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Reliability of Landing 3D Motion Analysis: Implications for Longitudinal Analyses


Medicine & Science in Sports & Exercise: November 2007 - Volume 39 - Issue 11 - p 2021-2028
doi: 10.1249/mss.0b013e318149332d

Purpose: Biomechanical measures quantified during dynamic tasks with coupled epidemiological data in longitudinal experimental designs may be useful to determine which mechanisms underlie injury risk in young athletes. A key component is the ability to reliably measure biomechanical variables between testing sessions. The purpose was to determine the reliability of three-dimensional (3D) lower-extremity kinematic and kinetic variables during landing in young athletes measured within a session and between two sessions 7 wk apart.

Methods: Lower-extremity kinetics and kinematics were quantified during a drop vertical jump. Coefficient of multiple correlations (CMC), intraclass correlation coefficients (ICC (3, k), ICC (3, 1)), and typical error (TE) analyses were used to examine within- and between-session reliability.

Results: There were no differences in within-session reliability for peak angular rotations between planes with all discrete variables combined (sagittal ICC ≥ 0.933, frontal ICC ≥ 0.955, transverse ICC ≥ 0.934). Similarly, the between-session reliability of kinematic measures were not different between the three planes of motion but were lower than the within-session ICC. The within- and between-session reliability of discrete joint moment variables were excellent for all sagittal (within ICC ≥ 0.925, between ICC ≥ 0.800) and frontal plane moment measures (within ICC ≥ 0.778, between ICC ≥ 0.748). CMC analysis revealed similar averaged within-session (CMC = 0.830 ± 0.119) and between-session (CMC = 0.823 ± 0.124) waveform comparisons.

Conclusion: The majority of the kinematic and kinetic variables in young athletes during landing have excellent to good reliability. The ability to reliably quantify lower-extremity biomechanical variables of young athletes during dynamic tasks over extended intervals may aid in identifying potential mechanisms related to injury risk factors.

1Cincinnati Children's Hospital Medical Center, Sports Medicine Biodynamics Center and Human Performance Laboratory, Cincinnati, OH; 2University of Kentucky, Department of Kinesiology and Health Promotion and Biodynamics Laboratory, Lexington, KY; 3University of Cincinnati, College of Medicine, Departments of Pediatrics and Orthopaedic Surgery and Biomedical Engineering, Cincinnati, OH; and 4Rocky Mountain University of Health Professions, Graduate Program in Athletic Training, Provo, UT

Address for correspondence: Kevin R. Ford, M.S., Cincinnati Children's Hospital, 3333 Burnet Avenue; MLC 10001, Cincinnati, OH 45229;E-mail:

Submitted for publication September 2006.

Accepted for publication June 2007.

Multidisciplinary approaches are typically used to define the potential mechanisms that underlie increased injury risk of musculoskeletal injury and to identify specific factors predictive of injury risk in sports. Specifically, motion analysis techniques have been used to identify potential risk factors for knee injuries(6,9,18,21,26). However, a majority of these investigations are limited to cross-sectional comparisons between groups (i.e., male versus females). Though cross-sectional comparisons may be important to the overall understanding of the biomechanics potentially related to increased injury risk for subsets of athletes, the biomechanical findings are not typically coupled with injury outcomes. Studies that focus on injury surveillance (epidemiology) are invaluable for the determination of the magnitude of the ACL injury incidence disparity between male and female athletes (1). The combination of biomechanical analysis and injury-surveillance techniques within a single study design, however, are not widely used, though the study designs are critical for the delineation of biomechanical factors that underlie injury risk. In a recent coupled biomechanical-epidemiological study, variables quantified with 3D motion analysis techniques predicted ACL injury risk in female athletes with high sensitivity and specificity (13). Incorporation of longitudinal experimental designs into prospective injury risk factor studies may allow for the combination of both epidemiological and biomechanical techniques to address important sports medicine and orthopedic problems. However, a key component to the use of motion analysis techniques to define these problems, especially with longitudinal study designs, is the ability to reliably measure biomechanical variables in individuals both within and between testing sessions.

Kadaba et al. (17) conducted one of the first studies to investigate the reliability of quantitative motion analysis techniques. They examined kinematic and kinetic data from normal gait and found that most often the data were more reliable within a session than between different testing sessions. Error in reapplication of reflective markers was cited as a potential factor for the lower reliability measures, especially in the frontal and transverse planes (17). Both Ferber et al. (7) and Queen et al. (28) found similar trends of improved within-session reliability compared with between testing days during 3D motion analysis of running. These authors also cited marker placement as the most likely cause of decreased between-session reliability. In addition, increased reliability has previously been found when comparing sagittal versus frontal and transverse motions (3,7,17,28). Typical out-of-plane rotations (frontal and transverse) may be more sensitive to reapplication of markers (7,29). However, frontal and transverse plane variables may provide essential information during maneuvers, which may relate to an injury (9,12,13,18,21). Reliability of biomechanical variables, specifically during landing maneuvers, is not well defined in the literature. The examination of the reliability of these variables is important, because a rapid deceleration, such as occurs during landing from a jump, is frequently identified during ACL injuries (24).

The studies discussed above tested subjects between 1 d and approximately 1 wk apart (7,17,28). In coupled biomechanical and epidemiological studies, a longer period of time may be required for the determination of testing reliability, as the biomechanical variables may be measured before a sport season, and subsequent injury and exposure data are recorded for the entire season. The length of a sport season varies according to the sport and level of play; however, a length of 2-3 months is fairly typical in high school sports. If an injury occurs during the season, the preseason quantification of biomechanical variables is considered to be a reasonable representation of the biomechanical profile for that athlete. Therefore, high fidelity in repeated testing of these complex measures is of tantamount importance to the generation of valid study data. Without a strong understanding of potential errors inherent to motion analysis techniques, longitudinal and epidemiological investigations using these techniques are greatly limited.

Applications involving serial measurements of young athletes throughout maturational development are contingent on reliable data acquisition. Quatman and colleagues (27) evaluated young female and male athletes to determine whether changes in maturational development status would affect kinetic patterns during a drop vertical jump. They evaluated young athletes for a 1-yr time span and found that young females do not develop increased lower-extremity power with maturation in a fashion similar to males. This indicates that incorporating additional motion analysis techniques in longitudinal evaluation of young athletes may be warranted if the measures are demonstrated to be reliable over time.

The purpose of the present study was to determine the reliability of three-dimensional (3D) lower-extremity kinematic and kinetic variables during landing in young athletes. Specifically, the reliability of within-session measurements and those made between two relatively extended sessions (approximately 7 wk) were analyzed, using rigorous statistical methods. The first hypothesis was that biomechanical variables quantified during landing in young athletes would be reliable between two sessions. The corollary hypotheses tested, which were developed from a thorough analysis of the previously published literature, were that the within-session measures would provide greater reliability than the between-session measures, and the sagittal plane variables would provide greater reliability than the frontal and transverse plane variables.

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Subjects were included in the current reliability study from a cohort in a prospective, longitudinal study. Middle school and high school soccer players were tested before their upcoming fall season (58 female, 71 male). Approximately 7 wk later, the basketball players from the same middle and high school were recruited for testing before their upcoming winter season (65 female, 71 male). From this large cohort of subjects, 11 athletes were identified who participated in both testing session because they played on both the soccer and basketball teams tested. These 11 subjects were used in the current study to determine the within- and between-session reliability of lower-extremity biomechanical variables (three female, eight male; height:1.64 ± 0.10 to 1.64 ± 0.10 m, typical error = 0.006 m; mass: 53.4 ± 13.0 to 54.5 ± 13.2 kg, typical error = 0.8 kg; 6.7 ± 1.4 wk between sessions). A power analysis from a previous test-retest training study using similar methods, variables, and time interval required a minimum of eight subjects (22). In addition, earlier reliability studies have used a similar sample size (3,28).

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Informed written consent, approved by Cincinnati Children's Hospital institutional review board, was obtained from the parent or parental guardian of each subject. Child assent was also obtained from each subject before study participation. Each subject was instrumented with 37 retroreflective markers (Fig. 1) placed on the sacrum, left PSIS, sternum, and bilaterally on the shoulder, elbow, wrist, ASIS, greater trochanter, midthigh, medial and lateral knee, tibial tubercle, midshank, distal shank, medial and lateral ankle, heel, dorsal surface of the midfoot, lateral foot (fifth metatarsal), and toe (between the second and third metatarsals). A static trial was first collected in which the subject was instructed to stand still with foot placement standardized to the laboratory coordinate system. This static measurement was used as each subject's neutral (zero) alignment; subsequent kinematic measures were referenced in relation to this position. Each subject performed a drop vertical jump (DVJ), which consisted of starting on top of a 31-cm box with their feet positioned 35 cm apart (distance measured between toe markers) (9). They were instructed to drop directly down off the box and immediately perform a maximum vertical jump. The DVJ was employed to examine landing mechanics that might be related to the mechanics of similar movements in both soccer and basketball. The first landing on the force platforms (i.e., the drop from the box) was used for analysis. Three successful trials were recorded for each subject.



Trials were collected with EVaRT (version 4, Motion Analysis Corporation, Santa Rosa, CA) using a motion analysis system consisting of eight digital cameras (Eagle cameras, Motion Analysis Corporation, Santa Rosa, CA) positioned in the laboratory and sampled at 240 Hz. Before data collection, the motion analysis system was calibrated with a two-step process, first using a static calibration frame to orient the cameras with respect to the laboratory coordinate system and, second, using dynamic wand data to fine tune camera positions, calculate the lens distortion maps, and calculate the lens focal length. Two force platforms (AMTI, Watertown, MA) were sampled at 1200 Hz and time synchronized with the motion analysis system. The force platforms were embedded into the floor and positioned 8 cm apart so that each foot would contact a different platform during the stance phase of the drop vertical jump.

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Data analysis.

Data were imported into Visual3D (Version 3.65, C-Motion, Inc., Rockville, MD) and MATLAB (Version 7.0, The Mathworks, Natick, MA) for data reduction and analysis. 3D Cartesian marker trajectories from each trial were filtered through a low-pass fourth-order Butterworth filter at a cutoff frequency of 12Hz. 3D joint angles were calculated for both the left and right side according to the cardan/euler rotation sequence (5). To minimize possible peak impact errors, the force plate data were filtered through a low-pass fourth-order Butterworth filter at a cutoff frequency of 12 Hz (4). These data were used with the kinematic data to calculate joint moments, using inverse dynamics (32). Geometric primitives were used to model the inertial properties of each body segment (11). Specifically, the foot, shank, and thigh were defined as a frustra of cones, and the pelvis as cylinder. Each segment's mass was defined from Dempster's proportions presented by Winter (32). Net external moments are described in this paper and represent the external load on the joint.

The vertical ground-reaction force (VGRF) data were used to calculate initial contact (IC) with the ground immediately after the subject dropped from the box. IC was defined when VGRF first exceeded 10 N. Toe-off (TO) was subsequently calculated after IC when the VGRF fell below 10 N. Kinematic and kinetic data were normalized to 100% of the stance phase (between IC and TO). The following discrete variables were calculated during the stance phase for each trial: maximum VGRF, maximum joint moment, and maximum and minimum joint angle for the hip, knee, and ankle. The discrete variables were chosen on the basis of their frequent use in relation to possible injury risk and gender comparison studies (9,13,18,21,26). In addition, these variables are often assessed in interventional study designs in which test-retest measures are quantified for similar time intervals. By convention, hip flexion, hip adduction, hip internal rotation, knee extension, knee adduction, knee internal rotation, ankle dorsiflexion, and ankle inversion were represented as positive values. Vertical jump height was calculated from the difference between the standing and maximum vertical trajectory of the estimated center of mass of the body.

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Statistical analysis.

The discrete variables and waveforms from each trial from both sides (left and right) and sessions were used for the within-session reliability (11 subjects, two sides, two sessions, N = 44 sides). The mean of three trials from both the left and right sides were used for between-session reliability (11 subjects, two sides, N = 22). Intraclass correlation coefficients (ICC) were used to examine within (ICC (3, k)) and between (ICC (3, 1)) session reliability of discrete variables. The ICC classifications of Fleiss (less than 0.4 was poor, between 0.4 and 0.75 was fair to good, and greater than 0.75 is excellent) were used to describe the range of ICC values (8). Coefficients of multiple correlations (CMC) were used to compare each joint angle and moment waveform for the entire stance phase, according to Kadaba et al. (17). The 95% confidence intervals (CI) were calculated for ICC and CMC variables to compare the results (7). Typical errors (TE) were also calculated within and between sessions and are reported in the measurement units of degrees or newton-meters per kilogram for joint angle and moment calculations (15). Statistical analyses were conducted in SPSS (Version 12.0, SPSS Inc. Chicago, IL).

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The within-session ICC values were higher than the between-session ICC values for all measured variables combined (within ICC = 0.903, CI 0.86-0.95; between ICC = 0.768, CI 0.72-0.82). The within- and between-session averaged CMC values, however, were not different (within CMC = 0.830, CI 0.77-0.89; between CMC = 0.823, CI 0.76-0.88). Reliability of these data are displayed in Table 1. There were no differences between planes in within- and between-session reliability for peak angular rotations with all discrete variables combined (sagittal plane ICC = 0.830, CI 0.69-0.97; frontal plane ICC = 0.899, CI 0.83-0.97; 0.895, transverse plane ICC = 0.869, CI 0.75-0.99; Table 1). Typical errors are presented in Table 1 and represent the average within-subject differences both within and between sessions. The average sagittal plane typical error at each joint was 3.1 ± 1.6° compared with 1.8 ± 0.5° in the frontal plane and 2.3 ± 0.8° in the transverse plane. Kinematic waveforms for a representative subject are presented in Figure 2.





The between-session kinematic ICC variables calculated for each joint and rotation for initial contact and the total excursion during stance are displayed in Table 2. During the stance phase of the drop vertical jump, the dependent variable with poorest reliability was knee flexion angle at initial contact (ICC = 0.404). No differences between measurements of peak angle, total angular excursion, or angle at initial contact were observed in the analysis of all angular joint rotations combined (peak ICC = 0.773, CI 0.69-0.86; initial contact ICC = 0.712, CI 0.61-0.82; excursion ICC = 0.685, CI 0.60-0.77; Table 2).



To determine variability in peak versus total joint excursion measurements, we examined the subject with the largest between-day mean differences for each discrete measure of peak angle and rotation, and we compared this variable with the same subject's corresponding angular excursion measure (7). The mean peak angle variability between all joints was greater (P = 0.02; 8.6 ± 4.2°) than the matching subjects' angular excursion differences between tests of 4.8 ± 2.9°. In contrast to the prior method, the same comparisons were performed for the individual subject with the largest between-day difference for each angular excursion measure and the corresponding peak angle measurement. Mean angular excursion was greater than the peak angles (P = 0.001). Differences in mean angular excursion variability across all joints were greater (P = 0.001; 9.1 ± 4.1°) than the corresponding peak angle difference between tests of 4.8 ± 3.9°.

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The mean values and reliability measures of joint moments are presented in Table 1. Comparison of between-session waveforms are presented in Figure 3. The within- and between-session reliability of discrete (ICC) joint moment variables were excellent for all sagittal (within-session ICC ≥ 0.904, between-session ICC ≥ 0.800) and frontal plane moment measures (within ICC ≥ 0.779, between-session ICC ≥ 0.748, Table 1). Knee internal rotation moment demonstrated the lowest reliability among the kinetic measurements, with fair to good within-session (ICC = 0.666) and between-session (ICC = 0.592) reliability. Maximum jump height showed excellent within- and between-session reliability during the DVJ (within ICC = 0.988; between ICC = 0.936). Typical errors for joint moments are presented in Table 1. The average joint moment typical error was 0.16 ± 0.04 N·m·kg−1 in the sagittal plane, 0.10 ± 0.03 N·m·kg−1 in the frontal plane, and 0.08 ± 0.02 N·m·kg−1 in the transverse plane. The typical errors were slightly improved when examining the differences within a session and between two sessions in kinematic data (within, 2.1 ± 0.8°; between, 2.8 ± 1.4°; P = 0.049) but were not different when examining joint moments (within, 0.13 ± 0.05 N·m·kg−1; between, 0.11 ± 0.05 N·m·kg−1; P = 0.3).



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The purpose of the present study was to determine the reliability of 3D lower-extremity kinematic and kinetic variables during landing in young athletes. The first hypothesis was that biomechanical variables quantified during a drop vertical jump in young athletes would be reliable when assessed in longitudinal sessions. The current findings indicate that the majority of the kinematic and kinetic variables in young athletes during landing have excellent to good reliability. In addition, the typical errors and waveform comparisons are also presented to further examine the differences between and within session. To our knowledge, this is the first investigation to demonstrate the reliability within session and between two extended sessions of a dynamic landing task in young athletes. A novel aspect of the current study was that the subjects were not recruited for the purpose of obtaining test-retest reliability. The investigators and test subjects were unaware during the motion analysis testing session that the subjects were to be included in reliability analyses. Therefore, this represents a realistic test-retest assessment of variability in lower-extremity 3D variables from a large-scale, longitudinal study. Cautious data interpretation is necessary in longitudinal studies. For example, in Figure 1 the subject had a similar knee abduction angle pattern, but the peak difference is approximately 25%. However, the absolute between-day difference was only 2.5°, which relates well to the typical error presented in Table 1 for all subjects (2.3°). Therefore, a percentage difference between testing sessions may not be the best approach when evaluating injury risk or longitudinal changes on the basis of the relatively small knee abduction range of motion.

Within-session reliability of discrete variables was higher than between two extended sessions, which supports the first corollary hypothesis. Other investigators have reported similar results during gait (17,30) and jogging (7,28): that within-day repeatability is greater than between-day repeatability. Multiple factors may lead to increased variability during repeated motion analysis testing. These factors may include marker-placement errors, referenced static alignment, task difficulty, and neuromuscular development of the population. An additional related concern with data collection via video-based motion analysis systems is the susceptibility of kinematic cross-talk errors (25). Cross-talk errors result from the primary (flexion) calculations of the embedded axis "bleeding" into the cal culations of secondary axes (abduction-adduction, internal-external). Ramakrishnan and Kadaba (29) preformed a sensitivity analysis to estimate the errors attributable to incorrectly defining the embedded axes at the hip and knee. During gait, the sagittal plane measures at the hip and knee were relatively unaffected by analytical displacement of the flexion-extension axes (29). In contrast, they reported large errors for hip and knee abduction-adduction and internal-external rotations that they attributed to cross-talk (29). Errors may be reduced by standardized marker placement (single investigator) and through standardized reference static alignment (foot placement and posture).

Marker reapplication between testing days may lead to a constant offset in between-day joint angular calculations. Ferber et al. (7) found that during jogging, between-day reliability was greater in joint angular excursion than peak measures, which may be more sensitive because of marker-placement errors. They suggest that the total joint excursion may be a better variable when accessing a subject's running mechanics across multiple days or after outcomes (7). The present findings are contrary to those observed during running in that no between-day ICC differences were found during a DVJ in peak angle, initial contact, or total angular excursion measures. Kadaba et al. (17) attributed lower reliability measures between days to the reapplication of reflective markers. The current study used only one experienced tester to apply the markers in all trials; however, marker-placement error may lead to measurable differences in angles and moments (29). A permanent marker or site tattoo was not used in this study; these techniques may help accurate reapplication of marker locations if necessary. Although the specific origins of error between testing days cannot be uncoupled (e.g., offset, crosstalk), these observed differences suggest that further biomechanical investigation, combined with simultaneous bone pin analysis, may be beneficial.

Another potential origin of both within- and between-session error, given the age and maturity of subjects and challenging nature of the task, may have been altered motor performance. For example, the repeatability of vertical jumping declines when the complexity of the task is increased from no approach to three and seven strides (33). However, to help control for these origins of variability, subjects in the current study were provided an overhead goal (target) during testing to standardize the jump performance and to provide standardized extrinsic motivation to encourage maximal effort during the task (10). The height of the target was determined from a series of maximum countermovement jumps performed before the DVJ. This standardization should have limited potential differences in motor performance. The overall performance (vertical height achieved during the drop vertical jump) demonstrated excellent between-session reliability (ICC = 0.936, TE = 1.8 cm). Previous studies of maximum vertical jump with both double-leg and single-leg take-offs report high reliability in measurements taken during theses tasks (33). We are currently investigating additional test sessions at a variety of time points within this population to better understand the potential sources of error during the performance of this task.

Our second corollary hypothesis was that sagittal plane variables would have higher reliability than frontal and transverse plane variables. This hypothesis was only partially supported by the experimental findings. Joint moment reliability in the sagittal plane was higher than in the secondary planes. However, further analysis of the data demonstrates that the within-session reliability of the kinetic data was actually high in all three planes. The sagittal plane joint moments exhibited greater reliability than the sagittal plane peak angles. Steinwender et al. (30) have reported similar results of increased kinetic reliability in the sagittal plane in normal children. There were no kinematic differences in reliability between sagittal, frontal, and transverse plane during the drop vertical jump in discrete measures when examined with intraclass correlations. In contrast, others have reported that during gait andrunning, the frontal and transverse plane joint motions demonstrate decreased reliability compared with the sagittal plane (3,7,17,28). Comparison of waveforms (CMC) did indicate higher reliably in the sagittal plane, likely related to the larger motions that occur in this plane.

The demonstrated reliability of kinematic and kinetic variables during the drop vertical jump also indicates that similar techniques may be used to evaluate the effects of interventions designed to reduce injury risk factors in young athletes (12). The effects of neuromuscular training on measures related to increased risk of ACL injury, including frontal plane knee motion and torque, in female athletes, have been investigated (23). Similar to the current study, they tested athletes with a drop vertical jump with 7 wk between evaluations. They determined that neuromuscular training reduces biomechanical risk factors associated with ACL injury (13,23). The presented reliability values may be limited to young athletes who play both basketball and soccer. In addition, this investigation did not quantify the potential variability related to between-test measures of greater than 7 wk. Further investigation should determine test-retest reliability in other types of athletes for longer between-test time periods.

Reliable kinematic and kinetics data can be obtained during a drop vertical jump in young athletes using motion analysis techniques measured over extended time intervals. A brief recommendation of specific techniques that may improve the reliability and, ultimately, the efficacy of repeated motion analysis measurements is presented below.

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Data collection.

System calibration (video and analog) is typically based on manufacturer recommendations. It should be consistently performed before each data-collection session. Modern systems can be calibrated efficiently, even with different configured camera and force plate locations. However, in the current study, standardized camera and force plate locations were used during repeated measurement sessions. Verification and quality control of calibration procedures is recommended, especially in laboratories that use multiple camera and force plate configurations (14). As described previously, marker reapplication has been attributed to lower reliability (17). Therefore, the use of one well-trained investigator to place markers on the subjects may be useful to maintain reliable test measures for extended periods of time. Initial pilot testing and ongoing verification of consistent application of markers is suggested. Reference static alignment (neutral pose) should be standardized with consistent foot placement and posture. In addition, standardized instructions and data-collection procedures should be employed with all test sessions.

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Data analysis and interpretation.

Selection of marker set and analysis methods should be based on each project's purpose, with the advantages and disadvantages of each clearly evaluated. For example, additional marker configurations (i.e., clusters) have been shown to be reliable between sessions (7). A multiple-trial average has been suggested to improve the reliability and should be considered especially with measurements over extended time intervals (16). The reliability of each dependent variable, inclusive of joint rotation (i.e., flexion/extension, abduction/adduction, internal/external), measurement (i.e., angular displacement, moment), and time (i.e., maximum, initial contact, excursion) should be examined critically for each movement pattern studied. Reliability over multiple measurement sessions may be improved by using different analysis procedures. Cautious interpretation of results should be practiced on the basis of the limitations inherent to the methods and instrumentation. For example, it is clear that excessive marker motion relative to the underlying bone may limit accurate estimation of segmental kinematics (19). This topic has been reviewed extensively with studies investigating bone-anchored markers, optimized marker locations, and filtering techniques (2,19,20,31). In the current study, we attempted to avoid maker placements over areas of large muscle mass. In addition, we used a filtering strategy that was based on the literature and residual analysis (4,21,32). Additional optimization and filtering strategies may prove useful in future studies conducted in combination with bone-anchored pins.

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In conclusion, discrete variables can be compared from multiple sessions; however, certain variables have improved reliability compared with others. It is suggested that careful interpretation of each statistical measure (ICC, CMC, TE) be used when determining the dependent variables of interest (angular rotations and moments).

This work was supported by NIH/NIAMS Grant R101-AR049735-03. The authors would like to acknowledge the entire Sports Medicine Biodynamics Center, Jon Divine, Jeff Robbins, Boone County Kentucky School District, and Ton van den Bogert for support of our longitudinal biomechanical studies. The authors would like to acknowledge the members of the American College of Sports Medicine-Biomechanics Interest Group and Scott McLean for evoking important discussions related to the reliability of motion analysis data during the 2006 Annual Meeting.

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1. Agel, J., E. A. Arendt, and B. Bershadsky. Anterior cruciate ligament injury in national collegiate athletic association basketball and soccer: a 13-year review. Am. J. Sports Med. 33:524-530, 2005.
2. Benoit, D. L., D. K. Ramsey, M. Lamontagne, L. Xu, P. Wretenberg, and P. Renstrom. Effect of skin movement artifact on knee kinematics during gait and cutting motions measured in vivo. Gait Posture 24:152-164, 2006.
3. Besier, T. F., D. L. Sturnieks, J. A. Alderson, and D. G. Lloyd. Repeatability of gait data using a functional hip joint centre and a mean helical knee axis. J. Biomech. 36:1159-1168, 2003.
4. Bisseling, R. W., and A. L. Hof. Handling of impact forces in inverse dynamics. J. Biomech. 39:2438-2444, 2006.
5. Cole, G. K., B. M. Nigg, J. L. Ronsky, and M. R. Yeadon. Application of the joint coordinate system to three-dimensional joint attitude and movement representation: a standardization proposal. J. Biomech. Eng. 115:344-349, 1993.
6. Decker, M. J., M. R. Torry, D. J. Wyland, W. I. Sterett, and J. R. Steadman. Gender differences in lower extremity kinematics, kinetics and energy absorption during landing. Clin. Biomech. 18:662-669, 2003.
7. Ferber, R., I. McClay Davis, D. S. Williams 3rd, and C. Laughton. A comparison of within- and between-day reliability of discrete 3D lower extremity variables in runners. J. Orthop. Res. 20:1139-1145, 2002.
8. Fleiss, J. L. The Design and Analysis of Clinical Experiments. New York, NY: Wiley, 1986.
9. Ford, K. R., G. D. Myer, and T. E. Hewett. Valgus knee motion during landing in high school female and male basketball players. Med. Sci. Sports Exerc. 35:1745-1750, 2003.
10. Ford, K. R., G. D. Myer, R. L. Smith, R. N. Byrnes, S. E. Dopirak, and T. E. Hewett. Use of an overhead goal alters vertical jump performance and biomechanics. J. Strength Cond. Res. 19:394-399, 2005.
11. Hanavan, E. P., Jr. A Mathematical Model of the Human Body. Wright-Patterson Air Force Base, OH: Air Force Aerospace Medical Research Lab, 1964, pp. 1-207.
12. Hewett, T. E., G. D. Myer, and K. R. Ford. Anterior cruciate ligament injuries in female athletes: part 1, mechanisms and risk factors. Am. J. Sports Med. 34:299-311, 2006.
13. Hewett, T. E., G. D. Myer, K. R. Ford, et al. Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: a prospective study. Am. J. Sports Med. 33:492-501, 2005.
14. Holden, J. P., W. S. Selbie, and S. J. Stanhope. A proposed test to support the clinical movement analysis laboratory accreditation process. Gait Posture 17:205-213, 2003.
15. Hopkins, W. G. Measures of reliability in sports medicine and science. Sports Med. 30:1-15, 2000.
16. Hunter, J. P., R. N. Marshall, and P. McNair. Reliability of biomechanical variables of sprint running. Med. Sci. Sports Exerc. 36:850-861, 2004.
17. Kadaba, M. P., H. K. Ramakrishnan, M. E. Wootten, J. Gainey, G. Gorton, and G. V. Cochran. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. J.Orthop. Res. 7:849-860, 1989.
18. Kernozek, T. W., M. R. Torry, H. Cowley, and S. Tanner. Gender differences in frontal and sagittal plane biomechanics during drop landings. Med. Sci. Sports Exerc. 37:1003-1012, 2005.
19. Leardini, A., L. Chiari, U. Della Croce, and A. Cappozzo. Human movement analysis using stereophotogrammetry. Part 3. Soft tissue artifact assessment and compensation. Gait Posture 21:212-225, 2005.
20. Manal, K., I. McClay, S. Stanhope, J. Richards, and B. Galinat. Comparison of surface mounted markers and attachment methods in estimating tibial rotations during walking: an in vivo study. Gait Posture 11:38-45, 2000.
21. McLean, S. G., S. W. Lipfert, and A. J. van den Bogert. Effect of gender and defensive opponent on the biomechanics of sidestep cutting. Med. Sci. Sports Exerc. 36:1008-1016, 2004.
22. Myer, G. D., K. R. Ford, S. G. McLean, and T. E. Hewett. The effects of plyometric versus dynamic stabilization and balance training on lower extremity biomechanics. Am. J. Sports Med. 34:490-498, 2006.
23. Myer, G. D., K. R. Ford, J. P. Palumbo, and T. E. Hewett. Neuromuscular training improves performance and lower-extremity biomechanics in female athletes. J. Strength Cond. Res. 19:51-60, 2005.
24. Olsen, O. E., G. Myklebust, L. Engebretsen, and R. Bahr. Injury mechanisms for anterior cruciate ligament injuries in team handball: a systematic video analysis. Am. J. Sports Med. 32:1002-1012, 2004.
25. Piazza, S. J., and P. R. Cavanagh. Measurement of the screw-home motion of the knee is sensitive to errors in axis alignment. J.Biomech. 33:1029-1034, 2000.
26. Pollard, C. D., I. M. Davis, and J. Hamill. Influence of gender on hip and knee mechanics during a randomly cued cutting maneuver. Clin. Biomech. 19:1022-1031, 2004.
27. Quatman, C. E., K. R. Ford, G. D. Myer, and T. E. Hewett. Maturation leads to gender differences in landing force and vertical jump performance: a longitudinal study. Am. J. Sports Med. 34:806-813, 2006.
28. Queen, R. M., M. T. Gross, and H. Y. Liu. Repeatability of lower extremity kinetics and kinematics for standardized and self-selected running speeds. Gait Posture 23:282-287, 2006.
29. Ramakrishnan, H. K., and M. P. Kadaba. On the estimation of joint kinematics during gait. J. Biomech. 24:969-977, 1991.
30. Steinwender, G., V. Saraph, S. Scheiber, E. B. Zwick, C. Uitz, and K. Hackl. Intrasubject repeatability of gait analysis data in normal and spastic children. Clin. Biomech. 15:134-139, 2000.
31. van den Bogert, A. J., G. D. Smith, and B. M. Nigg. In vivo determination of the anatomical axes of the ankle joint complex: an optimization approach. J. Biomech. 27:1477-1488, 1994.
32. Winter, D. A. Kinetics: forces and moments of force. In: Biomechanics and Motor Control of Human Movement. New York, NY: John Wiley & Sons, Inc., 2005, pp. 86-117.
33. Young, W., C. MacDonald, T. Heggen, and J. Fitzpatrick. An evaluation of the specificity, validity and reliability of jumping tests. J. Sports Med. Phys. Fitness 37:240-245, 1997.


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