Gait & Posture 39 (2014) 915–919
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Full length article
The effects of limb dominance and fatigue on running biomechanics Allison M. Brown a,*, Rebecca A. Zifchock b, Howard J. Hillstrom c a
Adjunct Assistant Professor, Department of Rehabilitation and Movement Sciences, Rutgers Biomedical and Health Sciences, Newark, NJ, USA Department of Civil & Mechanical Engineering, United States Military Academy, West Point, NY, USA c Director, Leon Root, MD Motion Analysis Laboratory, Hospital for Special Surgery, New York, NY, USA b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 December 2011 Received in revised form 25 July 2013 Accepted 2 December 2013
Purpose: To establish whether lower extremity limb dominance has an effect on overground running mechanics. Background: In attempts to resolve unilateral pathology, physical therapists often use the restoration of symmetry as a clinical milestone. While lower limb dominance has been shown to affect lower extremity mechanics during dynamic tasks such as jump landing, its effect on running gait is poorly understood. Further, despite the role of fatigue in running mechanics and injury, the interaction between fatigue and limb dominance has yet to be examined. Methods: Three-dimensional kinematic and kinetic data were collected on 20 females during overground running. Data were collected prior-to and following a treadmill run to exertion. Dominant and non-dominant limb data were compared in the fresh-state using a paired t-test. A 2-way repeatedmeasures ANOVA was used to test for an interaction between fatigue and limb dominance. Results: There were no significant differences between the kinematic or kinetic patterns of the dominant and non-dominant lower extremities during fresh-state overground running. Fatigue was not shown to interact with limb dominance. Conclusion: Limb dominance did not affect kinematic or kinetic side-to-side differences. Therefore, physical therapists can continue to use resolution of lower extremity symmetry as a goal of therapy without having to account for limb dominance. The lack of an interaction between fatigue and limb dominance indicates that the dominant and non-dominant limbs fatigue at a similar rate. ß 2013 Elsevier B.V. All rights reserved.
Keywords: Exertion Kinematics Kinetics Limb laterality
1. Introduction Understanding lower extremity biomechanics during running is important for the prevention and treatment of lower extremity injuries. One area of interest includes differences in side-to-side gait mechanics. In attempts to resolve unilateral pathology, physical therapists often use the restoration of symmetry as a clinical milestone. However, there is some evidence that large sideto-side differences may be attributed to lower limb dominance during dynamic tasks such as jump landing [1,2]. Thus, it is important that clinicians understand the effects of lower limb dominance on running gait and the potential implications of using the contralateral limb as a basis for comparison when establishing treatment goals.
* Corresponding author at: Department of Rehabilitation and Movement Sciences, School of Health Related Professions, Rutgers Biomedical and Health Sciences, 65 Bergen Street, Room 714, Newark 07107, NJ, USA. Tel.: +1 973 972 2141. E-mail address: [email protected] (A.M. Brown). 0966-6362/$ – see front matter ß 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gaitpost.2013.12.007
Similarly, when studying injured runners, investigators often choose to compare data from their injured limb with that of the healthy runner’s dominant limb. This implies an assumption of lower extremity symmetry, such that the biomechanics of the dominant limb are representative of those in the non-dominant limb. However if, during overground running, symmetry does not exist between the dominant and non-dominant limbs, the choice to examine only one limb may significantly affect study results. Lower limb dominance, and its effect on running gait, is a poorly understood concept. While there is an abundance of research directed at upper extremity dominance and its associated phenomena, far less attention has been paid to the effects of lower extremity dominance on biomechanics. Still, much of the current literature suggests that both the dominant and nondominant limbs play important roles in lower extremity tasks. In their investigation of lower-extremity kinetics during slow, preferred, and fast walking speeds, Seeley et al [3] found increased dominant limb impulses during the propulsive phase of fast-speed walking. This suggests that limb dominance may have a further effect on gait mechanics with increased speeds such as seen during running. Additionally, studies have found limb dominance to affect
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A.M. Brown et al. / Gait & Posture 39 (2014) 915–919
lower extremity kinematics during a jump landing [2]. These findings suggest that limb dominance may affect lower extremity kinematics during tasks requiring increased shock absorption and force attenuation, such as during running gait. To our knowledge, only one study has examined for the effects of limb dominance on running biomechanics [4]. This study examined kinetics of the dominant and non-dominant lower limbs during overground running. Although no side-to-side differences were found, this study was limited by its small sample size (N = 5). To-date, no study has thoroughly examined limb laterality and lower extremity kinematics or kinetics during overground running. The effects of fatigue are another important consideration for any running-related research. Kinetic, kinematic and electromyographic changes have been well-documented following fatigue in both healthy and injured runners [5–9]. Fatigue alters running biomechanics in as little as 15 min of running [6], impacting both the casual and long distance runner. If the dominant and nondominant limbs do, in fact, behave differently during overground running, it is possible that they may fatigue at different rates. Thus, fatigue may magnify the kinematic and kinetic differences between limbs. To date, no studies have examined the potential interaction between limb dominance and fatigue during running gait with respect to lower extremity kinematics and kinetics. Therefore, the purpose of this study was to examine the effect of limb laterality and fatigue (before and after a run to exertion) on lower extremity kinematics and kinetics during overground running. Specifically, this study focused on hip, knee, and ankle variables that have been previously associated with running injury [8,10]. Based on previously documented biomechanical gender differences during running [11], inclusion in this study was limited to female runners. Two hypotheses were tested in this study: first (H1), due to differences in the role that lower extremities are thought to play during gait, it was hypothesized that lower extremity kinematics and kinetics will differ between the dominant and non-dominant limbs during fresh-state overground running. Second (H2), it was hypothesized that lower extremity kinematics and kinetics of the dominant and non-dominant limb will be affected differently by fatigue. 2. Methods 2.1. Participants Twenty healthy female runners (age 29 6 years, height 1.6 0.1 m, mass 56.8 5.2 kg) participated in this study. All runners were rear foot strikers who were running a minimum of 15 miles per week for at least one year and were able to run at least one 9 min mile. Participants were excluded if they had any history of lower extremity surgery or if they reported any injury to the trunk, pelvis or lower extremities in the six months prior to data collection. Written informed consent was obtained for this study protocol, which was approved by the Hospital for Special Surgery Institutional Review Board.
Fig. 1. Six degree of freedom marker set utilized to track the segment coordinate systems for kinematic and kinetic data calculation.
C7, right and left ASIS, and sacrum. The pelvis was defined and tracked by the right and left ASIS and sacrum. The thighs were defined and tracked by a virtual (functionally-determined) hip joint center, medial, and lateral femoral condyles, and an array cluster on the thigh. The shanks were defined and tracked by medial and lateral femoral condyles, the tibial tuberosity, medial and lateral malleoli, and an array cluster on the shank. The hind feet were defined and tracked by the distal toe, proximal, and distal calcaneus (aligned based on the bisection of the posterior calcaneus), and the medial and lateral calcaneus (Fig. 2). All joint angles were referenced relative to the proximal segment with the trunk and pelvis referenced relative to the lab. All testing was
2.2. Instrumentation A 12-camera Motion Analysis Corporation system (Motion Analysis Corporation; Santa Rosa, CA) and four forceplates (Bertec Corp.; Columbus, OH, and AMTI; Watertown, MA) were used for the collection of kinematic and kinetic data. Passive, retroreflective markers were placed in specified locations on the trunk, pelvis, and lower extremities for the tracking of segment coordinate systems during dynamic running trials (Fig. 1). These markers were used to define and track a custom six-degree-of-freedom model that included a trunk, pelvis, thighs, shanks, and hindfeet. The trunk was defined and tracked by markers placed on the sternal notch,
Fig. 2. Rearfoot marker placement directly on the skin of the calcaneus via customized holes cut from the heel cup.
A.M. Brown et al. / Gait & Posture 39 (2014) 915–919
conducted with participants wearing a neutral, laboratoryprovided running shoe (New Balance, 1062; Boston, MA, USA) with customized holes cut from the heel cup to allow marker placement directly on the calcaneus. Video and forceplate data were sampled at 120 and 4800 Hz, respectively.
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data were collected during over ground running in the same manner as during fresh-state data collection. The elapsed time from runners’ completion of the treadmill run until the post-exertion data collection was 7 0.002 min. 2.4. Data processing
2.3. Experimental protocol 2.3.1. Establishing limb dominance The dominant lower extremity was established based on participants’ verbal report of which limb they would use to kick a soccer ball. This self-report method of determining lower extremity dominance was found to have 97.7% agreement with task performance and a 96% test-retest agreement [12]. 2.3.2. Three-dimensional motion analysis Data collection began with the capture of static and dynamic trials to calibrate the anatomic coordinate systems. Next, markers used solely for anatomical definitions were removed so that only tracking markers remained during running trials. To ensure repeatability among conditions, all tracking markers remained on the participants for the duration of testing. To begin running data collection, participants were positioned at the start of a 25 m runway that extended a minimum of 8 m beyond either end of the data capture volume. They were instructed to run along the runway and through the data capture volume for the collection of five acceptable overground running trials. An acceptable trial was defined as one where the runner struck at least one forceplate while running at a speed of 3.35 m/s (10%). To ensure consistency across trials, velocity was recorded with two photoelectric timers placed 4 m apart. When the runner broke the first photoelectric beam, a timer began. The timer then turned off when the runner broke the second photoelectric beam. 2.3.3. Fatiguing run After the collection of five acceptable overground trials on each limb, participants performed a fatiguing treadmill run (24.6 7.0 min). This run was performed at a self-selected pace expected to induce fatigue within 40 min. Participants were encouraged to select a running speed that reflected their five kilometer race pace. Prior to beginning the run, participants were educated on the Borg Rating of Perceived Exertion (RPE) Scale [13]. Throughout the run, RPE data were collected every three minutes. The run was terminated once the runner rated their exertion a 17/20 (‘‘very hard’’). Upon completion of the treadmill run, fatigued-state
Data from five trials on each limb were processed and analyzed using a customized code written in Visual 3-D (C-Motion, Inc.; Rockville, MD) and LabView (National Instruments; Austin, TX). Marker trajectories and forceplate signals were smoothed using bidirectional second-order low-pass Butterworth filters achieving fourth-order attenuation with 08 phase lag at cutoff frequencies of 8 and 50 Hz, respectively. Joint angles were calculated using an X, Y, Z (Flexion, Abduction, Rotation) Euler decomposition sequence. Inverse dynamics calculations [14] using ground reaction force data, kinematic data, and segment inertial properties [15,16] were utilized to calculate internal joint moments. Internal joint moments were normalized to participant height and mass. Kinematic variables of interest included: peak hip flexion, adduction and internal rotation, peak knee flexion, abduction, and internal rotation and peak ankle dorsiflexion and eversion angles. Kinetic variables included: peak hip extensor and abductor, peak knee extensor and adductor, and peak ankle plantarflexor and invertor internally referenced moments. Peak kinematic and kinetic values were extracted from the stance phase of each individual trial (Fig. 3). Within each limb, variables were then averaged across each of the five trials for both fresh and fatigued-state conditions. 2.5. Statistical analyses A paired t-test was utilized to test for differences between the dominant and non-dominant limb during fresh-state overground running. A 2-way repeated measures ANOVA was used to examine the effect of fatigue on dominant and non-dominant limb kinematics and kinetics. Significance was set at P 0.05, and a trend was operationally-defined as P 0.10. 3. Results 3.1. Joint kinematics Means and standard deviations for hip, knee, and ankle joint kinematic variables of the dominant and non-dominant limbs in both the fresh and fatigued state are included in Table 1. During
Knee flexion/extension angle (Degrees)
120 100 80 60 40 20 0 0
10
20
30
40
50
60
70
80
90
100
Percent of Gait Cycle Fig. 3. Knee flexion values across the gait cycle. Shaded region indicates stance phase of gait cycle from peak variables of interest were extracted. Dotted circle identifies peak knee flexion value extracted from the loading response phase of stance.
A.M. Brown et al. / Gait & Posture 39 (2014) 915–919
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Table 1 Means and standard deviations for hip, knee, and ankle joint kinematic variables of the dominant and non-dominant limbs in both the fresh and fatigued state. Fatigued-state
Fresh-state Kinematic variables (8)
Dominant
Non-dominant
Dominant
Non-dominant
Hip flexion Hip adduction Hip internal rotation Knee flexion Knee abduction Knee internal rotation Ankle dorsiflexion Ankle eversion
39. 6 (5.4) 16.8 (3.1) 5.6 (8.4) 39.6 (5.4) 4.7 (3.1) 12.6 (11.2) 22.5 (7.1) 7.0 (7.5)
39.1 (3.7) 15.6 (2.3) 3.2 (7.2) 39.1 (3.7) 4.8 (3.4) 10.2 (8.2) 22.9 (5.1) 6.4 (5.2)
39.2 (4.8) 17.3 (3.2) 6.3 (8.3) 39.2 (4.8) 5.0 (3.4) 11.7 (11.7) 24.3 (8.0) 8.8 (6.7)
39.3 (7.0) 15.5 (2.6) 3.4 (7.1) 38.8 (3.3) 4.8 (3.6) 9.6 (7.6) 23.9 (5.0) 7.0 (5.9)
fresh-state running there were no differences in hip, knee or ankle kinematic variables between the dominant and non-dominant limbs (reject H1). In addition, there were no interactions between fatigue status and limb dominance for any of the kinematic variables measured (reject H2). 3.2. Joint kinetics Means and standard deviations for hip, knee, and ankle joint kinetic variables of the dominant and non-dominant limbs in both the fresh and fatigued state are included in Table 2. During fresh, non-fatigued running, there were no differences in hip, knee or ankle kinetics between the dominant and non-dominant limbs (reject H1). Similarly, there were no interactions detected between fatigue status and limb dominance with respect to joint kinetics (reject H2). 4. Discussion Existing literature has suggested there are differences in kinematic behavior of the dominant and non-dominant lower limbs during eccentric loading tasks such as performing a droplanding [1]. Despite these findings, no studies have thoroughly examined for such differences in joint kinematics or kinetics during running. While running and drop-landing are unique tasks, they are similar in the sense that they are both high impact activities requiring increased muscle activity for shock attenuation. Therefore, this study sought to determine whether similar lower extremity differences would be exhibited during the stance phase of running gait. Additionally, this study examined the effects of fatigue on lower extremity kinematics and kinetics with respect to limb dominance during overground running. The results of this study indicate that, in a fresh state, the dominant and nondominant limbs of runners do not exhibit differences in lower extremity kinematics or kinetics. While these findings differ from those of Brown et al [1], they are supported by previous studies examining overground walking gait. Gundersen et al [17] examined the effect of lower limb dominance on lower extremity kinematic asymmetry during walking. The authors found that, while some lower extremity asymmetries did exist, they could not be correlated with lower limb laterality. These findings have
immediate implications for the treatment of running injuries. Based on the current results, which show no differences between the dominant and non-dominant limbs, therapists and physicians can continue to strive for regaining lower extremity symmetry as part of their therapeutic outcomes. Additionally, these findings have clear methodological implications when conducting research on running gait. While this study does not dispute the fact that the lower limbs function in an asymmetrical manner during overground running, it supports the notion that, without biasing their findings, researchers can choose either the dominant or non-dominant limb for comparison to injured populations when examining running mechanics. The second aim of this study was to examine the effect that a fatiguing run may have on dominant and non-dominant lower limb running mechanics. It was hypothesized that the dominant and non-dominant limb would fatigue at different rates, thereby affecting lower extremity kinematics and kinetics. This hypothesis was not supported by the results of this study, which found no interaction between fatigue and limb dominance when examining joint kinematics or kinetics. In general, a run to fatigue has been shown to result in both kinematic and kinetic alterations [6,7]. However, the results of this study suggest that, in the presence of fatigue-related kinematic or kinetic alterations, differences are not shown to be magnified specifically at the dominant or nondominant limb. The results of this study do not suggest that side-to-side differences, or asymmetries are non-existent in runners. In fact, multiple studies have shown that there are asymmetries, which occur during running gait [18,19]. Rather, this study suggests that side-to-side differences are not likely due to lower limb dominance. This still leaves investigators with unanswered questions regarding the causes of these asymmetries. Further research should be conducted to determine whether these differences are innate, the result of training or possibly induced by marker placement asymmetry. Additionally, it is still unclear as to whether the presence of asymmetries can predispose runners to injury. This study was not without limitations. The lack of significant differences between the kinematic and kinetic behaviors of the dominant and non-dominant limbs and the absence of an effect of fatigue may in part be due to the selection of variables utilized for comparison. Kinematic and kinetic outcome measures were
Table 2 Means and standard deviations for hip, knee, and ankle joint moments of the dominant and non-dominant limbs in both the fresh and fatigued state. Fresh-state
Fatigued-state
Kinetic variables
Dominant
Non-dominant
Dominant
Non-dominant
Hip extensor (N-m/Kg) Hip abductor (N-m/Kg) Knee extensor (N-m/Kg) Knee adductor (N-m/Kg) Ankle plantarflexor (N-m/Kg) Ankle invertor (N-m/Kg)
1.7 (0.4) 2.2 (0.4) 2.6 (0.5) 0.2 (0.1) 2.8 (0.3) 0.3 (0.2)
1.7 (0.4) 2.2 (0.3) 2.6 (0.3) 0.2 (0.1) 2.7 (0.3) 0.2 (0.2)
1.6 (0.4) 2.1 (0.4) 2.5 (0.5) 0.2 (0.1) 2.8 (0.3) 0.3 (0.2)
1.7 (0.4) 2.1 (0.3) 2.6 (0.4) 0.2 (0.1) 2.8 (0.3) 0.2 (0.2)
A.M. Brown et al. / Gait & Posture 39 (2014) 915–919
selected based upon previously documented fatigue-related changes within these variables. It is possible, however, that for the determination of side-to-side differences with respect to limb dominance, the use of joint kinematic and kinetic measures may not be a sensitive enough measure. A different, and more global quality of movement measure may be a more appropriate tool with which to elicit effects of limb dominance. Future studies will be focused on validating the use of quality of movement measure, such as the Gait Deviation Index [20] for use with runners. An additional limitation of this work was that it included only female participants. Due to the known biomechanical gender differences [11] these results may not be generalizable to a population of male runners. Future studies should examine similar hypotheses in male runners. 5. Conclusion Overall, during fresh-state (non-fatigued) running, limb dominance had essentially no effect on lower extremity joint kinematics or kinetics. This suggests that kinematic or kinetic side-to-side differences seen during overground running are not affected by lower limb dominance. Therefore, when treating injured runners, physical therapists can continue to focus on regaining lower extremity dynamic symmetry as a long-term goal of therapy. In addition, these results support the continued selection of either the dominant or non-dominant limb or an arbitrary selection of right or left limbs for comparison to an injured population. There was no interaction between fatigue status and limb dominance indicating that the dominant and non-dominant limbs fatigue at a similar rate. Therefore, side-to-side differences seen during fatigued-state running are not likely related to limb dominance. Acknowledgements The authors would like to acknowledge New Balance for the use of their shoes during data collection. This study was approved by Hospital for Special Surgery Institutional Review Board.
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References [1] Brown TN, Palmieri-Smith RM, McLean SG. Differences between sexes and limbs in hip and knee kinematics and kinetics during anticipated and unanticipated jump landings: implications for ACL injury. Br J Sports Med 2009. [2] Ford KR, Myer GD, Hewett TE. Valgus knee motion during landing in high school female and male basketball players. Med Sci Sports Exerc 2003;35(10): 1745–50. [3] Seeley MK, Umberger BR, Shapiro R. A test of the functional asymmetry hypothesis in walking. Gait Posture 2008;28(1):24–8. [4] Hamill J, Bates BT, Knutzen KM. Ground reaction force symmetry during walking and running. Res Q Exerc Sport 1984;55(3):289–93. [5] Christina KA, White SC, Gilchrist LA. Effect of localized muscle fatigue on vertical ground reaction forces and ankle joint motion during running. Hum Mov Sci 2001;20(3):257–76. [6] Derrick TR, Dereu D, McLean SP. Impacts and kinematic adjustments during an exhaustive run. Med Sci Sports Exerc 2002;34(6):998–1002. [7] Gerlach KE, White SC, Burton HW, Dorn JM, Leddy JJ, Horvath PJ. Kinetic changes with fatigue and relationship to injury in female runners. Med Sci Sports Exerc 2005;37(4):657–63. [8] Miller RH, Lowry JL, Meardon SA, Gillette JC. Lower extremity mechanics of iliotibial band syndrome during an exhaustive run. Gait Posture 2007;26(3): 407–13. [9] Nyland JA, Shapiro R, Stine RL, Horn TS, Ireland ML. Relationship of fatigued run and rapid stop to ground reaction forces, lower extremity kinematics, and muscle activation. J Orthop Sports Phys Ther 1994;20(3):132–7. [10] Noehren B, Davis I, Hamill J. ASB clinical biomechanics award winner 2006 prospective study of the biomechanical factors associated with iliotibial band syndrome. Clin Biomech (Bristol Avon) 2007;22(9):951–6. [11] Ferber R, Davis IM, Williams 3rd DS. Gender differences in lower extremity mechanics during running. Clin Biomech (Bristol Avon) 2003;18(4):350–7. [12] Coren S, Porac C. The validity and reliability of self-report items for the measurement of lateral preference. Br J Psychol 1978;69:207–11. [13] Borg G. Borg’s Perceived Exertion and Pain Scale, 104. Champaign, IL: Human Kinetics; 1998. [14] http://www.c-motion.com/v3dwiki/index.php?title=Inverse_Dynamics. [15] http://www.c-motion.com/v3dwiki/index.php?title=Segment_Properties. [16] Dempster WT, Gabel WC, Felts WJ. The anthropometry of the manual work space for the seated subject. Am J Phys Anthropol 1959;17(4):289–317. [17] Gundersen LA, Valle DR, Barr AE, Danoff JV, Stanhope SJ, Snyder-Mackler L. Bilateral analysis of the knee and ankle during gait: an examination of the relationship between lateral dominance and symmetry. Phys Ther 1989;69(8): 640–50. [18] Zifchock RA, Davis I, Hamill J. Kinetic asymmetry in female runners with and without retrospective tibial stress fractures. J Biomech 2006;39(15):2792–7. [19] Karamanidis K, Arampatzis A, Bruggemann GP. Symmetry and reproducibility of kinematic parameters during various running techniques. Med Sci Sports Exerc 2003;35(6):1009–16. [20] Schwartz MH, Rozumalski A. The gait deviation index: a new comprehensive index of gait pathology. Gait Posture 2008;28(3):351–7.
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Full length article
The effects of limb dominance and fatigue on running biomechanics Allison M. Brown a,*, Rebecca A. Zifchock b, Howard J. Hillstrom c a
Adjunct Assistant Professor, Department of Rehabilitation and Movement Sciences, Rutgers Biomedical and Health Sciences, Newark, NJ, USA Department of Civil & Mechanical Engineering, United States Military Academy, West Point, NY, USA c Director, Leon Root, MD Motion Analysis Laboratory, Hospital for Special Surgery, New York, NY, USA b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 December 2011 Received in revised form 25 July 2013 Accepted 2 December 2013
Purpose: To establish whether lower extremity limb dominance has an effect on overground running mechanics. Background: In attempts to resolve unilateral pathology, physical therapists often use the restoration of symmetry as a clinical milestone. While lower limb dominance has been shown to affect lower extremity mechanics during dynamic tasks such as jump landing, its effect on running gait is poorly understood. Further, despite the role of fatigue in running mechanics and injury, the interaction between fatigue and limb dominance has yet to be examined. Methods: Three-dimensional kinematic and kinetic data were collected on 20 females during overground running. Data were collected prior-to and following a treadmill run to exertion. Dominant and non-dominant limb data were compared in the fresh-state using a paired t-test. A 2-way repeatedmeasures ANOVA was used to test for an interaction between fatigue and limb dominance. Results: There were no significant differences between the kinematic or kinetic patterns of the dominant and non-dominant lower extremities during fresh-state overground running. Fatigue was not shown to interact with limb dominance. Conclusion: Limb dominance did not affect kinematic or kinetic side-to-side differences. Therefore, physical therapists can continue to use resolution of lower extremity symmetry as a goal of therapy without having to account for limb dominance. The lack of an interaction between fatigue and limb dominance indicates that the dominant and non-dominant limbs fatigue at a similar rate. ß 2013 Elsevier B.V. All rights reserved.
Keywords: Exertion Kinematics Kinetics Limb laterality
1. Introduction Understanding lower extremity biomechanics during running is important for the prevention and treatment of lower extremity injuries. One area of interest includes differences in side-to-side gait mechanics. In attempts to resolve unilateral pathology, physical therapists often use the restoration of symmetry as a clinical milestone. However, there is some evidence that large sideto-side differences may be attributed to lower limb dominance during dynamic tasks such as jump landing [1,2]. Thus, it is important that clinicians understand the effects of lower limb dominance on running gait and the potential implications of using the contralateral limb as a basis for comparison when establishing treatment goals.
* Corresponding author at: Department of Rehabilitation and Movement Sciences, School of Health Related Professions, Rutgers Biomedical and Health Sciences, 65 Bergen Street, Room 714, Newark 07107, NJ, USA. Tel.: +1 973 972 2141. E-mail address: [email protected] (A.M. Brown). 0966-6362/$ – see front matter ß 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gaitpost.2013.12.007
Similarly, when studying injured runners, investigators often choose to compare data from their injured limb with that of the healthy runner’s dominant limb. This implies an assumption of lower extremity symmetry, such that the biomechanics of the dominant limb are representative of those in the non-dominant limb. However if, during overground running, symmetry does not exist between the dominant and non-dominant limbs, the choice to examine only one limb may significantly affect study results. Lower limb dominance, and its effect on running gait, is a poorly understood concept. While there is an abundance of research directed at upper extremity dominance and its associated phenomena, far less attention has been paid to the effects of lower extremity dominance on biomechanics. Still, much of the current literature suggests that both the dominant and nondominant limbs play important roles in lower extremity tasks. In their investigation of lower-extremity kinetics during slow, preferred, and fast walking speeds, Seeley et al [3] found increased dominant limb impulses during the propulsive phase of fast-speed walking. This suggests that limb dominance may have a further effect on gait mechanics with increased speeds such as seen during running. Additionally, studies have found limb dominance to affect
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A.M. Brown et al. / Gait & Posture 39 (2014) 915–919
lower extremity kinematics during a jump landing [2]. These findings suggest that limb dominance may affect lower extremity kinematics during tasks requiring increased shock absorption and force attenuation, such as during running gait. To our knowledge, only one study has examined for the effects of limb dominance on running biomechanics [4]. This study examined kinetics of the dominant and non-dominant lower limbs during overground running. Although no side-to-side differences were found, this study was limited by its small sample size (N = 5). To-date, no study has thoroughly examined limb laterality and lower extremity kinematics or kinetics during overground running. The effects of fatigue are another important consideration for any running-related research. Kinetic, kinematic and electromyographic changes have been well-documented following fatigue in both healthy and injured runners [5–9]. Fatigue alters running biomechanics in as little as 15 min of running [6], impacting both the casual and long distance runner. If the dominant and nondominant limbs do, in fact, behave differently during overground running, it is possible that they may fatigue at different rates. Thus, fatigue may magnify the kinematic and kinetic differences between limbs. To date, no studies have examined the potential interaction between limb dominance and fatigue during running gait with respect to lower extremity kinematics and kinetics. Therefore, the purpose of this study was to examine the effect of limb laterality and fatigue (before and after a run to exertion) on lower extremity kinematics and kinetics during overground running. Specifically, this study focused on hip, knee, and ankle variables that have been previously associated with running injury [8,10]. Based on previously documented biomechanical gender differences during running [11], inclusion in this study was limited to female runners. Two hypotheses were tested in this study: first (H1), due to differences in the role that lower extremities are thought to play during gait, it was hypothesized that lower extremity kinematics and kinetics will differ between the dominant and non-dominant limbs during fresh-state overground running. Second (H2), it was hypothesized that lower extremity kinematics and kinetics of the dominant and non-dominant limb will be affected differently by fatigue. 2. Methods 2.1. Participants Twenty healthy female runners (age 29 6 years, height 1.6 0.1 m, mass 56.8 5.2 kg) participated in this study. All runners were rear foot strikers who were running a minimum of 15 miles per week for at least one year and were able to run at least one 9 min mile. Participants were excluded if they had any history of lower extremity surgery or if they reported any injury to the trunk, pelvis or lower extremities in the six months prior to data collection. Written informed consent was obtained for this study protocol, which was approved by the Hospital for Special Surgery Institutional Review Board.
Fig. 1. Six degree of freedom marker set utilized to track the segment coordinate systems for kinematic and kinetic data calculation.
C7, right and left ASIS, and sacrum. The pelvis was defined and tracked by the right and left ASIS and sacrum. The thighs were defined and tracked by a virtual (functionally-determined) hip joint center, medial, and lateral femoral condyles, and an array cluster on the thigh. The shanks were defined and tracked by medial and lateral femoral condyles, the tibial tuberosity, medial and lateral malleoli, and an array cluster on the shank. The hind feet were defined and tracked by the distal toe, proximal, and distal calcaneus (aligned based on the bisection of the posterior calcaneus), and the medial and lateral calcaneus (Fig. 2). All joint angles were referenced relative to the proximal segment with the trunk and pelvis referenced relative to the lab. All testing was
2.2. Instrumentation A 12-camera Motion Analysis Corporation system (Motion Analysis Corporation; Santa Rosa, CA) and four forceplates (Bertec Corp.; Columbus, OH, and AMTI; Watertown, MA) were used for the collection of kinematic and kinetic data. Passive, retroreflective markers were placed in specified locations on the trunk, pelvis, and lower extremities for the tracking of segment coordinate systems during dynamic running trials (Fig. 1). These markers were used to define and track a custom six-degree-of-freedom model that included a trunk, pelvis, thighs, shanks, and hindfeet. The trunk was defined and tracked by markers placed on the sternal notch,
Fig. 2. Rearfoot marker placement directly on the skin of the calcaneus via customized holes cut from the heel cup.
A.M. Brown et al. / Gait & Posture 39 (2014) 915–919
conducted with participants wearing a neutral, laboratoryprovided running shoe (New Balance, 1062; Boston, MA, USA) with customized holes cut from the heel cup to allow marker placement directly on the calcaneus. Video and forceplate data were sampled at 120 and 4800 Hz, respectively.
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data were collected during over ground running in the same manner as during fresh-state data collection. The elapsed time from runners’ completion of the treadmill run until the post-exertion data collection was 7 0.002 min. 2.4. Data processing
2.3. Experimental protocol 2.3.1. Establishing limb dominance The dominant lower extremity was established based on participants’ verbal report of which limb they would use to kick a soccer ball. This self-report method of determining lower extremity dominance was found to have 97.7% agreement with task performance and a 96% test-retest agreement [12]. 2.3.2. Three-dimensional motion analysis Data collection began with the capture of static and dynamic trials to calibrate the anatomic coordinate systems. Next, markers used solely for anatomical definitions were removed so that only tracking markers remained during running trials. To ensure repeatability among conditions, all tracking markers remained on the participants for the duration of testing. To begin running data collection, participants were positioned at the start of a 25 m runway that extended a minimum of 8 m beyond either end of the data capture volume. They were instructed to run along the runway and through the data capture volume for the collection of five acceptable overground running trials. An acceptable trial was defined as one where the runner struck at least one forceplate while running at a speed of 3.35 m/s (10%). To ensure consistency across trials, velocity was recorded with two photoelectric timers placed 4 m apart. When the runner broke the first photoelectric beam, a timer began. The timer then turned off when the runner broke the second photoelectric beam. 2.3.3. Fatiguing run After the collection of five acceptable overground trials on each limb, participants performed a fatiguing treadmill run (24.6 7.0 min). This run was performed at a self-selected pace expected to induce fatigue within 40 min. Participants were encouraged to select a running speed that reflected their five kilometer race pace. Prior to beginning the run, participants were educated on the Borg Rating of Perceived Exertion (RPE) Scale [13]. Throughout the run, RPE data were collected every three minutes. The run was terminated once the runner rated their exertion a 17/20 (‘‘very hard’’). Upon completion of the treadmill run, fatigued-state
Data from five trials on each limb were processed and analyzed using a customized code written in Visual 3-D (C-Motion, Inc.; Rockville, MD) and LabView (National Instruments; Austin, TX). Marker trajectories and forceplate signals were smoothed using bidirectional second-order low-pass Butterworth filters achieving fourth-order attenuation with 08 phase lag at cutoff frequencies of 8 and 50 Hz, respectively. Joint angles were calculated using an X, Y, Z (Flexion, Abduction, Rotation) Euler decomposition sequence. Inverse dynamics calculations [14] using ground reaction force data, kinematic data, and segment inertial properties [15,16] were utilized to calculate internal joint moments. Internal joint moments were normalized to participant height and mass. Kinematic variables of interest included: peak hip flexion, adduction and internal rotation, peak knee flexion, abduction, and internal rotation and peak ankle dorsiflexion and eversion angles. Kinetic variables included: peak hip extensor and abductor, peak knee extensor and adductor, and peak ankle plantarflexor and invertor internally referenced moments. Peak kinematic and kinetic values were extracted from the stance phase of each individual trial (Fig. 3). Within each limb, variables were then averaged across each of the five trials for both fresh and fatigued-state conditions. 2.5. Statistical analyses A paired t-test was utilized to test for differences between the dominant and non-dominant limb during fresh-state overground running. A 2-way repeated measures ANOVA was used to examine the effect of fatigue on dominant and non-dominant limb kinematics and kinetics. Significance was set at P 0.05, and a trend was operationally-defined as P 0.10. 3. Results 3.1. Joint kinematics Means and standard deviations for hip, knee, and ankle joint kinematic variables of the dominant and non-dominant limbs in both the fresh and fatigued state are included in Table 1. During
Knee flexion/extension angle (Degrees)
120 100 80 60 40 20 0 0
10
20
30
40
50
60
70
80
90
100
Percent of Gait Cycle Fig. 3. Knee flexion values across the gait cycle. Shaded region indicates stance phase of gait cycle from peak variables of interest were extracted. Dotted circle identifies peak knee flexion value extracted from the loading response phase of stance.
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Table 1 Means and standard deviations for hip, knee, and ankle joint kinematic variables of the dominant and non-dominant limbs in both the fresh and fatigued state. Fatigued-state
Fresh-state Kinematic variables (8)
Dominant
Non-dominant
Dominant
Non-dominant
Hip flexion Hip adduction Hip internal rotation Knee flexion Knee abduction Knee internal rotation Ankle dorsiflexion Ankle eversion
39. 6 (5.4) 16.8 (3.1) 5.6 (8.4) 39.6 (5.4) 4.7 (3.1) 12.6 (11.2) 22.5 (7.1) 7.0 (7.5)
39.1 (3.7) 15.6 (2.3) 3.2 (7.2) 39.1 (3.7) 4.8 (3.4) 10.2 (8.2) 22.9 (5.1) 6.4 (5.2)
39.2 (4.8) 17.3 (3.2) 6.3 (8.3) 39.2 (4.8) 5.0 (3.4) 11.7 (11.7) 24.3 (8.0) 8.8 (6.7)
39.3 (7.0) 15.5 (2.6) 3.4 (7.1) 38.8 (3.3) 4.8 (3.6) 9.6 (7.6) 23.9 (5.0) 7.0 (5.9)
fresh-state running there were no differences in hip, knee or ankle kinematic variables between the dominant and non-dominant limbs (reject H1). In addition, there were no interactions between fatigue status and limb dominance for any of the kinematic variables measured (reject H2). 3.2. Joint kinetics Means and standard deviations for hip, knee, and ankle joint kinetic variables of the dominant and non-dominant limbs in both the fresh and fatigued state are included in Table 2. During fresh, non-fatigued running, there were no differences in hip, knee or ankle kinetics between the dominant and non-dominant limbs (reject H1). Similarly, there were no interactions detected between fatigue status and limb dominance with respect to joint kinetics (reject H2). 4. Discussion Existing literature has suggested there are differences in kinematic behavior of the dominant and non-dominant lower limbs during eccentric loading tasks such as performing a droplanding [1]. Despite these findings, no studies have thoroughly examined for such differences in joint kinematics or kinetics during running. While running and drop-landing are unique tasks, they are similar in the sense that they are both high impact activities requiring increased muscle activity for shock attenuation. Therefore, this study sought to determine whether similar lower extremity differences would be exhibited during the stance phase of running gait. Additionally, this study examined the effects of fatigue on lower extremity kinematics and kinetics with respect to limb dominance during overground running. The results of this study indicate that, in a fresh state, the dominant and nondominant limbs of runners do not exhibit differences in lower extremity kinematics or kinetics. While these findings differ from those of Brown et al [1], they are supported by previous studies examining overground walking gait. Gundersen et al [17] examined the effect of lower limb dominance on lower extremity kinematic asymmetry during walking. The authors found that, while some lower extremity asymmetries did exist, they could not be correlated with lower limb laterality. These findings have
immediate implications for the treatment of running injuries. Based on the current results, which show no differences between the dominant and non-dominant limbs, therapists and physicians can continue to strive for regaining lower extremity symmetry as part of their therapeutic outcomes. Additionally, these findings have clear methodological implications when conducting research on running gait. While this study does not dispute the fact that the lower limbs function in an asymmetrical manner during overground running, it supports the notion that, without biasing their findings, researchers can choose either the dominant or non-dominant limb for comparison to injured populations when examining running mechanics. The second aim of this study was to examine the effect that a fatiguing run may have on dominant and non-dominant lower limb running mechanics. It was hypothesized that the dominant and non-dominant limb would fatigue at different rates, thereby affecting lower extremity kinematics and kinetics. This hypothesis was not supported by the results of this study, which found no interaction between fatigue and limb dominance when examining joint kinematics or kinetics. In general, a run to fatigue has been shown to result in both kinematic and kinetic alterations [6,7]. However, the results of this study suggest that, in the presence of fatigue-related kinematic or kinetic alterations, differences are not shown to be magnified specifically at the dominant or nondominant limb. The results of this study do not suggest that side-to-side differences, or asymmetries are non-existent in runners. In fact, multiple studies have shown that there are asymmetries, which occur during running gait [18,19]. Rather, this study suggests that side-to-side differences are not likely due to lower limb dominance. This still leaves investigators with unanswered questions regarding the causes of these asymmetries. Further research should be conducted to determine whether these differences are innate, the result of training or possibly induced by marker placement asymmetry. Additionally, it is still unclear as to whether the presence of asymmetries can predispose runners to injury. This study was not without limitations. The lack of significant differences between the kinematic and kinetic behaviors of the dominant and non-dominant limbs and the absence of an effect of fatigue may in part be due to the selection of variables utilized for comparison. Kinematic and kinetic outcome measures were
Table 2 Means and standard deviations for hip, knee, and ankle joint moments of the dominant and non-dominant limbs in both the fresh and fatigued state. Fresh-state
Fatigued-state
Kinetic variables
Dominant
Non-dominant
Dominant
Non-dominant
Hip extensor (N-m/Kg) Hip abductor (N-m/Kg) Knee extensor (N-m/Kg) Knee adductor (N-m/Kg) Ankle plantarflexor (N-m/Kg) Ankle invertor (N-m/Kg)
1.7 (0.4) 2.2 (0.4) 2.6 (0.5) 0.2 (0.1) 2.8 (0.3) 0.3 (0.2)
1.7 (0.4) 2.2 (0.3) 2.6 (0.3) 0.2 (0.1) 2.7 (0.3) 0.2 (0.2)
1.6 (0.4) 2.1 (0.4) 2.5 (0.5) 0.2 (0.1) 2.8 (0.3) 0.3 (0.2)
1.7 (0.4) 2.1 (0.3) 2.6 (0.4) 0.2 (0.1) 2.8 (0.3) 0.2 (0.2)
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selected based upon previously documented fatigue-related changes within these variables. It is possible, however, that for the determination of side-to-side differences with respect to limb dominance, the use of joint kinematic and kinetic measures may not be a sensitive enough measure. A different, and more global quality of movement measure may be a more appropriate tool with which to elicit effects of limb dominance. Future studies will be focused on validating the use of quality of movement measure, such as the Gait Deviation Index [20] for use with runners. An additional limitation of this work was that it included only female participants. Due to the known biomechanical gender differences [11] these results may not be generalizable to a population of male runners. Future studies should examine similar hypotheses in male runners. 5. Conclusion Overall, during fresh-state (non-fatigued) running, limb dominance had essentially no effect on lower extremity joint kinematics or kinetics. This suggests that kinematic or kinetic side-to-side differences seen during overground running are not affected by lower limb dominance. Therefore, when treating injured runners, physical therapists can continue to focus on regaining lower extremity dynamic symmetry as a long-term goal of therapy. In addition, these results support the continued selection of either the dominant or non-dominant limb or an arbitrary selection of right or left limbs for comparison to an injured population. There was no interaction between fatigue status and limb dominance indicating that the dominant and non-dominant limbs fatigue at a similar rate. Therefore, side-to-side differences seen during fatigued-state running are not likely related to limb dominance. Acknowledgements The authors would like to acknowledge New Balance for the use of their shoes during data collection. This study was approved by Hospital for Special Surgery Institutional Review Board.
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