Implementing OPTIMAL Theory in lower extremity tasks to reduce risk of injury

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Mackenzie Ann Pierson (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/
Advisor
Christopher Rhea

Abstract: Anterior cruciate ligament (ACL) injury affects roughly 150,000 people each year, and the majority of those affected are women and girls. Major risk factors for sustaining an ACL injury are condensed into the following categories: (1) anatomical/structural, (2) hormonal, (3) genetic, and (4) neuromechanical. Of the risk factor categories, neuromechanical is the most modifiable. Training programs, or ACL injury prevention programs (IPPs), have been implemented with the goal of modifying movement to reduce neuromechanical risk factors. However, these programs have had limited success at reducing the total number of ALC injuries in sport. One area of refinement in ACL IPPs is the adoption of newer motor learning theories that have evolved in recent years. A relatively new motor learning theory, Optimizing Performance Through Intrinsic Motivation and Attention for Learning (OPTIMAL) Theory, contains three key components that have shown to aid motor learning, retention, and transfer: (1) external focus of attention, (2) autonomy of support, (3) and enhanced expectancies. However, OPTIMAL Theory has primarily been studied in upper extremity tasks. To close these gaps between motor learning and ACL IPPs, this dissertation had three purposes: (1) compare performance of an OPTIMAL Theory group to a control group in the jump/landing task of basketball rebounding, (2) examine the extent to which motor performance from the basketball rebounding task transferred to a maximal effort vertical jump task (i.e., a similar dynamic task) and (3) examine the extent to which motor performance from the basketball rebounding task transferred to a standing balance task (i.e., a static task). A total of 60 young healthy adults participated in a two-day study and were randomly assigned to the OPTIMAL (n=30; 21 (3.6) years; 172.2 (10.9) cm; 81.0 (22.8) kg; M=15, F=15) or the control group (n=30; 22.1 (3.3) years; 167.9 (9.7) cm; 71.6 (16.1) kg; M=10, F=20). Day one included pre- and post-testing of five rebounds, five maximal effort vertical jumps, and standing balance testing. In between the pre- and post-tests was a practice block that included 25 rebounds, with the OPTIMAL group receiving instructions that included external focus, autonomy of support, and enhanced expectancy components, whereas the control group was only given the task goal of rebounding the ball at the highest point. After a 24-hour retention period, all participants completed retention testing of all three tasks which mimicked the pre-testing. Analyses of variance were used to examine the extent to which the OPTIMAL Theory instructions/feedback influenced knee flexion and hip-knee alignment—known ACL injury risk factors—during the rebounding and maximal effort vertical jump task, as well as balance control testing. The results reported in manuscript 1 show that OPTIMAL Theory does have a significant impact on the learning and retention of knee flexion and hip-knee alignment when compared to the control group. The results in manuscript 2 show that the OPTIMAL Theory group transferred the more advantageous movement to a related task, even though no specific instructions were given. The results in manuscript 3 show that using OPTIMAL Theory-rooted instructions to alter movement in a dynamic task may not transfer to a static task. Collectively, these data suggest that OPTIMAL Theory can be used with dynamic, sport-based tasks to enhance lower extremity biomechanics that are known to relate to ACL injury risk, setting the stage for its inclusion in ACL.

Additional Information

Publication
Dissertation
Language: English
Date: 2021
Keywords
ACL, Injury, Learning, OPTIMAL Theory, Retention, Transfer
Subjects
Anterior cruciate ligament $x Wounds and injuries $x Prevention
Knee $x Mechanical properties

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