This study was part of a larger sprint running research project conducted during the XII European Veterans Athletics Championships held in Jyväskylä, Finland, in July 2000. During the championships, the highest ranked (according to the 100-m performance of the previous year) male (40–88 yr) and female (35–87 yr) sprinters were invited by personal letter to participate in the study. Besides these athletes, other sprinters who qualified for the semifinals in 100-m sprint events were contacted after the preliminary heats and informed about the study. Fifty-six males and 44 females representing 12 countries participated and gave their written consent for the study. Of the athletes, 37 males and 33 females (the fastest 1–4 finalists in each 5-yr age category) were selected for the final analyses. Approval for this study was granted by the Ethics Committee of the University of Jyväskylä. The physical characteristics of the selected athletes are shown in Table 1.
Training and competition history.
The subjects of the study completed a detailed questionnaire (translated into eight languages) about current and former training, competition performance, and injuries. In addition, the athletes participated in a brief interview lasting about 30 min during which specific information on training methods and competition background was obtained. On this occasion, the measurements of body weight and height, and reaction speed were carried out. This information could not be obtained from 10 males and 7 females (including all 35-yr-old females). On the basis of the questionnaire and personal interview, most of the subjects had in their youth competed in sprint running events and maintained regular year-round training. There were slight age-related differences in the type and volume of the physical training. However, the race times showed that the subjects were rather similar with regard to the relative level of competitive performance and represented the world-class level in each age class. The training and competition history of the athletes are given in Table 1.
Collection of video data.
The research project was carried out in close cooperation with the local organizing committee. One year before the championships, the research plan was presented to the competition organizers and arrangements for videotaping were made. For the use of the present study, three camera locations for videotaping procedures were agreed upon. All data were collected in a manner so as not to interfere with the competition.
During the championships 51 100-m sprint races, including 32 heats and 19 finals, were recorded by a four-person crew using two Peak Performance (HSC 200, Peak Performance Technologies, Inc.) high-speed cameras (200 Hz) with the panning video technique. The use of the panning video technique, in which the camera rotates on a single axis, enables one to cover large portions of the race by keeping runner’s image size sufficiently large for accurate analysis (4,5). The cameras were mounted on specialized tripods with pan and tilt decoders and genlocked to each other to synchronize the video frames. Before and after measurements calibration was done by calibration rods (height 3.7 m) placed in both ends of the track. The cameras were equipped with Tamron (f: 1:2.5/20–80 mm) and Rank Taylor Hobson Monital (f: 1:2.1/20–100 mm) zoom lenses. For the recordings, S-VHS tapes (Basf SE 60 min) were utilized.
During the 100-m events, the cameras were located parallel to the track at a distance of 93 m away from the first lane (behind back straight) and at the point of 32 m and 72 m from a starting line. The cameras were placed on videotaping racks 5 m above the ground level and the optical axes of the cameras focused downward toward the track at an angle of approximately 4°. The first camera (at the 32-m point) covered the first 60 m of the race (including the smoke from the starter’s pistol) and the second camera the latter half of the race (40–110 m). The angular motion of both of the cameras involved about 30° of rotation to capture the respective portion of the race. When the optical axes of the cameras intersected the plane of motion at right angles, the field of view for the lane one was approximately 7 m. To obtain horizontal velocity and stride parameters in different phases of the run, the distance markers were placed at 10-m intervals along both sides of the track. The position of markers was determined by placing the calibration rods in the middle of lanes of the track, and with the aid of cameras projecting the line from the calibration rods to both sides of the tracks indicating the 10-m points for analysis. The foreground markers (white plastic plates of 12 × 15 cm with black tape marks on) were placed on the curbs of track, and the background markers (black tapes of 5 cm wide) were taped to the track fences at the height of chest and head.
Reduction and analysis of video data.
For the purpose of the present study, the performances of the four fastest runners in the finals in each 5-yr age category of both genders were to be analyzed. In some of the races, however, there was such a large heterogeneity in performance that the videotaped view required for accurate time analysis was not wide enough to capture more than three runners. The small number of female competitors over 70 yr (one or two in each age class) further reduced the analysis to a total of 70 running performances.
The video records were analyzed by two researchers with the aid of a Panasonic VCR (AG-7355) and a Hewlett Packard (Pentium II) computer with a 17-inch monitor (ViewSonic GA 771). The computer was operated with a Motus 32 workstation (Peak Performance Technologies, Inc.). This software enabled one to sequentially encode every frame of the recorded tape with a number and thus provided an actual frame rate of 200 Hz. The progressive running velocity was determined from the time required to run between consecutive 10-m sequences. The sequence times were measured by capturing the video frames when the runner crossed the 10-m distance markers and by dividing the number of frames elapsed between adjacent 10-m lines by the frame rate. Stride length (from one foot contact to other foot contact) was determined by dividing the sequence distance by the number of strides within each sequence. Stride rate (Hz) was calculated by dividing number of strides within each sequence by the sequence time. Ground contact times during peak velocity phase (the fastest 10-m sequence) and deceleration phase (90 m) of the run were measured by determining the number of frames elapsed from foot contact to toe-off in the same foot, and the flight times (during peak velocity and deceleration phases) were measured from the frames elapsed from one foot toe-off to opposite foot ground contact. Contact and flight time results were averaged over four consecutive strides.
Identification of the direct performance measures from video data was clear. In all of the sprint races, there were slight differences between the performance level of the fastest runners, and thus there were few view obstructions due to the fellow competitors that could prevent precise definition of the instant of foot contact, toe-off, and crossing the distance markers. Also, the officials and stands in the field area were situated so that they did not obstruct the camera views. The beginning of the race could be detected from the smoke of the starting pistol, and the exact moment of crossing the finish line could be verified with the aid of the official competition results. With the frame rate of 200 Hz, the margin of error in temporal measurements due to accuracy of the analyzing system was 0.005 s. Possible sources of error related to spatial information were lens distortions and photographic perspective error due to the distance of the camera to the track. The effect of lens distortion on stride parameters is expected to be small and primarily random in nature because the results were averaged over several consecutive strides in each 10-m sequence. The perspective errors were minimized by positioning the cameras sufficiently far away (93 m) from the runners and are also considered to be small for the purpose of the present study. In this study, a potential source of variation is air resistance. Despite that during the competition the wind readings of all the 100-m sprint races were within the legal wind speed limit of +2.0 m·s−1, there were small differences in wind readings between the races (range from −1.8 m·s−1 to +0.9 m·s−1 in females and from −1.2 m·s−1 to +1.0 m·s−1 in males) and is thus expected to have some effect on the results. Otherwise, there were small variations in weather conditions (no rain; temperature 16–18°C; humidity 70–80%) during afternoon finals (5:00–8:30 p.m.).
ANOVA was used to determine differences in the dependent variables (velocity, SL, SR, CT, and FT) among age groups. In the case of significant F-value from ANOVA, the Tukey post hoc analysis was used to identify the significance of differences between each pair of age groups. To add the power of ANOVA and reduce the number of group comparisons, two adjacent 5-yr categories were combined (Tables 2 and 3). Accordingly, the power of detecting a significant (P < 0.05) age effect reached a level of 0.72 (stride rate) up to 1.00 (velocity). Both linear and polynomial regression analyses were performed to determine the rate of change in performance variables with age. Pearson’s correlation coefficient was used to examine the relationships between variables. Where appropriate, partial correlation was used to control the effect of age on these relationships. Statistical significance was set at the 0.05 level. All the analyses were performed using SPSS 9.0.1 for Windows (SPSS, Inc.).
Generally, males showed higher running velocity, higher SR, larger SL, and shorter CT than females. No clear gender difference was found in FT values. In the following, the main results will be examined separately for males and females.
The 100-m race times ranged from 11.14 ± 0.19 s (40–44 yr) to 17.80 ± 0.57 s (85–89 yr) in males and from 12.78 ± 0.20 s (35–39 yr) to 22.08 s (87 yr) in females (Fig. 1). Expressed as an average race velocity, the performance ranged from 8.98 ± 0.15 m·s−1 to 5.62 ± 0.18 m·s−1 in males and from 7.83 ± 0.12 m·s−1 to 4.53 m·s−1 in females. The average rate of decline in race velocity over the 50-yr age period was 5.8% and 6.9% per decade for males and females, respectively. However, the deterioration of performance was exponential rather than linear as shown by a second-degree polynomial curve fitting (Fig. 1).
The velocity curves of 100-m run are shown in Fig. 2, A and B, and the age-related differences in selected velocity values in Tables 2 and 3. The relative rate of age-associated decline in velocity over the first 10 m was 4.9% per decade for both genders. When controlled for age, average velocity during initial acceleration (0–10 m) correlated with race time both in males (r = −0.73; P < 0.001) and females (r = −0.78; P < 0.001).
In males, the distance required to reach the peak velocity (the fastest 10-m sequence) in the 40- to 49-yr-old runners (45 m) differed significantly (P < 0.05) from that in the 80–89 yr (25 m). However, the time to peak velocity (range 4.40–6.08 s) showed no significant differences between age groups. In 50–59 yr females, the distance to peak velocity (35 m) was significantly different from that (20 m) in 70–89 yr. No significant differences were observed between the age groups in time required to reach the peak velocity (range 4.15–5.58 s).
The runners’ peak velocity showed clear age group differences in males and females (Tables 2 and 3). The age-related declines in peak velocity for male and female runners were 5.9% and 6.0% per decade, respectively. When controlled for age, the peak velocity showed an inverse correlation to race time both in males (r = −0.84; P < 0.001) and females (r = −0.90; P < 0.001).
The relative decrease of velocity from peak velocity sequence to the final phase of the run (range ∼5–10% for males, and ∼6–18% for females) correlated significantly with age both in male (r = −0.51; P < 0.01) and female runners (r = −0.76; P < 0.001).
SR curves over the 100 m in Figures 2 C and D indicate that runners in all age groups and in both genders reached the maximum or near maximum SR between 10 and 20 m, whereupon SR decreased toward the end of the run. During acceleration, SR of the oldest male runners (80–89 yr) differed significantly from that of all other male groups (P < 0.05), and during the peak velocity phase from the 40- to 49- and 50- to 59-yr-old groups (P < 0.01) (Table 2). In females, there were no significant differences in SR values between adjacent age groups (Table 3). In males, average SR, initial acceleration SR, and SR during peak velocity sequence declined by 2.2%, 3.0%, and 1.9% per decade, respectively, whereas in females, the age-related declines in the average SR, acceleration SR, and peak velocity SR were 2.1%, 2.1%, and 1.6% per decade, respectively.
When controlled for age, SR during initial acceleration (0–10 m) correlated significantly with the velocity of this phase both in males (r = 0.49; P < 0.01) and in females (r = 0.60; P < 0.001). However, the average SR and SR during peak velocity and deceleration phases showed no significant correlation with velocity in either males or females. The decline in SR from the peak velocity sequence to the end of the run was associated with the reduction in velocity from the peak velocity sequence to the end of the run in males (r = 0.37; P < 0.05) and in females (r = 0.48; P < 0.01).
SL curves in Figures 2 E and F, and the results in Tables 2 and 3 show age-related differences in SL during all phases of the 100-m run in both genders. During the acceleration, male and female runners in younger age groups were able to take longer strides and to increase their SL up to around 50 m, whereas in older runners, the maximum SL was achieved earlier. From the peak velocity phase to the deceleration phase of the run, SL remained unchanged in both males and in females. In males, average SL, initial acceleration SL, and SL of the peak velocity sequence declined by 4.7%, 2.9%, and 5.0% per decade, respectively, whereas in females, the average SL, acceleration SL, and SL of peak velocity sequence decreased by 5.1%, 3.6%, and 5.2% per decade, respectively. When expressed as relative values (SL/height), the decline in SL during peak velocity with advancing age was 4.1% per decade for males and 4.9% per decade for females.
When controlled for age, the average SL over 100 m, and SL during peak velocity and deceleration phases of the run correlated with velocity of those phases in both genders (r > 0.43; P < 0.01 in all cases). However, SL during initial acceleration over the first 10 m showed no significant correlation with the 10-m velocity in either males or females.
Ground contact time.
The individual CT values during peak velocity are illustrated in Figure 3, A and B, and the age group differences in CT are shown in Tables 2 and 3. CT increased progressively as running velocity decreased with age. Also, the relative time spent in contact phase (% stride) increased linearly with age and velocity from an average 44% of stride time at 10.4 m·s−1 (40–44 yr) to 61% at 6.4 m·s−1 (85–88 yr) in males, and from an average 46% at 8.9 m·s−1 (35–39 yr) to 71% at 5.3 m·s−1 (80–87 yr) in females.
Significant correlations were found in both male and female runners between the velocities during peak velocity and final phases and CT during these phases (partial r < −0.61; P < 0.001 in all cases). However, there were no significant age-adjusted correlations between decrease in velocity from peak velocity sequence to final phase and the increase in CT from peak velocity sequence to final phase in either males or females.
The individual values of FT during peak velocity are shown in Figures 3, A and B, and the age group differences in Tables 2 and 3. There were significant differences in FT values between the oldest and the other age groups.
When controlled for age, FT during peak velocity and deceleration phases was significantly related to velocity of these phases (r = 0.48; P < 0.01 in both cases) in females. In males, FT during the peak velocity phase was associated with the velocity of this phase (r = 0.37; P < 0.05), whereas no such correlation was observed during the deceleration phase. Furthermore, FT of the peak velocity and deceleration phases showed a significant correlation to SL of those phases in males (partial r > 0.58; P < 0.001 in both cases) and in females (partial r > 0.68; P < 0.001 in both cases).
There are a number of studies of the relationship between progressive velocity and stride pattern in the 100-m run in younger sprinters (1,3,4,8,10,25,26). However, to the authors’ knowledge, the present study is the first to provide information of velocity and stride characteristics during each performance phase of the 100-m run in master sprinters. The major findings were as follows: 1) The sprinting velocity of elite male and female master athletes declined exponentially with age the differences becoming more evident after ∼65–70 yr of age. 2) Age-associated differences in velocity were rather similar in each phase of the run in both genders. 3) The deterioration of the overall 100-m performance with age was primarily related to decrease in SL and increase in CT with advancing age in both male and female sprinters.
Analysis of the acceleration phase of the run revealed that the time required to reach the peak velocity sequence remained unchanged, whereas the distance to peak velocity reduced significantly with age. Recent competition analysis (using laser devices) on young elite sprinters (28) indicated that the time required to reach peak velocity (males ∼6.0–6.5 s, females ∼5.0–6.5 s) is close to that found in the present study, but the distance to peak velocity (males 58–62 m, females 45–59 m) was, due to higher acceleration velocity, greater than that in these master sprinters.
The age-related decline in peak velocity in this study (∼6% per decade) is close to the corresponding value of ∼9% in Hamilton’s study (11). Compared with elite young runners, the peak velocity achieved by the fastest male (40 yr) and female (41 yr) sprinters in this study were 13% and 14% lower than the highest reported peak velocity values in young male (12.05 m·s−1) and female (10.87 m·s−1) sprinters, respectively (1,7).
The decrease of velocity from peak velocity sequence to the end of the run for the fastest male and female sprinters in this study (5–6%) were about the same as corresponding values for young male (2–7%) and female runners (3–8%) in the recent major championships (1,3,7,25,28). When interpreting the effect of age on loss of velocity in the 100-m sprint it must be remembered that in older runners the peak velocity is achieved earlier, therefore the distance of deceleration phase is longer than in elite young runners.
The results of the current study indicate that SR has no major role in explaining the age-related decline in running velocity until the age of 80. However, after 80 yr of age there was a marked decline in SR, which seems to contribute to the decline in sprint performance in the oldest male and female age groups. Similarly, Hamilton’s data (11) showed that SR remained steady from age 30 until the oldest age group.
The SR curves show three distinct phases. As illustrated in Figure 2, C and D, SR increases rapidly in the beginning of the race and the runners reach the maximum or near maximum SR already at 10–20 m. Thereafter, SR declines gradually toward the end of the run. In the last 10-m sequence, the decline in SR becomes more pronounced which is associated with a momentary increase in SL. Comparison of SR values of different velocity phases of the fastest runners in the current study with those values reported in major competitions (1,25) shows that the young sprinters are superior to master sprinters particularly in the ability to increase their SR during acceleration phase and in the ability to maintain high SR throughout the run. Interestingly, during the peak velocity sequence master sprinters had almost similar SR to elite young sprinters.
The ability to achieve high SR is thought to be affected by biological age and the development of the central nervous system (16). It has been reported that the highest running SR is reached already approximately at the age of 8 yr after which the trainability of SR becomes relatively limited (16). There is a paucity of data regarding to what extent the maximum SR in running or maximum frequency in other cyclic speed movements can be maintained with increasing age. Our findings and those of Hamilton (11) suggest that in competitive master sprinters the ability to achieve high frequency in cyclic whole-body sprinting movements does not change markedly before ∼80 yr of age. However, it should be noted that SR is closely related to SL. As the sprinters in younger age groups were able to generate greater SL, the air time increased, and consequently SR component was decreased. Due to this interdependence, the relatively small age-related decline in SR in these master sprinters may be partly influenced by changes in SL with age.
The reduction in SL plays a decisive role in the age-related deterioration in 100-m sprint performance in these world-class master sprinters. This is clearly illustrated by findings that during each velocity phase SL declined markedly with age whereas SR showed smaller differences. Our findings are in agreement with the observations of Hamilton (11), who found that the decline in peak running velocity in master sprinters was accounted for by significant shortening of SL with increasing age. The SL values and the magnitude of the age-related decline in SL in Hamilton’s study, ∼6% per decade, are consistent with our results in the peak velocity phase.
SL curves in Figure 2, E and F, indicate that the changes in SL over the course of the 100-m race are quite similar in different age groups. During the acceleration phase of the run there was a large increase in SL followed by a phase of almost constant SL toward the end of the run. In the last 10-m sequence there is a slight increase in SL in males and in younger female sprinters, possibly due to a change in running technique before the finishing line. A closer inspection of SL curves indicate that the runners in younger age groups were able to increase SL for a longer distance with a greater SL than older runners. In relation to this, because SR reaches maximal value already at 10–20 m in all runners, the higher acceleration and the peak velocities found in younger sprinters seem to be explained by a higher ability to generate SL.
Previous findings of Ae et al. (1) indicate that the SL curves of the world’s best male sprinters resemble in shape those of the fastest master sprinters in this study. However, compared with young sprinters, the absolute SL values of acceleration, peak velocity, and deceleration phases of the run were about 5%, 13%, and 11% lower, respectively, for master male sprinters (40–44 yr) and about 3%, 10%, and 10%, lower, respectively, for master female sprinters (35–39 yr). This comparison suggests that the ability to generate high SL during the peak velocity sequence, and to maintain it toward the end of the race, is an important characteristic of elite young sprinters and may explain the majority of the difference in velocity between master sprinters. The difference in the initial acceleration velocity seems to be related to differences both in SR and SL.
Because the deterioration in the sprint performance with age seems to be the most closely linked to the reduction in SL, the critical question is what influences SL. On the basis of knowledge available from younger subjects, it appears that SL in running is determined largely by the ability to develop great vertical forces during the ground contact phase (24,30). According to Mero and Komi (24), during maximum velocity of ∼11 m·s−1, the sprinter has to produce a vertical force of more than three times his body weight, on one leg. Furthermore, because the contact phase in high-velocity sprinting lasts ∼80–100 ms (and the propulsion phase only for ∼60% of that time), the athlete must be capable of producing this force very rapidly. Related to this, a high percentage of fast-twitch (Type IIa, IIx) muscle fibers has shown to be predictive of great force production in young sprinters (23). To our knowledge, there is presently only one study (9) available on the impact of the aging process on the force production capacity in master national level sprinters and jumpers. This study demonstrated that vertical jump height in a standardized squat jump on a force platform decreased from ∼0.33 m in 40- to 49-yr-olds to ∼0.19 m in the athletes over 70. Also, the results of that study showed that the athletes over 70 were able to generate 58% of the maximum power output produced by 40- to 49-yr-old athletes. Similarly, evidence from untrained people has clearly indicated that explosive-strength characteristics of the leg muscles decline with age, especially from the sixth decade onward in both genders (2,15,19). This age-related decline in the muscle’s ability to produce force rapidly seems to be primarily attributed to a decrease in muscle mass caused by a loss and an atrophy of fibers, in particular of Type II fibers (15,19,20). Also, it has been demonstrated that the decreased rate of force production with age may be affected by decrease of the nervous system’s ability to activate muscles rapidly (14). However, it must be emphasized that running movements with repetitive part-phases impose unique demands on the force production capabilities of the neuromuscular system and thus the speculations of limiting factors for force production in sprinting based on acyclic single-repetition explosive movements is difficult. To understand the underlying mechanism for explaining the age-related reduction in force production and SL in sprint running, there is a need for sport-specific studies examining interrelationships between stride parameters, ground reaction forces, electromyography, and muscle fiber characteristics.
Ground contact time.
CT was found to be an important stride parameter related to differences in velocity between age groups in both male and female runners. Consistent with the current results, studies have demonstrated that a short CT is a decisive factor for world-class performance among younger athletes (18,22,25). As an example, competition analysis at the World Championships by Moravec et al. (25) revealed that in the fastest male sprinters, CT during the peak velocity phase (11.6–11.8 m·s−1) were as low as 80–82 ms. Those values are 15% below the corresponding values for the fastest male sprinters (40–44 yr) in this study. Also, the sprint stride of young and master sprinters differs in time spent in contact and flight phases. In young male sprinters (25), the relative time spent in contact is ∼38% compared with ∼44% in the youngest male sprinters in our study. These comparisons suggest that the distinguishing feature of the elite young athletes is their ability to minimize CT while increasing FT and the length of the stride.
CT in sprint running is found to be associated with factors such as stride technique (18) and leg stiffness (17). For example, Kuitunen et al. (17) reported that high ankle joint stiffness was related to short CT. The great leg stiffness, in turn, is suggested to be dependent on preactivation of the muscles (24) and on the stretch reflex potentiation during contact (6), which leads to a faster transition from the braking phase to the propulsion phase. Also, it is obvious that a shortened CT requires a rapid recruitment of the more powerful Type II motor units. Finally, a mechanical factor that is known to contribute to short CT is the small distance between body’s center of gravity and first ground contact point (18). Whether there are age-related changes in leg stiffness, stretch reflex response, and muscle activation during stretch-shortening cycle type muscle action is not known yet.
The present data showed that as running velocity decreased with age, there was a gradual decline in FT and SL. However, a closer inspection of individual data in Figure 3, A and B, indicate little or no decline in FT before age ∼70 yr in males and ∼65 yr in females. Therefore, it appears that in these masters sprinters across a range of velocity from ∼5.3 m·s−1 to ∼7.5 m·s−1, there is a rather linear increase in FT and SL with increasing running velocity, whereas at higher velocities (7.5–10.4 m·s−1) the increment in FT for a given increase in running velocity is proportionally smaller than that in SL. Thus, the interesting question is why FT does not change consistently as a result of change in running velocity and SL at higher velocities. Because FT is determined mainly by runner’s resultant take-off velocity, it may be that until ∼65–70 yr of age there were only small differences in impulses generated during propulsion portion of the contact phase. Regarding changes in CT with age, it seems that these equivalent impulses were achieved by different combinations of effective force and CT. For the faster younger runners, the impulse was achieved by the application of greater ground-reaction forces during briefer contact times, whereas older runners applied lesser ground forces during longer contact times (30). The possible explanation for the small or no change in FT, despite increases in SL as velocity is increased, could be the differences in take-off and landing characteristics of the stride (e.g., higher take-off angle in slower runners) (4).
This study has certain limitations in addition to its cross-sectional design. First, even though the present analysis was limited to the very best sprinters in each age group, we do not know the effect of genetic or nonphysiological factors on our findings. For example, based on our interview, the oldest female runners (70–87 yr) had not competed in athletics in their youth and may not have similar genetic constitution or training background to younger female sprinters. Second, the evaluation of the effect of age on biomechanical parameters is complicated by differences in the running velocity and height of the athletes. Studies on young runners have shown that SR and SL increase linearly at the lower velocities but at the higher velocities (∼7.0–9.5 m·s−1) SR increases to a relatively greater extent than SL (21). Hoffmann (12,13) found that among younger sprinters, SR tended to increase whereas SL tended to decline as the height (and leg length) of the athlete decreased, and thus it is possible that some portion of differences in SR and SL with age in older runners could be explained by these factors. Finally, as only the very best sprinters were selected, there was a small number of subjects per group. Therefore, the results could be affected by sampling bias and Type II errors.
The results of the present study showed similar relative decline in velocity in all phases of the run with advancing age in elite master sprinters. The deterioration of overall 100-m performance with age was primarily related to reduction in stride length and increase in ground contact time. Insight into the nature of the decline in the sprinting ability with advancing age may, apart from being of basic scientific value, have implications for the planning of training programs for aging athletes.
The authors wish to thank the local organizing committee for good collaboration during the investigations. Many thanks are given to Tuomas Liikavainio, Kimmo Mustonen, Jenni Torppa, Martti Koljonen, Ilkka Myllylä, Juha Isolehto, Lauri Laakso, Timo Törmäkangas, and Markku Kauppinen for technical assistance and data analysis. Also, thanks are directed to the athletes participating in this study.
This study was supported in part by grants from the Finnish Ministry of Education, and Sports Institute Foundation.
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