M ultiple risk factors, both modifiable and nonmodifiable, are known to manifest within the noncontact anterior cruciate ligament (ACL) injury mechanism.1 I will primarily address neuromechanical contributions to injury risk, which are often a key focus at meetings of this nature; such factors are amenable to training and, hence, largely modifiable. I hope, however, that as the reader progresses through the document, the critical importance of underlying nonmodifiable factors within the resultant neuromechanical strategy will not be lost. Sexual dimorphism in modifiable neuromuscular factors linked to ACL injury is well documented, with the ‘‘female’’ movement pattern interpreted as riskier. Females, for example, land in a more extended posture,2 are more quadriceps dominant,1–3 and demonstrate altered muscle activation and coactivation4 and greater out-of-plane knee motions5 and loads6–9 than males. Neuromuscular training strategies continue to evolve in line with these findings and represent an ever-increasing and equally important research focus.10–12 Recent epidemiologic data, however, suggest that in spite of these ongoing initiatives and reported early successes,13,14 ACL injury rates and the associated sex disparity have not diminished.15 If current prevention methods delivered reasonable efficacy, one would assume that a noticeable reduction in these rates would already be evident. It appears, therefore, that current strategies fail to counter key factors implicated within the injury mechanism. In particular, understanding of the precise contributions of neuromuscular control and resultant biomechanics to the injury mechanism and their integration with nonmodifiable structural and hormonal factors remains limited. The current lack of insight into the neuromechanical contributions to noncontact ACL injury risk and, thus, how they can be effectively countered appears to arise through several key factors. The remainder of this paper will focus on some of these factors, in the hope that researchers of ACL injury mechanisms and prevention will begin to address them. Currently, potential neuromechanical predictors of injury risk are generated primarily from the laboratory-based assessment of ‘‘safe’’ movement tasks. Although much can be gained from evaluating high-risk sport postures within a controlled laboratory setting, inferring injury risk from such assessments is questionable. Hence, research that more effectively brings together the laboratory and field environments appears warranted. Sports in which ACL injuries are common are largely governed by a random and often complex series of dynamic events, requiring an equally complex, centrally coordinated response.16,17 Integrating more sport-relevant factors within the in vivo experimental testing environment may, therefore, provide further crucial insights into the causal factors of noncontact ACL injury, facilitating the development of more effective and adaptable prevention methods. Authors of recent studies have begun to acknowledge this fact by regularly incorporating into the experimental design fatiguing8,18,19 and decision-making20–22 tasks, factors inherent in realistic sport participation. Because each of these factors promotes substantial adaptation in the neuromechanical profile and, in particular, exaggerates variables considered high risk, including them when assessing injury predictors is critical. Further, recent data suggest that the combined effect of these tasks may represent a worst-case scenario in terms of injury risk, in which substantial compromise of spinal and, specifically, supraspinal control promotes ineffective decision, response, and resultant movement strategies.16 Along with increasing efforts to develop more realistic laboratory testing environments, a similar research groundswell is bringing the laboratory to the field. Such developments not only permit biomechanical assessments during actual ACL injury scenarios but also contribute substantially to the screening and, ultimately, diminution or elimination of high-risk neuromechanical factors. Model-based image-matching techniques, for example, using commercially available software applications, can estimate joint kinematics with reasonable accuracy from videos of actual injury events.23 Combining these data with neuromuscular measures obtained for similar movements within the laboratory setting may provide helpful insights into the neuromechanical profile of the ACL injury. Other recent developments, such as markerless motion capture techniques24 and wearable motion sensors,25 may similarly allow the assessment of lower limb joint mechanics during actual sport participation and, possibly, true injury scenarios. These devices may also provide an excellent method of screening for high-risk, sport-relevant neuromechanical profiles and, further, countering them through dynamic, real-time feedback techniques. These possibilities alone suggest that additional exploration of such methods is warranted. Current modeling methods, while obviously not representing an actual on-field assessment, also afford an important extension beyond in vivo, laboratory-based experimental methods. The recent development and Journal of Athletic Training 2008;43(5):538–540 g by the National Athletic Trainers’ Association, Inc www.nata.org/jat summary
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Open Access
Jan 1, 2016
Jun-Bae Kim + 2
