In the collegiate setting, anterior cruciate ligament (ACL) injuries account for 3–5% of all injuries sustained by athletes (14). Furthermore, 88% of these injuries resulted in a loss of practice or playing time of at least 10 days or more (14). These injuries can severely affect an athlete's season and possibly playing career, as nearly 50% do not return to sport after 2 years (1). There are several mechanisms of injury that can cause an ACL tear, but nearly 70% of these injuries are because of noncontact mechanisms (2,16). Recent research has suggested that athletes have an increased risk of ACL injury when landing in a knee valgus position (2,13). This may be because of a decrease in neuromuscular control before or during landing. Due to the length of missed time from competition and the understanding of what factors may predispose an athlete to ACL injuries, medical personnel have started to be proactive about screening to prevent injuries.
Several tests have been reported to help identify risk factors for ACL injury in collegiate athletes (17). Unfortunately, some of these tools are very expensive and can be time consuming. Not all clinical settings can afford expensive biomechanical equipment to provide screening of athletes. It can also be time consuming to screen athletes individually using high-speed motion capturing systems to evaluate biomechanical deficits; therefore, clinician friendly screening tools are being sought after.
The tuck jump assessment (TJA) was created as 1 such “clinician friendly” tool to identify lower extremity landing technique flaws during a plyometric activity (18,19). The TJA is a quick assessment of repetitive tuck jump performance, lasting 10 seconds, yet requiring a high level of effort, which may result in fatigue. There are 10 technique flaws that can be assessed as either having the apparent deficit or not during the TJA (17,19). These flaws are presented in Table 1. The deficits are summed up (0–10), and it has been suggested that patients or athletes having scores of 6 or higher should be selected for further technique training (17,19), although to date, no study has prospectively examined the association between injury and a specific TJA score.
On further evaluation of the TJA, Myer et al. (17) presented a model with the 10 technique flaws categorized into 5 different modifiable risk factors (17). These modifiable risk factors were ligament dominance, quadriceps dominance, leg dominance or residual injury deficits, trunk dominance (“core” dysfunction), and technique perfection (17). Table 1 shows the modifiable risk factor categorizations with the associated TJA technique flaws. Ligament dominance is suggested as an imbalance between the neuromuscular and ligamentous control of dynamic knee stability (17,18). Quadriceps dominance is suggested as an imbalance between knee extensor and flexor strength, recruitment, and coordination (17,18). Leg dominance is suggested as an imbalance between the 2 lower extremities in strength, coordination, and control (17,18). Trunk dominance is suggested as an imbalance between the inertial demands of the trunk and core control and the coordination to resist it (17,18). The final modifiable risk factor, technique perfection, was not defined in the literature. The grouping of the previous factors was based on expert consensus, and has not been empirically validated.
The grouping of these technique flaws into modifiable risk factors does not seem to have been categorized using statistical analyses, but rather developed as a result of expert consensus. There is strong evidence of the face validity of this tool (14,17) but to date, there does not seem to be data to support the construct validity. Therefore, the purpose of this paper is to investigate the underlying factor structure of the tuck jump assessment. It is our hypothesis that TJA is not a unidimensional construct. This is to say that the TJA will consist of more than 1 factor and therefore these factors will need to be interpreted using statistical evidence along with clinical expertise.
Experimental Approach to the Problem
A psychometric assessment was performed using exploratory factor analysis (EFA) on the 10 TJA technique flaws. Data from 2 previous studies were used to investigate the factor structure of the TJA, some of which has been previously published (8,13). These studies were both reviewed and approved by the Institutional Review Board (IRB) at Northern Arizona University, and informed consent was received from all participants.
This article is a secondary data analysis using deidentified data, and the IRB determined that this is not Human Subjects Research and therefore did not require additional review. The findings from this work can provide statistical evidence to support the grouping of the technique flaws into modifiable risk factors.
In this crosssectional study, an injury-free, healthy, student-athlete cohort (n = 90) between the ages of 18 and 24 (19.3 ± 1.2 years) and a general college cohort (n = 99) between the ages of 18 and 30 ([mean ± SD] 21.5 ± 2.0 years) were screened using the TJA.
There were 189 total participants (160 female, 29 male), 90 were Division I student-athletes and 99 were general college students. These 2 groups were part of different studies (10,15) and the deidentified data are analyzed herein. Participants in both studies received a clear description of the study risks and benefits and were given the opportunity to ask questions. All participants signed an informed consent document approved by the Institutional Review Board—before completing the original 2 studies. Student-athletes included in this study were 18–24 years old and current members of Division I women's athletic teams (basketball, volleyball, soccer, cross country, and track and field). General college students included in this study were 18–30 years old, currently enrolled in an undergraduate or graduate course of study. Self-report questionnaires (a health history questionnaire for student-athletes and Physical Activity Readiness Questionnaire (25) for the general college cohort) were administered to screen for any current or past injury and any positive responses to the screening were reviewed by a physical therapist (--) before testing. The physical therapist excluded 3 student-athletes and 2 participants in the general college cohort for whom testing would cause increase in pain or limit the ability to practice with the sport team.
The TJA was performed using instructions previously published by the test developers (19) and the research team (8). In summary, participants were told to jump repeatedly bringing the thighs parallel to the floor for 10 seconds with a high effort level while landing softly in the same footprint and immediately jumping again; jumping efforts were recorded (Sony Handyman, Sony Corp., San Diego, CA, USA and JVC camcorder, JVC Americas Corp., Wayne, NJ, USA) from a sagittal and frontal plane view. The participants were given no additional instructions, and were not informed of the scoring criteria for the TJA. After the TJA were recorded, raters used a previously published form to score technique flaws for each participant (19). Since participants jump multiple times, the rating is based on the overall performance for 10 seconds. The scoring criteria instructions (20) provide no further guidance on how to rate participants who demonstrate errors on some jumps but not others. The participants were rated as either demonstrating a technique flaw or not. The lack of more detailed instructions may be a reason for low interrater reliability seen with the TJA (8) as raters may interpret the instructions differently for the 10 seconds test. To decrease the possibility of low interrater reliability while scoring the TJA, the 2 rates came to consensus on 20 TJA before scoring the remaining videos.
These flaws were then summed up for a TJA total score (8,19). The member of the research team performing the statistical analyses (--) in this paper was blinded to the testing procedures along with the scoring of the TJA.
Demographic variables (age, height, and weight) between student-athletes and general college students were assessed as means using independent t-tests with alpha set at 0.05. After the TJA were scored, the data were entered into the Statistical Package for the Social Sciences (IBM SPSS, Inc., 22.0, Chicago, IL, USA). Due to the use of nominal data, phi-coefficients were calculated to measure the association between each of the binary variables. An exploratory factor analysis (EFA) was performed to determine how each of the TJA technique flaws loaded on individual factors. This sample was of adequate size due to 5–10 participants per variable (5). A principal component analysis (PCA) was used for factor extraction to determine how each variable loaded on a given factor. Eigen values (>1.0 (12)), percent of variance explained and a scree plot were used to determine the number of factors for the model (23). There is limited theoretical reason for the possible factors to be related. The factors were found to be uncorrelated, therefore a final orthogonal rotation (Varimax with Kaiser normalization) was applied to the model. This rotation assists with the interpretation of the factors and maintain the independence of the factors (23).
There were 189 participants in this study with 90 in the student-athlete cohort and 99 in the general college cohort. The analysis of the demographic variables showed that the participants in the cohorts were similar in height (p = 0.11) and weight (p = 0.12), but varied significantly in age (p < 0.001) as seen in Table 2.
The average number of technique flaws on the TJA total score was 5.42 ± 1.59. There was no difference in the TJA total score between those in the student-athlete cohort compared with those in the general college cohort (t187 = 0.83, p = 0.41). Participants not landing in the same footprint was the most commonly seen flaw (86.2% of participants), whereas foot contact timing not being equal was the least commonly seen flaw (12.7% of participants).
Assumption testing was performed for sampling adequacy, test of sphericity, and evidence of multicolinearity. The phi-correlation matrix and percent of variance for each technique flaw can be seen in Table 3. The Kaiser–Meyer–Olkin measure of sampling adequacy was met (p = 0.57), Bartlett's test of sphericity was satisfied ( = 137.25, p < 0.001), and the determinant score suggests a lack of multicolinearity (r = 0.474). These 3 findings suggested that the data were appropriate for an EFA to be used. The 5 highest eigen values were as follows: 1.78, 1.58, 1.25, 0.95, and 0.91. The PCA suggested a 3-factor model, which was also supported by the scree plot. This 3-factor model explained over 46% of the variance. A summary of the rotated factor loadings can be seen in Table 4.
The 3 factors as defined by the authors were fatigue, distal landing pattern, and proximal control (Table 3). The first factor, fatigue, consisted of 3 items. The factor had an eigen value of 1.77 and explained 17.66% of the variance in the model. The second factor, distal landing pattern, had 4 items with an eigen value of 1.59 and explained an additional 15.89% of the variance. The final factor, proximal control, had 3 items with an eigen value of 1.26 explaining 12.56% of the variance in the 3-factor model. There were 2 technique flaws that had negative loadings, thighs do not reach parallel (peak of jump) (−0.56) and pause between jumps (−0.78). There were no crossloadings of variables on multiple factors greater than 0.30.
The TJA was developed as a “clinician friendly” tool to help identify lower extremity landing technique flaws during a plyometric movement. The purpose of this article was to investigate the underlying factor structure of the TJA. It was our hypothesis that the factor structure of the TJA would not represent a unidimensional construct.
Based on the results from these analyses, it is suggested that the technique flaws for the TJA can be simplified into 3 factors explaining nearly 46% of the variance. The 3 factors were identified as fatigue, distal landing pattern, and proximal control due to the rotated factor loadings and conceptual relationships of the variables. These empirical factors are different from the proposed unidimensional construct or 5 modifiable risk factors that Myer et al. (17) created from biomechanical assessments (13,15).
The first factor of fatigue was created from the tuck jump technique flaws of thighs not equal side-to-side (during flight), pause between jumps and perfect technique declines before 10 seconds. Myer et al. (17) suggests that these technique flaws are related to the modifiable risk factors of leg dominance, trunk dominance (“core” dysfunction), and technique perfection, respectively. Leg dominance is an imbalance between the 2 lower extremities in strength, coordination, and control (17). Trunk dominance is an imbalance between the inertial demands of the trunk and core control and the coordination to resist it (17). As a participant becomes fatigued throughout the TJA it would be expected that the thighs are not of equal height during flight (17). This may be because of the dominant leg maintaining the strength, coordination, and control to reach a given height, whereas the non-dominant leg becomes fatigued more quickly.
The ground reaction forces during the takeoff phase of the tuck jump are the highest among common plyometric exercises (9). To absorb these high ground reaction forces it is essential to have appropriate muscle activation, otherwise injury may result (3). The faster the jumping rate, the quicker the lower extremity will fatigue, which has been reported to result in reduced joint kinematics and subsequent increased loading rate (4). It has been hypothesized that decreased eccentric contraction occurring with fatigue results in a more lengthened position at landing, which decreases the muscles ability to absorb the landing forces (4). This may have contributed to the loading of the factors in this study in that as the participant fatigues, the quadriceps and hip flexors are not only limited in their ability to concentrically contract to maintain thighs parallel to ground, but they are also limited in their eccentric lengthening. Therefore, when a participant has a faster jumping rate, with less time on the ground in between jumps, they will likely experience fatigue, altered joint kinematics, and perhaps increased loading rates compared with someone with a slower jumping rate.
Coventry et al. (7) found that body positioning changed during landing throughout a fatigue jumping protocol (5). It has been suggested that postural control and hip mechanics, specifically in the sagittal plane, decrease with fatigue (11). This current work supports this finding of decreased postural control and hip mechanics with fatigue because as a participant becomes more fatigued, the thighs did not remain equal side-to-side during the TJA. Viitasalo et al. (26) found that during a bout of continuous jumping, the efficiency of contraction of individual muscles decreased over jumping time as measured by the relationship between electromyography activity and force. Fatigue decreases the ability of the musculature to absorb and attenuate the force which can lead to an increased risk of injury (27).
Interestingly, the pause between jump variable had a negative rotated factor loading (−0.78) suggesting that as the length of time between jumps increases, the level of fatigue decreases. In other words, the longer the participant stayed on the ground in between jumps, the less fatigued the participant was at the end of the 10 seconds. This may suggest that the participant with less quadriceps/hip strength inherently reduced their jumping speed as a means to complete the plyometric task in which they were not appropriately prepared to perform. If this is the case, use of the TJA as a tool to determine return to play or ACL injury risk may not be appropriate at all in someone who lacks lower extremity muscle strength.
The second factor of the distal landing pattern was generated from excessive landing contact noise, foot contact timing not equal, foot placement not parallel (front to back) and does not land in the same footprint. These technique flaws were categorized into Myer et al. (17) quadriceps dominance, leg dominance, and trunk dominance. The authors are naming this factor distal landing pattern due to the fact that all of the tuck jump criteria refer to landing and the feet. As defined by Myer et al. (17), quadriceps dominance is an imbalance between knee extensor and flexor strength, recruitment, and coordination (13–15). During the tuck jump, the knee extensors have an extremely high level of electromyography activity when compared with other plyometric exercise (21). Pollard et al. (21) hypothesized that if the hip extensors are weak, the knee extensors may be overused to maintain the participants' body center of mass during landing. This imbalance can be seen in the sagittal plane when participants remain in an extended state at the knee when landing. This lack of co-contraction of the quadriceps and hamstrings causes the participant to land on a flat foot (20). When the participant lands on the entire foot the contact noise will be greater than when he/she lands on just the ball of the foot. Furthermore, it would be expected that all 4 of these technique flaws would be related because all of them focus on the foot during the landing phase of the TJA.
The third factor of proximal control was formed with the technique flaws of foot placement not shoulder width apart, lower extremity valgus at landing and thighs do not reach parallel (peak of jump). These criteria were grouped by Myer et al. (17) into ligament (foot placement not shoulder width apart and lower extremity valgus at landing) and trunk (thighs do not reach parallel) dominance. Ligament dominance is an imbalance between the neuromuscular and ligamentous control of dynamic knee stability, in other words, the participant's inability to control the lower extremity in the frontal plane during landing and cutting motions. Ligament dominance can occur when the participant relies more on the ligaments of the knee for absorption of the ground reaction forces rather than on the musculature of the lower extremity (12). This may be because of the lack of control at the hip, specifically in the frontal plane (17). Recent research suggests that as motion in the sagittal plane decreases, there is a greater amount of motion in the frontal plane, especially at the knee during landing (21). Further, an increased valgus moment has been linked to hip weakness, specifically as a means of compensating for hip abduction weakness in the stand limb (22). The participant may shift 1 foot posteriorly so that the feet are not shoulder width apart, to minimize the forces on a weaker limb (17). Even though the foot placement not being shoulder width apart seems like a distal landing concern, this uneven foot placement exacerbates the medial motion at the knee and knee valgus (femoral adduction, femoral internal rotation in relation to the hip, tibial external rotation in relation to the femur) (18). This valgus position is correlated with an increase in the ground reaction forces (13). This shows how participants who land with their feet not shoulder width apart will be more likely to also have a lower extremity valgus pattern occur. Thighs not reaching parallel during the height of the jump had a negative rotated factor loading (−0.56), suggesting that when participants did not reach this peak, there was an increase in the valgus position at the knee and the feet were not shoulder width apart at landing. Landing on the forefoot aligns the foot/ankle and knee complexes to readily absorb the forces during deceleration and maintains the appropriate amount of hip flexion (6). Yet when individuals land on the entire foot or on the rear foot, a greater amount of force is required to regain that amount of hip flexion (6).
The results of this work suggest that the TJA may load onto 3 different factors: fatigue, distal landing pattern, and proximal control. Each of the TJA technique flaws is related to one another within each given factor. These results may suggest that instead of a cumulative TJA technique flaw overall score, scores on the 3 factors may be more useful for injury screening.
The generalizability of the results may be limited due to the narrow age range and the small number of male participants. The age range for this work is narrow, with inclusionary criteria being 18–24 for the student-athlete cohort and 18–30 for the general college cohort. These results cannot be generalized to a high school population. The second limitation is that nearly 85% of the participants in this study were female (160/189); however, the majority of the literature on the TJA involves female participants, which may support the difference in the number of females and males in this work (17,18,20,24). A final limitation may be associated with measurement error from the low interrater reliability for the TJA (8). This was addressed by having the 2 raters come to consensus on 20 TJA before scoring the remaining videos, although error can be ruled out.
The results from this work suggest that when screening individuals using the TJA, a single cumulative score may not capture the full benefits of this tool. Extensive research was performed to create this “clinician-friendly” tool for injury screening (17–19) and recent research has even investigated the reliability of the tool (8), yet, to our knowledge, this is the first work to use psychometric methods to analyze the factor structure of the technique flaws for the TJA. It may be useful for future researchers to evaluate the consistency of rating pause between jumps from the clinician point of view compared with what is actually recorded with the photocell or contact plate to see how well they relate to one another. Additionally, it may be useful to consider an ordinal scale scoring criteria 0–2 with clear definitions for each category rather than a dichotomous rating of the flaw either being present or not present. Replication using this factor structure needs to be conducted before making large generalizations regarding the current findings.
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