ebook img

Trained Neuromechanical Adaptations Associated with ACL Injury Prevention Programs. by Tyler ... PDF

438 Pages·2011·6.97 MB·English
by  
Save to my drive
Quick download
Download
Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.

Preview Trained Neuromechanical Adaptations Associated with ACL Injury Prevention Programs. by Tyler ...

Trained Neuromechanical Adaptations Associated with ACL Injury Prevention Programs. by Tyler N. Brown A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Kinesiology) in The University of Michigan 2011 Doctoral Committee: Associate Professor, Riann Palmieri-Smith, Co-Chair Assistant Professor, Scott G. McLean, Co-Chair Professor, Ronald F. Zernicke Associate Professor, Richard E. Hughes © Tyler N. Brown 2011 Acknowledgements First and foremost, I would like to extend a heartfelt thank you to my advisors, Dr. Riann Palmieri-Smith and Dr. Scott McLean. I am not sure any words I put on this page will do justice the thanks and appreciation I owe you. I have gained considerable respect for both of you during this process. I will be forever grateful for the encouragement, feedback and guidance you have provided me throughout my time here at Michigan. I truly do appreciate the direction you have provide me and cannot thank you enough for taking the time to be my mentors. Thank You. To the members of my dissertation committee, thank you. To Dr. Richard Hughes and Dr. Ron Zernicke, I have tremendous respect for you both and I would like to extend a sincere thank you for all of your knowledge and experience that helped make this possible. I have truly learned a great deal from this experience and I hope to depart Michigan with some of your wisdom. Thank you lab members past and present. I appreciate your encouragement and support. Without all your help this would not have been possible. I truly appreciate you pitching in with a helping hand when I needed it. I hope I can be there to lend a helping hand when you need it, too. Thank you. ii Mark and Priya, without you both I would have no results. I truly owe you a sincere thank you for helping me out with Matlab code. You have saved me hours and hours of frustration and for that I am forever grateful. Thank you to all the undergraduate students who have come through the lab. There are too many to name and I would not want to leave anyone out so I will not try to mention everyone who deserves praise. I think you know who you are. Without your help I would not have made it through this project. You were a bigger help than you can imagine, thank you. Finally, I would like to thank my family. Without your emotional and financial support this would not have been possible. Thank you for the encouragement, understanding and patience. I cannot say enough. Thank you and without you I would not have made it. Thank you. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS ii LIST OF FIGURES vi LIST OF TABLES ix LIST OF APPENDICES xi ABSTRACT xii CHAPTER 1. Introduction 1 2. Quadriceps activation patterns predict sagittal plane knee kinetics during unilateral landings 9 3. Comparative training-induced lower limb joint biomechanical adaptations between uni-lateral and bi-lateral landings 30 4. Comparative adaptations of lower limb biomechanics during uni-lateral and bi-lateral landings after different neuromuscular-based ACL injury prevention protocols 49 5. Trained quadriceps and hamstrings activation changes predict modifications of hip and knee biomechanics following an ACL injury prevention program 78 6. Discussion 107 7. Conclusion 118 iv 8. Recommendations for future work 124 9. Literature review 127 APPENDICES 153 REFERENCES 407 v LIST OF FIGURES FIGURES Figure 2.1 Subjects will react to a random light stimulus and move in the appropriate direction upon landing from a forward jump. 14 Figure 2.2 Marker set for movement analysis (A) and model for skeleton kinematics and kinetics (B) of the right (contact) leg. 15 Figure 2.3 Mean (± SD) stance phase knee biomechanical patterns during the single- legged landing maneuver. 21 Figure 2.4 Relation between peak proximal tibia anterior shear force and rectus femoris pre-activity (A), and peak stance knee flexion moment with recuts femoris and vastus lateralis pre-activity (B) during single-legged landings. 22 Figure 3.1 The weekly schedule for the six-week neuromuscular training program consisting of exercises from four components (core strength and balance, plyometrics, resistance and speed). 36 Figure 3.2 A plot comparing the mean Pre and Post peak stance knee flexion posture for both unilateral and bilateral landings between the Trained and Control participants. 41 Figure 4.1 The weekly schedule for the six-week isolated component (CORE or PLYO) training programs consisting of exercises from either core stability and balance, or plyometric components. 56 vi Figure 4.2 A plot comparing the mean Pre and Post peak stance knee flexion posture for both unilateral and bilateral landings between the NM, CORE, PLYO and CON participants. 62 Figure 4.3 A plot comparing the mean Pre and Post peak stance knee abduction moments among the NM, CORE, PLYO and CON participants. 65 Figure 4.4 A plot comparing the mean Pre and Post peak stance hip flexion angle among the NM, CORE, PLYO and CON participants. 66 Figure 4.5 A plot comparing the mean Pre and Post peak stance hip flexion moment among the NM, CORE, PLYO and CON participants. 66 Figure 4.6 A plot comparing the mean Pre and Post peak stance hip adduction angle among the NM, CORE, PLYO and CON participants. 67 Figure 4.7 A plot comparing the mean Pre and Post peak stance hip adduction moment among the NM, CORE, PLYO and CON participants. 67 Figure 4.8 A plot comparing the mean Pre and Post peak stance knee flexion moment among the NM, CORE, PLYO and CON participants. 68 Figure 4.9 A plot comparing the mean Pre and Post peak stance knee abduction angle among the NM, CORE, PLYO and CON participants. 68 Figure 5.1 Mean (± SD) stance phase hip biomechanical patterns during the single-legged landing maneuver at both the post and pre-training time points. 91 Figure 5.2 Mean (± SD) stance phase knee biomechanical patterns during the single- legged landing maneuver at both the post and pre-training time points. 92 vii Figure 5.3 Relation between pre-post change in lateral hamstrings pre-activity with peak stance hip flexion moment (A) and peak anterior knee joint reaction force (B) during single-legged landings for the NM group. 95 Figure 5.4 Relation between pre-post change in peak stance knee abduction angle with VL:LH co-contraction ratio for the CORE group. 96 Figure 5.5 Relation between pre-post change in peak stance knee abduction angle with vastus lateralis pre-activity for the CON group. 96 Figure 5.6 Relation between pre-post change in peak stance hip flexion moment with rectus femoris pre-activity for the PLYO group. 97 viii LIST OF TABLES TABLES Table 2.1 Average RMS activation (mean ± SD) of the dominant limb for the pre- and re- activity phases of the single-legged landing maneuver 19 Table 2.2 Regression coefficients from the full stepwise regression models associating pre-activity muscle activation variables with key peak stance (0%–50%) phase knee joint biomechanical parameters 20 Table 3.1 Subject Characteristics 34 Table 3.2 Average Vertical Jump Height 40 Table 3.3 Peak Stance (0% - 50%) Phase Hip and Knee Rotations 41 Table 3.4 Initial Contact Hip and Knee Rotations 42 Table 4.1 Baseline Subject Characteristics 61 Table 5.1 Average change (post-training – pre-training) in RMS activation (mean ± SD) of the dominant limb following neuromuscular training for the pre-activity phase of the single-legged landing maneuver. 90 Table 5.2 Regression coefficients from the full stepwise regression models associating pre-post changes in preparatory muscle activation variables with training changes in peak stance (0%–50%) phase anterior knee joint reaction force. 93 ix

Description:
Figure 5.4 Relation between pre-post change in peak stance knee . ACL injury scenario, may help improve the future prevention model and . object. During a non-contact ACL injury episode, which account for up to 80 percent high-risk activities (e.g. basketball, field hockey, soccer, and volleyball)
See more

The list of books you might like

Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.