Optimizing whole-body kinematics to minimize valgus knee loading during sidestepping: Implications for ACL injury risk
Introduction
Anterior cruciate ligament (ACL) injuries in sport are common (Gianotti et al., 2009, Janssen et al., 2011). New Zealand and Australia spend approximately 17.4 million NZD (Gianotti et al., 2009) and 75 million AUD (Janssen et al., 2011) on ACL injuries each year. Extrapolating from figures reported by Gianotti et al. (2009) and current world population estimates (World Bank, 2010); the United States annually spend approximately 1 billion USD on ACL injury management. Approximately 55% of ACL injured athletes are not capable of returning to the same level of competition two years post-reconstruction (Dunn and Spindler, 2010), a percent that increases to 70% after three years (Roos et al., 1995), which were over double that of a comparable group of non-ACL injured athletes (Ekstrand et al., 1990, Roos et al., 1995). A rupture to the ACL can be considered one of the most severe knee injuries an athlete can sustain in sport.
More than one half of non-contact ACL injuries occur during sidestepping sport manoeuvres (Cochrane et al., 2007, Koga et al., 2010, Krosshaug et al., 2007). Biomechanical studies have shown that during the weight acceptance (WA) phase of sidestepping, which is from initial heel contact to the first trough in the vertical ground reaction force vector (Dempsey et al., 2007), peak valgus knee moments are up to 2-times larger than those observed during straight-line running (Besier et al., 2001). During weightbearing (i.e. stance) (Fleming et al., 2001) and when valgus knee moments are combined with anterior tibial translations, ACL strain is significantly elevated (Markolf et al., 1995, Withrow et al., 2006). These are similar to the loading patterns needed to increase ACL strain and/or reach injurious loading thresholds in-silico (McLean et al., 2004, McLean et al., 2008, Quatman et al., 2011, Shin et al., 2011). Reducing valgus knee loading during sport tasks like sidestepping is therefore considered an appropriate countermeasure to reduce ACL injury risk.
Hewett et al. (2005) has shown peak valgus knee moments during landing are good predictors of ACL injury. Peak valgus knee moments (Besier et al., 2001, Chaudhari et al., 2005, Dempsey et al., 2007, McLean et al., 2005) and peak in-vivo ACL strain (Cerulli et al., 2003) are generally observed during WA. Consequently, one focus of ACL injury prevention training intervention is to reduce valgus knee moments during the WA phase of sidestepping (Cochrane et al., 2010, Dempsey et al., 2009), when ACL injury risk is thought to be the greatest.
Both neuromuscular (Myer et al., 2005) and balance (Cochrane et al., 2010) training have been shown to reduce valgus knee moments during landing and sidestepping. However, these studies have not measured and/or identified the kinematic mechanisms contributing to these observed reductions in knee loading. Hip (McLean et al., 2005), trunk (Dempsey et al., 2007) and arm kinematics (Chaudhari et al., 2005) have been shown to be associated with peak valgus knee moments during sidestepping, while lateral trunk stability has been shown to be associated with rate of ACL injury (Zazulak et al., 2007). Although associations between upper body biomechanics and knee loading have been identified, they are heuristic in nature, providing limited causal information when applied to complex, multi-body, dynamic movements like sidestepping.
Full-body in-silico simulations, with optimization computational methods have been used previously to identify causal relationships between whole-body (WB) kinematics and peak varus knee moments during walking (Fregly et al., 2007). The open-source musculoskeletal modeling software OpenSim (simtk.org, Stanford, CA) allows for in-silico simulations of human movement to be created from three-dimensional (3D) motion data. The residual reduction algorithm (RRA) within OpenSim is an optimization tool capable of altering a simulation's kinematics to reduce peak knee joint loading during sidestepping. Using this modeling framework and these computational tools, our aim was to identify causal relationships between WB kinematics and peak valgus knee moments during the WA phase of sidestepping.
Section snippets
Methods
The experimental procedure consisted of three phases: (1) experimental motion data collection; (2) skeletal modeling and residual force/moment reduction; and (3) minimizing peak valgus knee torques by optimizing WB kinematics (Fig. 1).
Thirty-four male Western Australian Amateur Football players completed the UWA sidestepping protocol at 5 ms−1 (Besier et al., 2001, Dempsey et al., 2007). All experimental procedures were approved by the University of Western Australia Human Research Ethics
Results
Pre-to-post kinematic optimization, peak mean valgus knee moments during UnSS were significantly reduced by 44.2 Nm (106.1±48.6 to 61.9±36.4 Nm) (p=0.045). Peak mean flexion and internal rotation knee moments increased by 24.1 Nm (252.2±80.2 to 276.3±69.4 Nm) and 1.1 Nm (7.6±6.9 to 8.7±7.7 Nm), respectively (Fig. 4).
Pre-to-post kinematic optimization, unique 3D kinematic changes were used by each simulation to reduce peak valgus knee moments. However, only nine of a possible 37 critical joint
Discussion
Associations between upper body posture and peak valgus knee moments during sidestepping have been reported previously in the literature (Chaudhari et al., 2005, Dempsey et al., 2007, McLean et al., 2005). For example, lateral trunk flexion (Dempsey et al., 2007) and constraining an athlete's arms to their mid-line (Chaudhari et al., 2005) likely restricted their upper body CoM from moving medially during sidestepping, resulting in the observed increases in peak valgus knee moments. Results
Conflict of interest
There were no financial or personal relationships with other people or organizations that could have biased the presented work.
Acknowledgments
The authors would like to acknowledge the assistance of Prof. Caroline Finch, Dr Tim Doyle and Dr Dara Twomey in attaining the experimental data for this simulation work. We thank the Australian National Health and Medical Research Council (grant number 400937 to Prof. Finch, Prof. Lloyd and Prof Elliott) and the Western Australian Medical Health and Research Infrastructure Fund (Prof. Lloyd) for their support of this study. CJ Donnelly would like to thank the Canadian Society for Biomechanics
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