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Changes in patellofemoral pain resulting from repetitive impact landings are associated with the magnitude and rate of patellofemoral joint loading
Clinical Biomechanics 53 (2018) 31–36
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Changes in patellofemoral pain resulting from repetitive impact landings are associated with the magnitude and rate of patellofemoral joint loading
T
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Lee T. Atkinsa, , C. Roger Jamesb, Hyung Suk Yangc, Phillip S. Sizer Jrb, Jean-Michel Brisméeb, Steven F. Sawyerb, Christopher M. Powersd a
Department of Physical Therapy, Angelo State University, San Angelo, TX, USA Department of Rehabilitaion Sciences and Center for Rehabilitation Research, Texas Tech University Health Sciences Center, Lubbock, TX, USA c Kinesiology and Sport Management Division, University of South Dakota, Vermillion, SD, USA d Division of Biokinesiology & Physical Therapy, University of Southern California, Los Angeles, CA, USA b
A R T I C L E I N F O
A B S T R A C T
Keywords: Patella Kinetics Patellofemoral joint reaction force
Background: Although a relationship between elevated patellofemoral forces and pain has been proposed, it is unknown which joint loading variable (magnitude, rate) is best associated with pain changes. The purpose of this study was to examine associations among patellofemoral joint loading variables and changes in patellofemoral pain across repeated single limb landings. Methods: Thirty-one females (age: 23.5(2.8) year; height: 166.8(5.8) cm; mass: 59.6(8.1) kg) with PFP performed 5 landing trials from 0.25 m. The dependent variable was rate of change in pain obtained from selfreported pain scores following each trial. Independent variables included 5-trial averages of peak, time-integral, and average and maximum development rates of the patellofemoral joint reaction force obtained using a previously described model. Pearson correlation coefficients were calculated to evaluate individual associations between rate of change in pain and each independent variable (α = 0.05). Stepwise linear multiple regression (αenter = 0.05; αexit = 0.10) was used to identify the best predictor of rate of change in pain. Findings: Subjects reported an average increase of 0.38 pain points with each landing trial. Although, rate of change in pain was positively correlated with peak force (r = 0.44, p = 0.01), and average (r = 0.41, p = 0.02) and maximum force development rates (r = 0.39, p = 0.03), only the peak force entered the predictive model explaining 19% of variance in rate of change in pain (r2 = 0.19, p = 0.01). Interpretation: Peak patellofemoral joint reaction force was the best predictor of the rate of change in pain following repetitive singe limb landings. The current study supports the theory that patellofemoral joint loading contributes to changes in patellofemoral pain.
1. Introduction Patellofemoral pain (PFP) is a common yet complex multifactorial condition that can affect one's quality of life (Davis and Powers, 2010; Powers et al., 2012; Witvrouw et al., 2014). Patellofemoral pain has been cited as the most common lower extremity injury among runners (Taunton et al., 2002), and is reported to affect females 2 to 10 times more often than males (Fulkerson, 2002; Fulkerson and Arendt, 2000; Robinson and Nee, 2007). A hallmark sign of PFP is the onset or exacerbation of anterior knee pain with high impact activities such as running, (Ho et al., 2014; Noehren et al., 2012) and landing from a jump (Willson et al., 2008). Furthermore, reduction or abolishment of PFP typically occurs during activities characterized as having reduced
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patellofemoral joint (PFJ) loading (Crossley et al., 2015). It has been proposed that PFP can be caused by elevated patellofemoral joint reaction forces (PFJRFs) (Dye, 2005; Goodfellow et al., 1976). However, research relating PFJRFs and PFP has not confirmed this hypothesis. For example, persons with PFP exhibit lower peak PFJRFs compared to healthy controls during walking (Chen and Powers, 2014; Heino-Brechter and Powers, 2002), running (Chen and Powers, 2014), and stair ambulation (Brechter and Powers, 2002; Chen and Powers, 2014). It has been proposed that the lower peak PFJRFs may be the result of compensatory behavior to minimize patellofemoral joint loading during functional tasks. A high PFP prevalence among persons who engage in high impact activities such as running suggests that the PFJ loading rate may be
Corresponding author at: Angelo State University Physical Therapy, ASU Station #10923, San Angelo, TX 76909-0923, USA. E-mail addresses: [email protected] (L.T. Atkins), [email protected] (C.R. James), [email protected] (H.S. Yang), [email protected] (P.S. Sizer), [email protected] (J.-M. Brismée), [email protected] (S.F. Sawyer), [email protected] (C.M. Powers). https://doi.org/10.1016/j.clinbiomech.2018.02.006 Received 21 June 2017; Accepted 6 February 2018 0268-0033/ © 2018 Elsevier Ltd. All rights reserved.
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to account for muscle co-contraction in the biomechanical model (see details below). Double differential EMG electrodes were placed on the skin over the muscle bellies and parallel to the fibers of the biceps femoris, semitendinosus, and medial and lateral gastrocnemius muscles using previously described techniques (Rainoldi et al., 2004). The skin was cleaned using abrasive gel and isopropyl alcohol and electrodes were positioned and secured with double-sided tape. Next, EMG signals were collected as participants performed 3, 5-s maximum voluntary isometric contractions (MVIC) of the hamstrings and gastrocnemius using a dynamometer (Biodex, System 3, Shirley, NY, USA). Prior to MVIC testing for each muscle group, subjects performed one or two submaximal practice trials to familiarize themselves with the task and minimize any potentially confounding learning effects. For the hamstrings MVIC, subjects were positioned with their hips and knees at 85° and 90° respectively. For the gastrocnemius MVIC, subjects were seated with their hips flexed to 85°, their knee fully extended, and their ankle plantar flexed to 15°. During each MVIC, subjects were secured with straps and instructed to contract with maximal effort. Following MVIC testing, 14-mm reflective markers were placed on the first, second, and fifth metatarsal heads, medial and lateral malleoli and femoral epicondyles, iliac crests, anterior and posterior superior iliac spines. Additionally, rigid clusters of at least 3 non-collinear tracking markers were secured to the thighs, legs, and feet of each subject. A cluster of 4 markers secured to the mid-trunk was aligned such that it was in a plane approximately parallel to the frontal plane of the subject's trunk while in a static standing position (Fig. 1A). A static standing trial was recorded and used to define the local segmental coordinate systems and joint axes. All anatomic calibration markers, except those on the pelvis, were removed prior to the SLL trials.
more predictive of symptom behavior than peak loading (Schaffler et al., 1989). This premise is supported by Cheung and Davis, who reported that following a running retraining intervention, improved PFP symptoms were associated with a reduced rate of lower limb loading (Cheung and Davis, 2011). Thus, it is conceivable that an elevated PFJ loading rate may evoke PFP (Cheung and Davis, 2011). An important step in designing optimal intervention strategies for persons with PFP is to gain a complete understanding of the relationship between PFJ loading and changes in PFP. Therefore, the purpose of the current study was to examine the associations among various measures of PFJ loading (peak PFJRF, PFJRF rate, PFJRF impulse) and changes in perceived PFP across repeated single limb landings (SLL). Based on previous literature, we hypothesized that PFJRF loading rate would be more predictive of PFP rate of change than peak PFJRF or PFJRF impulse. 2. Methods 2.1. Subjects Thirty-one females (mean (SD) age, 23.5 (3.8); height, 166.8 (5.8) cm; mass, 59.6 (8.1) kg; body mass index, 21.5 (2.9) kg/m2) with PFP were recruited for this study. Participants were included if they were between 18 and 45 years of age, had a body mass index < 30 kg/m2, and rated their level of physical activity from 5 to 9 on the Tegner Activity Scale (Tegner and Lysholm, 1985; Willson et al., 2008; Willson and Davis, 2008). Participants older than 45 years of age were excluded to minimize the potential influences of patellofemoral joint osteoarthritis. Additionally, participants must have reported insidious onset PFP of at least 3 weeks duration that was reproducible with at least 2 of the following activities: isometric quadriceps contraction, prolonged sitting, kneeling, squatting, running, or jumping. Operationally, PFP was defined as retro- or peripatellar pain (vague or localized) rated at minimum of 3 and maximum of 8 out of 10 on an 11-point visual analog scale. Potential subjects were excluded if they were non-English speaking, had prior knee surgery or traumatic patellar dislocation, neurological involvement that would influence performance of single limb landings, were pregnant, or were taking pain medication at time of testing. Participants underwent a physical exam by a licensed physical therapist with 8 years of experience to rule out other potential knee pathologies (i.e. ligamentous instability, meniscus injury, and large knee effusion). Approximately 30% of screened individuals were excluded based on these criteria. The study protocol was approved by the Institutional Review Board of the affiliated university. Prior to participation all subjects provided written informed consent. 2.2. Instrumentation Three-dimensional kinematic data were recorded at 250 Hz using an 8-camera Vicon Nexus motion capture system (Vicon, Centennial, CO, USA). Ground reaction force data were recorded at 2000 Hz using an inground force plate (Bertec, Columbus, OH, USA). Electromyography (EMG) data were recorded at 2000 Hz using a telemetered EMG system (Delsys Trigno, Boston, MA, USA). The EMG system had an input impedance > 10 Gohms, common mode rejection ratio > 80 dB and baseline noise < 0.75 μV root-mean-square. 2.3. Procedures Participants donned standard shoes (New Balance Inc., Boston, MA, USA), a sports top, and spandex shorts. Height and body weight were measured and recorded. The symptomatic or most painful knee (in the case of bilateral pain) was identified and the subject was prepared for testing. Electromyographic data from the knee flexor muscles were obtained
Fig. 1. (A) Anatomic and tracking marker placement. (B) Starting position for single limb landing task.
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PFJRF (Fig. 2) (Powers et al., 2014; Teng and Powers, 2014). Subjectspecific knee kinematic and kinetic data (i.e. knee flexion angle and adjusted knee extension moment) from the SLL trials were used as model inputs. Additional model inputs were obtained using data from previous studies and included the quadriceps effective lever arm length (van Eijden et al., 1986) and the relationship between quadriceps force and PFJRF (van Eijden et al., 1987). To account for co-contraction during the SLL trials, the knee flexor moment was estimated using SIMM modeling software (Motion Analysis Corporation, Santa Rosa, CA, USA). The SIMM lower limb model used in this study contained musculotendon actuators along with information regarding peak isometric muscle force, optimal muscle fiber length, tendon slack length, and pennation angle for the lower extremity muscles (Delp and Loan, 2000). Muscles within the SIMM software were represented as a series of 3-D vectors constrained to wrap over underlying structures. The SIMM model inputs included subject-specific knee joint kinematics and normalized knee flexor muscle EMG data. Knee joint kinematics were used to estimate contraction velocity and muscle tendon lengths for each knee flexor muscle while the normalized EMG data represented the level of muscle activation. It was assumed that semitendinosus and biceps femoris short head activation were the same as semimembranosus and biceps femoris long head activation, respectively (Lloyd and Besier, 2003). The torques from each individual knee flexor muscle were then estimated, summated, and normalized to body mass in order to represent the knee flexor moment. The knee flexor moment (obtained via SIMM modeling software) was then added to the net knee extension moment (obtained via inverse dynamics calculations) to provide a more accurate, adjusted knee extension moment that accounted for antagonistic muscle activation. The first step in calculating the PFJRF was to determine the quadriceps effective lever arm length. This was achieved by fitting a nonlinear equation to data from a previous study that described the quadriceps effective lever arm length at each knee flexion angle (van Eijden et al., 1986). The quadriceps force was determined by calculating the quotient of the adjusted knee extension moment divided by the quadriceps effective lever arm length for each knee flexion angle. Finally, the time-series PFJRF was calculated by multiplying the quadriceps force by a constant that described the relationship between the quadriceps force and PFJRF as a function of knee flexion angle (van Eijden et al., 1987). The dependent variable of interest was the rate of change in PFP across the landing trials. The independent variables of interest included maximum and average PFJRF loading rates (N/kg/s), peak PFJRF (N/
2.4. Single limb landing trials Subjects performed 5 SLL trials using their self-selected landing strategy. For each trial, subjects hung from a height-adjustable bar that was suspended to the ceiling then dropped 0.25 m and landed on a force plate (Fig. 1B). The 0.25 m drop height was chosen based on pilot testing which revealed that repetitive landings from this height elicited an increase in PFP but not to the extent that subjects were unable to complete 5 successful trials. A SLL trial was considered successful if subjects landed within the borders of the force plate using only their involved limb and maintained their balance for approximately 2 s. Prior to performing the first SLL trial, subjects were allowed several practice trials to familiarize themselves with the task. For each trial, ground reaction forces, kinematic and EMG data were collected. Prior to the first trial, subjects rated their pain level using an 11-point (0 to 10) scale to obtain baseline PFP. Immediately following each trial, subjects rated their pain during the previous SLL using the same scale. This procedure resulted in a total of 6 pain scores per subject. 2.5. Data reduction The rate of change in PFP was obtained by calculating the slope of a least-squares regression line that was fitted to the self-reported pain values that corresponded to the SLL trials. Kinematic and kinetic data were reconstructed and low-pass filtered (fourth-order, Butterworth, 10 Hz) using Vicon Nexus software (v2.3, Vicon, Centennial, CO, USA). Kinematic data were then imported into Visual 3D (v5, C-Motion, Germantown, MD, USA), where a six-degrees-of-freedom link segment model was applied. The thigh, leg, and foot segments were modeled as frusta of cones and the pelvis segment was modeled as a cylinder. Joint kinematics were calculated as movement of the distal segment relative to the proximal segment. Net internal joint moments were calculated using inverse dynamics and normalized to each subject's body mass. Raw EMG signals were band-pass filtered (20–450 Hz) within the Trigno hardware then rectified and low-pass filtered (fourth-order, Butterworth, 6 Hz) in Matlab (MATLAB, MathWorks, Natick, MA, USA) to create a linear envelope (Tsai and Powers, 2013). The greatest EMG value within the linear envelope obtained from either the MVIC or landing trials was used to normalize the EMG signals (Tsai and Powers, 2013). 2.5.1. Patellofemoral joint reaction force model A previously described 2D model of the PFJ was used to estimate the
Net Knee Extension Momenta
Internal Knee Flexion Momentb
Step 1 Adjusted Knee Extension
Step 2 Quadriceps Effective Lever Armc
Step 3 Quadriceps Force
Sagittal Knee Joint Anglea Step 4 Patellofemoral Joint Reaction Force
Relationship between Quadriceps and Patellofemoral Joint Reaction Forced
Fig. 2. Flow diagram of PFJRF model. a) Data obtained from motion capture system. b) Data obtained from SIMM EMG-driven musculoskeletal modeling software. c) Data obtained from van Eijden et al. 1986 d) Data obtained from van Eijden et al.1987.
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kg), and PFJRF time integral (N ∗ s/kg). All variables were calculated during the deceleration phase of landing which began the instant the vertical ground reaction force (vGRF) exceeded 20 N and ended when the vertical velocity of the pelvis equaled zero.
Table 1 Descriptive results for the PFJRF-related independent variables of interest.
2.6. Statistical analyses The individual relationships between the rate of change in PFP (dependent variable) and the PFJRF variables of interest (independent variables) were examined using Pearson correlation coefficients (α = 0.05). If more than one independent variable was significantly associated with the rate of change in PFP, a stepwise linear multiple regression (αenter = 0.05; αexit = 0.10) was calculated to determine which of the independent variables was most predictive. The linearity assumption for all regression analyses was confirmed by visually assessing scatter plots of each independent variable plotted against the dependent variable. The assumptions of homoscedasticity and homogeneity of variance for the residuals was visually confirmed by assessing the relationships of the standardized residuals plotted against the standardized predicted values (Portney and Watkins, 2009). Statistical analyses were performed using SPSS statistical software (SPSS Inc. v24, Chicago, IL, USA). The strength of the observed correlation coefficient values were interpreted as follows: excellent relationships were 0.75 or greater; moderate to good relationships were 0.50 to 0.75; fair relationship were 0.25 to 0.50; and little or no relationships were < 0.25 (Portney and Watkins, 2009).
Independent variables of interest
Mean (SD)
Range
Peak PFJRF (N/kg) PFJRF time integral (N ∗ s/kg) Average rate of PFJRF development (Nm/kg/s) Maximum rate of PFJRF development (Nm/kg/s)
47.8 (17.0) 6.4 (4.2) 333.1 (104.5) 738.9 (288.9)
12.2–87.1 0.3–15.3 117.5–536.0 61.0–1318.6
Abbreviations: PFJRF, patellofemoral joint reaction force; Table 2 Correlation results between the rate of change in the PFP and the PFJRF-related independent variables of interest. Covariate with PFP ⁎
Peak PFJRF (N/kg) Average rate of PFJRF development (Nm/kg/s)⁎ Maximum rate of PFJRF development (Nm/kg/s)⁎ PFJRF time integral (N ∗ s/kg)
Pearson r
p-Value
0.44 0.42 0.39 0.15
0.01 0.02 0.03 0.43
Abbreviation: PFP, patellofemoral pain; PFJRF, patellofemoral joint reaction force. ⁎ p < 0.05.
(r = 0.15, p = 0.43). With respect to the step-wise regression analysis, only peak PJFRF entered the regression model for predicting the rate of change in PFP (r = 0.44, p = 0.01) explaining 19% of the variance in the rate of change in PFP. The unstandardized coefficient for the peak PFJRF was positive indicating that an increase in peak PFJRF was predictive of an increase in the rate of change in PFP with each successive landing trial (Table 3).
3. Results Twenty-seven subjects reported an incremental increase in PFP during the 5 SLL trials while 4 subjects reported no change in PFP. On average, the mean (SD) rate of change in PFP with each successive SLL trial was 0.38 (0.30) points per trial (Fig. 3) (range: 0.00 to 1.00 points per trial). The mean, SD, and range for each PFJRF-related independent variable is presented in Table 1. The rate of change in PFP was significantly associated with 3 of the 4 of the PFJRF-related independent variables. The strength of these associations was fair and included the following: peak PFJRF (r = 0.44, p = 0.01), average PFJRF loading rate (r = 0.42, p = 0.02), and maximum PFJRF loading rate (r = 0.39, p = 0.03) (Table 2). The PFJRF time-integral variable was not associated with the rate of change of PFP
4. Discussion Results from the current study support the theory that the rate of change in PFP is related to PFJ loading, as 3 out of 4 PFJRF-related variables (peak PFJRF and maximum and average PFJRF rates) were associated with changes in PFP. These variables were positively correlated with the rate of change in PFP, indicating that persons who landed with greater rates of PFJRF loading and peak PFJRF magnitudes experienced greater increases in PFP with successive SLL trials. Thus, PFJRF loading may in part, explain the high prevalence of PFP among
Patellofemoral Pain Reported Over Consecutive Single Limb Landing Trials 5
Reported Pain Level
4 3
y = 0.38x + 0.43
2 1 0 -1
baseline
trial 1
trial 2
trial 3
trial 4
trial 5
Time Figure 3. Regression line of best fit based on average PFP reported by subjects at baseline and immediately following each single limb landing trial. Error bars represent one standard deviation.
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2013), and there is evidence that PFP is associated with greater levels of psychological distress (Jensen et al., 2005). Furthermore, it has been recommended that valid pain models include both mechanical and psychological domains (Kittelson et al., 2014; Mueller and Maluf, 2002). Thus, had our predictive model included additional mechanical (e.g. PFJ contact area and stress) and psychological variables (e.g. selfperceived health status (Jensen et al., 2005) or coping mechanisms (Witvrouw et al., 2000)) it is conceivable that more of the variance in the rate of change in PFP would have been explained. Nonetheless, given the multifaceted nature of PFP, and the fact that nearly one fifth of the variance in rate of change in PFP was explained by a single mechanical variable (i.e. peak PFJRF), the current study highlights the need for continued research aimed at increasing understanding of the relationships between PFJRF and PFP. There are several limitations of this study that should be acknowledged. First, our PFJRF estimates were based on a two-dimensional PFJ model that did not account for the influence of frontal and transverse plane motions on the magnitude or rate of the PFJRFs. Additionally, the PFJ model used in the current study utilized several input variables that were not subject specific. For example, the quadriceps lever arm was estimated as a function of knee flexion angle based on previously published data (van Eijden et al., 1986). While the knee flexion data was specific to each subject, the lever arm at varying degrees of knee flexion was similar for all subjects. Thus, inter-subject differences in structural characteristics that could have affected the quadriceps lever arm were not accounted for in our model. As noted above, our model estimated PFJRFs as opposed to PFJ stress. It is possible that peak stress and/or rate of stress development may have provided greater insight into the mechanisms underlying PFP development. Lastly, the current study only examined 5 SLL trials, whereas many functional activities that provoke PFP involve a greater number of impacts (i.e. running). Thus, had the subjects in this study performed a greater number of trials, it is conceivable that our results may have differed.
Table 3 Stepwise multiple regression results for predicting the rate of change in PFP from the PFJRF-related independent variables. Model R
Model R2
Model pvalue
Predictor variable
Unstandardized coefficient
0.44
0.19
0.01
Peak PFJRF (N/ kg)
0.008
Abbreviation: PFP, patellofemoral pain; PFJRF, patellofemoral joint reaction force; SLL, single limb landing.
individuals that engage in repeated impact activities such as running. The PFJRF time-integral, which is representative of the total magnitude of PFJ loading, was not correlated with the rate of change in PFP. This suggests that for short duration impact activities such as running and landing from a jump, the overall magnitude of the PFJRF may not be related to PFP behavior. However, it is possible that the PFJRF time-integral may play a larger role in PFP development during non-impact activities of longer duration such as squatting and prolonged static sitting. Contrary to our proposed hypothesis, peak PFJRF (as opposed to PFJRF rate) emerged as the best predictor variable, explaining nearly 20% of the variance in rate of change in PFP. The positive correlation indicates that subjects who landed with greater peak PFJRFs experienced a greater rate of change in PFP from one SLL trial to the next. Although significant, the clinical relevance of this relationship may appear inconsequential given the magnitude of the unstandardized coefficient (increase of 0.008 pain points per landing per 1 N/kg increase in peak PFJRF). However, when interpreting this relationship, one should consider that typical PFP provocation during repetitive impact activities such as running is relatively slow and gradual (Noehren et al., 2012). Given that runners take approximately 1000 steps (or landings) per mile (Davis and Futrell, 2016), the cumulative effects indicated by this relationship may be relevant. As mentioned above, several studies have reported that persons with PFP exhibit pain-avoidance behaviors as evidenced by lower PFJRFs during activities such as walking, stair ambulation, and running compared to asymptomatic controls (Brechter and Powers, 2002; Chen and Powers, 2014; Heino-Brechter and Powers, 2002). Given the fact that 87% of the participants in the current study experienced a progressive increase in symptoms with repetitive SLLs suggests that compensatory pain-avoidance behaviors (if present) were not entirely successful in modulating symptoms. Given the relatively high demand and constrained nature of the landing task used in the current study, it is likely that compensatory options were limited. This is consistent with the premise that the availability of compensatory behaviors is reduced as the demand of a task increases (Higgins, 1977). Although not the best predictor, the maximum and average PFJRF rates also were independent predictors of the increase in PFP. This finding supports the work of Cheung and Davis (Cheung and Davis, 2011) who reported that the use of movement retraining to reduce lower limb loading rates in runners with PFP was effective in diminishing symptoms. However, the peak PFJRF and measures of PFJRF rate of development as measured in the current study were highly correlated with each other (r-values ranging from 0.77 to 0.89). As such, it is recommended that clinicians focus on interventions that minimize peak PFJRFs and rates of PFJRF development when managing patients with PFP. There are several possible explanations as to why more variance in the rate of change in PFP was not explained by our predictive model. First, PFJ stress (PFJRF/PFJ contact area) is thought to be an important mechanical stimulus for invoking PFP (Farrhoki et al., 2011; Fulkerson, 2004), and PFJ contact area was not measured in the current study. Additionally, pain has been defined as an unpleasant sensory and emotional experience involving an individual's perception (Venes,
5. Conclusion The magnitude and rate of change in PFP across repeated SLL trials was significantly and positively correlated with the magnitude and rate of PFJRF development. Of the PFJRF-related variables examined, the peak PFJRF was the best predictor of the rate of change in PFP. The results of this study suggest that strategies aimed at reducing peak and rate of PFJRFs during impact activities may be beneficial in minimizing the exacerbation of PFP. For example, one potential strategy for minimizing PFJ loading during SLL activities may be to implement a forward trunk lean. According to Teng et al., a 7° increase in forward trunk lean while running resulted in a significant reduction in peak PFJRF (Teng and Powers, 2014). Based on this finding it is feasible that the same strategy (i.e. forward trunk lean) may be effective for minimizing the PFJRF rate as well. Acknowledgements This work was supported by a research grant from the Texas Physical Therapy Foundation (Austin, TX, USA). The authors would like to acknowledge Alexander Drusch, MS, Kinyata Cooper, BS, Tzu-Chieh Liao, PT, PhD for their contributions to this study. Conflict of interest The authors certify that they have no affiliations with or financial involvement in any organization or entity with a direct financial interest in the subject matter or materials discussed in the article. Approval This study protocol was approved by the Institutional Review Board 35
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at Texas Tech University Health Sciences Center.
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References Brechter, J.H., Powers, C.M., 2002. Patellofemoral joint stress during stair ascent and descent in persons with and without patellofemoral pain. Gait Posture 16 (2), 115–123. Chen, Y.-J., Powers, C.M., March 2014. Comparison of three dimensional patellofemoral joint reaction forces in persons with and without patellofemoral pain. J. Appl. Biomech. http://dx.doi.org/10.1123/jab.2011-0250. Cheung, R.T.H., Davis, I.S., 2011. Landing pattern modification to improve patellofemoral pain in runners: a case series. J. Orthop. Sports Phys. Ther. 41 (12), 914–919. http://dx.doi.org/10.2519/jospt.2011.3771. Crossley, K.M., Callaghan, M.J., van Linschoten, R., November 2015. Patellofemoral pain. BMJ, h3939. http://dx.doi.org/10.1136/bmj.h3939. Davis, I.S., Futrell, E., 2016. Gait retraining. Phys. Med. Rehabil. Clin. N. Am. 27 (1), 339–355. http://dx.doi.org/10.1016/j.pmr.2015.09.002. Davis, I.S., Powers, C.M., 2010. Patellofemoral pain syndrome: proximal, distal, and local factors—international research retreat, April 30–May 2, 2009, Baltimore, Maryland. J. Orthop. Sports Phys. Ther. 40 (3), A1–A48. http://dx.doi.org/10.2519/jospt.2010. 0302. Delp, S.L., Loan, J.P., 2000. A computational framework for simulating and analyzing human and animal movement. Comput. Sci. Eng. 2 (5), 46–55. Dye, S.F., 2005. The pathophysiology of patellofemoral pain: a tissue homeostasis perspective. Clin. Orthop. NA (436), 100–110. http://dx.doi.org/10.1097/01.blo. 0000172303.74414.7d. van Eijden, T.M., Kouwenhoven, E., Verburg, J., Weijs, W.A., 1986. A mathematical model of the patellofemoral joint. J. Biomech. 19 (3), 219–229. van Eijden, T.M., Weijs, W.A., Kouwenhoven, E.K., Verburg, J., 1987. Forces acting on the patella during maximal voluntary contraction of the quadriceps femoris muscle at different knee flexion/extension angles. Cells Tissues Organs 129 (4), 310–314. http://dx.doi.org/10.1159/000146421. Farrhoki, S., Keyak, J.H., Powers, C.M., 2011. Individuals with patellofemoral pain exhibit greater patellofemoral joint stress: a finite element analysis study. Osteoarthr. Cartil. 19 (3), 287–294. http://dx.doi.org/10.1016/j.joca.2010.12.001. Fulkerson, J.P., 2002. Diagnosis and treatment of patients with patellofemoral pain. Am. J. Sports Med. 30 (3), 447–456. Fulkerson, J.P., 2004. Disorders of the Patellofemoral Joint. Fourth. Lippincott Williams & Wilkins, Philadelphia. Fulkerson, J.P., Arendt, E.A., 2000. Anterior knee pain in females. Clin. Orthop. 372, 69–73. Goodfellow, J., Hungerford, D.S., Woods, C., 1976. Patello-femoral joint mechanics and pathology. 2. Chondromalacia patellae. J. Bone Joint Surg. (Br.) 58-B (3), 291–299. Heino-Brechter, J., Powers, C.M., 2002. Patellofemoral stress during walking in persons with and without patellofemoral pain. Med. Sci. Sports Exerc. 34 (10), 1582–1593 Oct 2002. Higgins, J.R., 1977. Human Movement: An Integrated Approach. Mosby, Incorporated. Ho, K.-Y., Hu, H.H., Colletti, P.M., 2014. Powers CM. Running-induced patellofemoral pain fluctuates with changes in patella water content. Eur J Sport Sci. 0 (0), 1–7. http://dx.doi.org/10.1080/17461391.2013.862872. Jensen, R., Hystad, T., Baerheim, A., 2005. Knee function and pain related to psychological variables in patients with long-term patellofemoral pain syndrome. J. Orthop. Sports Phys. Ther. 35 (9), 594–600. http://dx.doi.org/10.2519/jospt.2005.35.9.594. Kittelson, A.J., George, S.Z., Maluf, K.S., Stevens-Lapsley, J.E., 2014. Future directions in painful knee osteoarthritis: harnessing complexity in a heterogeneous population.
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Clinical Biomechanics journal homepage: www.elsevier.com/locate/clinbiomech
Changes in patellofemoral pain resulting from repetitive impact landings are associated with the magnitude and rate of patellofemoral joint loading
T
⁎
Lee T. Atkinsa, , C. Roger Jamesb, Hyung Suk Yangc, Phillip S. Sizer Jrb, Jean-Michel Brisméeb, Steven F. Sawyerb, Christopher M. Powersd a
Department of Physical Therapy, Angelo State University, San Angelo, TX, USA Department of Rehabilitaion Sciences and Center for Rehabilitation Research, Texas Tech University Health Sciences Center, Lubbock, TX, USA c Kinesiology and Sport Management Division, University of South Dakota, Vermillion, SD, USA d Division of Biokinesiology & Physical Therapy, University of Southern California, Los Angeles, CA, USA b
A R T I C L E I N F O
A B S T R A C T
Keywords: Patella Kinetics Patellofemoral joint reaction force
Background: Although a relationship between elevated patellofemoral forces and pain has been proposed, it is unknown which joint loading variable (magnitude, rate) is best associated with pain changes. The purpose of this study was to examine associations among patellofemoral joint loading variables and changes in patellofemoral pain across repeated single limb landings. Methods: Thirty-one females (age: 23.5(2.8) year; height: 166.8(5.8) cm; mass: 59.6(8.1) kg) with PFP performed 5 landing trials from 0.25 m. The dependent variable was rate of change in pain obtained from selfreported pain scores following each trial. Independent variables included 5-trial averages of peak, time-integral, and average and maximum development rates of the patellofemoral joint reaction force obtained using a previously described model. Pearson correlation coefficients were calculated to evaluate individual associations between rate of change in pain and each independent variable (α = 0.05). Stepwise linear multiple regression (αenter = 0.05; αexit = 0.10) was used to identify the best predictor of rate of change in pain. Findings: Subjects reported an average increase of 0.38 pain points with each landing trial. Although, rate of change in pain was positively correlated with peak force (r = 0.44, p = 0.01), and average (r = 0.41, p = 0.02) and maximum force development rates (r = 0.39, p = 0.03), only the peak force entered the predictive model explaining 19% of variance in rate of change in pain (r2 = 0.19, p = 0.01). Interpretation: Peak patellofemoral joint reaction force was the best predictor of the rate of change in pain following repetitive singe limb landings. The current study supports the theory that patellofemoral joint loading contributes to changes in patellofemoral pain.
1. Introduction Patellofemoral pain (PFP) is a common yet complex multifactorial condition that can affect one's quality of life (Davis and Powers, 2010; Powers et al., 2012; Witvrouw et al., 2014). Patellofemoral pain has been cited as the most common lower extremity injury among runners (Taunton et al., 2002), and is reported to affect females 2 to 10 times more often than males (Fulkerson, 2002; Fulkerson and Arendt, 2000; Robinson and Nee, 2007). A hallmark sign of PFP is the onset or exacerbation of anterior knee pain with high impact activities such as running, (Ho et al., 2014; Noehren et al., 2012) and landing from a jump (Willson et al., 2008). Furthermore, reduction or abolishment of PFP typically occurs during activities characterized as having reduced
⁎
patellofemoral joint (PFJ) loading (Crossley et al., 2015). It has been proposed that PFP can be caused by elevated patellofemoral joint reaction forces (PFJRFs) (Dye, 2005; Goodfellow et al., 1976). However, research relating PFJRFs and PFP has not confirmed this hypothesis. For example, persons with PFP exhibit lower peak PFJRFs compared to healthy controls during walking (Chen and Powers, 2014; Heino-Brechter and Powers, 2002), running (Chen and Powers, 2014), and stair ambulation (Brechter and Powers, 2002; Chen and Powers, 2014). It has been proposed that the lower peak PFJRFs may be the result of compensatory behavior to minimize patellofemoral joint loading during functional tasks. A high PFP prevalence among persons who engage in high impact activities such as running suggests that the PFJ loading rate may be
Corresponding author at: Angelo State University Physical Therapy, ASU Station #10923, San Angelo, TX 76909-0923, USA. E-mail addresses: [email protected] (L.T. Atkins), [email protected] (C.R. James), [email protected] (H.S. Yang), [email protected] (P.S. Sizer), [email protected] (J.-M. Brismée), [email protected] (S.F. Sawyer), [email protected] (C.M. Powers). https://doi.org/10.1016/j.clinbiomech.2018.02.006 Received 21 June 2017; Accepted 6 February 2018 0268-0033/ © 2018 Elsevier Ltd. All rights reserved.
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to account for muscle co-contraction in the biomechanical model (see details below). Double differential EMG electrodes were placed on the skin over the muscle bellies and parallel to the fibers of the biceps femoris, semitendinosus, and medial and lateral gastrocnemius muscles using previously described techniques (Rainoldi et al., 2004). The skin was cleaned using abrasive gel and isopropyl alcohol and electrodes were positioned and secured with double-sided tape. Next, EMG signals were collected as participants performed 3, 5-s maximum voluntary isometric contractions (MVIC) of the hamstrings and gastrocnemius using a dynamometer (Biodex, System 3, Shirley, NY, USA). Prior to MVIC testing for each muscle group, subjects performed one or two submaximal practice trials to familiarize themselves with the task and minimize any potentially confounding learning effects. For the hamstrings MVIC, subjects were positioned with their hips and knees at 85° and 90° respectively. For the gastrocnemius MVIC, subjects were seated with their hips flexed to 85°, their knee fully extended, and their ankle plantar flexed to 15°. During each MVIC, subjects were secured with straps and instructed to contract with maximal effort. Following MVIC testing, 14-mm reflective markers were placed on the first, second, and fifth metatarsal heads, medial and lateral malleoli and femoral epicondyles, iliac crests, anterior and posterior superior iliac spines. Additionally, rigid clusters of at least 3 non-collinear tracking markers were secured to the thighs, legs, and feet of each subject. A cluster of 4 markers secured to the mid-trunk was aligned such that it was in a plane approximately parallel to the frontal plane of the subject's trunk while in a static standing position (Fig. 1A). A static standing trial was recorded and used to define the local segmental coordinate systems and joint axes. All anatomic calibration markers, except those on the pelvis, were removed prior to the SLL trials.
more predictive of symptom behavior than peak loading (Schaffler et al., 1989). This premise is supported by Cheung and Davis, who reported that following a running retraining intervention, improved PFP symptoms were associated with a reduced rate of lower limb loading (Cheung and Davis, 2011). Thus, it is conceivable that an elevated PFJ loading rate may evoke PFP (Cheung and Davis, 2011). An important step in designing optimal intervention strategies for persons with PFP is to gain a complete understanding of the relationship between PFJ loading and changes in PFP. Therefore, the purpose of the current study was to examine the associations among various measures of PFJ loading (peak PFJRF, PFJRF rate, PFJRF impulse) and changes in perceived PFP across repeated single limb landings (SLL). Based on previous literature, we hypothesized that PFJRF loading rate would be more predictive of PFP rate of change than peak PFJRF or PFJRF impulse. 2. Methods 2.1. Subjects Thirty-one females (mean (SD) age, 23.5 (3.8); height, 166.8 (5.8) cm; mass, 59.6 (8.1) kg; body mass index, 21.5 (2.9) kg/m2) with PFP were recruited for this study. Participants were included if they were between 18 and 45 years of age, had a body mass index < 30 kg/m2, and rated their level of physical activity from 5 to 9 on the Tegner Activity Scale (Tegner and Lysholm, 1985; Willson et al., 2008; Willson and Davis, 2008). Participants older than 45 years of age were excluded to minimize the potential influences of patellofemoral joint osteoarthritis. Additionally, participants must have reported insidious onset PFP of at least 3 weeks duration that was reproducible with at least 2 of the following activities: isometric quadriceps contraction, prolonged sitting, kneeling, squatting, running, or jumping. Operationally, PFP was defined as retro- or peripatellar pain (vague or localized) rated at minimum of 3 and maximum of 8 out of 10 on an 11-point visual analog scale. Potential subjects were excluded if they were non-English speaking, had prior knee surgery or traumatic patellar dislocation, neurological involvement that would influence performance of single limb landings, were pregnant, or were taking pain medication at time of testing. Participants underwent a physical exam by a licensed physical therapist with 8 years of experience to rule out other potential knee pathologies (i.e. ligamentous instability, meniscus injury, and large knee effusion). Approximately 30% of screened individuals were excluded based on these criteria. The study protocol was approved by the Institutional Review Board of the affiliated university. Prior to participation all subjects provided written informed consent. 2.2. Instrumentation Three-dimensional kinematic data were recorded at 250 Hz using an 8-camera Vicon Nexus motion capture system (Vicon, Centennial, CO, USA). Ground reaction force data were recorded at 2000 Hz using an inground force plate (Bertec, Columbus, OH, USA). Electromyography (EMG) data were recorded at 2000 Hz using a telemetered EMG system (Delsys Trigno, Boston, MA, USA). The EMG system had an input impedance > 10 Gohms, common mode rejection ratio > 80 dB and baseline noise < 0.75 μV root-mean-square. 2.3. Procedures Participants donned standard shoes (New Balance Inc., Boston, MA, USA), a sports top, and spandex shorts. Height and body weight were measured and recorded. The symptomatic or most painful knee (in the case of bilateral pain) was identified and the subject was prepared for testing. Electromyographic data from the knee flexor muscles were obtained
Fig. 1. (A) Anatomic and tracking marker placement. (B) Starting position for single limb landing task.
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PFJRF (Fig. 2) (Powers et al., 2014; Teng and Powers, 2014). Subjectspecific knee kinematic and kinetic data (i.e. knee flexion angle and adjusted knee extension moment) from the SLL trials were used as model inputs. Additional model inputs were obtained using data from previous studies and included the quadriceps effective lever arm length (van Eijden et al., 1986) and the relationship between quadriceps force and PFJRF (van Eijden et al., 1987). To account for co-contraction during the SLL trials, the knee flexor moment was estimated using SIMM modeling software (Motion Analysis Corporation, Santa Rosa, CA, USA). The SIMM lower limb model used in this study contained musculotendon actuators along with information regarding peak isometric muscle force, optimal muscle fiber length, tendon slack length, and pennation angle for the lower extremity muscles (Delp and Loan, 2000). Muscles within the SIMM software were represented as a series of 3-D vectors constrained to wrap over underlying structures. The SIMM model inputs included subject-specific knee joint kinematics and normalized knee flexor muscle EMG data. Knee joint kinematics were used to estimate contraction velocity and muscle tendon lengths for each knee flexor muscle while the normalized EMG data represented the level of muscle activation. It was assumed that semitendinosus and biceps femoris short head activation were the same as semimembranosus and biceps femoris long head activation, respectively (Lloyd and Besier, 2003). The torques from each individual knee flexor muscle were then estimated, summated, and normalized to body mass in order to represent the knee flexor moment. The knee flexor moment (obtained via SIMM modeling software) was then added to the net knee extension moment (obtained via inverse dynamics calculations) to provide a more accurate, adjusted knee extension moment that accounted for antagonistic muscle activation. The first step in calculating the PFJRF was to determine the quadriceps effective lever arm length. This was achieved by fitting a nonlinear equation to data from a previous study that described the quadriceps effective lever arm length at each knee flexion angle (van Eijden et al., 1986). The quadriceps force was determined by calculating the quotient of the adjusted knee extension moment divided by the quadriceps effective lever arm length for each knee flexion angle. Finally, the time-series PFJRF was calculated by multiplying the quadriceps force by a constant that described the relationship between the quadriceps force and PFJRF as a function of knee flexion angle (van Eijden et al., 1987). The dependent variable of interest was the rate of change in PFP across the landing trials. The independent variables of interest included maximum and average PFJRF loading rates (N/kg/s), peak PFJRF (N/
2.4. Single limb landing trials Subjects performed 5 SLL trials using their self-selected landing strategy. For each trial, subjects hung from a height-adjustable bar that was suspended to the ceiling then dropped 0.25 m and landed on a force plate (Fig. 1B). The 0.25 m drop height was chosen based on pilot testing which revealed that repetitive landings from this height elicited an increase in PFP but not to the extent that subjects were unable to complete 5 successful trials. A SLL trial was considered successful if subjects landed within the borders of the force plate using only their involved limb and maintained their balance for approximately 2 s. Prior to performing the first SLL trial, subjects were allowed several practice trials to familiarize themselves with the task. For each trial, ground reaction forces, kinematic and EMG data were collected. Prior to the first trial, subjects rated their pain level using an 11-point (0 to 10) scale to obtain baseline PFP. Immediately following each trial, subjects rated their pain during the previous SLL using the same scale. This procedure resulted in a total of 6 pain scores per subject. 2.5. Data reduction The rate of change in PFP was obtained by calculating the slope of a least-squares regression line that was fitted to the self-reported pain values that corresponded to the SLL trials. Kinematic and kinetic data were reconstructed and low-pass filtered (fourth-order, Butterworth, 10 Hz) using Vicon Nexus software (v2.3, Vicon, Centennial, CO, USA). Kinematic data were then imported into Visual 3D (v5, C-Motion, Germantown, MD, USA), where a six-degrees-of-freedom link segment model was applied. The thigh, leg, and foot segments were modeled as frusta of cones and the pelvis segment was modeled as a cylinder. Joint kinematics were calculated as movement of the distal segment relative to the proximal segment. Net internal joint moments were calculated using inverse dynamics and normalized to each subject's body mass. Raw EMG signals were band-pass filtered (20–450 Hz) within the Trigno hardware then rectified and low-pass filtered (fourth-order, Butterworth, 6 Hz) in Matlab (MATLAB, MathWorks, Natick, MA, USA) to create a linear envelope (Tsai and Powers, 2013). The greatest EMG value within the linear envelope obtained from either the MVIC or landing trials was used to normalize the EMG signals (Tsai and Powers, 2013). 2.5.1. Patellofemoral joint reaction force model A previously described 2D model of the PFJ was used to estimate the
Net Knee Extension Momenta
Internal Knee Flexion Momentb
Step 1 Adjusted Knee Extension
Step 2 Quadriceps Effective Lever Armc
Step 3 Quadriceps Force
Sagittal Knee Joint Anglea Step 4 Patellofemoral Joint Reaction Force
Relationship between Quadriceps and Patellofemoral Joint Reaction Forced
Fig. 2. Flow diagram of PFJRF model. a) Data obtained from motion capture system. b) Data obtained from SIMM EMG-driven musculoskeletal modeling software. c) Data obtained from van Eijden et al. 1986 d) Data obtained from van Eijden et al.1987.
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kg), and PFJRF time integral (N ∗ s/kg). All variables were calculated during the deceleration phase of landing which began the instant the vertical ground reaction force (vGRF) exceeded 20 N and ended when the vertical velocity of the pelvis equaled zero.
Table 1 Descriptive results for the PFJRF-related independent variables of interest.
2.6. Statistical analyses The individual relationships between the rate of change in PFP (dependent variable) and the PFJRF variables of interest (independent variables) were examined using Pearson correlation coefficients (α = 0.05). If more than one independent variable was significantly associated with the rate of change in PFP, a stepwise linear multiple regression (αenter = 0.05; αexit = 0.10) was calculated to determine which of the independent variables was most predictive. The linearity assumption for all regression analyses was confirmed by visually assessing scatter plots of each independent variable plotted against the dependent variable. The assumptions of homoscedasticity and homogeneity of variance for the residuals was visually confirmed by assessing the relationships of the standardized residuals plotted against the standardized predicted values (Portney and Watkins, 2009). Statistical analyses were performed using SPSS statistical software (SPSS Inc. v24, Chicago, IL, USA). The strength of the observed correlation coefficient values were interpreted as follows: excellent relationships were 0.75 or greater; moderate to good relationships were 0.50 to 0.75; fair relationship were 0.25 to 0.50; and little or no relationships were < 0.25 (Portney and Watkins, 2009).
Independent variables of interest
Mean (SD)
Range
Peak PFJRF (N/kg) PFJRF time integral (N ∗ s/kg) Average rate of PFJRF development (Nm/kg/s) Maximum rate of PFJRF development (Nm/kg/s)
47.8 (17.0) 6.4 (4.2) 333.1 (104.5) 738.9 (288.9)
12.2–87.1 0.3–15.3 117.5–536.0 61.0–1318.6
Abbreviations: PFJRF, patellofemoral joint reaction force; Table 2 Correlation results between the rate of change in the PFP and the PFJRF-related independent variables of interest. Covariate with PFP ⁎
Peak PFJRF (N/kg) Average rate of PFJRF development (Nm/kg/s)⁎ Maximum rate of PFJRF development (Nm/kg/s)⁎ PFJRF time integral (N ∗ s/kg)
Pearson r
p-Value
0.44 0.42 0.39 0.15
0.01 0.02 0.03 0.43
Abbreviation: PFP, patellofemoral pain; PFJRF, patellofemoral joint reaction force. ⁎ p < 0.05.
(r = 0.15, p = 0.43). With respect to the step-wise regression analysis, only peak PJFRF entered the regression model for predicting the rate of change in PFP (r = 0.44, p = 0.01) explaining 19% of the variance in the rate of change in PFP. The unstandardized coefficient for the peak PFJRF was positive indicating that an increase in peak PFJRF was predictive of an increase in the rate of change in PFP with each successive landing trial (Table 3).
3. Results Twenty-seven subjects reported an incremental increase in PFP during the 5 SLL trials while 4 subjects reported no change in PFP. On average, the mean (SD) rate of change in PFP with each successive SLL trial was 0.38 (0.30) points per trial (Fig. 3) (range: 0.00 to 1.00 points per trial). The mean, SD, and range for each PFJRF-related independent variable is presented in Table 1. The rate of change in PFP was significantly associated with 3 of the 4 of the PFJRF-related independent variables. The strength of these associations was fair and included the following: peak PFJRF (r = 0.44, p = 0.01), average PFJRF loading rate (r = 0.42, p = 0.02), and maximum PFJRF loading rate (r = 0.39, p = 0.03) (Table 2). The PFJRF time-integral variable was not associated with the rate of change of PFP
4. Discussion Results from the current study support the theory that the rate of change in PFP is related to PFJ loading, as 3 out of 4 PFJRF-related variables (peak PFJRF and maximum and average PFJRF rates) were associated with changes in PFP. These variables were positively correlated with the rate of change in PFP, indicating that persons who landed with greater rates of PFJRF loading and peak PFJRF magnitudes experienced greater increases in PFP with successive SLL trials. Thus, PFJRF loading may in part, explain the high prevalence of PFP among
Patellofemoral Pain Reported Over Consecutive Single Limb Landing Trials 5
Reported Pain Level
4 3
y = 0.38x + 0.43
2 1 0 -1
baseline
trial 1
trial 2
trial 3
trial 4
trial 5
Time Figure 3. Regression line of best fit based on average PFP reported by subjects at baseline and immediately following each single limb landing trial. Error bars represent one standard deviation.
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2013), and there is evidence that PFP is associated with greater levels of psychological distress (Jensen et al., 2005). Furthermore, it has been recommended that valid pain models include both mechanical and psychological domains (Kittelson et al., 2014; Mueller and Maluf, 2002). Thus, had our predictive model included additional mechanical (e.g. PFJ contact area and stress) and psychological variables (e.g. selfperceived health status (Jensen et al., 2005) or coping mechanisms (Witvrouw et al., 2000)) it is conceivable that more of the variance in the rate of change in PFP would have been explained. Nonetheless, given the multifaceted nature of PFP, and the fact that nearly one fifth of the variance in rate of change in PFP was explained by a single mechanical variable (i.e. peak PFJRF), the current study highlights the need for continued research aimed at increasing understanding of the relationships between PFJRF and PFP. There are several limitations of this study that should be acknowledged. First, our PFJRF estimates were based on a two-dimensional PFJ model that did not account for the influence of frontal and transverse plane motions on the magnitude or rate of the PFJRFs. Additionally, the PFJ model used in the current study utilized several input variables that were not subject specific. For example, the quadriceps lever arm was estimated as a function of knee flexion angle based on previously published data (van Eijden et al., 1986). While the knee flexion data was specific to each subject, the lever arm at varying degrees of knee flexion was similar for all subjects. Thus, inter-subject differences in structural characteristics that could have affected the quadriceps lever arm were not accounted for in our model. As noted above, our model estimated PFJRFs as opposed to PFJ stress. It is possible that peak stress and/or rate of stress development may have provided greater insight into the mechanisms underlying PFP development. Lastly, the current study only examined 5 SLL trials, whereas many functional activities that provoke PFP involve a greater number of impacts (i.e. running). Thus, had the subjects in this study performed a greater number of trials, it is conceivable that our results may have differed.
Table 3 Stepwise multiple regression results for predicting the rate of change in PFP from the PFJRF-related independent variables. Model R
Model R2
Model pvalue
Predictor variable
Unstandardized coefficient
0.44
0.19
0.01
Peak PFJRF (N/ kg)
0.008
Abbreviation: PFP, patellofemoral pain; PFJRF, patellofemoral joint reaction force; SLL, single limb landing.
individuals that engage in repeated impact activities such as running. The PFJRF time-integral, which is representative of the total magnitude of PFJ loading, was not correlated with the rate of change in PFP. This suggests that for short duration impact activities such as running and landing from a jump, the overall magnitude of the PFJRF may not be related to PFP behavior. However, it is possible that the PFJRF time-integral may play a larger role in PFP development during non-impact activities of longer duration such as squatting and prolonged static sitting. Contrary to our proposed hypothesis, peak PFJRF (as opposed to PFJRF rate) emerged as the best predictor variable, explaining nearly 20% of the variance in rate of change in PFP. The positive correlation indicates that subjects who landed with greater peak PFJRFs experienced a greater rate of change in PFP from one SLL trial to the next. Although significant, the clinical relevance of this relationship may appear inconsequential given the magnitude of the unstandardized coefficient (increase of 0.008 pain points per landing per 1 N/kg increase in peak PFJRF). However, when interpreting this relationship, one should consider that typical PFP provocation during repetitive impact activities such as running is relatively slow and gradual (Noehren et al., 2012). Given that runners take approximately 1000 steps (or landings) per mile (Davis and Futrell, 2016), the cumulative effects indicated by this relationship may be relevant. As mentioned above, several studies have reported that persons with PFP exhibit pain-avoidance behaviors as evidenced by lower PFJRFs during activities such as walking, stair ambulation, and running compared to asymptomatic controls (Brechter and Powers, 2002; Chen and Powers, 2014; Heino-Brechter and Powers, 2002). Given the fact that 87% of the participants in the current study experienced a progressive increase in symptoms with repetitive SLLs suggests that compensatory pain-avoidance behaviors (if present) were not entirely successful in modulating symptoms. Given the relatively high demand and constrained nature of the landing task used in the current study, it is likely that compensatory options were limited. This is consistent with the premise that the availability of compensatory behaviors is reduced as the demand of a task increases (Higgins, 1977). Although not the best predictor, the maximum and average PFJRF rates also were independent predictors of the increase in PFP. This finding supports the work of Cheung and Davis (Cheung and Davis, 2011) who reported that the use of movement retraining to reduce lower limb loading rates in runners with PFP was effective in diminishing symptoms. However, the peak PFJRF and measures of PFJRF rate of development as measured in the current study were highly correlated with each other (r-values ranging from 0.77 to 0.89). As such, it is recommended that clinicians focus on interventions that minimize peak PFJRFs and rates of PFJRF development when managing patients with PFP. There are several possible explanations as to why more variance in the rate of change in PFP was not explained by our predictive model. First, PFJ stress (PFJRF/PFJ contact area) is thought to be an important mechanical stimulus for invoking PFP (Farrhoki et al., 2011; Fulkerson, 2004), and PFJ contact area was not measured in the current study. Additionally, pain has been defined as an unpleasant sensory and emotional experience involving an individual's perception (Venes,
5. Conclusion The magnitude and rate of change in PFP across repeated SLL trials was significantly and positively correlated with the magnitude and rate of PFJRF development. Of the PFJRF-related variables examined, the peak PFJRF was the best predictor of the rate of change in PFP. The results of this study suggest that strategies aimed at reducing peak and rate of PFJRFs during impact activities may be beneficial in minimizing the exacerbation of PFP. For example, one potential strategy for minimizing PFJ loading during SLL activities may be to implement a forward trunk lean. According to Teng et al., a 7° increase in forward trunk lean while running resulted in a significant reduction in peak PFJRF (Teng and Powers, 2014). Based on this finding it is feasible that the same strategy (i.e. forward trunk lean) may be effective for minimizing the PFJRF rate as well. Acknowledgements This work was supported by a research grant from the Texas Physical Therapy Foundation (Austin, TX, USA). The authors would like to acknowledge Alexander Drusch, MS, Kinyata Cooper, BS, Tzu-Chieh Liao, PT, PhD for their contributions to this study. Conflict of interest The authors certify that they have no affiliations with or financial involvement in any organization or entity with a direct financial interest in the subject matter or materials discussed in the article. Approval This study protocol was approved by the Institutional Review Board 35
Clinical Biomechanics 53 (2018) 31–36
L.T. Atkins et al.
at Texas Tech University Health Sciences Center.
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