Lower-extremity injuries are the most common sports injuries during practice and competition (1). Sports injuries are often associated with inadequate planning and execution of training sessions, improper joint alignment and movement, and weakness in muscles, tendons, and ligaments. Acute sports injuries primarily affect athletes in power events, such as jumping, sprinting, landing, and sharp changes in direction. Injuries prevent athletes from executing full training programs and delay their return to competition, and in extreme cases, may have long-term or career-ending consequences. Biomechanical analysis has proven crucial in objective quantification and understanding of injury mechanisms and has been used in improving injury risks in training and competition (2).
In sports biomechanics, motion analysis systems (MAS) are widely used in attempts to improve the performance and techniques of athletes, and to evaluate injury mechanisms (2). Ideally, MAS could be used for individualized, quantitative rehabilitation applications targeting specific deficits in a given athlete. Evaluation of the postoperative progress, gait and running patterns, sports techniques, and detection of motor control and functional deficits are the main targets of motion capture based sports biomechanics research. MAS have been used effectively in risk assessment and injury prevention of athletes (2). MAS also can be used as a biofeedback system to increase the efficiency of neuromuscular training and in lowering injury risks (3).
MAS can be classified into two general categories: (i) wearable and (ii) nonwearable sensors (4). The Table provides a brief description of the technology, application, advantages, and disadvantages of wearable and nonwearable MAS.
Biomechanical modeling and computer simulation platforms allow for analysis of the musculoskeletal system during sports movements (5–7). Quantitative information of movement patterns, static and dynamic balance, posture, and motor and sensory control can be obtained using modeling and simulation. Interactions between athletes and environment also can be simulated using these platforms. In addition to the temporospatial, kinematic (joint angle, angular velocity, and angular acceleration), and kinetic data (ground reaction force, joint moments, mechanical power, and work), which can be obtained from MAS, musculoskeletal simulation programs can provide information on joint contact forces, muscle forces and power, muscle-tendon unit (MTU) velocity and length changes, and activation level of muscles (5,6) (Fig.).
In this review, we discuss selected injury mechanisms and risk factors of some of the most common lower-limb musculoskeletal injuries, including anterior cruciate ligament (ACL), patellofemoral, and hamstring injuries. The discussion focuses on approaches using kinematic and kinetic information on injury assessment. Furthermore, we evaluated the efficacy of musculoskeletal modeling and dynamic simulation tools in helping our understanding of injury mechanisms related to these injuries.
Lower-Extremity Sports Injuries
ACL injuries are among the most common knee injuries in athletes. About 2 million ACL injuries occur every year around the world (2). The surgical treatment of an ACL injury costs about US $17,000, excluding expenses associated with the rehabilitation (8). The primary function of the ACL is to resist anterior translation and medial rotation of the tibia relative to the femur. ACL loss is typically associated with knee instability, which may lead to further knee injuries and increased risk for knee joint degeneration (2). In vitro loading and in vivo studies indicate that anterior tibial translation causes large ACL strains at low flexion angles (30° and below) (9). ACL injury often has a disruptive effect on an athlete’s career and quality of life (2,8). ACL injury may lead to chronic knee instability, cartilage injury, meniscus tears, and osteoarthritis. Half of all the ACL patients suffer from knee pain and dysfunction, within 10 to 20 years, regardless of the mode of intervention: surgical repair or conservative treatment (10).
ACL injuries can occur due to direct contact with another athlete. However, two-thirds of all ACL tears occur in noncontact situations (11). Noncontact ACL injuries usually occur while executing sudden movements, such as landing from a jump, single leg landing, cutting maneuvers, sudden decelerations, or combinations of these patterns (10,11). Female athletes landing with excessive hip and knee angles, knee valgus, internally rotated tibia, and pronated feet are at increased risk of ACL injury (10). Poor trunk control and trunk motion with a shifted body on the weight-bearing leg also have been associated with increased risk for ACL tears (10). In alpine skiers, internal tibial rotation with a fully extended or a flexed knee beyond 90° has been shown to cause the noncontact ACL injury (10). Contact ACL injuries are usually related to forceful valgus stress and are often accompanied by medial meniscus and medial collateral ligament injury (10).
Three-dimensional (3D) kinematic analysis of landing from a drop vertical jump (DVJ) is one of the most common methods used in ACL injury risk assessment (12). Besides risk assessment, MAS can be used to provide targeted feedback training aimed at altering movement patterns (3). For example, Ford et al. (3) reported that they were able to reduce knee abduction load and posture from baseline to posttraining during a DVJ by using kinetic- and kinematic-based real-time biofeedback during repetitive double-leg squats. Tuck jumps also have been used as a screening movement for ACL injuries (12). DVJ and tuck jump assessments are thought to help identify quadriceps dominance, leg dominance, residual injury deficits, trunk dominance, and poor technique (13).
To prevent ACL injuries, understanding biomechanical mechanisms for ligament overloading is essential. Noncontact ACL injuries are thought to be related to poor neuromuscular control, leading to inadequate biomechanical characteristics (2). For this reason, injury prevention interventions are often targeted at improving neuromuscular control (2). For best outcomes, ACL injury prevention programs should be multicomponent (10). Programs including strengthening, aerobic conditioning, plyometrics, neuromuscular training with feedback related to body mechanics, and landing pattern corrections are the most common rehabilitation and prevention methods.
ACL prevention programs focusing on neuromuscular training include proprioception and balance training, symmetry among lower limbs, and joint alignment feedback during squatting, lunging, cutting, jumping, and landing movements. Strength training prevention programs try to achieve lower-limb symmetries, proper muscle coordination, and accepted muscle strength ratios between the quadriceps and hamstrings muscle groups. Plyometric programs should target proper jumping and landing techniques, and cutting movements should be made such as to decrease strains on joints and ligaments.
Patellofemoral injuries are mainly due to overuse rather than a traumatic injury. They typically occur in conjunction with anterior knee pain, especially in athletic populations performing repetitive jumping movements. Patellofemoral pain may account for 25% to 40% of all knee problems seen in a sports injury clinic (14). Patellofemoral maltracking is thought to cause patellofemoral pain, arthritis, instability, and focal chondral disease (15). Possible contributors to patellofemoral pain are kinematic abnormalities, abnormal patellar tracking, high patellofemoral joint compressive stresses, increased Q-angles, reduced quadriceps length, malalignment of the lower extremity, quadriceps weakness, muscle/soft tissue tightness, and overuse (16).
High knee abduction moments have been implicated with patellofemoral pain and injuries (17). Increased external knee flexion moments and anterior tibial shear forces have been shown to increase tibial shear forces, patellofemoral joint reaction forces, patellofemoral pain, and patellar tendinopathy (18). Knee extensor and hip abductor strength insufficiencies have been proposed to lead to overuse running injuries including patellofemoral pain (19). Female runners who have patellofemoral pain often present with hip abductor and extensor weaknesses, and an increased range of hip internal rotation (19). In a study of 600 novice recreational runners, it has been shown that high eccentric hip abductor strength lowered the risk of developing patellofemoral pain (20).
A more erect landing pattern also has been associated with an increased risk of acute and overuse patellofemoral injury (21). Landing with a reduced hip flexion angle has been associated with increased quadriceps activation and reduced hip extensor activation, thereby reducing ground reaction forces (21).
Increased external knee flexion moment, and the related increase in quadriceps activity in an erect trunk position lead to high anterior tibial shear forces, patellofemoral joint reaction forces, and patellar tendon forces, which are all associated with ACL injuries, patellofemoral pain, and patellar tendinopathy.
Reducing hip flexion during the landing of DVJ has been shown to increase knee abduction moments, and high knee abduction moments are associated with increased risks for patellofemoral pain and ACL injury (17,21). Hip and trunk movements are related to knee joint injury risks (21).
Hamstring injuries are classified as acute and chronic. They are among the most common lower-extremity sports injuries. Hamstring injuries often lead to long-term dysfunction, difficulty to return to play, and recurrent future injuries (22). Hamstring injuries have a high incidence in soccer (23) and sprinting (24). During the 2016 Rio de Janeiro Olympic Games, hamstring injuries were the most common muscle injury among all sports (46.2%), and in sprinters (60%) (25).
Previous hamstring injury is the best predictor for a future hamstring injury, followed by increasing age (26). Many athletes may have a second, and then multiple hamstring injuries. Running and sprinting, especially the deceleration of the swing leg just prior to foot strike, has been identified as the primary phase for Type I acute hamstring strain injuries in sprinting. During dancing, slide tackling, and high kicking, hip flexion movement with knee extension may lead to Type II hamstring strains because the hamstring muscles are stretched beyond their limit (27).
Biomechanical risk factors associated with hamstring injuries are insufficient lumbopelvic motor control and stability, overuse, and weakness in the hamstring muscles (28). Neuromuscular coordination of the muscles in the lumbopelvic region may influence the hamstring functioning. Lumbopelvic instability can lead to change in the length-tension relationship of the hamstring muscle, which may increase the risk of hamstring injuries (29). The gluteus maximus and hamstring act as synergistic muscles for hip extension. If the gluteus maximus is weak, the hamstring often takes over the role of primary hip extensor to compensate this weakness, which contributes to hamstring overload (29).
Horizontal ground reaction force and eccentric peak torque at the end of the swing phase of sprinting have been related to high-speed running hamstring injuries (30). Asymmetries in muscle force between limbs are thought to have a negative effect on sprint mechanics, which cannot be precisely evaluated with today's biomechanical analysis techniques (31). Therefore, the need for approaches other than MAS to calculate time-dependent contractile properties of muscles, such as force, length, and contraction velocity arises.
Analyzing Mechanisms of Lower-Limb Injuries through Musculoskeletal Modeling and Simulation
The simplest form of motion analysis is performed by the human eye. But this method is subjective and qualitative, although it is used much more frequently in coaching and rehabilitation assessment than any formal and quantitative method. Two-dimensional (2D) video analysis has been used frequently in the assessment of human movement since it is simple and often provides much of the required information. However, sports injuries often have an off-2D component, thus requiring 3D analysis tools.
MAS enable the recording of 3D position and orientation of body segments, and ground reaction forces, as well as electromyographic (EMG) signals. Data from MAS can be used as input for modeling and simulation of movements, using computational tools, such as Anybody (5) and OpenSim (6). Because in vivo measurement of muscle forces is complicated and requires invasive surgical approaches, it is not practical. Also, measuring in vivo human muscle forces can be ethically questionable. Similarly, bone-to-bone contact forces in an intact joint, muscle contraction velocity, and muscle length changes are not easily obtained using MAS. Therefore, computational musculoskeletal modeling and simulation environments have been developed in biomechanics research to calculate length changes of contractile elements within muscles, contraction velocities, force, power, and work for individual muscles, and to estimate in vivo joint contact forces (24).
It has been suggested that lower-limb malalignment, weakness, and poor conditioning are risk factors for ACL injuries (32). Movements in all three anatomical planes of the knee affect ACL stresses and strains (2,11). Knee valgus and varus moments, internal tibial rotation moment, and anterior shear forces are common mechanisms for noncontact ACL injury. Understanding the force-sharing patterns among muscles crossing the knee allows for identification of each muscles’ contribution to ACL loading and injury.
One of the first musculoskeletal knee models was developed by Pandy and Shelburne (33). They designed a 2D, sagittal-plane knee model to predict the forces in the knee ligaments induced by isometric contractions of 11 muscles. They simulated quadriceps leg raises, maximum isometric knee extensions, and maximum isometric knee flexion motions. They found that hamstring muscle forces produce a posterior shear force on the tibia that reduces ACL strains. However, this ACL protective effect only worked for knee flexion angles between 15° and 60°, and was ineffective outside that range. The main limitations of that initial knee model were that it was 2D, it had a constant patellar ligament length, and the tibial plateau and patellar facet were assumed to be flat.
Noyes et al. (34) and Jonathan et al. (35) performed simulations to identify differences in knee kinematics and kinetics between males and females. They found that women had greater knee extension and valgus moments than men during the landing phase of the stop-jump task. Their model did not include any muscles, not allowing them to predict muscle forces during the landing tasks. Ali et al. (36) performed simulations for single-leg landings performed from increasing vertical heights and reaching increasing horizontal distances. They found that increasing quadriceps forces increased the noncontact ACL injury risk, while increasing hamstring and gastrocnemius forces and increasing ankle plantarflexion angle reduced the risk. The knee was modeled as a revolute joint without ACL. Therefore, the results of that study required further corroboration.
To determine the causes of ACL injuries in female athletes during noncontact impact activities, Kar and Quesada (37) developed a knee joint model that allowed for mediolateral translation, adduction-abduction rotation, and internal-external rotation. The ACL was modeled as a passive tissue attaching to femur and tibia. Knee flexion, valgus and internal/external moments, knee flexion, valgus and internal/external angles, ACL strains, and internal forces were calculated. They observed a lack of symmetry between the left and right knees for valgus angles, valgus moments, and muscle activations in female athletes, which are thought to be among the main risk factors for ACL injury. In addition, the absence of a model for the patellofemoral joint, and the lack of complete EMG recordings (only the rectus femoris, vastus lateralis, bicep femoris, and gastrocnemius were measured) made it difficult to validate the model. Roldan et al. (38) performed simulations of walking, running, cross-over cutting, sidestep cutting, jumping, and jumping on one leg for 12 young participants. They predicted ACL length, strain, and tensile force, and found that the ACL was subjected to multidirectional loading.
Maniar et al. (39) used a 37 degree of freedom, full-body, musculoskeletal model to investigate the role of the major lower-limb muscles on knee joint loading during unanticipated sidestep cutting maneuvers, a movement considered a high risk for ACL injury. They showed that knee-spanning as well as nonknee-spanning muscles considerably contribute to anteroposterior shear joint force, frontal plane knee joint varus/valgus moment, and transverse plane knee joint internal/external rotational moment during the weight acceptance phase of the sidestep movement. Specifically, they found that the hamstring (biceps femoris long head and medial hamstrings), soleus, and gluteal muscles can unload the ACL during the sidestep cutting task. They concluded that optimizing the function of these muscles should be of high priority in ACL prevention programs. This example illustrates how musculoskeletal modeling can be used to investigate cause-effect relationships between muscle forces and joint loads, which in turn may help improve the effectiveness of preventative and rehabilitative interventions.
MAS have been used effectively in evaluating patellofemoral injury mechanisms. For example, Besier et al. (40) simulated a musculoskeletal model to predict knee muscle forces during walking and running in a group of patients with patellofemoral pain and pain-free control subjects. They found that the patients with patellofemoral pain had higher normalized muscle forces (forces normalized to the maximum isometric muscle force for each muscle) than pain-free controls. Muscle forces are the main contributors to joint contact forces, thus the increased forces may have resulted in increased joint contact stresses, which in turn may have caused the pain observed in the patients. Besier et al. (40) also found that females had greater normalized hamstring and gastrocnemius muscle forces during walking and running compared to males, which is in agreement with the experimental findings in the literature (41,42). Yet, the patellofemoral joint contact force was not calculated in this study.
Besier et al. (43) combined neuromusculoskeletal and finite element modeling to estimate patellar cartilage stress during stair climbing in patients with patellofemoral pain and compared the results to pain-free controls. They found no significant differences between patients and pain-free controls. However, females displayed greater peak patellar cartilage stress compared to males. This finding may contribute to the justification of the greater prevalence of patellofemoral pain in females compared to males.
Olbrantz et al. (44) determined patellofemoral stresses for drop landings in healthy females. Visual feedback of the ground reaction forces helped the participants to reduce patellofemoral joint stresses. Therefore, visual feedback may be used in teaching landing mechanics. Kernozek et al. (45) compared inverse dynamics and inverse dynamics coupled with static optimization techniques for determining the quadriceps force for estimating patellofemoral joint stress. They found that patellofemoral joint stresses obtained from the combination of inverse dynamics and static optimization were higher than those obtained from inverse dynamics alone, indicating that the choice of approach in the prediction of the muscle forces plays a major role in calculating the stresses in patellofemoral joint models. This uncertainty in the selection of the solution approach for the muscle force distribution problem is an important limitation of the musculoskeletal simulation programs. Unfortunately, it is still impossible to measure all muscle forces in humans during movement, and therefore model validation is impossible except using approaches in which muscle forces can be measured directly. Such approaches have repeatedly shown that it is impossible at this time, to predict individual muscle forces with any certainty across a wide range of movements (46).
Musculoskeletal simulation platforms estimate muscle lengths, velocity and force, and thus are well-suited to study hamstring strain injuries. Experiments on isolated muscles and single fibers have shown that the amount of muscle/fiber strain is directly related to muscle damage (47). Musculotendon strain is typically defined as the change in length relative to the musculotendon length measured during standing. Estimation of MTU strain provides an opportunity to understand strain-type injuries. Another important parameter calculated using musculoskeletal simulations is the muscle work. Positive and negative works occur during concentric and eccentric contractions, respectively. These phases are sometimes referred to as “producing” power and “absorbing” power. Based on musculoskeletal simulations, it is thought that most of the hamstring strain injuries happen during the early stance phase and late swing phase of high speed running (24,48,49). However, if this is indeed the case is virtually impossible to demonstrate using experimental approaches.
Thelen et al. (48) and Chumanov et al. (50) used musculoskeletal models to understand the function of human hamstring muscles during high-speed running. They investigated whether the hamstrings are susceptible to injury during the late swing phase of sprinting, when the hamstrings are active and lengthening, or during the stance phase, when contact loads occur. In both studies, the mechanics of the hamstring muscle were studied using a forward dynamics approach during high-speed treadmill running. Thelen et al. (48) found that the peak length of the hamstring MTU occurs during the terminal swing phase. However, they had only one subject to obtain the required kinematic data. Chumanov et al. (50) concluded that the large inertial loads during high-speed running make the hamstrings susceptible to injury during the late swing phase. Because the load patterns between treadmill and overground running differ, these data may not reflect real sprinting conditions.
Schache et al. (24) performed simulations using a 3D musculoskeletal model to understand the mechanics of hamstring muscles in overground sprinting. They calculated joint moments, MTU forces, strains, velocity, power, and work. They found that peak forces and strains for the hamstring muscles occur during the terminal swing phase, thus creating the highest risk for injury during that phase of sprint running.
Musculoskeletal modeling and simulation tools provide a practical and quantitative way to investigate the mechanics of musculoskeletal sports injuries by examining the relationships between muscle forces and joint loads during movements with a high risk of injury. Arguably, the biggest challenge facing scientists is the inability to measure individual muscle forces experimentally, and thus the inability to validate muscle force predictions obtained theoretically. The most common approach to calculate individual muscle forces has been to formulate an optimization problem (46). However, muscle forces obtained from optimization-based approaches should be cautiously evaluated because the predicted muscle functions are highly sensitive to changes in mechanical and architectural properties of the MTU, especially the tendon slack length, of which accurate experimental determination is challenging (51). Therefore, solving the muscle force distribution problem, which has its origins in the beginnings of modern biomechanics, remains one of the major challenges facing musculoskeletal modeling and simulation community.
Nevertheless, integrating musculoskeletal simulation platforms with injury prevention programs is a useful approach, as it helps identify candidate mechanical events that may cause musculoskeletal injury, and allows for musculoskeletal injuries to be simulated without jeopardizing athletes. For such approaches to be useful, it is necessary to obtain subject-specific musculoskeletal models with accurate anatomical and physiological structures. Improvements in musculoskeletal simulation also may be achieved by performing real-time data analysis and providing real-time feedback to subjects.
The authors declare no conflicts of interest and do not have any financial disclosures.
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