Drop jump landing knee valgus angle; normative data in a physically active population

2010 ◽  
Vol 11 (2) ◽  
pp. 56-59 ◽  
Author(s):  
Lee Herrington ◽  
Allan Munro
2021 ◽  
Vol 7 (2) ◽  
pp. 148-163
Author(s):  
Saeed Ghobadi Nezha ◽  
◽  
Seyyed Hosein Hoseini ◽  
Ali Asghar Norasteh ◽  
◽  
...  

Objective: Dynamic Knee Valgus (DKV) causes many acute and chronic knee injuries during deceleration and landing in sports activities and can affect the athletes’ specific skills. This study aims to investigate the effect of 6 weeks of progressive jump-landing training on jump shooting accuracy and knee valgus angle in male basketball players with DKV. Methods: This is a quasi-experimental study conducted on 24 male basketball players with DKV in Mazandaran, who were selected purposefully. They were randomly divided into training and control groups. Their jump shooting accuracy was assessed by Boddington’s Basketball Jump Shooting Accuracy Test, and their DKV angle was measured by a two-dimensional evaluation method in Quinoa software. In addition to their routine exercises, the training group performed the jump-landing training for 6 weeks at 3 sessions per week, while the control group performed their usual exercises only. In order to compare the two study groups, ANCOVA was used. Results: The progressive jump-landing program had a significant effect on jump shooting accuracy (P=0.001) and DKV angle (P=0.001). Conclusion: Six weeks of progressive jump-landing training can improve the jump shooting accuracy and knee valgus angle in basketball players with DKV. Therefore, jump landing exercises are recommended for improving the jump shooting skill and knee alignment in these athletes.


2005 ◽  
Vol 37 (Supplement) ◽  
pp. S398 ◽  
Author(s):  
Darin A. Padua ◽  
Stephen W. Marshall ◽  
Anthony I. Beutler ◽  
Marlene DeMaio ◽  
Barry P. Boden ◽  
...  

2013 ◽  
Vol 48 (2) ◽  
pp. 161-171 ◽  
Author(s):  
Jena Etnoyer ◽  
Nelson Cortes ◽  
Stacie I. Ringleb ◽  
Bonnie L. Van Lunen ◽  
James A. Onate

Context: Instruction can be used to alter the biomechanical movement patterns associated with anterior cruciate ligament (ACL) injuries. Objective: To determine the effects of instruction through combination (self and expert) feedback or self-feedback on lower extremity kinematics during the box–drop-jump task, running–stop-jump task, and sidestep-cutting maneuver over time in college-aged female athletes. Design: Randomized controlled clinical trial. Setting: Laboratory. Patients or Other Participants: Forty-three physically active women (age = 21.47 ± 1.55 years, height = 1.65 ± 0.08 m, mass = 63.78 ± 12.00 kg) with no history of ACL or lower extremity injuries or surgery in the 2 months before the study were assigned randomly to 3 groups: self-feedback (SE), combination feedback (CB), or control (CT). Intervention(s): Participants performed a box–drop-jump task for the pretest and then received feedback about their landing mechanics. After the intervention, they performed an immediate posttest of the box–drop-jump task and a running–stop-jump transfer test. Participants returned 1 month later for a retention test of each task and a sidestep-cutting maneuver. Kinematic data were collected with an 8-camera system sampled at 500 Hz. Main Outcome Measure(s): The independent variables were feedback group (3), test time (3), and task (3). The dependent variables were knee- and hip-flexion, knee-valgus, and hip- abduction kinematics at initial contact and at peak knee flexion. Results: For the box–drop-jump task, knee- and hip-flexion angles at initial contact were greater at the posttest than at the retention test (P < .001). At peak knee flexion, hip flexion was greater at the posttest than at the pretest (P = .003) and was greater at the retention test than at the pretest (P = .04); knee valgus was greater at the retention test than at the pretest (P = .03) and posttest (P = .02). Peak knee flexion was greater for the CB than the SE group (P = .03) during the box–drop-jump task at posttest. For the running–stop-jump task at the posttest, the CB group had greater peak knee flexion than the SE and CT (P ≤ .05). Conclusions: Our results suggest that feedback involving a combination of self-feedback and expert video feedback with oral instruction effectively improved lower extremity kinematics during jump-landing tasks.


2005 ◽  
Vol 37 (Supplement) ◽  
pp. S398 ◽  
Author(s):  
Darin A. Padua ◽  
Stephen W. Marshall ◽  
Anthony I. Beutler ◽  
Marlene DeMaio ◽  
Barry P. Boden ◽  
...  

2021 ◽  
Vol 53 (8S) ◽  
pp. 142-143
Author(s):  
Tracy Zaslow ◽  
Eva Ciccodicola ◽  
Nicole Mueske ◽  
Mia Katzel ◽  
Curtis Vandenberg ◽  
...  

2013 ◽  
Vol 18 (3) ◽  
pp. 10-13 ◽  
Author(s):  
Nelson Cortes ◽  
James Onate

Context:Clinical assessment tools are needed to identify individual athletes who possess elevated risk for anterior cruciate ligament injury. Existing methods require expensive equipment and the investment of a large amount of time for data processing, which makes them unfeasible for preparticipation screening of a large number of athletes.Objective:To assess the extent of agreement between LESS and the iLESS classifications of jump landing performance and the level of agreement between ratings assigned by a novice evaluator and an expert evaluator.Methods:Ratings of drop-jump landings from 20 video recordings of NCAA Division I collegiate athletes, which were randomly selected from a large database.Results:The dichotomous iLESS score corresponded to the dichotomous classification of LESS score for 15 of 20 cases rated by the expert evaluator and 17 of 20 cases rated by the novice evaluator. For the iLESS, only 2 scores out of 20 differed between the evaluators.Conclusions:A high level of agreement was observed between the LESS and iLESS methods for classification of jump- landing performance. Because the iLESS method is inexpensive and efficient, it may prove to be valuable for preparticipation assessment of knee injury risk.


The Knee ◽  
2020 ◽  
Vol 27 (1) ◽  
pp. 183-191 ◽  
Author(s):  
Yingchao Yin ◽  
Shilun Li ◽  
Ruipeng Zhang ◽  
Jialiang Guo ◽  
Zhiyong Hou ◽  
...  

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