Drazen Glisic. Copyright by Drazen Glisic 2015

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1 Patellar Tendon Loading during Laboratory Controlled Vertical and Horizontal Jump-Landing Tasks Towards the Development of a tool to assess the risk of Developing Jumper s Knee by Drazen Glisic A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Exercise Sciences University of Toronto Copyright by Drazen Glisic 2015

2 Patellar Tendon Loading during Laboratory-Controlled Vertical and Horizontal Jump-Landing Tasks Towards the Development of a tool to assess the risk of Developing Jumper s Knee Abstract Drazen Glisic Master of Science Department of Exercise Sciences University of Toronto 2015 Empirical and theoretical evidence support the notion that patellar tendinopathy can develop due to the loads associated with various movement patterns. The objective of the current study was to determine whether visually observable trunk and lower extremity kinematic variables were related to patellar tendon loading. Full body three-dimensional kinematics and force platform data were collected for 35 participants. Patellar tendon stress values were estimated through a single equivalent model. Three relationships were found to be consistent across all studied tasks; a smaller trunk flexion angle, and greater knee flexion range of motion and peak knee flexion angles were associated with an increased peak patellar tendon stress, and an increased patellar tendon stress impulse, respectively. If a causal relationship between patellar tendon loading and risk of patellar tendinopathy exists, there may be a basis for using the four tasks for patellar tendinopathy prediction and prevention. ii

3 Table of Contents List of Tables... iv List of Figures... viii List of Appendices... ix 1 Introduction Research Question Null Hypothesis Review of Literature Patellar Tendinopathy Injury Model Risk Factors for Patellar Tendinopathy Justification for Methodology Experimental Tasks Kinematic Variables Methods Participants Vertical and Horizontal Landing Tasks Data Acquisition (Instrumentation) Data Processing and Analyses Statistical Analyses Results Discussion Conclusion References Appendices Appendix A: Victorian Institute of Sport Assessment Scale -Patella Appendix B: Physical Activity Readiness Questionnaire iii

4 List of Tables Table 3.1. Physical characteristics of the participants (Mean ± SD) Table 3.2. VISA-P scores for male and female participants (Mean ± SD) Table 3.3. List of markers being placed on the participants and their function (i.e., calibration only, tracking only, or both calibration and tracking) Table 3.4. Regression Coefficients for the equation above to predict the instantaneous moment arm (MA in cm) of the patellar tendon as a function of knee joint flexion angle (θ in degrees). Taken from Herzog and Read [48]. Coefficient values are given in double precision (D) notation Table 3.5. List of Kinetic and Kinematic Variables Table 4.1. Results of the two-way repeated measures ANOVA for σ peak Table 4.2. Results of the two-way repeated measures ANOVA for σ imp Table 4.3. Results of the two-way repeated measures ANOVA for σ rate Table 4.4. Means and standard deviations (±SD) for the three patellar tendon stress characteristics across the four tasks Table 4.5. Means and standard deviations (±SD) for the three patellar tendon stress characteristics across the four tasks, by sex Table 4.6. Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak ) in the UNI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table 4.7. Results of the four multiple regression models used to predict impulse (σ imp ) in the UNI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table 4.8. Results of the four multiple regression models used to predict stress rate of development (σ rate ) in the UNI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as iv

5 predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table 4.9. Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak ) in the BI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict stress rate of development (σ rate ) in the BI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict impulse (σ imp ) in the BI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak ) in the UNI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict impulse (σ imp ) in the UNI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict stress rate of development (σ rate ) in the UNI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as v

6 predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict impulse (σ imp ) in the BI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak ) in the BI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the four multiple regression models used to predict stress rate of development (σ rate ) in the BI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors Table Results of the repeated measures two-way ANOVA for the sagittal plane segment and joint RoMs. a Trunk RoM; b Knee RoM; c Ankle RoM Table Results of the repeated measures two-way ANOVA for the sagittal plane segment and joint angles at CL. a Trunk Angle at CL; b Knee Angle at CL; c Ankle Angle at CL Table Results of the repeated measures two-way ANOVA for the sagittal plane segment and joint angles at IC. a Trunk Angle at IC; b Knee Angle at IC; c Ankle Angle at IC Table Results of the repeated measures two-way ANOVA for the peak sagittal plane segment and joint angles. a Peak Trunk Angle; b Peak Knee Angle; c Peak Ankle Angle Table Means (SD), and ranges for sagittal plane trunk, knee, and ankle RoMs across the four tasks vi

7 Table Means (SD) for sagittal plane trunk, knee, and ankle RoMs across the four tasks, by sex Table Means (SD) and ranges for sagittal plane trunk, knee, and ankle angles at CL across the four tasks Table Means (SD) for sagittal plane trunk, knee, and ankle angles at CL across the four tasks, by sex Table Means (SD), and ranges for sagittal plane trunk, knee, and ankle angles at IC across the four tasks Table Means (SD) for sagittal plane trunk, knee, and ankle angles at IC across the four tasks, by sex Table Means (SD), and ranges for peak sagittal plane trunk, knee, and ankle angles across the four tasks Table Means (SD) for peak sagittal plane trunk, knee, and ankle angles across the four tasks, by sex vii

8 List of Figures Figure 2.1. Model of an acute injury mechanism Figure 2.2. Models of Chronic ("overuse") injury mechanisms... 7 Figure 3.1. Anterior (a) and posterior (b) views of the full body marker set-up Figure 3.2. Relative ( joint ) and absolute ( segment ) angles Figure 3.3. Summary of approach used to quantify patellar tendon stress Figure 4.1. Visual3D screen capture at CL of athletes who landed in a manner associated with relatively lower peak patellar tendon stress magnitude (a) and higher peak patellar tendon stress magnitude (b) viii

9 List of Appendices Appendix A: Victorian Institute of Sport Assessment Scale -Patella Appendix B: Physical Activity Readiness Questionnaire ix

10 1 Introduction Patellar tendinopathy (PT) is a common clinical condition characterized by activity-related anterior knee pain, and focal patellar tendon tenderness [1]. It has been linked with decreased functional capacity, chronic pain, lost playing time and early retirement in athletes [2]. As a consequence, there is considerable motivation to develop preventive strategies. Patellar tendinopathy is more frequently reported in basketball and volleyball players than in athletes competing in other sports such as ice hockey, track and field, wrestling, handball, and soccer [3, 4]. Therefore, it is possible that between-sport differences in physical demands influence, in part, the potential for the onset and progression of PT. Relative to other sports, basketball and volleyball players are frequently required to jump and land during practices and games; these activities impose tensile loads on an athlete s patellar tendons [5]. The magnitude of these loads may not exceed the tissue failure tolerance initially, but if performed repetitively and without adequate recovery conditions, these movements can reduce the load-bearing tolerance of the tendon, and decrease the margin of safety (difference between tolerance and applied load) to zero (failure) [6]. Not only are applied musculoskeletal loads influenced by the nature of task(s) performed, but applied loads can also be influenced by a number of intrinsic (personal) and extrinsic environmental factors. For instance, quadriceps strength, flexibility, skill level, weight, and body mass index are examples of personal factors that have been associated with the 1

11 2 development of PT [7]. Janssen et al. [8] suggested that all these factors interact together to increase the loads imposed on the patellar tendon by influencing an athlete s landing kinetics and kinematics. Using multiple regression analyses, they found that sex, quadriceps strength, ankle dorsiflexion velocity and trunk flexion velocity were related to the patellar tendon force peak magnitude and its rate of development when landing from a lateral block-jump task [8]. Furthermore, when the trunk and lower extremity kinetics and kinematics in healthy controls were compared to athletes with a history (past or present) of PT, the group to which an athlete belonged could be distinguished from his/her landing kinetics and kinematics [9-11]. Although these findings do suggest that biomechanical analyses could be used to identify athletes who may be predisposed to developing PT, the measurement of the aforementioned kinematics (i.e., trunk flexion and ankle dorsi-flexion velocities) currently requires sophisticated instrumentation, effectively prohibiting such approaches from being employed in the field. From a practical standpoint, it would be beneficial to examine if there are visually observable kinematic features that could be used as surrogates of PT loading during controlled landing tasks. The link between landing kinematics and kinetics and PT reporting makes it plausible that a movement screen can be created that is capable of identifying athletes who are at higher risk of developing the condition. As such, a movement screen could be used as an injury prevention tool if it is capable of exposing personal characteristics that could increase the risk of developing musculoskeletal disorders. The use of a drop vertical jump as a movement screening task has been successful in identifying females who are at higher risk of suffering a non-contact anterior cruciate ligament (ACL) injury [12]. One advantage of

12 3 the drop vertical jump is that it can be analyzed quantitatively [12] or qualitatively [13, 14] to assess ACL injury risk, as key movement characteristics (i.e., kinematic variables associated with ACL loading) can be accurately and reliably identified using motion capture systems or via visual observation [13-15]. This makes it usable in both laboratory and field settings. A similar movement-screening task has yet to be established for assessing PT risk. However, given the abovementioned logic, it is feasible that individuals who execute controlled jump-landing tasks in ways that result in relatively high rates and/or magnitudes of patellar tendon loading may be more likely to develop PT. Against this backdrop, the objectives of this thesis were to: (1) quantify the patellar tendon stress during four laboratory-simulated vertical and horizontal landing tasks; and (2) examine whether associations exist between visually observable landing kinematic variables and patellar tendon stress magnitudes (peaks), impulses, and rates. The specific research question posed and null hypothesis tested in this thesis are stated below Research Question Are there visually observable body segment and/or joint kinematic variables during controlled laboratory-based vertical and horizontal landing tasks that are related to patellar tendon stress magnitudes (peak), impulses, and rates? Null Hypothesis No relationships exist between the abovementioned characteristics of the patellar tendon stress-time curves and body segment and joint kinematic variables.

13 2 Review of Literature 2.1 Patellar Tendinopathy The patellar tendon is the extension of the common tendon of insertion of the quadriceps femoris muscle, with attachments at the inferior pole of the patella and the tibial tuberosity [16]. Patellar tendinopathy (PT), colloquially referred to as jumper s knee, is a clinical condition that affects the patellar tendon at either one of its attachments, or along the main body of the tendon [17, 18]. PT is thought to develop as a result of micro (tendinosis) or partial tearing, or degeneration of the tendon [19], although the causal relationship between histological observations and pain reporting is not well understood [20, 21]. Individuals with PT commonly experience pain that is localized to the proximal attachment (inferior pole of patella), or to the main body of the patellar tendon [22]. In addition to the pain associated with the condition, those with PT can experience reduced functional capacity, early retirement, as well as chronic pain that persists after retirement from sport [2]. The condition is common in sports that require repetitive jumping, climbing, kicking or running, with the highest prevalence being reported in basketball and volleyball players [3, 4, 18]. In a study conducted by Lian et al. [4], the prevalence of PT was examined near the end of the competitive season in athletes participating in a number of sports including: basketball; volleyball; soccer; track and field; ice hockey; road cycling; and orienteering. They found that basketball and volleyball players had the highest prevalence of current symptoms (44.6% and 31.9%, respectively) and previous symptoms (48% and 55%, 4

14 5 respectively). Furthermore, in a similar study by Zwerver et al. [3] it was found that PT affected 14.4% of volleyball and 11.8% of basketball players, the highest and third highest reported prevalence in the seven sports investigated. These data suggest that there is something common about sport demands (extrinsic factors) and/or player capacities (intrinsic factors) that make basketball and volleyball athletes more susceptible to PT. It could be hypothesized that the amount of jumps and landings the athletes perform, and the way in which jumping and landing exertions are performed are contributory. However, causal mechanisms remain unknown, likely due to the multifactorial and complex nature of athletic injuries. 2.2 Injury Model Although tools for diagnosing PT and plausible explanations for its cause have been proposed [1, 16, 23], the etiology of PT is not completely understood. It is likely that PT causation is multifactorial, but some insight into its onset and progression may be gained by considering PT through the application of a generic mechanical injury model [24]. In such a model, injury is defined as tissue damage that occurs when the applied load (tissue demand) exceeds the tissue failure tolerance (tissue capacity). Mechanically speaking, tissues can be overloaded in two ways: acutely and chronically. An acute injury occurs when a one-time supra-maximal load exceeds the tissue tolerance and results in failure (Figure 2.1). Chronic injuries occur when applied loads are initially sub-failure, but with sustained (Figure 2.2a) or repeated (Figure 2.2b) application of the sub-failure load with inadequate conditions for recovery, cumulative trauma results due to a lowering of the tissue tolerance [6]. As the tolerance decreases, the margin of safety the difference

15 6 between the applied load and the tissue s load tolerance approaches zero and loads that were once safely applied can now lead to tissue failure or injury [6]. Characterizing an injury as either acute or chronic is challenging because the final mechanism tissue demand exceeding the tissue tolerance is the same in both cases. If the loading history of the tissue is not known, then chronic injuries can easily be mistaken as acute injuries. PT causation may be better understood using a chronic injury model. In this way, damage to the patellar tendon substance and/or its osteoligamentous junctions occurs as a result of cumulative loading; its load-bearing tolerance decreases as it is exposed to repeated subfailure loads without adequate conditions for recovery. Failure Tolerance Point of Injury Load Margin of Safety Time Figure 2.1. Model of an acute injury mechanism. Tissue damage (injury) results when the magnitude of a one-time load application exceeds the failure load tolerance of the tissue (adapted from McGill 1997 [25]).

16 7 b) a) Failure-Tolerance Load Margin-of-Safety Applied-Load Point-of-Injury Time b) a) Failure-Tolerance Load Margin-of-Safety Point-of-Injury Time Figure 2.2. Models of Chronic ("overuse") injury mechanisms Sustained (a) or repeated (b) load application outpaces tissue recovery processes, leading to reduced failure load tolerance. Again, tissue damage (injury) results when the applied load exceeds the failure load tolerance (adapted from McGill 1997 [25]). The development and progression of PT may be further explained by considering how the patellar tendon is loaded and how it might behave under specific types of loading based on its composition, structure and mechanical function. The patellar tendon is composed of thick bundles of collagen fibers [26] that are all aligned in the same direction. The

17 8 arrangement of the collagen fibers and their mechanical properties give the tendon high tensile strength and a high elastic modulus [24]. The patellar tendon s structure and composition allow it to transfer quadriceps femoris muscle tension to the tibia. It is the transfer of tensile load that places the greatest stress on the tendon and is hypothesized to be the common mechanism involved in the development of PT [27]. In addition to determining the type of applied load the tendon is most suited to support, the composition of the patellar tendon also determines its mechanical properties and its response to load. Due to the tendon s viscoelastic nature, its mechanical properties and its response to load are both time- and strain-rate dependent [24]. As such, the amount of stress developed within a tendon in response to loading depends on the magnitude of the applied load, the rate at which the load is applied, and the cross-sectional area of the tendon. The tendon s viscoelastic properties also determine the rest required for proper recovery of the tissues, and whether the applied stress causes unrecoverable (plastic) or reversible (elastic) deformation [24]. Therefore, when athletes are performing movements in their respective sports, PT stress is not only dependent on the absolute magnitudes of the applied loads, but it also depends on time-varying characteristics of the applied loads, and on its crosssectional area. 2.3 Risk Factors for Patellar Tendinopathy Musculoskeletal injury causation is likely multifactorial [24], and thus not surprisingly, a number of PT risk factors have been identified [28]. Below is a brief summary of some factors that have been cited as having the ability to increase an individual s risk of

18 9 developing PT together with a brief discussion of how these factors can be interpreted using the injury model presented above. As mentioned previously the prevalence of PT is higher in basketball and volleyball players than in athletes who compete in a number of different sports [3, 4]. Within the framework of a mechanical load tolerance injury model, the amount of jump-landings volleyball and basketball players perform could be one potential reason for the high rates of PT in these athletes, compared to other sports. The knee is responsible for transferring load and dissipating mechanical energy during jump-landings [29] and a portion of this load will be transferred through the patellar tendon. During jump-landings, net knee extensor moments are of generally higher magnitudes than those produced in tasks such as running, cutting and kicking [5, 30], which would place a greater mechanical load-bearing responsibility on the patellar tendon. Therefore, basketball and volleyball players may increase their risk of developing PT as a result of the high-magnitude loads that their tendons have to support. In accordance with the injury model introduced above, not only may the magnitudes of applied tendon loads be implicated in the development of PT, but the frequency, rate, and total duration of loading must also be considered. Biological tissues may exhibit degradation with repeated and prolonged mechanical exposures if adequate rest/recovery conditions are not met [24]. The performance of a single landing is unlikely to cause PT, but if repeated multiple times over a training session, season or career, then the likelihood of PT may increase. Cumulative exposure is a function of the amount of time spent in

19 10 training and competition and the length of an individual s career or involvement in sport or physical activity. In basketball and volleyball, the two sports with the highest rates of PT, a relationship between training time and PT incidence has been found: a direct relationship exists between training time and percentage of volleyball player s with PT [17], and basketball players with PT spend more time in sport-specific training than those without PT [4]. A comparison of injury rates between basketball and volleyball players who participate at various competitive levels reveals that the rate of injury increases in accordance with the competition levels [4, 31-33]. With progression to higher competition levels, the number of hours allocated to sport-specific training and competition would also increase together with the cumulative load imposed on the tendon. As a consequence, reducing the number and severity of such injuries might not be attainable by altering the biomechanics of jumping alone, but attention must also be paid to training, practice and, competition schedules. Although the performance of landing tasks will always impose stresses within the patellar tendon, it may also be important to compare jumping and landing techniques between individual athletes. Differences in lower extremity landing kinematics during a dropvertical jump [9] and a spike jump [10], have been noted between athletes with a history of PT when compared to those without a history, independent of sex. Specifically, athletes who previously had PT but were asymptomatic at time of testing, landed with an increased knee flexion velocity, and a decreased knee and ankle range of motion [9, 10].

20 11 Furthermore, in a group of 11 volleyball players a regression equation that included the peak knee flexion angle, the peak tibial external rotation moment, the peak vertical ground reaction force and its time derivative, and the peak time derivative of the knee extensor moment was able to predict the presence of PT [11]. A similar study showed that multiple regression analyses could predict 52% of the patellar tendon force variance using ankle dorsi-flexion velocity and trunk flexion velocity, along with sex and quadriceps strength, as predictors, and 70% of the patellar tendon force loading rate variance could be explained by the interaction of ankle dorsi-flexion velocity, sex and quadriceps strength [8]. These studies suggest that there is a relationship between landing kinematics and PT loading characteristics. Based on the tendon s material properties, application of higher loads and a more rapid rate of force development could potentially increase the stress developed in the patellar tendon. Such a loading scenario could increase the potential for patellar tendon damage acutely or chronically (i.e., if rest and recovery conditions are inadequate) and could cause a more rapid accumulation of trauma in the tendon [6]. Several studies have found PT rates to be higher in men than women [3, 4, 20, 34], suggesting that there may be sex-based differences in the way the patellar tendon is loaded and/or in its load-bearing tolerance. Sex-based differences in neuromuscular control [35-37] and morphology [38] have been reported in the literature, and both of these factors could influence PT based on the injury model presented above. Specifically, between-sex neuromuscular differences could lead to differences in patellar tendon loading characteristics between men and women (e.g., greater body-mass-normalized knee extensor moments [39]), and morphological differences between men and women (e.g.,

21 12 patellar tendon cross-sectional area [40]) could result in between-sex differences in the load-bearing tolerance of the patellar tendon. In addition to the risk factors mentioned above, playing surface [17], athletes flexibility, quadriceps strength, and weight have been hypothesized to be risk factors for the development of PT [7]. Exactly how each of these factors relates to PT is unknown, and it is possible that there are still a number of other variables present that affect PT but have not been thoroughly investigated. For example, the rules of the sport in addition to the equipment used, may also effect an individual s chance of developing PT. Determining an injury mechanism is not trivial, and although how an individual lands may play a large and interactive role, it is important to acknowledge the complexity of injury causation. With that being said the current study only investigated some of the potential factors involved. 2.4 Justification for Methodology How body movements are coordinated and controlled could alter injury risk by changing the loads applied to tissues [41] and the load-bearing tolerance of said tissues. Movement can be constrained by a host of interacting personal (structural and functional), task, and environmental characteristics [42]. As a consequence, athletes may have preferred or learned patterns of movement coordination and control, but their movement behaviour is also moulded by the requirements of the sport. This rationale is the basis for movement screening, wherein a controlled task is used in an attempt to expose a movement pattern or characteristic that may place an individual at a higher risk of developing an injury. Being

22 13 able to identify individuals that are at a greater risk of injury based on their movement behaviour in controlled tasks may be a valuable tool, provided that the task(s) chosen expose the characteristics of interest. Hewett et al. [12] developed a screening task that exposes movement characteristics that are linked with higher risk of sustaining non-contact ACL injury. They reported that a lower extremity valgus position and higher knee abduction moments exhibited during the performance of controlled drop vertical jumps were risk factors for sustaining ACL injuries in young women, and a biomechanical rationale has been proposed for why these kinematic and kinetic quantities are predictive [12]. By relating injury incidence with lower extremity biomechanics during a drop vertical jump landing, they were able to provide a screening tool for identifying females who are at a higher risk of ACL injury in sporting contexts [12]. The same group validated a nomogram that can be used in clinics to predict ACL injury [13], and proposed that a 2D analysis of a controlled task could be used effectively to screen athletes, in place of a full 3D analysis [15]. There are a variety of clinical screens that are widely used and have shown promise in predicting lower extremity injury [43]. Many involve identifying key kinematic features exhibited by athletes during the performance of standardized movements in a controlled environment. The underlying assumption is that observations made during the screening task(s) can be used to identify personal traits that may predispose athletes to injury (e.g., neuromuscular control deficits) without the need to simulate specific sporting conditions.

23 14 The literature concerning PT has presented evidence for a relationship between an individual s landing kinetics and kinematics and a previous or current presence of PT [9, 10]. These studies suggest that individuals with PT or a history of PT can be identified in a controlled laboratory setting but it is not possible to infer causation on the basis of such investigations. Based on the biomechanical rationale presented above in Section 2.2, patellar tendon loading characteristics during the performance of controlled movements may be related to PT risk. That is, it could be hypothesized that athletes who habitually execute tasks in manner that results in high-magnitude or -rate of patellar tendon loading may be at greater risk of developing PT by virtue of their preferred/learned patterns of movement coordination and control. Although a direct relationship between patellar tendon loading and risk of PT has not been established, in the current study an assumption was made that increased patellar tendon loading could be used as a surrogate for PT risk. Thus if specific movement behaviours associated with the patellar tendon stress variables were found in the current study, then they could potentially be related to PT risk as well, providing further evidence to support the continued development of a movement screen for PT risk. 2.5 Experimental Tasks The four tasks that were analyzed in the study consisted of unilateral and bilateral landings, performed in the vertical and horizontal directions (i.e., landing from vertical and horizontal jumps). The landing tasks were chosen because of the demands they place on the patellar tendon through eccentric action of the knee extensor muscles [5, 29], and because it was assumed that such tasks could feasibly be performed in field settings.

24 15 Variations of the vertical landing tasks included in this study have been extensively used in the ACL and strength and conditioning literature because the tasks are highly controllable across athletes [9]. In the unilateral version of the vertical task (UNI-VER), athletes were instructed to place their hands on their hips (iliac crests) and support themselves unilaterally on top of a raised platform [44]. They then stepped-off the platform and performed a single-leg landing [44]. The non-support leg while standing on the platform was considered the test leg [44]. In the bilateral version of the task (BI-VER), athletes were instructed to step-off of a raised platform while maintaining their hands on their iliac-crests, and immediately jump as high as they can [12]. For both vertical tasks, the platform height was standardized to the height of each athlete s tibial tuberosity. By standardizing the platform height, and instructing athletes to step-off and not jump-off, whole-body centre-of-mass (CoM) velocities were kept relatively consistent within participants, and thus the external task demands were considered to be crudely normalized within participants. Horizontal landing tasks were included in the study because there is evidence to suggest that horizontal landings develop a higher peak patellar tendon force and force rate of development [5]. In an investigation of the patellar tendon forces associated with the stop-jump task, the horizontal landing portion (i.e., landing from a horizontal jump, prior to take-off for the vertical jump) had a higher average peak patellar tendon force and force rate of development compared to the vertical landing portion (i.e., landing occurring after the vertical jump was executed) [5]. When the horizontal landing is performed as part of the stop-jump task athletes initially accelerate to an average speed of 4.5m/s and then

25 16 landed [5]. In the current study an attempt was made to modify the task so it would be easier to control across athletes, thus the two horizontal landing tasks began with a static position. During the unilateral (UNI-HOR) and bilateral (BI-HOR) versions of the horizontal landing task, athletes were asked to execute a two-foot broad jump onto the force plate from a pre-determined distance, 60% and 75% of their height, respectively. The UNI-HOR required participants to perform a single-leg landing [45]. In the BI-HOR athletes performed a two-foot landing. Arm position in both tasks was controlled by having participants place their hands on their iliac crests. By standardizing the distance from the starting position to the force plate in the task, the goal was to normalize the effort each athlete gave and to keep the external demands consistent across athletes. 2.6 Kinematic Variables The kinematic variables chosen for analyses in the study are presented in the Methods section Table 3.5. They included discrete sagittal plane variables representing trunk segment and knee and ankle joint displacements (peaks and at specific events during landing), and total ranges-of-motion (peak-to-peak during landing). They were selected for two primary reasons: (1) a biomechanical relationship was hypothesized to exist between the kinematic variables and the outcome variables of interest; and (2) practitioners may be able to observe these variables in athletes without the need for costly motion capture systems. Based on the injury model presented previously, a change in the impulse, peak patellar tendon force or patellar tendon force rate of development may modify an individual s risk

26 17 of developing PT. Therefore the chosen kinematic variables had to be capable, hypothetically, of modifying the patellar tendon loading characteristics in some way, or be indicative of how the patellar tendon is being loaded. Trunk segment and lower extremity joint angular displacements can conceivably affect patellar tendon loading characteristics by influencing the inertial forces, centre of pressure (CoP) location, duration of the landing, and the patellar tendon moment arm. Moreover, previous research has demonstrated that patellar tendon loading magnitudes and patterns are associated with trunk flexion and ankle dorsi-flexion velocities [8]. There are a number of possible landing scenarios in which the kinematic variables can affect loading of the patellar tendon. One possible way is if an athlete lands with less trunk flexion. This could cause greater ankle dorsi-flexion and flexion of the knee, which could increase the moment about the knee and the associated patellar tendon force. The trunk and segment ranges-of-motion can also be related to the duration of the landing. If the athlete exhibits smaller range-of-motion throughout the kinematic chain, this could indicate that they are completing the landing in a shorter time frame, which can affect the peak patellar tendon stress rate of development, the patellar tendon impulse, and the peak patellar tendon stress [46, 47]. Unlike the trunk and ankle kinematics, the sagittal plane knee flexion angle has a more direct effect on the patellar tendon forces, and thus the developed stress, because (1) it could potentially increase the moment about the knee; and (2) the regression equation being used to compute the moment arm of the patellar tendon includes knee flexion [48].

27 18 By observing the kinematic variables, some insight may be gained into determining what movement patterns are associated with greater mechanical loading demands on the patellar tendon. Although ankle dorsi-flexion and trunk flexion velocities have been associated with peak patellar tendon forces and patellar tendon force rates of development [8], these kinematic variables may be difficult to visually observe. From a practical standpoint it would be beneficial to determine if kinematic variables related to PT can be identified via visual observation (i.e., without the use of sophisticated, or costly equipment). Currently, joint and segment angular displacements, and ranges-of-motion are being identified via visual observation in reliable movement screens [13, 14]. Kinematic variables such as these may be easier for humans to observe without the use of sophisticated equipment [49, 50]. Thus the variables that were included as predictors of patellar tendon loading in this study were restricted to sagittal plane joint and segment angular displacements, and ranges-of-motion.

28 3 Methods 3.1 Participants Thirty-five healthy (17 male and 18 female) basketball and volleyball players were recruited from the University of Toronto s Men s and Women s Varsity Basketball and Volleyball teams. Physical characteristics of the athletes are presented in Table 3.1. Athletes were excluded from the experiment if they had: any current lower extremity or back injury (past 6 months) that would prevent them from completing any of the four tasks; a self-reported history of lower limb surgery; equilibrium disorders; and/or any other orthopaedic or neurological conditions that could influence their lower limb mechanics during the tasks performed. Individuals with PT were included, provided that they were cleared by the team physician/therapists to participate in practices and games. All athletes filled out an informed consent document and a Physical Activity Readiness Questionnaire (PAR-Q) (Appendix C). Athletes also completed a Victorian Institute of Sport Assessment Patella (VISA P) (Appendix A) scale to assess patellar tendon symptoms, function, and their ability to undertake sport [51]. VISA-P scores for all athletes are included in Table 3.2. Testing was performed at the University of Toronto s Musculoskeletal Biomechanics and Injury Prevention Laboratory, and all recruitment, experimental, and informed consent processes were approved by the University of Toronto s Office of Research Ethics prior to study commencement. 19

29 20 Table 3.1. Physical characteristics of the participants (Mean ± SD). Sex Age Height (cm) Mass (kg) Male 20.8 ± ± ±9.69 Female 20.3 ± ± ±8.45 Table 3.2. VISA-P scores for male and female participants (Mean ± SD). Sex VISA-P Score Male 78.4 ±16.1 Female 91.9 ± Vertical and Horizontal Landing Tasks Athletes were led through a standardized warm-up, consisting of five body weight squats, and three counter-movement jumps. After completion of the warm up, athletes performed four tasks consisting of unilateral and bilateral landings, performed in the vertical and horizontal direction (i.e., landing from vertical and horizontal jumps). For the data collections, women wore a sports brassiere, and both men and women wore spandex bottoms and their own training shoes and socks. Athletes were allowed to practice each task until they indicated and demonstrated that they were comfortable performing it. Minimal instruction or feedback concerning jumping technique was given in order to avoid a potential coaching effect on the subject s natural performance of the task [52]. Trials were conducted by having athletes make contact with the plate using their dominant foot, defined as the leg they would use to kick a soccer ball. Each task was performed five times. The order of the tasks was randomized between athletes, but tasks were

30 21 blocked within athletes to streamline the data collection. A 3-minute break was provided between each task. In the unilateral vertical landing task (UNI-VER), athletes stepped-off of a raised platform that was adjusted to tibial tuberosity height (31 cm 53 cm). After stepping off, a singleleg landing was performed onto the stepping leg (e.g., if body was supported on the right foot during the step-off, they landed on their left foot). In the bilateral version of the vertical landing tasks, athletes performed a drop vertical jump (BI-VER) [12]. Similar to the UNI-VER, athletes started on the raised platform and proceeded to step-off. They were instructed to jump as high as possible after contacting the ground, and then perform a bilateral landing. The unilateral horizontal landing task (UNI-HOR) consisted of athletes performing a two-foot broad jump and landing on one foot. In the bilateral horizontal task (BI-HOR), participants performed a two-foot broad jump onto a two-foot landing. For both the UNI-HOR and BI-HOR, participants were instructed to land on a pre-determined target, which was taped at a distance equal to 60% and 75% of the athlete s height, respectively. For all tasks, athletes were asked to place their hands on their iliac crests in order to control for arm position. They were also instructed to stick the landing (i.e., control the final position); the task was repeated if the participant failed to do so. 3.3 Data Acquisition (Instrumentation) Body segment kinematics were tracked using 14 mm retroreflective markers secured to participants via non-allergenic double-sided adhesive collars, zinc oxide tape and tensor bandages. The whole-body marker set-up consisted of 74 calibration, tracking and dual-

31 22 purpose (calibration and tracking) markers attached directly to skin overlying anatomical landmarks or to rigid bodies (Table 3.3). The primary investigator performed all landmarking via palpation for consistency across participants. As depicted in Figure 3.1, markers were placed bilaterally on: sides of the temple; sides of the forehead; acromion process; medial and lateral humeral epicondyle; styloid process of the radius; styloid process of the ulna; base of the 1 st and 5 th proximal phalanges; dorsal surface of the base of the 3 rd proximal phalange; iliac crest; anterior superior iliac spine; posterior superior iliac spine; greater trochanter of femur; medial and lateral femoral condyle; tibial tuberosity; medial and lateral malleoli; calcaneus; base of the first and fifth proximal phalange (foot); and the dorsal surface of the base of the 3 rd proximal phalange (foot). Single tracking markers were placed on: the center of the forehead; spinous process of seventh cervical vertebrae; suprasternal notch; and xiphoid process. Three skin markers were attached to the upper arms and thighs, and two skin markers were placed on each shank and forearm. A rigid body with four non-collinearly arranged markers attached to it was placed on the posterior aspect of the trunk at the base of the rib cage.

32 23 Table 3.3. List of markers being placed on the participants and their function (i.e., calibration only, tracking only, or both calibration and tracking). Single Tracking Markers Bilateral Markers Location Middle of Forehead C7 Suprasternal Notch Xiphoid Process Trunk Rigid Body (4) Acromion Process Side of Temple Side of Forehead Lateral Epicondyle of Elbow Medial Epicondyle of Elbow Sylid Process of Radius Styloid Process of Ulna Base of 2 st Proximal Phalange Base of 5 th Proximal Phalange Dorsal Surface of Base of 3 rd Proximal Phalange Anetrior Superior Iliac Spine Posterior Superior Iliac Spine Iliac Crest Greater Trochanter Later Femoral Condyle Medial Femoral Condyle Tibial Tuberosity Medial Malleolus Lateral Melleolus Calcaneous Base of 1 st Proximal Phalange Base of 5 th Proximal Phalange Dorsal Surface of Base of 3 rd Proximal Phalange Upper Arm (3) Posterior Forearm (2) Thigh (3) Shank (2) Type Calibration and Tracking Calibration and Tracking Calibration and Tracking Calibration and Tracking Tracking Only Calibration and Tracking Calibration and Tracking Tracking Only Calibration and Tracking Calibration Only Calibration and Tracking Calibration and Tracking Calibration Only Calibration Only Tracking Only Calibration and Tracking Calibration and Tracking Calibration and Tracking Calibration and Tracking Calibration and Tracking Calibration Only Tracking Only Calibration and Tracking Calibration and Tracking Tracking Only Calibration and Tracking Calibration and Tracking Tracking Only Tracking Only Tracking Only Tracking Only Tracking Only Following the application of markers, a 5-second static calibration trial was collected with participants standing quietly in the anatomical position. The static calibration trial was followed by two standardized knee flexion/extension trials (i.e., left- and right-side) that were used off-line to compute knee joint centres and axes of rotation functionally (described below). Three-dimensional marker position data were collected at a sample rate of 200 Hz [12, 15, 35] using an 8-camera optoelectronic motion capture system (Oqus 1, Qualisys AB, Gothenburg, Sweden). Foot-ground contact forces were measured at rate of 2000 Hz using one in-ground force plate (BP600900, Advanced Mechanical Technology Inc., Watertown,

33 24 MA). Ground reaction force data and kinematic data were spatially and temporally synchronized, digitized, and stored using Qualisys Track Manager (version 2.8, Qualisys AB, Gothenburg, Sweden). Subsequent to calibration of the Oqus cameras, the four corners of the force plate were located within the lab coordinate system using the CalTesterPlus TM protocol and software (C-Motion, Inc., Germantown, MD) [53]. Figure 3.1. Anterior (a) and posterior (b) views of the full body marker set-up Yellow-Tracking only; Red-Calibration-Only; Blue- Dual-Purpose. 3.4 Data Processing and Analyses Three-dimensional kinematics of all modeled body segments were computed together with lower extremity joint angles and net joint knee moments using Visual3D software (Version 5, C-Motion, Inc., Germantown, MD). Prior to calculating these kinematic and kinetic quantities, marker position and ground reaction force data were smoothed using a dual-pass, second-order Butterworth filter with an effective cut-off frequency of 12 Hz [54]. The same cut-off frequency was used for marker and ground reaction force data in

34 25 order to minimize artifacts in the joint moment calculations as a result of foot-ground impacts [55]. In accordance with the general approach presented elsewhere [56], the procedure for deriving segment-fixed coordinate systems in Visual3D began with the calculation of segment endpoints. For all segments except the upper arms and thighs, endpoints were defined as the mid-point between the medial and lateral static calibration markers at the proximal and distal ends of the segments. The distal endpoints of the upper arms and thighs were calculated as just described, but their proximal endpoints were estimated, respectively, by creating virtual markers that were offset 0.15 cm inferiorly from the acromion processes and medially from the greater trochanters at a distance of 25% of the length of a line between them. Adjustments were then made to the locations of the medial and lateral inter-condylar knee markers by projecting them onto a functional knee joint flexion/extension axis using marker data that were collected while participants performed 5 to 10 controlled open-chain knee flexion/extension cycles [57]. The origin of each segment-fixed coordinate system was positioned at its proximal endpoint, with its longitudinal axis aligned coincident to a line that joined the endpoints, and its anteriorlydirected axis defined as normal to the plane created by its calibration markers. (For all segments except the upper arms and thighs, a least-squares plane was fit to the four calibration markers.) The laterally-directed axes of each segment were calculated as the cross-product between unit vectors that were directed along the longitudinally- and anteriorly-directed axes. In this way, anatomically interpretable right-handed orthonormal coordinate systems were created for all segments, and homogeneous transformation

35 26 matrices were generated between these anatomical and technical segment-fixed coordinate systems in order to optimally reconstruct the position and orientation of body segments throughout the experimental trials [58]. The aforementioned technical segmentfixed coordinate systems were derived based on the positions of tracking markers that were collected in the static calibration trial for the sole purpose of reconstructing the anatomical coordinate systems during the experimental trials. Effectively, these procedures permitted the creation of a three-dimensional linked-segment model of the body from which joint angles were calculated. Joint angles were defined as the orientation of distal segments with respect to their adjacent proximal segments [59]. The rotation matrix describing the relative three-dimensional orientation between the anatomical coordinate system affixed to adjoining segments was decomposed using the recommended Cardan sequence of rotations [60, 61]. An absolute trunk (segment) angle was also computed (i.e., orientation of the trunk segment with respect to the laboratory coordinate system). Trunk and lower-extremity kinematics were computed throughout each task, but were only analyzed between two events : (1) initial ground contact (IC); and (2) completion of landing (CL). During the BI-VER only the first landing phase was analyzed (i.e., after stepping off the raised platform). A vertical ground reaction force threshold of 10 N was used to define IC [8]. CL was defined as the instant of time when the linked-segment model centre-of-mass (CoM = weighted-sum of all body segment mass locations) reached a minimum vertical position in the laboratory coordinate system. All events were visually inspected during post-processing for quality control purposes. Kinematic variables derived

36 27 included: instantaneous trunk, knee and ankle joint angles at IC and CL; peak knee and ankle joint, and trunk segment angles between IC and CL; trunk, hip, knee and ankle joint range-of-motion (RoM = angle at CL angle at IC ). Given the purpose of this study, only flexion/extension components of the lower extremity joint angles were analyzed (these were hypothesized to be visually observable). The trunk segment angle was measured with respect to the vertical laboratory coordinate system axis (i.e., ninety-degrees is horizontal) (Figure 3.2). When the thigh and shank were aligned as they were in the static calibration trials, the knee joint angles were defined as 0 (i.e., angles were reported as changes from relaxed upright standing) (Figure 3.2). Knee joint flexion was operationally defined to be a positive angle, and extension was defined as a negative angle. When the angle between the shank and foot was matched with its orientation in upright standing (approximately 90 ), ankle joint angle was defined as 0. In this way, ankle dorsi-flexion and plantar-flexion represented positive and negative angles, respectively (Figure 3.2).

37 28 θ trunk! θ hip! θ knee! y θ ankle! x Figure 3.2. Relative ( joint ) and absolute ( segment ) angles. A bottom-up inverse dynamics approach was used to calculate the net joint reaction force and moment at the knee. That is, the Newton-Euler equations of motion were solved first for the foot segment (to yield ankle joint reaction kinetics), and then for the shank segment (to yield knee joint reaction kinetics). Relevant body segment parameters (segment mass-inertial properties) used in the analyses were estimated using the default geometric models [62] and regression equations [63] embedded in Visual3D software, using participant-specific height, mass, and segment lengths as inputs. The procedure employed was based on standard methods that have been presented elsewhere [56]. The patellar tendon force was quantified in Visual3D using a single equivalent muscle modeling approach, wherein the net knee joint extensor moment of force (inverse dynamics output) was divided by the estimated patellar tendon moment arm. Moment

38 29 arms were estimated on a athlete-by-athlete basis using the following equation from Herzog and Read [48]: MA = B0 +B1(θ) + B2(θ) 2 +B3(θ) 3 +B4(θ) 4! Coefficients used in above stated regression equation are included in Table 3.4. Table 3.4. Regression Coefficients for the equation above to predict the instantaneous moment arm (MA in cm) of the patellar tendon as a function of knee joint flexion angle (θ in degrees). Taken from Herzog and Read [48]. Coefficient values are given in double precision (D) notation. Patellar Tendon B0 B1 B2 B3 B4 r D D D D Patellar tendon force was then divided by participant-specific patellar tendon crosssectional area (CSA) estimates from Carroll et al. (2008) to quantify the patellar tendon stress (σ). The CSA was calculated by multiplying each participant s body mass by 1.5mm 2 /kg, which was determined to be the value of the CSA for men and women when normalized to body weight [64]. The following variables were extracted/derived from the patellar tendon stress-time history: peak patellar tendon stress (σ peak ); rate of development of the patellar tendon stress (σ rate ); and the stress-time integral (impulse, σ imp ). Patellar tendon stress estimates were analyzed (instead of force estimates) to crudely account for potential between subject differences that may be related to anthropometrics [40, 65]. The σ peak was extracted from initial foot-ground contact to completion of the landing. The σ rate was calculated by dividing σ peak by the time to σ peak. The σ imp was calculated by integrating the stress-time curve between initial foot-ground contact and the completion of the landing. A summary of the approach to quantify patellar tendon stress is presented in Figure 3.3.

39 30 Moment Arm [4] Cross Sectional Area [5] σ Peak Stress Knee Moment (Inverse Dynamics) [N!m] Patellar Tendon Force [N] Patellar Tendon Stress (σ) [kpa] Stress Rate of Development Impulse t Figure 3.3. Summary of approach used to quantify patellar tendon stress. 3.5 Statistical Analyses A repeated measures analysis of variance (ANOVA) with one between-participant factor (sex) and one within-participant factor (task) was performed for each patellar tendon stress variable. Tukey s post-hoc testing was used to further investigate any significant relationships. Then, four separate statistical models were created using multivariate regression analyses for each task (i.e., UNI-HOR, BI-HOR, UNI-VER, BI-VER) and with each patellar tendon stress characteristic (i.e., σ peak, σ rate, σ imp ) as a dependent variable. The four models included trunk, knee and ankle RoMs, angles at CL, angles at IC, and peak flexion angles (Table 3.5). The significant relationships were then consolidated in order to compare across the three patellar tendon stress characteristics, and the four tasks. Twoway ANOVAs were conducted to compare all kinematic variables across the tasks and sex. All analyses were performed using statistical software (R: A Language and Environment for Statistical Computing; Vienna, Austria), with α levels set at 0.05 and p-values less than 0.05 being considered statistically significant.

40 31 Table 3.5. List of Kinetic and Kinematic Variables. Dependent Variables Kinetic Predictor Variables Kinematic (Ankle, Knee, Hip and Trunk) Peak Patellar Tendon Stress (σ peak ) Instantaneous Angle at Initial Contact (IC) Rate of Patellar Tendon Stress Development (σ rate ) Patellar Tendon Stress Impulse (σ imp ) Instantaneous Angle at Completion of Landing (CL) Peak Angle from Initial Contact to Completion of Landing Range of Motion (RoM) (angle at completion of landing angle at initial contact)

41 4 Results Results of the repeated measures ANOVAs, shown in Tables , reveal that there were no task*sex interaction effects in the three patellar tendon stress characteristics. However, a main effect of task was observed for σ peak (F(3, 130) = 11.99, p < ), and σ imp (F(3, 130) = 9.62, p < ), and main effects of both sex (F(1, 130) = 4.61, p = ) and task (F(3, 130) = 17.35, p < ) were observed for σ rate. Tukey s post-hoc testing showed that: the σ peak and σ imp were both significantly higher in the UNI-VER and UNI-HOR compared to the BI-VER and BI-HOR (Table 4.4); the patellar tendon stress rate of development was significantly higher in the UNI-VER than in the two bilateral tasks (Table 4.4); and stress rate of development was higher in the UNI-HOR compared to the BI-HOR (Table 4.4). There were no between sex differences in the σ peak and σ imp ; however, differences were observed in the σ rate (Table 4.5). Table 4.1. Results of the two-way repeated measures ANOVA for σ peak. Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals Table 4.2. Results of the two-way repeated measures ANOVA for σ imp. Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals

42 33 Table 4.3. Results of the two-way repeated measures ANOVA for σ rate. Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals Table 4.4. Means and standard deviations (±SD) for the three patellar tendon stress characteristics across the four tasks.!! Peak Stress (MPa) Stress Rate of Development (MPa/s) Impulse (MPa!s) BI-VER (9.05) (77.78) 4.88 (1.26) BII-HOR (5.26) (62.50) 5.27 (1.39) UNI-VER (7.13) (68.34) 6.70 (2.22) UNI-HOR (7.50) (73.59) 6.81 (2.31) Table 4.5. Means and standard deviations (±SD) for the three patellar tendon stress characteristics across the four tasks, by sex. Peak Stress (MPa) Stress Rate of Development (MPa/s) Impulse (MPa!s) Male Female Male Female Male Female BI-VER (11.71) (4.26) (101.27) (48.43) 4.69 (1.34) 5.06 (1.20) BI-HOR (6.57) (3.57) (76.00) (48.69) 4.91 (1.63) 5.61 (1.05) UNI-VER (8.84) (5.17) (76.44) (58.72) 6.68 (2.81) 6.73 (1.49) UNI-HOR (9.64) (4.73) (77.64) (71.82) 6.78 (2.77) 6.84 (1.85) Unilateral-Vertical Landing Task As shown in Tables 4.6 (Model 1) and 4.7 (Model 1), during the UNI-VER task the model containing sagittal plane trunk, knee and ankle RoM as predictors, explained 22.9% (p = ) of the variation in σ peak, and 76.7% (p < ) of the variation in σ imp. No individual predictors were related to σ peak ; however, a positive relationship was detected between σ imp and increased sagittal plane trunk, knee and ankle RoM (Table 4.7, Model 1). In addition to the RoM data, the model containing peak trunk, knee, and ankle sagittal

43 34 plane angles explained a significant part of the variance in both σ peak (r 2 = , p = ) and σ imp (r 2 = , p < ); a higher peak knee flexion angle was related to an increased σ peak (Table 4.6, Model 4) and σ imp (Table 4.7, Model 4). Although the body segment and joint angles at IC did not explain a significant proportion of the variance in σ peak, σ imp, or σ rate, the trunk, knee, and ankle sagittal plane angles at CL explained 26.8% of the variation in σ peak (p = ) and 71.6% of the variation in σ imp (p < ). A larger trunk flexion angle at CL was associated with a lower σ peak (p = ), and a larger dorsiflexion angle at CL was associated with a higher σ imp. None of the kinematic variables included in the analyses were related to σ rate during the UNI-VER task (Table 4.8, Models 1 to 4).

44 35 Table 4.6. Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak) in the UNI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 30) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle Table 4.7. Results of the four multiple regression models used to predict impulse (σ imp) in the UNI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 30) Estimate Std. Error t value Pr (> t ) Model < (Intercept) Trunk RoM Knee RoM Ankle RoM Model < (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model < (Intercept) Peak Trunk Angle Peak Knee Angle < Peak Ankle Angle

45 36 Table 4.8. Results of the four multiple regression models used to predict stress rate of development (σ rate) in the UNI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 30) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle Bilateral-Vertical Landing Task In the BI-VER task, the models containing sagittal plane RoM values (Model 1 in Tables ) were able to explain 37.1% (p = ), 31.4% (p = ), and 57.6% (p < ) of the variance in σ peak, σ rate, and σ imp, respectively. A smaller trunk RoM led to a greater σ peak (p = ) and σ rate (p = ), while increased knee RoM resulted in an increase in σ imp (p = ). The peak angles were also able to explain a significant amount of the variation in all three patellar tendon stress characteristics (Model 4 in Tables ); however, individual predictors were only related to the σ peak and σ imp. A higher peak trunk flexion angle was associated with a reduced σ peak (p = ), while greater peak knee flexion angles were associated with increased σ imp (p = ).

46 37 Similar to what was observed in the UNI-VER task, sagittal plane trunk, knee, and ankle angles at IC were not significantly related to the σ peak, σ rate, or σ imp (Model 3 in Tables ), although the models containing all four of these variables at CL as predictors were able to explain a significant proportion of the variance in the patellar tendon stress characteristics (Model 2 in Tables ). Thirty-six percent (p = ) of the variance in σ peak, 62.5% (p < ) of the variance in σ imp, and 25.4% (p = ) of the variance in σ rate, could be explained by the combination of joint and body segment angles at CL. Larger trunk flexion angles at CL were associated with a decreased σ peak (p = ), and a greater knee flexion angle was related to a higher σ imp (p = ). Once again no individual predictors were related to the σ rate (Table 4.10). Table 4.9. Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak) in the BI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) < Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle

47 38 Table Results of the four multiple regression models used to predict stress rate of development (σ rate) in the BI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) < Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle Table Results of the four multiple regression models used to predict impulse (σ imp) in the BI-VER task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model < (Intercept) Trunk RoM Knee RoM Ankle RoM Model < (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model < (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle

48 39 Unilateral-Horizontal Landing Task In the UNI-HOR task, the sagittal plane RoM values were able to explain 25.5% of the variance in σ peak (p = ) and 62.8% of the variance in σ imp (p < ) (Model 1 in Tables 4.12 and 4.13). In both models an increased knee RoM was associated with an increase in the respective patellar tendon stress characteristic. In addition to the models containing RoMs, models including trunk, knee, and ankle angles at CL explained a significant amount of the variance in σ peak (r 2 =0.3459, p = ) and σ imp (r 2 = , p < ) (Model 2 in Tables 4.12 and 4.13). Greater ankle dorsiflexion at CL had a significant, positive relationship with both σ peak (p = ), and σ imp (p < ). The only other model that explained a significant amount of the variance in the patellar tendon stress characteristics during the UNI-HOR task was the one containing the peak trunk, knee, and ankle angles. It was able to explain 53.8% (p < ) of the variance in σ imp (Model 4 in Table 4.13); in this model a higher peak knee flexion angle was associated with an in an increase in σ imp (p = ). No model was able to predict a significant amount of the variance in σ rate during the UNI- HOR task (Table 4.14).

49 40 Table Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak) in the UNI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) < Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) < Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle Table Results of the four multiple regression models used to predict impulse (σ imp) in the UNI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model < (Intercept) Trunk RoM Knee RoM < Ankle RoM Model < (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL < Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model < (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle

50 41 Table Results of the four multiple regression models used to predict stress rate of development (σ rate) in the UNI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) < Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle Bilateral-Horizontal Landing Task Once again the models containing sagittal plane trunk, knee, and ankle RoMs, angles at CL, and peak angles were the only models capable of explaining a significant amount of the variance in the patellar tendon stress characteristics. In the BI-HOR task the joint and segment RoMs, angles at CL, and peak angles explained 68.2% (p < ), 64.4% (p < ), and 65.7% (p < ), of the variance in σ imp, respectively (Models 1, 2 and 4 in Table 4.15). Increased knee RoM (p < ) and larger peak knee flexion angles (p < ) were related to an increase in the σ imp. In addition to the σ imp, the model containing segment and joint angles at CL was able to explain the variance in σ peak (r 2 =

51 , p = ) (Model 2 in Table 4.16). In this model more trunk flexion at CL was associated with a lower σ peak (p = ). No other model was able to explain a significant amount of the variance in the patellar tendon stress characteristics during the BI-HOR task (Tables ). Table Results of the four multiple regression models used to predict impulse (σ imp) in the BI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model < (Intercept) Trunk RoM Knee RoM < Ankle RoM Model < (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model < (Intercept) Peak Trunk Angle Peak Knee Angle < Peak Ankle Angle

52 43 Table Results of the four multiple regression models used to predict peak patellar tendon stress (σ peak) in the BI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) < Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) < Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle Table Results of the four multiple regression models used to predict stress rate of development (σ rate) in the BI-HOR task. Model 1 Range of Motion (RoM) as predictors; Model 2 Segment and Joint Angles at completion of landing (CL) as predictors; Model 3 Segment and Joint Angles at initial contact (IC) as predictors; Model 4 Peak Segment and Joint Angles as predictors. Predictor R 2 p F (3, 31) Estimate Std. Error t value Pr (> t ) Model (Intercept) Trunk RoM Knee RoM Ankle RoM Model (Intercept) Trunk Angle at CL Knee Angle at CL Ankle Angle at CL Model (Intercept) < Trunk Angle at IC Knee Angle at IC Ankle Angle at IC Model (Intercept) Peak Trunk Angle Peak Knee Angle Peak Ankle Angle

53 44 Kinematic Variables No task*sex interactions were found, however all 12 kinematic variables used in the regression analyses did differ across task (Tables ). In addition to this, knee RoM (Figure 4.18b), trunk, knee, and ankle angles at CL (Figure 4.19 a c), ankle angles at IC (Figure 4.20c), and peak trunk, knee, and ankle angles (Figure 4.21 a c) were significantly different between males and females. Descriptive statistics of the kinematic variables across the tasks and between sexes are presented in Tables Additionally two screen captures depicting athletes landing with relatively high and low patellar tendon loads, respectively, are shown in Figure 4.1. Table Results of the repeated measures two-way ANOVA for the sagittal plane segment and joint RoMs. a Trunk RoM; b Knee RoM; c Ankle RoM. a Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals b Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals c Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals

54 45 Table Results of the repeated measures two-way ANOVA for the sagittal plane segment and joint angles at CL. a Trunk Angle at CL; b Knee Angle at CL; c Ankle Angle at CL. a Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals b Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals c Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals Table Results of the repeated measures two-way ANOVA for the sagittal plane segment and joint angles at IC. a Trunk Angle at IC; b Knee Angle at IC; c Ankle Angle at IC. a Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals b Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals c Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals Table Results of the repeated measures two-way ANOVA for the peak sagittal plane segment and joint angles. a Peak Trunk Angle; b Peak Knee Angle; c Peak Ankle Angle. a Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals b Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals c Df Sum of Squares Mean Squares F Value Pr (>F) Task < Sex Task*Sex Residuals

55 46 Table Means (SD), and ranges for sagittal plane trunk, knee, and ankle RoMs across the four tasks.!! BI-VER BI-HOR UNI-VER UNI-HOR Trunk RoM ( ) (10.61) (7.37) (9.20) (6.30) Knee RoM ( ) (11.09) (11.94) (9.81) (8.57) Ankle RoM ( ) (7.89) (20.29) (7.27) (14.51) Table Means (SD) for sagittal plane trunk, knee, and ankle RoMs across the four tasks, by sex. Trunk RoM ( ) Knee RoM ( ) Ankle RoM ( ) Male Female Male Female Male Female BI-VER (10.54) (10.08) (10.80) (9.32) (7.45) (7.34) BI-HOR 9.83 (5.24) (8.87) (11.16) (10.38) (18.57) (22.19) UNI-VER (10.32) (7.97) (11.39) (7.34) (7.86) (5.99) UNI-HOR (5.89) (6.73) (9.24) (7.09) (14.75) (14.47) Table Means (SD) and ranges for sagittal plane trunk, knee, and ankle angles at CL across the four tasks.!! BI-VER BI-HOR UNI-VER UNI-HOR Trunk Angle at CL ( ) (13.41) (12.60) (13.98) (11.60) Knee Angle at CL ( ) (12.33) (13.16) (11.37) (9.72) Ankle Angle at CL ( ) (5.22) (6.05) (4.68) (5.60)

56 47 Table Means (SD) for sagittal plane trunk, knee, and ankle angles at CL across the four tasks, by sex. Trunk Angle at CL ( ) Knee Angle at CL ( ) Ankle Angle at CL ( ) Male Female Male Female Male Female BI-VER (13.06) (12.72) (11.70) (11.14) (4.67) (5.14) BI-HOR (11.40) (12.58) (12.18) (10.68) (5.51) (5.49) UNI-VER (13.82) (13.62) (12.55) (9.06) (5.06) (3.71) UNI-HOR (11.95) (10.72) (9.71) (9.30) (5.67) (5.34) Table Means (SD), and ranges for sagittal plane trunk, knee, and ankle angles at IC across the four tasks.!! BI-VER BI-HOR UNI-VER UNI-HOR Trunk Angle at IC ( ) (8.00) (9.80) (7.60) (7.25) Knee Angle at IC ( ) (6.58) (6.95) (4.48) (5.38) Ankle Angle at IC ( ) (5.70) (19.62) (5.30) (14.51) Table Means (SD) for sagittal plane trunk, knee, and ankle angles at IC across the four tasks, by sex. Trunk Angle at IC ( ) Knee Angle at IC ( ) Ankle Angle at IC ( ) Male Female Male Female Male Female BI-VER (7.29) (8.65) (5.94) (7.29) (6.59) (4.68) BI-HOR (8.00) (10.56) (4.59) (8.56) (18.33) (20.42) UNI-VER (6.40) (8.29) (3.39) (5.29) (6.14) (4.37) UNI-HOR (7.29) (6.69) 9.01 (5.46) 8.55 (5.45) (14.88) (14.55)

57 48 Table Means (SD), and ranges for peak sagittal plane trunk, knee, and ankle angles across the four tasks.!! BI-VER BI-HOR UNI-VER UNI-HOR Peak Trunk Angle ( ) (13.68) (12.69) (13.93) (11.55) Peak Knee Angle ( ) (12.29) (13.20) (11.25) (9.75) Peak Ankle Angle ( ) (26.99) (25.01) (10.90) (17.93) Table Means (SD) for peak sagittal plane trunk, knee, and ankle angles across the four tasks, by sex. Peak Trunk Angle ( ) Peak Knee Angle ( ) Peak Ankle Angle ( ) Male Female Male Female Male Female BI-VER (12.92) (13.30) (11.63) (11.14) (24.56) 1.45 (28.99) BI-HOR (11.92) (12.19) (12.28) (10.71) (23.83) 5.48 (24.64) UNI-VER (13.76) (13.58) (12.41) (8.99) (14.62) (4.93) UNI-HOR (11.89) (10.69) (9.70) (9.36) (18.18) (18.21) a) b) Figure 4.1. Visual3D screen capture at CL of athletes who landed in a manner associated with relatively lower peak patellar tendon stress magnitude (a) and higher peak patellar tendon stress magnitude (b).