Athletes with high knee abduction moments show increased vertical center of mass excursions and knee valgus angles across sport-specific fake-and-cut tasks of different complexities

Abstract

Young female handball players represent a high-risk population for anterior cruciate ligament (ACL) injuries. While the external knee abduction moment (KAM) is known to be a risk factor, it is unclear how cutting technique affects KAMs in sport-specific cutting maneuvers. Further, the effect of added game specificity (e.g., catching a ball or faking defenders) on KAMs and cutting technique remains unknown. Therefore, this study aimed: (i) to test if athletes grouped into different clusters of peak KAMs produced during three sport-specific fake-and-cut tasks of different complexities differ in cutting technique, and (ii) to test whether technique variables change with task complexity. Fifty-one female handball players (67.0 ± 7.7 kg, 1.70 ± 0.06 m, 19.2 ± 3.4 years) were recruited. Athletes performed at least five successful handball-specific sidestep cuts of three different complexities ranging from simple pre-planned fake-and-cut maneuvers to catching a ball and performing an unanticipated fake-and-cut maneuver with dynamic defenders. A k-means cluster algorithm with squared Euclidean distance metric was applied to the KAMs of all three tasks. The optimal cluster number of koptimal = 2 was calculated using the average silhouette width. Statistical differences in technique variables between the two clusters and the tasks were analyzed using repeated-measures ANOVAs (task complexity) with nested groupings (clusters). KAMs differed by 64.5%, on average, between clusters. When pooling all tasks, athletes with high KAMs showed 3.4° more knee valgus, 16.9% higher downward and 8.4% higher resultant velocity at initial ground contact, and 20.5% higher vertical ground reaction forces at peak KAM. Unlike most other variables, knee valgus angle was not affected by task complexity, likely due to it being part of inherent movement strategies and partly determined by anatomy. Since the high KAM cluster showed higher vertical center of mass excursions and knee valgus angles in all tasks, it is likely that this is part of an automated motor program developed over the players' careers. Based on these results, reducing knee valgus and downward velocity bears the potential to mitigate knee joint loading and therefore ACL injury risk.

Individualized Technique Feedback for Instant Technique Improvements and Knee Abduction Moment Reductions – A New Approach for ‘Sidestepping’ ACL Injuries?

“…Knee valgus angle and vertical impact velocity have been previously shown to be consistently increased in handball players showing high pKAM across handball-specific sidestep cuts of varying complexity. 21 The authors of that study therefore suggested these variables might be part of the players' inherent movement strategy. In that same dataset, the same task complexity used in the present study was the only one that, on average, provoked a rearfoot strike and, additionally, produced the highest pKAM across differently complex cutting maneuvers.…”

“…Next, the study did not investigate the long-term retention of the newly adopted technique or its transfer to unanticipated sidestep cutting scenarios. Although the knee valgus motion and vertical impact velocity have been proposed as components of a player's inherent movement strategy during handballspecific sidestep cuts with varying complexity, 21 it remains unclear whether training designed to alter cutting technique in preplanned cuts automatically translates to complex unplanned cuts. Lastly, it is important to acknowledge that the diverse nature of feedback approaches (visual, verbal, self-guided, expert, etc.)…”

“…Technique variables, i.e., foot strike angle (FSA), knee valgus angle (VAL), and vertical impact velocity of the center of mass (VIV), were chosen based on their established association with pKAM in female handball players. 20,21 Cut-off values were set conservatively using a previously collected dataset. 22 (Figure 1) Those cut-off values were:…”