Title

Principles of Motor Learning to Support Neuroplasticity After Acl Injury: Implications for Optimizing Performance and Reducing Risk of Second Acl injury

Document Type

Article

Publication Date

6-1-2019

Abstract

© 2019, The Author(s). Athletes who wish to resume high-level activities after an injury to the anterior cruciate ligament (ACL) are often advised to undergo surgical reconstruction. Nevertheless, ACL reconstruction (ACLR) does not equate to normal function of the knee or reduced risk of subsequent injuries. In fact, recent evidence has shown that only around half of post-ACLR patients can expect to return to competitive level of sports. A rising concern is the high rate of second ACL injuries, particularly in young athletes, with up to 20% of those returning to sport in the first year from surgery experiencing a second ACL rupture. Aside from the increased risk of second injury, patients after ACLR have an increased risk of developing early onset of osteoarthritis. Given the recent findings, it is imperative that rehabilitation after ACLR is scrutinized so the second injury preventative strategies can be optimized. Unfortunately, current ACLR rehabilitation programs may not be optimally effective in addressing deficits related to the initial injury and the subsequent surgical intervention. Motor learning to (re-)acquire motor skills and neuroplastic capacities are not sufficiently incorporated during traditional rehabilitation, attesting to the high re-injury rates. The purpose of this article is to present novel clinically integrated motor learning principles to support neuroplasticity that can improve patient functional performance and reduce the risk of second ACL injury. The following key concepts to enhance rehabilitation and prepare the patient for re-integration to sports after an ACL injury that is as safe as possible are presented: (1) external focus of attention, (2) implicit learning, (3) differential learning, (4) self-controlled learning and contextual interference. The novel motor learning principles presented in this manuscript may optimize future rehabilitation programs to reduce second ACL injury risk and early development of osteoarthritis by targeting changes in neural networks.

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