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Lower extremity joint stiffness characteristics during running with different footfall patterns a

a

b

Joseph Hamill , Allison H. Gruber & Timothy R. Derrick a

Department of Kinesiology, University of Massachusetts, Amherst, MA, USA

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Department of Health & Human Performance, Iowa State University, Ames, IA, USA Published online: 15 Oct 2012.

To cite this article: Joseph Hamill, Allison H. Gruber & Timothy R. Derrick (2014) Lower extremity joint stiffness characteristics during running with different footfall patterns, European Journal of Sport Science, 14:2, 130-136, DOI: 10.1080/17461391.2012.728249 To link to this article: http://dx.doi.org/10.1080/17461391.2012.728249

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European Journal of Sport Science, 2014 Vol. 14, No. 2, 130136, http://dx.doi.org/10.1080/17461391.2012.728249

ORIGINAL ARTICLE

Lower extremity joint stiffness characteristics during running with different footfall patterns

JOSEPH HAMILL1, ALLISON H. GRUBER1, & TIMOTHY R. DERRICK2 Department of Kinesiology, University of Massachusetts, Amherst, MA, USA, 2Department of Health & Human Performance, Iowa State University, Ames, IA, USA

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Abstract The purpose of this study was to examine the knee and ankle joint stiffness and negative joint work during running when participants utilised their preferred and non-preferred footfall pattern. A total of 40 healthy, young runners (20 habitual forefoot (FF) and 20 habitual rearfoot (RF) runners) served as participants in this study. Three-dimensional data were obtained using a motion capture system and a force platform. The participants completed over-ground trials in each of two conditions: 1. their natural footfall pattern; and 2. their non-preferred footfall pattern. Joint stiffness was calculated by the ratio of the change in joint moment and the change in joint angle during the energy absorption phase of support. Negative joint work was calculated as the integral of the joint power-time curve during the same time interval. It was observed that joint stiffness was different between the footfall patterns but similar for both groups within a footfall pattern. A stiffer knee and a more compliant ankle were found in the FF pattern and the opposite in the RF pattern. Negative work was greater in the ankle and less in the knee in the FF pattern and the reverse in the RF pattern. We conclude that runners, in the short term, can alter their footfall pattern. However, there is a re-organisation of the control strategy of the joint when changing from a FF to a RF pattern. This re-organisation suggests that there is a possible difference in the types of injuries that may be sustained between the FF and the RF footfall patterns.

Keywords: Joint stiffness, negative work, footfall patterns, running

Introduction The forefoot (FF) running footfall pattern has recently gained popularity for its claimed benefits of performance enhancement and prevention of running injuries (Lieberman et al., 2010; Romanov & Fletcher, 2007). However, limited evidence exists regarding the mechanical changes that occur with the initial adoption of the FF pattern. A study by Williams, McClay, & Manal (2000) reported select kinematic and kinetic parameters of the ankle and knee joints comparing habitual rearfoot (RF) runners converting to the FF pattern with habitual FF runners. These authors reported that only peak ankle plantar flexion moment and peak vertical ground reaction forces (GRFs) were different from a habitual FF pattern and a converted, or novice, FF pattern. The results from this study suggested habitual RF runners can successfully adopt the FF

pattern with little training. Comparing the differences between RF and FF running may be a necessary step to determining the efficacy of adopting a FF pattern for both a performance enhancement and an injury prevention perspective. Although adopting a FF pattern has been recommended by track coaches to improve performance, scientific investigations thus far have failed to find many significant differences between the footfall patterns. A FF pattern has been suggested to reduce the rate of running related overuse injuries due to the absence of the initial vertical GRF peak (Lieberman et al., 2010; Pratt, 1989). However, there is conflicting evidence in the literature such that some studies have found a relationship between vertical GRF variables and injury (Hreljac, Marshall, & Hume, 2000; Milner, Ferber, Pollard, Hamill, & Davis, 2006; Pohl, Mullineaux, Milner, Hamill &

Correspondence: J. Hamill, Department of Kinesiology, University of Massachusetts, 30 Eastman Lane, Amherst, MA 01003, USA. E-mail: [email protected] # 2012 European College of Sport Science

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Lower extremity joint stiffness characteristics Davis, 2008) while other studies have not (Bredeweg, 2011; Scott & Winter, 1990). It is clear that a foot-ground impact occurs regardless of the footfall pattern employed and that runners must attenuate the foot-ground impact (Hamill, Derrick, & Holt, 1995). It is also clear that the lower extremity plays a particularly important role in shock absorption (Derrick, Hamill, & Caldwell, 1998). Given the primary role that stiffness plays in the neuromuscular control of movement (Winter, Patla, Prince, Ishac, & Gielo-Perczak, 1998) as well as injury and performance (Butler, Crowell III, & Davis, 2003) it is important to note how footfall pattern may affect stiffness. One measure that has been used to estimate lower extremity stiffness is vertical stiffness. It can be derived from a massspring model that represents the sum of the constituent stiffness of the lower extremity (McMahon & Cheng, 1990; McMahon, Valiant, & Frederick, 1987). Vertical stiffness is computed from the maximum vertical GRF which is dependent on locomotor speed. Because vertical stiffness is calculated from a measure representing the whole body centre of mass, it cannot distinguish the particular joints that contribute to the total stiffness of the limb. Therefore, it has been suggested that stiffness in individual joints on the lower extremity may be a more relevant measure for this purpose (Hamill, Moses, & Seay, 2009). Joint stiffness is an alternative measure that can be used to indirectly measure factors related to lower extremity injuries because it can be related mechanically to the attenuation of loads transmitted through the body. Joint stiffness is based on the joint moment-joint angle relationship and is modelled as a torsion spring. Joint stiffness may be calculated directly from the moment-angle relationship by: Joint Moment ¼ kDh where the joint moment is a surrogate for the load, k is the torsion spring stiffness and the displacement is the change in the joint angle (Du). This spring, with units of N m/degree, can undergo an angular deflection when acted on by the joint moment. For a given displacement, a stiffer spring will transmit a greater load than a more compliant spring. The stiffness of a torsion spring is not a true mechanical stiffness but is merely a representation of the torsion stiffness and is often referred to as ‘quasi-stiffness’ (Latash & Zatsiorsky, 1993). Determining quasistiffness of a joint thus can give an indication of the resistance to the deformation of a joint or the compliance of the joint. Joint stiffness has been implicated in overuse injuries and degenerative diseases (Hamill et al., 2009). For example, a more compliant joint will

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attenuate the load placed on the joint to a greater extent than a stiffer joint. The actions of the ankle and knee joints are sources of the compliance of the lower extremity during running. However, in heel toe running, the knee has been identified as a primary locus for shock attenuation while the ankle joint is used much less in this capacity (Derrick et al., 1998). McMahon et al. (1987) illustrated that increased energy absorption occurs when running with a more flexed knee (that is, a greater range of knee motion resulting in a more compliant knee). Since the actions of the ankle and knee joints are a major source of the compliance of the lower extremity during running, it may be that running with different footfall patterns may cause the ankle and knee joints to contribute differentially to the lower extremity stiffness because of the differences in joint motion and joint moments generated with each pattern. Stefanyshyn and Nigg (1998) modelled the ankle as a torsion spring and reported differences in ankle stiffness during distance (RF footfall pattern) and sprint (FF footfall pattern) running. They reported different joint stiffness values for each footfall pattern, the runners ran at distinctly different running speeds. In another study, Laughton, McClay, and Hamill (2003) also calculated joint stiffness in natural RF runners when running with both RF and FF patterns in order to determine shock absorption when running in orthotics. They suggested that there was a change in the stiffness values between the ankle and the knee when changing footfall patterns while in orthotic and nonorthotic conditions. It is clear that runners can alter their footfall patterns with the ankle and knee joint altering their roles in term of joint stiffness. However, it is not clear that habitual RF and habitual FF runners alter their stiffness patterns to the same degree when changing to a non-habitual pattern. This change in footfall pattern suggests that there may also be a change in energy absorption when footfall patterns change. Therefore, the purpose of this study was to examine the knee and ankle joint stiffness and negative work during running when participants utilised their preferred and non-preferred footfall pattern. It was hypothesised that there would be alterations in the joint stiffness and resulting negative work as a result of changing footfall patterns and that the contributions of the ankle and knee joints would be different in the different footfall patterns. That is, the ankle joint would be more compliant in an FF pattern compared to the RF pattern while the knee would be stiffer in the FF pattern compared to the RF pattern. It was also hypothesised that joint stiffness patterns between the ankle and knee joint will be the same for a given footfall pattern regardless of runner’s footfall preference.

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Methods

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Participants Twenty habitual RF runners (13 males and 7 females) and 20 habitual FF runners (14 males and 6 females) participated in this study. They  ¼ 25:9, s  ranged in age from 18 to 41 years (X  ¼ 1:8, s 6.1 years), in height from 1.6 to 1.9 m (X 0.09 m) and in body mass from 49.1 to 90.9 kg  ¼ 70:1, s10.1 kg). Participants were excluded (X if they had a history of neurological or cardiovascular problems and if they had experienced an injury to their lower extremity or back within the last year. All participants had been running for at least 15 years and ran at least 16 km/week at a running speed of 3.5 m/s for long duration runs. Participants were classified into the FFhabitual and RFhabitual groups by performing five over ground running trials in the laboratory at their preferred speed. Prior to the data collection session, participants were classified as RFhabitual runners if initial ground contact was made with the foot in dorsiflexion and generated an initial impact peak in the vertical GRF. Participants were classified as FFhabitual runners if initial ground contact was made with the foot in plantar flexion and there was an absence of the initial vertical impact peak. Prior to participation in the study, ethical approval for this study was obtained from the University Institutional Review Board and all participants signed an informed consent form approved by the University Institutional Review Board in accordance with University policy. Experimental set-up Three dimensional lower extremity motion was recorded with an eight camera Oqus system (Qualisys, Inc., Gothenberg, Sweden) surrounding the centre of a 25 m runway. A large (1.2 m0.6 m) force platform flush with the ground was located in the centre of the collection volume. Sampling for the kinematic and kinetic data was accomplished at 240 and 1200 Hz, respectively. Photoelectric sensors (Lafayette Instrument Company, Lafayette, IN, USA), placed 6 m apart, were used to monitor running speed. Protocol Each participant wore form fitting clothing and running shoes provided by the laboratory. Retroreflective markers were placed on the right lower extremity and pelvis of the participant according to a previously published convention (McClay & Manal, 1999). A standing calibration was recorded to determine the local coordinate systems, the location of joint centres and the foot, leg, thigh and pelvis

segment lengths of each participant. Participants were then instructed to run across the force platform without targeting the force platform or adjusting stride characteristics. Each participant performed 10 successful running trials at 3.5 m/s95% with their habitual running pattern and 10 successful trials with their non-habitual footfall pattern with the order of presentation of the conditions randomised for each participant. A successful trial was one in which the participant contacted the force platform with their right foot at the requisite running speed without adjusting their stride. The participants were instructed on how to run in their non-habitual pattern. For the non-habitual RF pattern, the participants were instructed to make initial contact on the heel, while for the non-habitual FF pattern, the participants were instructed to make ground contact on the ball of the foot while preventing the heel from touching the ground. Adequate time was given for each participant to practice their nonpreferred pattern. Data analysis Qualisys Track Manager Software (Qualisys, Inc., Gothenberg, Sweden) was used to track the marker positions and export the data into.C3D format. Raw kinematic and kinetic data were further processed using Visual 3D software (C-Motion, Inc, Rockville, MD, USA). A fourth order, zero-lag Butterworth digital low-pass filter was used to process raw kinematic and kinetic data with a cut-off frequency of 12 and 50 Hz, respectively. Three-dimensional lower extremity joint angles were calculated with respect to the proximal segment using an Xyz Cardan rotation sequence, a sequence representing flexion/extension, abduction/adduction and axial rotation (Cole, Nigg, Ronsky, & Yeadon, 1993). Lower extremity internal joint moments and powers were calculated using NewtonEuler inverse dynamics approach. The stance phase of each condition determined based on a 10 N threshold. Subsequently, the moments and angles were interpolated from initial contact to toeoff to 101 data points with each point representing 1% of the stance phase. Stiffness for the ankle and knee joints was determined for the energy absorption phase (Hamill et al., 2009). The energy absorption phase was defined as the point at which the ankle angle reached maximum dorsiflexion in midstance of the support period. The angular distance from initial touchdown to the maximum dorsiflexion angle in midstance was calculated as was the magnitude of the moment at the same points. A linear fit of the slope of the torque-angle profile produced the magnitude of the ankle stiffness (Figure 1a). A similar protocol was

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Lower extremity joint stiffness characteristics

Figure 1. Schematic of the calculation of ankle (a) and knee (b) joint stiffness. Slope of the dashed line represents the joint stiffness during the energy absorption phase of the support period. FC  initial foot contact; MS  midstance; TO  toeoff; kankle  ankle stiffness; kknee  knee stiffness.

conducted to calculate knee joint stiffness (Figure 1b). Negative mechanical work in the joints during the shock absorption phase of the running stride was evaluated by integrating the negative joint powertime profile over the energy absorption phase.

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RFhabitual and FFhabitual groups revealed no statistically significant difference between the habitual and non-habitual footfall patterns for both groups. There was no significant interaction between FFhabitual and RFhabitual groups running with a FF or RF pattern for ankle joint stiffness (p0.83) and for negative ankle work(p0.30) (see Figure 2). There was also no statistically significant difference between the FFhabitual and RFhabitual groups for ankle stiffness and negative ankle work in either the FF or RF pattern (p 0.38 and p 0.64, respectively). Therefore, the main effects of footfall pattern were collapsed across the FFhabitual and RFhabitual groups. There was a statistically significant difference between the main effect of RF and FF conditions for ankle joint stiffness when the groups means were collapsed (p 0.01; ES 2.3) with the FF pattern revealing a more compliant ankle than the RF  ¼ 7:1 N m=degree, pattern (X s1.9 versus  ¼ 12:1 N m=degree, s2.9, respectively). When X the group means were collapsed for negative work, there was a significant main effect across conditions (pB0.01; ES 2.3). Negative work done at the ankle for the FF pattern was greater than that for  ¼ 57:8 N m, s12.1 versus the RF pattern (X  ¼ 31:9 N m, s8.9, respectively). X For knee joint stiffness and for negative knee work, neither there were no significant interactions

Statistical analysis Initially, Levene’s test of the homogeneity of variance was used to determine if the variability between the habitual and non-habitual was different. The individual trial data for all parameters were averaged for each participant for statistical analysis. The 10 trial mean joint stiffness and negative mechanical work values for each participant in each condition were analysed statistically using a two-way analysis of variance with a Group factor (FFhabitual and RFhabitual) and a repeated footfall factor (FF and RF). A criterion alpha level (a 0.05) was set a priori. In order to further evaluate mean differences, effect size (ES) was calculated to express such differences relative to the pooled standard deviation. Cohen (1988) proposed that ES values of 0.2, 0.5 and 0.8 represents small differences, moderate differences and large differences, respectively. Results The test for homogeneity of variance to discern if there was any difference in the variance between the habitual and non-habitual footfall patterns of both the

Figure 2. Mean (SD) across all participants of each group performing each footfall pattern of ankle stiffness (a) and ankle negative work (b) during the energy absorption phase of the support period.

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Figure 3. Mean (SD) across all participants of each group performing each footfall pattern of knee stiffness (a) and knee negative work (b) during the energy absorption phase of the support period.

(p0.55 and p 0.96, respectively) nor was there a difference between groups for either the FF or RF patterns (p 0.69 and p0.92, respectively; see Figure 3). Therefore, the means of knee joint stiffness were collapsed across the FFhabitual and RFhabitual groups. For knee stiffness, a statistically significant difference between the RF and FF conditions was observed (p0.01; ES 2.4) with the FF pattern resulting in a stiffer knee joint than the  ¼ 11:5 N m=degree, s2.4 versus RF pattern (X  X ¼ 6:5 N m=degree, s 1.6, respectively). When the group means were collapsed, there was a significant main effect across conditions (p 0.01; ES 1.5). Negative work done at the knee for the FF pattern was less than that for the RF pattern  ¼ 20:5 N m, s7.5 versus X  ¼ 36:3 N m, s  (X 12.9, respectively). Figure 4 illustrates the relationship between ankle and knee joint stiffness in both the preferred and non-preferred footfall patterns for both the RFhabitual and FFhabitual groups. Discussion The purpose of this study was to examine the knee and ankle joint stiffness and negative work during human running when participants utilised their preferred and non-preferred footfall pattern. We

Figure 4. Mean ankle and knee joint stiffness for both the rearfoot (RF) and forefoot (FF) footfall patterns for: (a) the habitual FF group; and (b) the habitual RF group during the energy absorption phase of the support period.

hypothesised that there would be alterations in the joint stiffness and negative work as a result of changing footfall patterns and that the contributions of the ankle and knee joints would be different between each footfall pattern. The results of this study support the first hypothesis. We found that there was a re-organisation of the ankle and knee joint stiffness such that when using the FF footfall pattern, the ankle was more compliant than the knee while, when using the RF footfall pattern, the opposite was observed. Large ES were observed between RF and FF patterns for ankle and knee stiffness in each habitual group (ES 2.3) and (ES 2.4), respectively. The large effect sizes (ES 0.8) suggest that clinical differences may exist between all the stiffness values for each footfall pattern (Cohen, 1988). These results are in accordance with Laughton et al. (2003) who noted similar changes in ankle and knee joint stiffness when evaluating orthotics in combination with footfall patterns. Additionally, we hypothesised that habitual FF and RF runners could adapt to their nonpreferred footfall pattern. Based on the non-significant interactions, we also accepted the second hypothesis that, when participants changed from the preferred to their non-preferred footfall pattern, there would be no difference in the re-organisation of the joint stiffness between groups performing the same footfall pattern. The ES for the interactions

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Lower extremity joint stiffness characteristics were small (ES B0.2) indicating that the differences in ankle and knee joint stiffness between groups within each footfall pattern were not meaningful (Cohen, 1988). The kinematic and kinetic values associated with each footfall pattern fell within the ranges reported in the literature (DeWit, DeClerq, & Aerts, 2000; McClay & Manal, 1995; Williams et al., 2000). Ankle and knee joint stiffness was also similar to the values obtained from the literature for the FF and RF footfall patterns (Hamill et al., 2009; Laughton et al., 2003). It should be noted that in all of the previous studies that altered footfall patterns, only runners with a habitual RF pattern were used. In the present study, we used both habitual RF and habitual FF runners to determine if kinetic changes would be comparable between preferred and nonpreferred footfall patterns. It has been suggested that, based on the massspring model, the ankle joint is the dominant joint during locomotion (Farley & Gonzalez, 1996). During the energy absorption period, this appears to be true only when using a FF footfall pattern. The ankle joint is certainly more compliant, thus absorbing a greater amount of energy, during FF running than during RF running. On the other hand, knee joint stiffness exhibited the reverse pattern. That is, it is stiffer in the FF pattern, absorbing less energy, and more compliant, absorbing a greater amount of energy, during the RF pattern (see Figure 4). A joint that is compliant will absorb the energy of the foot-ground impact to a greater extent than a stiff joint. Energy absorption can be accomplished by either the ankle or the knee joint or both. Christiansen, Bayly, and Silva (2008) found a greater increase in transmissibility of imposed accelerations to the rat tibia upon removal of the knee joint compared to removal of the foot; therefore, the knee may have a more profound effect on the ability to absorb energy than the ankle. In addition, in human studies, Derrick et al. (1998) reported that the knee joint is the locus of the greatest impact energy absorption during running with the ankle significantly less effective. However, in that study, all of the participants were habitual RF runners. In the current study, it was verified that greater ankle stiffness and a more compliant knee joint resulted in greater impact energy absorption occurring at the knee rather than the ankle during the RF pattern for both the habitual RF and the habitual FF runners (in their non-habitual pattern). However, when running with a FF pattern, a more compliant ankle and stiffer knee results in greater absorption at the ankle. To maintain the posture of the lower extremity in FF running, a relatively stiff knee is necessary. Again, this transition of altered joint stiffness in FF running was verified in both groups when utilising a FF

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footfall pattern. When altering footfall patterns, we observed that there was a necessary re-organisation of the control strategy to accommodate the lower extremity joint energy absorption. The re-organisation of the functions of the ankle and knee joints in FF and RF running suggest that, while both are running tasks, they may represent different skills. In order to control the deformation of the joint or the change in the joint moment, we speculate that different musculature would be required. That is, the pattern of muscle activation and the degree of force development may be different between footfall patterns. This re-organisation could possibly result in injury when changing from one’s habitual pattern to their non-preferred pattern during the period when the participant is first trying to change their footfall pattern (Butler et al., 2003). In the short term, injury may result from the possible change in muscle activation pattern until the muscles adapt to the new stress. However, it is unknown as to whether these changes are beneficial or injurious in the long term. The results of this study also indicate that runners, regardless of their habitual footfall pattern, can change to the opposite footfall pattern over a relatively short term. With relatively little practice, both types of habitual footfall pattern runners could accommodate to their non-preferred pattern. However, this study does not lend support as to whether this change could be accomplished permanently or if this change would result in positive or negative adaptations. In a forward dynamics modelling study, Miller, Russell, Gruber, and Hamill (2009) suggested that the choice of a footfall pattern is task specific. That is, if a runner wants to run economically or to run fast or to sprint, then they will choose the most appropriate footfall pattern for each task. Therefore the ability to successfully alter footfall pattern is already an established motor programme inherent to an individual. It may be the initiation of the opposite footfall pattern at preferred, nonsprinting speeds that is the novel task. Conclusions There was no difference in joint stiffness or negative joint work between habitual FF runners and habitual RF runners when performing the FF pattern. The same was true for the RF pattern when performed habitually or non-preferred. While runner’s appear to be able to change their footfall pattern rather easily in the short term, altering one’s footfall pattern from either FF to RF or RF to FF requires a significant change in the actions of the ankle and knee in terms of joint stiffness. A FF pattern requires a compliant ankle and a stiffer knee while the RF pattern requires a stiff ankle and a more compliant

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knee. The alteration of the ankle and knee stiffness patterns was matched with associated changes in negative joint work. We conclude that the control strategy for running changes depends on the footfall pattern and this has implications for injury potential and performance. Future research should be concerned with the effects of these alterations in ankle and knee mechanics when the change from one footfall pattern to the other is made over a long period of time.

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Lower extremity joint stiffness characteristics during running with different footfall patterns.

The purpose of this study was to examine the knee and ankle joint stiffness and negative joint work during running when participants utilised their pr...
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