Reframing ACL Rehabilitation Through a Complex Systems Perspective
Recognizing the gap between clinical and sport environments
Imagine you are the point guard in a chaotic basketball game dribbling the ball up the court. Your team is trailing by 2 points with 10 seconds remaining in the game. The crowd is chanting, your coach is calling the final play of the game. As you cross half-court, your teammate comes to set a screen at the top of the key. You switch your visual gaze swiftly between your defender, your teammate, and your teammate's defensive player. You integrate this environmental information, survey the court from your peripheral vision, and anticipate based on your perception of the environment which play you should make next. Do you dribble off the screen for a 3-pointer to try and win the game? Is your teammate going to slip the screen and have a wide-open lane to the basket? Or will you have an open lane to drive for a layup?
This common sports scenario requires effective integration of sensory information pertaining to the individual (the point guard), environment (unfolding of the play) and task (i.e., shoot, drive, or pass) to execute the appropriate movement solution for the end goal, scoring a basket to win the game. It is important to acknowledge that the goal in sport is generally to produce skillful behavior (i.e., shoot consistently, pass accurately, drive effectively). Additionally, notice how the basketball scenario is constrained by 3 major components: the individual, environment, and task. Rooted in dynamical systems theory, the interactive nature of these 3 components can provide a framework for clinical practice to progress difficulty and create variability in practice. The purpose of this blog is to present a shift from relying on the traditional linear nature of rehabilitation where each of these components are manipulated individually, to instead a model that acknowledges the interactive nature of these components that occurs in sport.
The Traditional Lens of “Neuromuscular Control” – Linear Progression
The term “neuromuscular control” is complex, multi-factorial, and used in various applications through orthopedic and sports rehabilitation. Most of the time, neuromuscular control refers to the ability for an athlete/patient to engage in a dynamic skilled movement to reach a fundamental goal without injury.1 However, the traditional conceptualization of neuromuscular control implemented in orthopedic and sports rehabilitation practice focuses on the biomechanical components of the movement and lacks neurologic and ecological consideration for the organization of movement. Organization of movement in this context refers to neurophysiologic and perception-action coupling that occurs to produce and engage in a skilled movement, such as shooting a basketball in a game.
The traditional lens of neuromuscular control takes a linear approach to the learning process and may only provide an explanation for a limited amount of the current understanding of injury risky movement and decisions made in sport. The intermediate stage interventions during rehabilitation from ACL injury or reconstruction are typically prescribed using repetitive and technical demonstrations by the clinician that primarily focus on manipulating biomechanics to reduce movement patterns related to injury risk and improve ‘coordination’ of physical movement. In other words, treatment is traditionally catered towards a single underlying risk factor for ACL injury and/or reinjury (see Figure 1 left image). In this treatment approach, the progress made in rehabilitation is a stepwise model towards ‘technical proficiency’ in movement relative to a predetermined criterion. In other words, commonly landing from a jump with no knee valgus consistently every repetition is deemed proficient in that particular movement. Only when such technical proficiency is shown, the patient moves on to more complex and possibly cognitively demanding movements (e.g., landing after jumping to an overhead target or dual task while jumping). However, the linear nature of complete movement proficiency prior to additional challenge negates the interactive nature of the task, environment, and individual. By integrating an interactive complex systems model (see Figure 2 right image), movement proficiency can be achieved, and motor learning optimized.
The Complex Systems Perspective of ACL injury Risk and Rehabilitation
As modeled by Karl Newell (1986, 1996),2,14 motor behavior is an emergent process from interactions of key individual, environmental, and task constraints or boundaries (Figures 1 and 2). Individual constraints include both structural (e.g., height, weight) and functional (e.g., cognition, instruction, motivation) characteristics. Environmental constraints refer to those such as the conditions of ambient light, terrain, and opponents. Lastly, task constraints may be the rules of the motor task, equipment being used, and size of the movement space to name a few. In the following section, we will demonstrate how rehabilitation might manipulate these constraints to approach therapy through complex systems perspective.
Traditional movement outcomes such as isolated biomechanical variables certainly mediate ACL injury risk.3 However, human movement does not occur in isolation and is impacted by environmental interactions often dependent on the task goal. For example, biomechanical analysis of a side cut with soccer dribble in the research laboratory or predictable clinic would lack environmental constraints (such as teammates and defenders) with the task goal primarily focused on the cutting movement rather than the end result of a successful pass to a teammate avoiding defenders. Additionally, individual considerations for neurocognitive capacity and decision-making impact an individual’s movement decisions. ACL injury commonly occurs while vision is focused on environmental constructs such as sports balls or opponent players.4 In strategy sports such as basketball and soccer, vision is the dominant sensory system and is essential for environmental negotiation and decision planning.
Thus, to appropriately challenge the neuromuscular control in rehabilitation from ACLR or in injury risk reduction programs, it is important to acknowledge motor skill learning as discontinuous changes over time. That is, motor learning is not a linear trajectory of improvement. Furthermore, ACL injury risk can be thought of as a product of a dynamic interactive model between the task, environment and individual. In order to integrate this interactive model, a paradigm shift in how interventions are thought of and employed clinically is necessary. That is, clinicians can instruct in such a way, that the athlete is given a movement problem to solve (e.g., cut around the cone and kick the ball into the goal) rather than the solution to the problem (e.g., avoid a stiff cut by increasing knee flexion and lowering center of mass). This simple alteration in how the intervention is prescribed affords the learner (athlete/patient) to choose a natural movement solution, and the clinician is still able to inspect and analyze movement mechanics. The therapist can then manipulate environmental factors such as cone distance and speed of the drill etc., but the task goal remains on the outcome (successfully kicking into a goal) rather than the strategy (increasing knee flexion).5
Figure 1: This figure represents an example of a linear (left) and interactive (right) approach towards ACL injury risk.
Constraints-Led Approach Improves Specificity of Rehabilitation Efforts
The constraints led approach (CLA) is a coaching and/or teaching methodology which takes a context specific approach to facilitate motor learning and skill acquisition (Figure 2). The CLA model aims to encourage the performer (i.e., your athlete at risk for ACL injury or after ACLR) to find their own movement solutions based on constraints and the goal to be achieved. To do so, manipulation of the individual, task, and environment are necessary in practice to induce variability which is needed for motor learning. As depicted in Figure 2, a key component in facilitating dynamic movement solutions is considering which constraints you (the clinician) can manipulate. Similarly, how you manipulate them also plays a critical role in optimizing the rehabilitation process. Through integrating movement degeneracy and representative task design into practice, the CLA augments rehabilitation efforts by acknowledging complex systems interactions.
Figure 2: Schematic of the Constraints-Led Approach - https://perceptionaction.com/cla/
Movement Degeneracy is a term that describes the ability to perform an actionable goal in a variety of ways.6 Traditional injury prevention and rehabilitation programs structure skill practice and movement in such a way that variability (exploration of degrees of freedom) is often minimized. For example, in a squat, a clinician often constrains movement within the boundary of knees behind toes, no knee adduction and encourage a posterior weight shift for hip dominance. The hope is that this ‘trained’ movement solution will transfer to jump landings and even into a double leg landing in the chaos of sport. The CLA approach encourages exploration of movement where athletes experiment with various movement solutions. Thus, a squat in rehabilitation might instead include manipulating feedback (i.e., yes/no mechanical instructions instead of detailed explicit instructions) or the environment (i.e., placing foam rollers in front of the knees) surrounding the movement to facilitate self-discovery.
Representative Task Design is a concept developed by Egon Brunswik (1956) which emphasizes that a key concept in learning is understanding the constraints and organization that is necessary for each skilled task.7 In other words, in strategy sports the goal is never movement such as properly executing a side cut. Instead, an athletes’ goal might be to deceive the defense into becoming open to receive a pass. The athlete organizes their movement solution to effectively perform a side-cut in such a way that it conforms to the environmental affordances (i.e., location of other opponents or teammates).4 Advances in rehabilitation technology incorporating immersive environments (e.g., smart phone virtual reality)8 or incorporating external focus of attention,9,10 visual distractions, visual and/or physical targets can improve task design to encourage athletes to explore movement solutions even at early stages in recovery.
Integrating a dynamical systems approach to prevention and rehabilitation requires a paradigm shift away from repetitive practice designs and over utilizing verbal cues to perfect movement technique. Rather, this complex system perspective of ACL injury risk encompasses acknowledging various interacting components or elements that give rise to risk factors associated with primary or secondary injury. The complex system perspective can positively challenge clinicians to shape rehabilitation practices within the dynamical systems context and allow the athlete/patient to explore their own movement capabilities through the manipulation of constraints. Such that the knowledge gained is transferrable to the demands of their respective sport. This methodology is highly effective in acquiring new skills and refining motor learning in already known skills. Table 1 represents example factors in which clinicians can manipulate based upon the individual, task, and environment to incorporate variability in practice, and follow the CLA framework.
infographic-augmenting rehabilitation interventions with a complex systems approach.pdfIn Summary:
The goal of this blog was to raise awareness of the linear prescription of current rehabilitation efforts as it pertains to ACL injury. Since sport environments are highly chaotic, this linear lens might fail to fully prepare athletes for return to sport integration. We hope this post challenges clinicians to consider progression beyond isolated linear interventional methods and begin encompassing interactive dynamic situations that more closely mimic the realistic nature of sport.
Meredith Chaput
Meredith Chaput is an Assistant Professor of Physical Therapy in the School of Kinesiology and Rehabilitation Sciences. Chaput completed her undergraduate education in Exercise Science at the University of Minnesota Duluth and her Doctorate in Physical Therapy at Creighton University. After completing her doctoral training, she completed a post-professional residency in Sports Physical Therapy at Vanderbilt Orthopaedics Nashville and Belmont University and is a Board-Certified Clinical Specialist in Sports Physical Therapy. Currently, Chaput co-directs the CNSlab in conjunction with Drs. Matt Stock and Grant Norte within the Institute of Exercise Physiology and Rehabilitation Sciences. Chaput’s research investigates compensatory nervous system plasticity after lower extremity musculoskeletal injury with the goal to develop neurotherapeutic interventions for orthopedic rehabilitation. Her research integrates functional magnetic resonance imaging (fMRI) and laboratory metrics of functional performance and visual-cognition.
Harjiv Singh
Harjiv is currently the Performance and Development Scientist with the Orlando Magic. He received his PhD in Motor Control and Learning from the University of Nevada, Las Vegas. He aims to combine his educational experiences in motor learning, data science, and biomechanics, to understand how the brain and body correlate seamlessly to coordinate skilled movement behavior (learning and performance) and to translate this into tangible KPI's for coaches and clinicians alike.
References:
1. Hewett TE, Myer GD, Ford KR, et al. Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes. Am J Sports Med. 2005;33(4):492-501.
2. Newell KM. Constraints on the Development of Coordination. In: Wade MG, Whiting HTA, eds. Motor Development in Children: Aspects of Coordination and Control. Springer Netherlands; 1986:341-360. doi:10.1007/978-94-009-4460-2_19
3. Silvers-Granelli HJ, Bizzini M, Arundale A, Mandelbaum BR, Snyder-Mackler L. Does the FIFA 11+ Injury Prevention Program Reduce the Incidence of ACL Injury in Male Soccer Players? Clin Orthop. 2017;475(10):2447-2455. doi:10.1007/s11999-017-5342-5
4. Krosshaug T, Nakamae A, Boden BP, et al. Mechanisms of anterior cruciate ligament injury in basketball: video analysis of 39 cases. Am J Sports Med. 2007;35(3):359-367. doi:10.1177/0363546506293899
5. Faltus J, Criss CR, Grooms DR. Shifting Focus: A Clinician’s Guide to Understanding Neuroplasticity for Anterior Cruciate Ligament Rehabilitation. Curr Sports Med Rep. 2020;19(2):76-83. doi:10.1249/JSR.0000000000000688
6. Edelman GM, Gally JA. Degeneracy and complexity in biological systems. Proc Natl Acad Sci. 2001;98(24):13763-13768. doi:10.1073/pnas.231499798
7. Brunswik E. Perception and the Representative Design of Psychological Experiments, 2nd Ed. University of California Press; 1956:xii, 154.
8. Rausch M, Simon JE, Starkey C, Grooms DR. Smartphone virtual reality to increase clinical balance assessment responsiveness. Phys Ther Sport Kidlington. 2018;32:207-211. doi:http://dx.doi.org.proxy.library.ohio.edu/10.1016/j.ptsp.2018.05.017
9. Gokeler A, Benjaminse A, Hewett TE, et al. Feedback Techniques to Target Functional Deficits Following Anterior Cruciate Ligament Reconstruction: Implications for Motor Control and Reduction of Second Injury Risk. Sports Med. 2013;43(11):1065-1074. doi:10.1007/s40279-013-0095-0
10. Benjaminse A, Gokeler A, Dowling AV, et al. Optimization of the Anterior Cruciate Ligament Injury Prevention Paradigm: Novel Feedback Techniques to Enhance Motor Learning and Reduce Injury Risk. J Orthop Sports Phys Ther. 2015;45(3):170-182. doi:10.2519/jospt.2015.4986
11. Burcal CJ, Needle AR, Custer L, Rosen AB. The Effects of Cognitive Loading on Motor Behavior in Injured Individuals: A Systematic Review. Sports Med. 2019;49(8):1233-1253. doi:10.1007/s40279-019-01116-7
12. Granados C, Wulf G. Enhancing Motor Learning Through Dyad Practice: Contributions of Observation and Dialogue. Res Q Exerc Sport Wash. 2007;78(3):197-203. doi:http://dx.doi.org.proxy.library.ohio.edu/10.1080/02701367.2007.10599417
13. Gu Q, Zou L, Loprinzi PD, Quan M, Huang T. Effects of Open Versus Closed Skill Exercise on Cognitive Function: A Systematic Review. Front Psychol. 2019;10. doi:10.3389/fpsyg.2019.01707
14. Newell, K. M. Change in movement and skill: learning, retention, and transfer. Dexterity and its Development, 1996; 393-429.