Identified in repetitive jumping sports, overuse injuries such as patellar tendinopathy have increased (23), with a prevalence range from 10% in college athletes (39) to 32% in elite basketball athletes (23). Although the overall prevalence across different sports indicates that every fifth elite athlete will suffer patellar tendinopathy within his or her career, it is particularly concerning that 55% of elite basketball players have reported current or previous patellar tendinopathy symptoms (23). Classified as a degenerating overuse knee injury (33), patellar tendinopathy is diagnosed using a combination of a history of activity related pain (25), tenderness on palpation (26), and a patellar tendon abnormality (PTA) on diagnostic imaging (9), with the severity of patellar tendinopathy assessed via the Victorian Institute of Sport Assessment (VISA) score (37).
With controversy surrounding the precise etiology of patellar tendinopathy, a combination of internal and external risk factors is thought to play a part in its development (10,33). Although the presence of a PTA is used to confirm diagnosis of patellar tendinopathy (9,26), PTAs have also been identified as a risk factor in the development of patellar tendinopathy (8). Asymptomatic athletes with a PTA have an increased likelihood of developing patellar tendinopathy (7,8), especially males who are twice as likely to develop a PTA compared with females (9). Regardless of this supporting evidence of PTA as a risk factor for patellar tendinopathy development, the clinical importance of PTA changes has not yet been identified because the size of the PTA varies over time and is unable to predict patellar tendinopathy symptoms (18). Nevertheless, identification of asymptomatic athletes with a PTA using a different landing strategy may provide a method to identify these athletes at risk of developing patellar tendinopathy.
With repetitive landing being identified as the primary risk factor of patellar tendinopathy (10,33), symptomatic athletes with patellar tendinopathy (5,34) and asymptomatic athletes with a PTA (13) have been associated with altered lower limb landing strategies. The critical characteristics associated with these altered landing strategies are knee and hip joint motion. During a dynamic landing, maximum knee joint flexion is the strongest predictor of symptomatic patellar tendinopathy (34) and asymptomatic athletes with a PTA compared to athletes with normal patellar tendons displayed increased knee joint flexion at initial foot–ground contact (IC) and different hip movement strategies, whereby they extend their hip during landing as opposed to flexing (13).
As these altered landing strategies primarily occur in the sagittal plane, it may be possible to identify athletes with these altered landing patterns, using a simple two-dimensional video camera as opposed to three-dimensional motion analysis. Although three-dimensional lower limb motion analysis is considered the gold standard of assessing landing technique, it is costly and extensively time- and space-consuming (29). It is therefore not practical for all coaches and/or clinicians to use this method. A successful alternative method for validly screening athletes at risk in a cost-, time-, and space-effective method is to screen athletes using a two-dimensional video analysis, previously used in anterior cruciate ligament injuries (31). Nevertheless, the two-dimensional video analysis may be limited if there is a lack of validity between three- and two-dimensional measures (29). Therefore, further research is warranted to determine whether this analysis method could be implemented to develop a movement screening tool for another knee joint injury—patellar tendinopathy.
Other risk factors associated with patellar tendinopathy that are also readily measurable and modifiable, which may be included with a movement screening tool to assess injury risk, include increased adiposity (10,17), decreased lower limb flexibility (26,39), and higher vertical jump height (25). If any of these modifiable risk factors are meaningful predictors of an increased risk of developing a PTA and therefore patellar tendinopathy, these risk factors should be used to screen athletes. Athletes identified as being at risk can then have modification strategies implemented to reduce injury risk and/or prevalence of patellar tendinopathy.
As PTAs tend to emerge during the developmental adolescent years (9), it is imperative that risk factors associated with patellar tendinopathy within the junior pre-elite population be identified and understood to develop effective preventative measures to aid in the reduction of patellar tendinopathy injury rates (36). As such, a prevention-through-prediction model aims to reduce injury rates by predicting athletes at risk of developing patellar tendinopathy within the sporting population, with implementation of risk modification strategies to reduce the patellar tendinopathy incidence rate. Therefore, the present study aimed to (i) determine the risk factors that are most influential in predicting the incidence and severity of PTA in junior pre-elite basketball athletes, and (ii) to develop an easily implemented, valid, and reliable movement screening tool based on critical risk factors associated with patellar tendinopathy that can be used by coaches and/or clinicians to identify athletes who are at a higher risk of developing patellar tendinopathy. We hypothesized that the criteria of altered hip and knee motion strategies during a stop–jump task, lower limb flexibility, increased adiposity, and increased vertical jump performance will allow a prevention-through-prediction approach to determine (i) the presence and (ii) the severity of a PTA in asymptomatic individuals.
METHODS
Participants
Twenty-two junior pre-elite male basket-ball athletes (mean age = 17.7 ± 1.5 yr, height = 183 ± 10 cm, mass = 78.0 ± 14.7 kg) with no current signs or symptoms of patellar tendinopathy were recruited from junior pre-elite rural representative teams. The presence of a PTA (Table 1) (7) was assessed by an experienced musculoskeletal sonographer (M.J.; PRP Imaging Bathurst NSW Australia) using a 12-MHz linear array ultrasound transducer (Toshiba, Aplio XG, Japan). Body composition was estimated by a dual-energy x-ray absorptiometry (DEXA; XR800, Norland, Cooper Surgical Company) using a supine whole-body scan performed by a qualified technician (scanning resolution = 6.5 × 13.0 mm, scanning speed = 130 mm·s−1). The participants’ height, body composition, anthropometric dimensions, static dorsiflexion (26), static hamstring (39) and quadriceps flexibility (14), and maximum vertical jump height (25,36) were measured before determining their dominant lower limb on the basis of their preferred kicking leg (15). Of the 22 participants recruited, 10 participants with a PTA and no current signs of patellar tendinopathy were individually matched for height, mass, and test limb to 10 of the participants with normal patellar tendons (13). Participants were matched because of significant lower limb movement asymmetry displayed for some critical kinetic and kinematic variables associated with patellar tendinopathy during the horizontal landing phase in a stop–jump task (12). The lower limb with the larger PTA area (mm2) (13) was selected for analysis if a participant had bilateral PTA. Written informed consent was obtained from each participant before data collection, with parental/guardian consent obtained for minors. All methods were approved by the institution’s Human Research Ethics Committee (2011/071).
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TABLE 1: PTA measurements.
Experimental task
Since a substantial component of basketball play is rapid acceleration, deceleration (28), and repetitive landing (27), the stop–jump task was chosen as the experimental task. The stop–jump task involved five phases, which included a horizontal preparation, horizontal landing, horizontal take-off, vertical preparation, and vertical landing phase (12). Each participant was required to perform the horizontal preparation phase accelerating forwards for 10 m toward a force platform. The average approach speed during the horizontal preparation phase (mean approach speed = 5.1 ± 0.3 m·s−1) was measured using infrared timing lights (Speed Light; Swift Sports Equipment, Lismore, Australia). Upon approaching of the force platform, each participant jumped horizontally off one lower limb and stopped abruptly using a simultaneous two-foot landing with one foot wholly contacting a force platform (horizontal landing phase). Participants then immediately jumped vertically upward (horizontal take-off phase) off the ground to strike a ball suspended from the ceiling (vertical preparation phase), with both hands (vertical jump height = 52 ± 7 cm). Participants performed the vertical landing phase by landing on both feet a second time (vertical landing phase). Participants performed five successful stop–jump movements for each lower limb. A successful stop–jump was defined as a participant obtaining an adequate approach speed of between 4.5 and 5.5 m·s−1 during the horizontal preparation phase, placing a foot wholly on the force platform during the horizontal landing phase, and contacting ball suspended from the ceiling with both hands. The approach speed was based on the 10-m sprint time (1) and average sprint duration of 2.1 s (2) as these are typical values in junior elite male basketball. During task familiarization, jump height effort was standardized among the participants by positioning the ball at the maximum height each participant could touch the ball with both hands after performing the horizontal landing phase of the stop–jump task.
Experimental procedure
Before completing a 5-min warm-up on a cycle ergometer (Monark 828E, Varburg, Sweden), a static trial was performed. Each participant was then familiarized with the stop–jump task before performing five successful dynamic stop–jump trials. During each dynamic trial, the ground reaction forces generated at landing were recorded (2400 Hz) using a multichannel force platform with built-in charge amplifier (Type 9281CA; Kistler, Winterthur, Switzerland) embedded in the floor and connected to a control unit (Type 5233A; Kistler, Winterthur, Switzerland). The participants lower limb and trunk motion was recorded in three dimensions using a Qualisys Oqus 300 camera system (240 Hz; Qualisys AB, Gøeborg, Sweden), and in two dimensions using a digital video camera (30Hz; GZ-MG465; JVC, Yokohama, Japan). Passive reflective markers were placed on each participant’s lower limbs, pelvis, and torso, on the shoe at the first and fifth metatarsal head, mid anterior foot and calcaneous, lateral and medial malleolus, lateral and medial femoral epicondyle, four-marker cluster placed on the leg and thigh, greater trochanter, anterior superior iliac spine, posterior superior iliac spine, iliac crest, sternal notch, xiphoid process, acromion, lumbosacral (L5–S1) intervertebral joint space, thoracolumbar (T12–L1) intervertebral joint space, bilaterally on the ribcage at the level of the T12–L1 intervertebral joint space and immediately superior to the iliac crest marker, and five tracking markers placed on the lumbar region. To avoid losing view of the passive reflective markers, the participants wore minimal clothing (shorts) and their own socks and athletic running shoes.
Data reduction and analysis
Analysis of the three-dimensional kinematic and kinetic data was performed using Visual 3D software (Version 4; C-Motion, Germantown, MD). The raw kinematic coordinates, ground reaction forces, free moments, and center of pressure data were initially filtered using a fourth-order zero-phase-shift Butterworth digital low pass filter (fc = 18 Hz) before calculating individual ground reaction forces and joint kinematics. Segment masses were defined from Zatsiorsky et al. (40) for the foot, shank, and thigh segments, and from Pearsall et al. (32) for the pelvis, lumbar, thorax, and trunk segments. Segmental inertial properties of each segment were modeled using geometric primitives (20), with the foot, shank, and thigh defined as a frusta of a right cone (16), and the pelvis, lumbar, thorax, and trunk defined as elliptical cylinders (35). With respect to the Cardanic axes of the local joint coordinate system, intersegmental joint angles were expressed for knee and hip joint angles as flexion–extension, adduction–abduction, and internal–external rotation; and trunk angles as extension–flexion, right–left lateral flexion, and left–right rotation. Using the 18-Hz filtered kinetic data, the horizontal landing phase was defined from IC when the vertical ground reaction force exceeded 10 N to the first local minimum, to peak knee joint flexion angle (Kneemax). For each trial, the maximum vertical jump height (11), knee and hip joints, and trunk segment kinematics at IC and at the time of the KneeMax were calculated.
The two-dimensional video data of the lower limb and trunk motion were analyzed using Silicon Coach Pro (Version 6; Silicon Coach Ltd., Dunedin, New Zealand) software. Based on the passive reflective markers, knee and hip joint and trunk segment angles were calculated in the sagittal plane at IC and at the time of the KneeMax. The DEXA scan was analyzed (IlluminatusDXA, ver. 4.2.0), and total body fat mass (22) quantities were calculated.
Statistical analysis
Multiple regression analysis (forward method) was used to determine substantial factors in estimating PTA (i) presence and (ii) severity (area of the PTA within the patellar tendon (24); dependent variables). A “small” correlation has previously been defined as a correlation coefficient of 0.10. Consequently, we considered correlation coefficients of >0.10 as “substantial” (21). All independent variables were continuous and included seven different three-dimensional variables during the landing phase (knee and hip joint angle and trunk segment angle at IC and at the time of the KneeMax and hip joint angle range of motion [ROM; hip joint angle at Kneemax minus hip joint angle at IC]) and five other variables (dorsiflexion, hamstring and quadriceps flexibility, maximum vertical jump height, and adiposity). As three-dimensional motion analysis is considered the gold standard of assessing landing technique (29), only the three-dimensional joint angles were included within the regression analysis. All joint angles included in the statistical analysis were representative of the average of the five successful trials performed by each participant. Independent variables identified as substantial predictors of PTA presence (0 = no evidence of PTA, 1 = evidence of any degree of PTA) from the respective regression analysis were included in a discriminate analysis to correctly classify PTA presence using a leave-one-out classification. However, because two-dimensional compared with three-dimensional motion analysis is more practical and cost- and time-efficient for coaches and/or clinicians (29), the validity of two-dimensional data collection was determined using a linear regression equation to calculate the magnitude of the standard error of the estimate (SEE) between two- (independent variable) and three-dimensional (dependent variable) kinematic data. All regressions and discriminate analyses were performed using PASW statistical package (Version 17.0.1; SPSS, Inc., Chicago, IL). Intrarater reliability of the lower limb and trunk segment two-dimensional kinematic data analysis were assessed for six participants on three separate occasions using consecutive trial pairs of the two-dimensional kinematic data (Analysis Sessions 1 and 2 and Analysis Sessions 2 and 3) using the typical error of measurement (TEM; calculated as a coefficient of variation), percent change in the mean, and intraclass correlation using Microsoft Excel (21). Means and SD were calculated for independent and dependant variables for the participants with PTA and their counterparts with normal patellar tendons during the horizontal landing phases of the stop–jump task.
RESULTS
Means ± SD of the variables used within the multiple regression analyses to predict PTA (i) presence and (ii) severity are illustrated in Table 2. The multiple regression model indicated that the substantial predictors of (i) presence (equation 1) were hip joint ROM (R2 = 0.474), knee flexion at IC (R2 = 0.112), and quadriceps flexibility (R2 = 0.090); and (ii) those of severity (equation 2) of PTA were VISA (R2 = 0.392) and hip ROM (R2 = 0.124), with the standard error of each equation as 0.30 and 12.33, respectively. Discriminant analysis indicated that the respective predictors were able to classify the presence of PTA with 95% accuracy and 95% cross-validation.
TABLE 2: Means ± SD of independent and dependent variables.
Note: a result that exceeds 1 indicates the predicted presence of PTA. A result of <1 indicates no predicted presence of PTA.
Note: a result indicates the area (mm2) of PTA.
The two-dimensional kinematic variables showed excellent reproducibility between Analysis Sessions 1 and 2 and Analysis Sessions 2 and 3, regarding percent change in the mean, TEM, and test–retest correlation (Table 3). The equation of the two-dimensional kinematic data to predict three-dimensional kinematic data is shown in Table 4.
TABLE 3: Intrarater reliability of the two-dimensional kinematic data for 6 participants: Analysis Sessions 1 versus 2 and Analysis Sessions 2 versus 3.
TABLE 4: Equations to convert two-dimensional (2D) joint angles to their three-dimensional (3D) equivalent (y′) during the landing phase of the stop–jump task.
DISCUSSION
Injuries in sport can substantially affect an athlete’s career, which is why many sporting teams have recently adopted a prevention-through-prediction approach that involves movement screening tools (30). In planning and implementing movement screening assessment tools, it is critical that the influence of injury-specific risk factors be identified to enable appropriate modification strategies to be developed and implemented in this case to reduce the risk of patellar tendinopathy. The results of the present study identify substantial variables that enable the prediction of PTA presence and severity in junior pre-elite basketball athletes, which will allow risk factor modification strategies to be implemented and therefore enable a prevention-through-prediction approach to reduce the risk of patellar tendinopathy in athletes.
In identifying variables to predict a PTA in asymptomatic athletes, the importance of lumbopelvic control within rehabilitation programs for patellar tendinopathy was confirmed within this present study because the hip joint ROM was the primary risk factor that predicted both the presence and the severity of a PTA. That is, asymptomatic athletes with a PTA compared to athletes with normal patellar tendons used a different hip movement strategy, whereby they displayed a negative hip joint ROM, indicating that they extended their hip joint while landing as opposed to flexing, which was consistent with previous findings (13). Furthermore, the larger magnitude of this negative hip joint ROM predicted an increase PTA area in participants with a PTA. By using this different hip movement strategy, the PTA athletes require greater forward translation of the center of mass in relation to the base of support because the center of mass is at a greater posterior location at IC, which, in turn, may increase the tensile and compressive loads on the proximal part of the patellar tendon and contribute to the development of a PTA (13). As tendons have the ability to adapt through alterations in the extracellular matrix according to changes in loads (4), these changes in the loads may result in histological adaptation toward a compression-resistant morphology (19), and, therefore, its ability to then withstand high-tensile loads is diminished and injury occurs (3).
In addition to altered hip kinematics, asymptomatic athletes with a PTA also landed with greater knee flexion at IC compared to athletes with normal patellar tendons. Landing with greater knee flexion results in a more posterior line of action of the patellar tendon, which may further contribute to higher patellar tendon compressive loading on the posterior aspect of the patellar tendon through a greater quadriceps tendon force-to-patellar tendon force ratio (6) and by greater tensile loading of the superficial fibers of the patellar tendon on the anterior surface of the patella (3). Because histological adaptations occur as a result of increased patellar tendon tension and compressive loads (19), this present study provides further evidence that the direction of the load that the patellar tendon sustains is more critical than the magnitude of this load in the development of a PTA (13) and the role compressive loads play within patellar tendinopathy development.
Reduced quadriceps flexibility was the only other substantial predictor of the presence of a PTA in asymptomatic athletes. This supports previous research that has associated this variable as a risk factor in the development of patellar tendinopathy (39). Although the relationship between patellar tendinopathy and flexibility is not conclusive, it has been suggested that reduced flexibility may lead to greater load exerted on the tendon (39). However, asymptomatic athletes with a PTA have been shown to dissipate similar patellar tendon loads during stop–jump task landings compared to athletes with normal patellar tendons (13), suggesting that quadriceps flexibility may not affect the magnitude of the load sustained by the patellar tendon during landing. Such a finding further suggests that quadriceps flexibility may influence the direction of this load, which may be more critical in the development of patellar tendinopathy.
Identification of quadriceps flexibility, hip joint ROM and knee joint angle at IC during stop-jump task as meaningful predictors of the presence of a PTA allows these variables to be used as the movement screening criteria to predict asymptomatic athletes at risk of developing a PTA. If an athlete is identified at higher risk of developing a PTA (i.e., a result score of >1.0 in equation 2), risk modification strategies can be implemented to potentially reduce further progression of the asymptomatic PTA into patellar tendinopathy. With an SEE of 0.30 in equation 2, a score as low as 0.7 indicates that referral for further biomechanical screening with diagnostic imaging is warranted. This movement screening criterion is therefore a functional and valid tool that can be easily implemented at a community sporting level to allow coaches and/or clinicians to screen their asymptomatic athletes to predict a PTA presence, thereby allowing risk factor modification strategies to be used by these athletes at an increased risk of developing patellar tendinopathy.
In relation to severity of the PTA within asymptomatic athletes, the VISA score was the second strongest predictor after hip joint ROM. Because the VISA score is used to aid in the diagnosis of patellar tendinopathy, with a score lower than 80 indicating patellar tendinopathy (38), the lower VISA score predicts a larger PTA area within asymptomatic athletes. Although the VISA is a highly reliable test (37) and is sensitive to changes in severity allowing it to be used to monitor rehabilitation progress of athletes recovering from patellar tendinopathy (37), the clinical importance of area of a PTA and VISA in athletes with patellar tendinopathy remains unclear.
Although the predictors of the presence and severity of a PTA during landing were assessed with the criterion of three-dimensional analysis, using a two-dimensional video analysis to screen athletes at risk would not be valid if there was a lack of consistency between three- and two-dimensional data (29). Nevertheless, within the current study, there is consistent relationship between three- and two-dimensional data indicated by the low SEE, which shows that two-dimensional data may be used to estimate three-dimensional data with a low amount of error (between 1.6° and 5.3°; Table 4). Furthermore, the intratester reliability of performing the two-dimensional analysis indicates that there is an excellent reliability for the lower limb and trunk segments (TEM ∼1%–2%; Table 3). Therefore, based on the results of this study, it is suggested that two-dimensional motion analysis is a reliable and valid alternative for coaches and clinicians relative to the costly criterion three-dimensional motion analysis to predict three-dimensional data values.
The authors acknowledge potential limitations within this study. The asymptomatic participants with a PTA incorporated in this study included athletes with both unilateral and bilateral PTA. Given that the etiology of bilateral and unilateral patellar tendinopathy is suggested to be different (10,17), this inclusion may have influenced the results of this current study. Further limitations of this study include the age and skill level of the target population included for participation. Because participants were limited to junior pre-elite athletes, it is unknown if the results observed in this study could be replicated in other age groups and/or competition levels such as elite athletes. Therefore, we recommended that future research investigate an additional independent sample of athletes to confirm this current study’s findings, and based on the three criteria, effective preventative measures are developed to aid in the reduction of patellar tendinopathy injury.
CONCLUSIONS
Hip ROM and knee joint angle at IC during the stop–jump task landing as well as quadriceps flexibility were substantial predictors of the presence of a PTA. These variables can be easily measured and identified using a simple and reliable movement screening criteria to allow coaches and/or clinicians to screen and determine the presence and severity of a PTA, thereby enabling risk factor modification strategies to be developed and implemented, reducing the risk of developing patellar tendinopathy.
The authors thank Michael Jones and PRP Diagnostic Imaging for funding the ultrasounds in this study and Charles Sturt University laboratory staff for performing the DEXA scans. There are no other conflicts of interest from any of the authors. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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