The aim of this study was to investigate differences of the spatiotemporal parameters in a 100-m sprint among elite, sub-elite, and non-elite sprinters with a unilateral transtibial amputation. Using publicly available Internet broadcasts, we analyzed 125, 19, and 33 records from 30 elite, 12 sub-elite, and 22 non-elite sprinters, respectively. For each sprinter’s run, the average velocity, step frequency, and step length were calculated using the number of steps in conjunction with the official race time. Average velocity was greatest in elite sprinters (8.71±0.32 m/s), followed by the sub-elite (8.09±0.06 m/s) and non-elite groups (7.72±0.27 m/s). Although there was a significant difference in average step frequency between the three groups, the effect size was small and the relative difference among the three groups was 3.1%. Statistical analysis also revealed that the average step length was longest in elite sprinters, followed by the sub-elite and non-elite groups. These results suggest that the differences in sprint performance between the three groups is mainly due to the average step length rather than step frequency.
Citation: Hobara H, Hashizume S, Kobayashi Y, Mochmaru M (2016) Spatiotemporal Parameters of 100-m Sprint in Different Levels of Sprinters with Unilateral Transtibial Amputation. PLoS ONE 11(10): e0163712. https://doi.org/10.1371/journal.pone.0163712
Editor: Gaël Guilhem, INSEP, FRANCE
Received: February 8, 2016; Accepted: September 13, 2016; Published: October 4, 2016
Copyright: © 2016 Hobara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by JSPS KAKENHI Grant Number 26702027. https://www.jsps.go.jp/english/index.html.
Competing interests: The authors have declared that no competing interests exist.
Running-specific prostheses (RSPs) with energy storing capabilities have attracted more and more individuals with lower extremity amputations to running as a form of exercise and athletic competition. More recently, RSPs have allowed amputee runners to compete at athletic levels achieved never before [1, 2]. Theoretically, the average velocity during a 100-m sprint is the product of the average step frequency and average step length. Although both parameters are inversely correlated, an increase in one factor will result in an improvement in sprint velocity, as long as the other factor does not undergo a proportionately similar or larger decrease. Because spatiotemporal parameters are modifiable by sprint training sessions , identifying factors affecting these parameters of 100-m sprints in unilateral transtibial amputees will provide coaches and practitioners with a basis for better evaluation of sprint performance and aid in the development of more effective training methods for amputee sprinters.
Several studies have compared biomechanical characteristics between able-bodied sprinters and amputee runners using RSPs during maximal and submaximal running [4–7]. The results of these studies are useful to aid in developing ideal coaching and training regimes by identifying the underlying differences between the two groups. On the other hand, despite the fact that examining sprint performance among different levels of sprinters is useful for training-conditioning programs and the design of effective talent development, less research attention has been given to examining differences in biomechanical characteristics in amputee sprinters using RSPs.
In a previous study, Gajer et al.  compared sprint performance between faster and slower groups in able-bodied athletes. The authors found that the faster group had a longer stride length during the entire 100-m race than the slower group. Further, Hunter et al.  and Weyand et al.  also suggested that step length has strong association with the sprint velocity in able-bodied athletes. However, it is unknown if this longer step length of elite group can be observed in amputee sprinters using RSPs.
The aim of this study was to investigate the differences in the spatiotemporal parameters of a 100-m sprint among elite, sub-elite, and non-elite sprinters with a unilateral transtibial amputation. We hypothesized that the differences in sprint performance between the three groups would mainly be due to the average step frequency rather than step length.
Since our data set was obtained from publicly-available Internet broadcasts, we did not obtain informed consent. Institutional review board (Environment and Safety Headquarters, Safety Management Division, AIST) approval was obtained prior to the study. We analyzed 177 races of 64 sprinters with lower extremity amputations from publicly available Internet broadcasts. Based on the classification system created by the International Paralympic Committee (IPC; http://www.paralympic.org/), we included the Men’s T44 class (defined as any athlete with lower limb impairment/s that meets the minimum disability criteria for lower limb deficiency, impaired lower limb passive range of motion, impaired lower limb muscle power, or leg length difference). These races included several Paralympics, the IPC Athletics World Championships, and other national- and international-level competitions from 2004 to 2015 (Table 1). Individual races were excluded from the analysis if the athlete did not complete the race or the athlete’s body was not visible throughout the entire race. T44 sprinters who did not use RSPs were also excluded. Analyzing publicly available data from sport competitions for research purposes were performed by Salo et al.  for sprint running using 52 able-bodied sprinters, and by Hobara et al.  for prosthetic sprinting using 36 able-bodied and 42 amputee sprinters (25 unilateral and 17 bilateral transtibial amputees).
EL, SEL and NEL indicates elite-, sub-elite and non-elite group, respectively.
In the present study, we separated the whole population into three groups based on qualification standards. The elite group (EL) consisted of sprinters who satisfied the A-Qualification Standards of the Men’s T44 class (12.20 s) in the analyzed races (London 2012 Paralympic Games Qualification Guide-Athletics, 2011). The sub-elite group (SEL) consisted of sprinters who could not reach the A-Qualification Standards, but satisfied B-Qualification Standards (12.50 s). The non-elite group (NEL) consisted of sprinters who could not reach the B-Qualification Standards. Consequently, the EL, SEL, and NEL groups consisted of 125 (30 sprinters), 19 (12 sprinters), and 33 (22 sprinters) data, respectively.
As stated in previous studies [12–14], we determined the average velocity (V100) of each individual by dividing the official race distance (100 m) by the official race times (trace), which were obtained from each competition’s official website; thus, (1) In the present study, we calculated average step frequency (fstep) as (2) where Nstep is the number of steps, which was manually counted by the authors. Because V100 is the product of fstep and average step length (Lstep), we calculated the Lstep by (3) If we could not count the number of steps, we excluded the data from our analyses. The last step before the finish line was considered to be the last step. If an athlete’s foot was located on the finish line, we considered it as a step .
Before the statistical analyses, Levene’s test was performed to ensure that the assumptions of normality and homogeneity of variance were met. Since the assumptions were violated in our data, the Kruskal-Wallis test was used to compare V100, fstep, and Lstep among the EL, SEL, and NEL groups. We also calculated the effect size (ES) for the Kruskal-Wallis test using Cramer’s V. From this effect size calculation, the results were interpreted as small (0.1 to 0.3), medium (0.3 to 0.5), or large (higher than 0.5). If a significant main effect was observed, the Mann-Whitney U test with a Bonferroni correction as post hoc multiple comparison was repeated for all combinations in each variables. Because there were three Mann-Whitney U tests in each variable, the alpha levels were set at 0.016 (0.05/3) and 0.003 (0.01/3). Statistical significance was set at P < 0.05. These statistical analyses were executed using SPSS version 19 (IBM SPSS Statistics Version 19, SPSS Inc., Chicago, IL).
Fig 1 shows the fstep–Lstep plot for all the individuals in the three groups. Dotted lines indicate the times predicted using the combination of fstep and Lstep. As shown in Fig 2, V100 exhibited a significant main effect on the groups (X2(2) = 112.66, P < 0.01, ES = 0.57). V100 was greatest in EL, followed by SEL and NEL (P < 0.003 for all comparisons). Statistical analyses revealed that fstep had a significant effect on the groups (Fig 3; X2(2) = 13.184, P < 0.01), and fstep in EL was significantly higher than SEL (P < 0.016) but not NEL. However, the relative difference between EL and SEL was 3.1%, and the ES of the Kruskal-Wallis test was 0.19 (small). Lstep also displayed a significant effect on the groups (Fig 4; X2(2) = 72.58, P < 0.01, ES = 0.45). Statistical analyses also revealed that Lstep was longest in EL, followed by SEL and NEL (P < 0.003 for all comparisons).
Unfilled circles, gray triangles, and filled squares indicate the data for elite (EL), sub-elite (SEL) and non-elite (NEL) groups, respectively. Dotted lines denote the official race times calculated using the combination of fstep and Lstep.
Asterisks (**) indicate significant differences with EL at P < 0.01. Daggers (††) indicate significant differences with SEL at P < 0.01.
An asterisk (*) indicates a significant difference with EL at P < 0.05.
The aim of this study was to investigate the differences in the spatiotemporal parameters of a 100-m sprint among elite, sub-elite, and non-elite sprinters with a unilateral transtibial amputation. In the present study, V100 was greatest in EL sprinters, followed by SEL and NEL (Fig 2). Although a statistically significant difference in fstep between the three groups was identified (Fig 3), the ES for this effect was small (0.19). On the other hand, Lstep was the longest in EL, followed by SEL and NEL (ES = 0.45, medium; Fig 4). Therefore, the results of the present study support our initial hypothesis that the differences in sprint performance between the three groups would mainly be due to the Lstep rather than the fstep.
In a previous study, Hunter et al.  introduced a deterministic model for sprint running, especially for both the fstep and Lstep. Based on the deterministic model, determinants of fstep and Lstep could be partly explained by the relative horizontal and vertical ground reaction force impulse, segment positions, segment inertial parameters, and air resistance. For the ground reaction forces, Rabita et al.  found that elite able-bodied sprinters in their study had a 9.7% greater force production capacity than sub-elite able-bodied sprinters. Furthermore, Fortier et al.  and Slawinski et al.  reported that elite able-bodied sprinters showed better force production capacity during the sprint start and subsequent steps than sub-elite able-bodied sprinters. Therefore, the differences in Lstep among elite, sub-elite, and non-elite sprinters with a unilateral transtibial amputation in our study may be attributed to force production capacity during sprinting.
There are certain considerations that must be acknowledged when interpreting the results of the current study. First, we assumed the athletes completed a step exactly at the 100-m mark [12–14]. However, a previous study  subtracted a distance of 0.55 m and a time of 0.52 s from the calculations of averaged step frequency and step length based on their pilot test. Thus, the reliability and accuracy of the current data should be interpreted carefully. Second, we calculated the average step length using the number of steps taken and the official race time as data. However, Nagahara et al.  demonstrated that not all the steps in a 100-m sprint have the same length and frequency, indicating that the average step frequency and step length in the present study may not necessarily be representative of any particular part of the sprint. Thus, continuous changes in spatiotemporal parameters during 100-m sprint in the three groups should be investigated. Third, we only investigated athletes with unilateral transtibial amputations who participated in official competitions, such as the Paralympic Games, IPC Athletics World Championship, IPC European Championship, and other national and international competitions. Thus, caution should be exercised in interpretation and generalization of these findings to other classes, such as transfemoral amputees and bilateral transtibial amputees. Finally, although most of T44 sprinters from 2004 to 2015 generally used Flex-Foot Cheetah® (Össur), Cheetah® Xtreme™ (Össur), or 1E90 Sprinter (Ottobock) RSPs, we did not determine each individual’s RSPs, which might indirectly influence the spatiotemporal parameters during sprinting [20–21]. Consequently, the differences in RSPs used within each of the groups may affect the spatiotemporal parameters of the 100-m sprint. Further research is required on whether and how sprint performance changes among different levels of sprinters with unilateral transtibial amputations.
In summary, we investigated differences in the spatiotemporal parameters of a 100-m sprint among elite, sub-elite, and non-elite sprinters with a unilateral transtibial amputation. The results of the present study suggest that the differences in sprint performance between the three groups is mainly due to the Lstep rather than the fstep.
- Conceptualization: HH.
- Funding acquisition: HH.
- Investigation: HH.
- Methodology: HH.
- Project administration: HH.
- Supervision: YK MM.
- Validation: HH SH.
- Visualization: HH YK.
- Writing – original draft: HH.
- Writing – review & editing: HH SH YK MM.
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