score for the LS (78%, p < 0.001) and of FPKPA > 33° (59%, p < 0.001) (Fig. 1).

Fig. 1
figure 1

Rate of frontal plane 2D scores classified according to the peak knee abduction moment (KAM) values out of the interquartile range (IQR). High KAM and low KAM represent the values (N*m/BW) between third quartile and maximum and first quartile and minimum, respectively

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Discussion

The main finding of the present study was the association between the 2D scoring system and the knee joint loading (KAM) in the biomechanical assessment of a deceleration task. Lower 2D scores were associated with higher KAM, thus confirming the study hypothesis. Both inter-and intra-rater reliability was excellent (ICC > 0.94) for the 2D criteria and total score. Such a qualitative 2D scoring system could, therefore, be a useful tool in ACL injury risk screening and prevention.

Despite the overwhelming evidence that pressing (therefore deceleration tasks) is a major situational pattern for ACL injury in football [6, 11, 18, 27], there is a lack of research on this kind of task. The research suggests that high-intensity engagements involving deceleration tasks are drawn into ACL injury causation in footballers. Nonetheless, a stronger focus should be paid to the quality of movement execution. The performance-injury conflict is largely debated in the sports medicine community. A recent investigation by Dos’Santos et al. highlighted that the most performant players adopt less safe biomechanical patterns in sport-specific movements [17]. The authors advocated the need for a broader consciousness on performance-injury conflict and for preventative screenings.

As already demonstrated, KAM and medial knee motion during a deceleration task may predict an ACL injury [15]. In the present study, low 2D score both on frontal plane and considering the whole scoring system (Fig. 2) were associated with knee joint overloading (i.e., high KAM). The integrated frontal plane assessment (i.e., LS criteria, the FPKPA, and the GRF vector) demonstrated great sensibility and specificity for the KAM: only 4% of the athletes were misclassified in the high-KAM group and 2% in the low-KAM group (Fig. 1). Thus, a good level of accuracy has been reached despite the quick and cost-effective fashion. The total score was significantly correlated with the KAM. Furthermore, a total score between 0/10 and 4/10 was associated with an average 0.8 N*m/BW higher KAM compared to those obtaining at least 8/10. The latter finding supports the use of the 2D total score as a simple—but comprehensive—indication of an athlete’s movement quality in the deceleration task.

Fig. 2
figure 2

A–B Example of deceleration tasks performed with low knee abduction moment and corresponding high 2D score; C–D example of deceleration tasks performed with high knee abduction moment and corresponding low 2D score

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Such an evaluation may be applied to screen for additional preventative neuromuscular training, both in the primary and secondary prevention setting. Despite the open debate on the use of targeted neuromuscular training [4, 24], “at-risk” athletes were found to benefit more from additional customized programs focused on movement quality [20]. Given the growing incidence of ACL injuries in males and (recently) in females footballers [1, 3, 13], large-scale screenings and a broader awareness of the ACL injury risk are desirable.

Therefore, there are two main advantages of the qualitative movement assessment that we proposed. On one hand, it might provide detailed insights for ACL professionals on athlete’s biomechanics and potential ACL injury risk. On the other hand, it might provide quick feedback to the athlete regarding his/her movement quality and progress over the additional training or prolonged rehabilitation.

For the first time, a qualitative 2D scoring system has been described and validated for a deceleration task. Such an assessment might be part of a wider test battery, alongside jumping tasks (drop vertical jump, hops) and cutting maneuvers, to better discriminate safe and dangerous movement strategies across various injury risk situational patterns.

The present study has some limitations. First, the data were collected on healthy athletes during a single session. Therefore, it was not possible to provide the sensibility of the 2D scoring system in a test–retest condition after a preventative training program, nor in the discrimination between ACL-intact and ACL-reconstructed patients. Second, no muscle activation data were collected during the task execution. The integration of EMG-driven data might provide interesting insights into the interaction between athletes’ movement quality and muscular strength. Moreover, the investigation was conducted in a controlled laboratory environment. Therefore, despite the footballers were required to perform the tasks at their maximum speed, the external focus (e.g., ball, opponents) was limited. Future studies might consider inserting this evaluation in a real-world scenario. Indeed, recent studies on neuromuscular control in ACL-injured patients argued that neurocognitive alterations might happen during the situational patterns leading to an ACL injury [2, 9, 10]. Therefore, the introduction of additional focuses could help to discriminate between safe and unsafe return to the field. Last, only valid trials (i.e., entire foot strike on the force platform) were investigated. The analysis of the non-valid trials might be considered in future studies for a wider comprehension of players’ movement quality in real scenarios.

The proposed qualitative assessment of a deceleration task could be introduced in a test battery for the primary and secondary ACL injury risk stratification alongside with most common movements, e.g., single- and double-leg hops and cutting maneuvers. In the day-by-day clinical practice, professionals might benefit from an accurate and cost-effective tool to investigate athletes’ movement quality during a sport-specific situational pattern frequently found in ACL injury causation. Such screening could promote the indication for movement re-training and targeted neuromuscular training both in primary and secondary ACL injury prevention.

Conclusion

The qualitative 2D scoring system here described successfully identified football players with high KAM during a deceleration task. The application of this qualitative movement assessment based on a detailed and accurate scoring system is suitable to identify players and patients with high KAM (high knee abduction moments) and target additional training in the scenario of the primary and secondary ACL injury risk reduction.