During running and hopping, the body can be modeled as a spring-mass system when rebounding from the ground (Cavagna et al., 1988). Energy is stored and released from the lower extremities when they deform (Butler et al., 2003). To utilize this energy, some stiffness of the extremity is required (Butler et al., 2003). Stiffness contributes to the rebound of energy during the stretch-shortening cycle (SSC) (Butler et al., 2003). Although Watsford et al. (2010) have associated increased stiffness with injury in athletics, recent evidence suggests a relationship between increased lower extremity stiffness and enhanced athletic performance, specifically in distance running (Barnes et al., 2013, 2015; Spurrs et al., 2003). Barnes et al. (2015) reported that warming up with a weighted vest resulted in an increased running velocity as well as improved running economy. They attributed these improvements to an increase in leg stiffness. Spurrs et al. (2003) also reported an increase in leg stiffness and improved 3-km running times when male distance runners completed a 6-week plyometric training program. Similar changes in leg stiffness were reported when runners followed a 6-week hill interval program (Barnes et al., 2013). During a nine week plyometric intervention, Saunders et al. (2006) attributed improvements in running economy of highly trained runners to increased muscle-tendon stiffness. Arampatzis et al. (2001) evaluated leg stiffness during drop jumps and found that increased stiffness was associated with increased rebound velocity.
Many of the previously mentioned studies suggest that increased stiffness is associated with improved running economy (Barnes et al., 2013, 2015; Saunders et al., 2006; Spurrs et al., 2003). Running economy is a factor closely related with running performance (Conley and Krahenbuhl, 1980; Conley et al., 1984). Conley and Krahenbuhl (1980) concluded that in a nationally prominent 10 km race, 65.4% of the variation in performance of highly trained athletes with similar maximal aerobic power (VO2max) could be explained by running economy. Since running economy is positively impacted by leg stiffness and running performance is influenced by running economy, leg stiffness is a valuable metric to assess in distance runners.
Lower extremity stiffness can be biomechanically assessed through vertical jump tests, which are relatively quick and non-strenuous. McGuigan et al. (2006) described the eccentric utilization ratio (EUR), which is computed from performance of two types of jumps, a squat jump (SJ) and a countermovement jump (CMJ). Jump height and peak power are determined and used to compute the EUR. Another method to assess stiffness is the five-jump test (Saunders et al., 2006). With this assessment, the participant completes five straight leg jumps, while instructed to jump as high as possible and with as little ground contact as possible. Farley et al. (1991) used a similar procedure in order to estimate effective vertical stiffness (k) of the lower extremity muscle-tendon system. Due to the spring-mass model (Cavagna et al., 1988), k can be calculated from the vertical ground reaction force (vGRF) exerted on a force plate during ground contact time.
Although several studies have investigated changes in leg stiffness in runners in response to training or warm up procedures (Arampatzis et al., 2001; Barnes et al., 2013, 2015; Saunders et al., 2006; Spurrs et al., 2003), few studies, if any, have measured changes in leg stiffness due to competition (i.e., race). For example, Hunter and Smith (2007) measured vertical leg stiffness towards the beginning and the end of a one-hour high-intensity run. They found a non-significant decrease in leg stiffness towards the end of the race compared to the beginning. Similar to Hunter and Smith (2007), the relationship between fatigue and leg stiffness has been studied, but only within the same laboratory session (Morin et al., 2006). Current literature lacks information on the influence of fatigue on leg stiffness across multiple days especially in response to competition. Therefore, the current investigation seeks to answer the following research question: how will lower extremity stiffness change during the two days following an exhaustive competition? It was hypothesized that lower extremity stiffness would decrease significantly the day after the exhaustive competition.
Twenty-two active Division I collegiate cross-country runners (13 males, 9 females, 19.5 ± 1.4 yr) (Table 1) were recruited, granted informed consent and volunteered for this study. Participants completed a short Health History Questionnaire prior to participation and were free of any history of major medical issues including metabolic and cardiovascular diseases, vestibular disorders, or musculoskeletal problems. All participants were members of the university’s cross-country team and were medically cleared to participate in the study. In addition, any individuals who had surgery or an injury that prevented participation in physical activity within the last six months of the study were excluded. All study methods were submitted to and approved by the university’s institutional review board.
Sex, age, body mass, height and weekly running volume for the 22 participants. Data expressed as mean ± standard deviation.
|Sex||13 M, 9 F|
|Age (yr)||19.5 ± 1.4|
|Mass (kg)||61.2 ± 6.3|
|Body Height (m)||1.7 ± 0.1|
|Weekly Running Volume (km·wk-1)||84.5 ± 51.2|
Design and Procedures
To determine the changes in leg stiffness of runners in response to a cross-country race, participants were tested over the course of three sessions spread over four days (Figure 1). Three jumping protocols were conducted to access leg stiffness: 1) the SJ, 2) the CMJ, and 3) the vertical hopping test (HT). The first day of testing was conducted 24 hours prior to competition. On day two, each participant competed in a cross country race (5 km for women, 8 km for men). 24 and 48 hours following competition participants reported for the same testing procedure as on day one.
Each participant was familiarized with the testing procedure one week prior to testing. Each participant completed a 5-min self-paced warm-up on a stationary bike (Monark 828E; Vansbro, Sweden). Participants then performed three SJs and three CMJs (30 s of rest intervals between jumps) on two embedded force plates (AMTI Optima OPT464508; Watertown, MA) sampled at 1000 Hz. The fourth jump was recorded if two jump heights in the set were outside of a threshold of 2.5 cm (Carroll et al., 2018). During each jump, participants positioned a plastic dowel across their shoulders to prevent arm swing which has been reported to influence jump performance (Figure 2) (Harman et al., 1990). Participants performed the SJ by flexing the knees to 900and holding for three seconds prior to maximally jumping on a command. In the CMJ, rather than holding the position for three seconds, participants jumped immediately following the knee flexion. Participants then performed the HT in which they completed 10 s of in-place, bilateral straight leg maximal hopping. Participants were instructed to hop as high as possible without pausing in between and hopped in sync with a digital metronome (Metronome, ONYX Apps) set to 2.2 Hz. Footwear was controlled across all testing days.
Vertical ground reaction force (vGRF) data were exported and processed via a custom script written in GNU Octave Version 4.3 (Eaton et al., 2016). vGRF data from both force plates were filtered using a 4th order Butterworth lowpass filter (cutoff frequency = 50 Hz) and summed. Subsequently, the following variables were calculated and averaged from the three trials of SJ and CMJ vGRF data: peak concentric force (N), rate of force development (N/s), impulse (Ns), takeoff velocity (m/s), peak power (N/s), peak landing force (N), and jump height (cm). Peak power was then normalized using body mass. The eccentric utilization ratio (EUR) was computed from the ratio between CMJ peak power and SJ peak power (McGuigan et al., 2006). Leg stiffness (k) was calculated from vGRF during ground contact time of the 5th-7th hop from the HT as previously described by Farley et al. (1991). A threshold of 2 N was used to define the takeoff and landing. The following equations were used to find the resonant period of vibration (ω) and subsequently k using the period (T) of the 5th-7th hop and mass (M)
Differences in SJ height, CMJ height, EUR, and leg stiffness were assessed for significance using a repeated measures ANOVA over the course of three testing days. Gender was included as a covariate within the ANOVA. Post-hoc analysis was used to test the mean differences for significance between days. The level of significance was set a priori at ≤ 0.05. Minimal detectable change (MDC) was calculated for k with a 95% level of confidence using methods previously described (Gardinier et al., 2013; Price et al., 2017).
Descriptive statistics were computed for all dependent variables (Table 2). Results of the repeated measures ANOVA revealed significant main effects for leg stiffness, but not for SJ height, CMJ height, nor the EUR.
Squat jump (SJ) height, countermovement jump (CMJ) height, eccentric utilization ratio (EUR), and leg stiffness (k) were computed over three testing days (pre-race, 24 h post-race and 48 h post-race). *Significant difference between pre-race (24 h) and post-race (24 h). #Significant difference between post-race (24 h) and post-race (48 h).
|Pre-Race (24 h)||Post-Race (24 h)||Post-Race (48 h)|
|SJ Height (cm)||26.89 ± 5.54||25.99 ± 5.65||27.19 ± 5.63|
|CMJ Height (cm)||30.81 ± 5.79||29.89 ± 6.06||31.44 ± 6.47|
|EUR||1.23 ± 0.1||1.20 ± 0.09||1.22 ± 0.1|
|k (kN · m-1)||36.84 ± 4.96*||33.11 ± 8.05*, #||36.30 ± 6.88#|
The Mauchly’s test for sphericity indicated that the assumption of sphericity for the SJ was met, X2(2) = 3.52, p > 0.05, but there was no significant main effect on SJ height over the three days F(2, 42) = 1.82, p > 0.05.
According to the Mauchly’s test, the assumption of sphericity for CMJ height was met, X2(2) = 3.03, p > 0.05. There was no significant main effect on CMJ height, F(2, 42) = 3.48, p > 0.05.
The Mauchly’s test indicated that the assumption of sphericity for the EUR was met, X2(2) = 2.93, p > 0.05, although no significant main effect on the EUR during the three days was found, F(2, 42) = 1.64, p > 0.05.
The Mauchly’s test confirmed that the assumption of sphericity was met for k, X2(2) = 2.85, p > 0.05. Moreover, there was a significant main effect on k over the three days F(2, 42) = 3.901, p = 0.028 (Figure 3). k decreased significantly from 36.84 ± 4.96 kN·m-1 to 33.11 ± 8.05 kN·m-1 from 24 h pre-race to 24 h post-race (p = 0.025) and then increased significantly from 24 h post-race to 48 h post-race (36.30 ± 6.88 kN·m-1), (p = 0.017). No significant difference was found from 24 h pre-race to 48 h post-race, p > 0.05. MDC was calculated for test-retest reliability of k (9.06 kN·m- 1). Hopping frequency was calculated and tested to verify whether there were deviations throughout the three days. No significant difference in hopping frequency was found during the post-hoc analysis, F(2, 42) = 2.07, p > 0.05.
This study aimed to observe and record how leg stiffness changed in response to an exhaustive competition. The findings of this study support the hypothesis that lower extremity stiffness significantly decreased in the 24 hours after a cross country race, but returned to baseline within 48 hours post-race. Specifically, k decreased significantly after the race and then increased significantly two days following the race. Although leg stiffness was significantly reduced as a result of racing, no other jumping measures (SJ height, CMJ height, and EUR) demonstrated any significant changes from baseline.
Previous published reports have equivocal findings with regard to the acute changes in leg stiffness as a result of athletic performance (Avela et al., 1999; Horita et al., 1999; Hunter and Smith, 2007; Nicol et al., 2007). For example, Hunter and Smith (2007) reported decreased vertical leg stiffness following a one-hour high-intensity run. Nicol et al. (2007) found decreased SSC performance in response to a marathon. SSC performance was assessed using a sprint test, a CMJ, a drop jump, and a forward five hop test. These two studies are in agreement with our results, however, stiffness was only assessed at the end or immediately following the exhaustive bout. Although these researchers did not assess SSC performance in the days following the run, there is evidence that recovery is bimodal with a secondary decline in performance two to three days after exercise (Avela et al., 1999; Horita et al., 1999; Nicol et al., 2007). Faulkner et al. (1993) suggested that the two declines in SSC performance were due to two separate injuries. The first of which was mainly mechanical and the secondary injury was more of an inflammatory response. Given that participants in this study were only assessed twice following the race and that the race was substantially shorter than both a marathon and an hour long run, it is hard to ascertain whether the measured leg stiffness decline was the result of a mechanical or inflammatory etiology.
The alterations in leg stiffness pre- and post-race found in this study are in agreement with the previously mentioned reports, but Oliver et al. (2015) reported results that seemingly contradict our findings. They assessed leg stiffness pre- and post-match in a group of youth rugby players over the course of an entire competitive season. They reported that leg stiffness significantly decreased over the course of a season, however, no significant decline was reported when comparing leg stiffness pre- and post-match. While they did not find significance in leg stiffness acutely, their findings support the premise that fatigue has an impact on leg stiffness. It is also possible that acute changes in leg stiffness due to fatigue are more sensitive to a running event than rugby. Possibly supporting this explanation is the fact that they reported acute changes in CMJ performance. Oliver et al. (2015) suggest that the CMJ is a movement specific test for rugby as it mirrors some of the mechanisms involved in the sport. Since running is commonly represented as a simple spring mass model (Farley et al., 1991), it is possible that the HT used in this study was a more running-specific test. This may also explain why significant differences were only found for leg stiffness and not for the SJ, CMJ, nor the EUR.
There could be several mechanisms which explain why leg stiffness was significantly reduced immediately following a cross country race. A maximal cross country race may elicit decreased neuromuscular performance which influences muscle reflexes as well as muscle and joint stiffness (Komi, 2000). One suggestion for these findings is decreased pre-activation of the lower extremity muscles. Pre-activation, or feedforward muscle activity, plays a primary role in determining leg stiffness (Lehnert et al., 2016; Oliver and Smith, 2010). Nicol et al. (2007) found that following a marathon run, knee flexion immediately following foot contact increased significantly. This may be due to reduced pre-activation of the fatigued muscle resulting in a less rapid rebound. Reduced pre-activation has also been found following a 10-km run (Paavolainen et al., 1999) as well as following a marathon (Avela and Komi, 1998). This may suggest lower pre-activation in the runners of this study during the HT following competition. A less rapid rebound due to this may have led to the decreased k value found in this study. As we did not measure surface electromyography during data collection, there is no possibility to confirm this potential explanation.
Clinically, there may be several important ramifications of the current investigation. Since the HT protocol used to find k requires very little time and is non-strenuous in nature, it can be easily implemented within a competitive season with no ill-effects on runners. While it is not practical for coaches to use force plates to assess leg stiffness in runners, Lloyd et al. (2009) reported that leg stiffness calculated from a contact mat was comparable to a gold standard. Therefore, it may be possible for coaches to monitor leg stiffness frequently during a training cycle. Since increased leg stiffness has been associated with improved running economy and performance (Arampatzis et al., 2001; Barnes et al., 2013, 2015; Saunders et al., 2006; Spurrs et al., 2003), coaches may want to use leg stiffness to appropriately schedule workouts and rest days. While the aggregate k significantly decreased and increased in the two days following the race, there were five participants whose k values did not follow this trend (Figure 4). It is possible that these runners did not provide a maximal effort in the race or that these runners had a faster recovery cycle than the other runners. Alternatively, runners may have required more than one practice session for the HT. Oliver et al. (2014) found that following exercise simulating soccer, leg stiffness increased in half of the participants and decreased in the other half. Pruyn et al. (2013) also suggested that changes in stiffness values throughout a rugby season were found at an individual level. Coaches should be aware that multiple data points will be necessary to understand how runners’ leg stiffness responds to training. Multiple data points will also help establish a true baseline prior to making coaching decisions based on leg stiffness values.
There were a few study limitations that should be noted. The male cross country competition was 8 km, while the female competition was 5 km. Therefore, it is possible that there was a discrepancy between leg stiffness between the males and females due to different race distances and a potentially different level of fatigue. In addition, we assumed that all participants provided a maximal effort in the race, but this was not assessed. The first two data collection days (24 h pre-race, 24 h post-race) were tightly controlled with regard to the run that was completed in the preceding 24 hours. However, following the second data collection, participants completed a weekly long run where the run distance and run surface varied between participants. This may have increased the variability of results on the third day of data collection (48 h post-race), but this had no effect on the preceding data collections. Despite these limitations, since these results were collected within a competitive collegiate cross country season, it increased the ecological validity and potential application of these results.
In a group of collegiate distance runners, leg stiffness significantly decreased in the 24 hours following a maximal effort cross country race. Despite the lack of sensitivity in the SJ, CMJ, and the EUR, significant changes in k in this study suggest that leg stiffness may be a valuable assessment metric in monitoring and coaching distance runners. The findings of this study suggest that future research should focus on how leg stiffness fluctuates throughout a microcylce in response to different types of workouts and recovery efforts.
The author kindly thanks J. Vernarelli for assistance with data and statistical analysis.
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