Pacing of Women and Men in Half-Marathon and Marathon Races

The Use of a Just Noticeable Difference Approach to Improve Perceptual Acuity Ability in Male Runners

We were interested in micro-variations in an athlete's psychophysical state that separate peak exertion from physiological collapse. Thus, we measured perceptual acuity in runners using a classic psychophysical approach, the just noticeable difference (JND) on two standard stimuli runs at treadmill speed corresponding to 70%VO2max and 80%VO2max. Thirty-four male runners (M age = 35.26, SD = 7.33 years) first performed a maximal treadmill test to determine the speed of a standard exercise bout for the JND trials. The JND trials consisted of four 5-minute running bouts on a treadmill with 5-minute rests between bouts. For bouts 1 and 3, participants ran at the standard stimuli pace, but for bouts 2 and 4, they adjusted their speeds to achieve a level of exertion at a JND above/below the SS. They achieved differences in the final 30 seconds of the VO2 between each JND bout and the previous standard stimuli at just above (JND-A) and just below (JND-B) the JND perceived exertions. We used a Generalized Linear Model analysis to compare the JND-A and JND-B within and between ventilatory threshold groups (lower/higher) in absolute and relative VO2 and in terms of the total JND magnitude. The magnitude of JND-A was greater than that of JND-B at 70%VO2max and 80%VO2max in absolute units (70%VO2 Δ = 2.62; SE = 0.37; p < .001; 80%VO2 Δ = 1.67; SE = 0.44; p = .002) and in relative units (70%VO2max Δ = 4.70; SE = 0.66; p < .001; 80%VO2max Δ = 2.96; SE = 0.80; p = .002). The total magnitude was greater in the 70%VO2max trial than 80%VO2max in absolute units (70%VO2 M = 3.78, SE = 0.31 mL·kg-1·min-1; 80%VO2 M = 2.62, SE = 0.37 mL·kg-1·min-1; p = .020) and in relative units (70%VO2max M = 6.57, SE = 0.53%VO2max; 80%VO2max M = 4.71, SE = 0.64%VO2max; p = .030). The JND range narrowed when physiologic demand increased, for both physical (speed) and psychological (RPE) variables.

Pacing Variation in Multistage Ultramarathons: Internet-Based Cross-Sectional Study

BACKGROUND

Ultramarathon running is the most popular ultraendurance competition in terms of the number of races and runners competing annually worldwide; however, no study has compared pacing and performance over a long period.

OBJECTIVE

This study analyzes the pacing of successful finishers and nonfinishers in multistage ultramarathons worldwide.

METHODS

A total of 4079 athletes (men=3288; women=791) competing in 99 multistage ultramarathon events from 1983 to 2021 were analyzed, including the number of participants, age, gender, rank, and running speed of successful finishers.

RESULTS

The results showed a significant increase in the number of events (n=338) and a significant increase in the number of finishers and nonfinishers (n=5575) in the ultramarathons worldwide during this period. The general linear models (GLMs) of pacing variation showed nonsignificant effects for gender (F1,36.2=2.5; P=.127; ηp2=0.063) and age group (F10,10=0.6; P=.798; ηp2=0.367), but it showed a significant interaction (gender × age) effect (F10,2689=2.3; P=.008; ηp2=0.009). Post hoc analyses showed that men have a higher pacing variation than women in the under 30 years (U30), U35, U45, and U50 groups. Additionally, the fastest women's age group (U35) had the lowest pacing variation. The GLM of pacing variation by gender and event distance showed significant effects for both gender (F1,3=18.5; P<.001; ηp2=0.007) and distance (F2,3=20.1; P<.001; ηp2=0.015). Post hoc analyses showed a growing pacing variation with increasing race distance for both men and women. In addition, men had a higher variation in long events. Furthermore, there was a significant main effect for both genders (F1,3=33.7; P<.001; ηp2=0.012) and rank (F1,3=136.6; P<.001; ηp2=0.048) on performance, with men being faster than women. Pacing varied greatly due to gender (F1,3=4.0; P=.047; ηp2=0.001), with a lower (ie, more even) pacing variation for male athletes in the top 3 finishers. Male nonfinishers showed a higher performance than female nonfinishers (F1,1340=25.6; P<.001), and no difference was identified for pacing variation (F1,789=1.5; P=.228) based on gender. In addition, a weak but significant correlation (r=-0.130; P<.001) was identified between the average running speed and pacing variation for both female and male nonfinishers.

CONCLUSIONS

In summary, multistage ultramarathon competitions showed an increasing number of competitors and a higher performance challenge. Men have a higher pacing (ie, less even) variation than women, especially observed in longer events. A higher pacing variation was associated with lower performance for men, women, and nonfinishers.

Participation and performance characteristics in half-marathon run: a brief narrative review

Half-marathon (HM) is a running sport of increasing popularity in both sexes and in all age groups worldwide during the last years. Many studies have examined several aspects of HM, such as performance and participation trends, sex and age differences, physiological correlates, and training; however, no comprehensive review has ever been contacted to summarize the recently accumulated knowledge. Therefore, the aim of the present study was to review all previous research in this sport, focusing on participation and performance aspects. It was shown that HM runners had similar anthropometric and physiological characteristics as full-marathon runners which should be attributed to the affinity of these two races in terms of metabolic demands. Performance in HM was related with superior scores in aerobic capacity (maximal oxygen uptake, anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, body mass index, body fat percentage and skinfold thickness). Considering the popularity of HM race and the lack of many original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical applications, since the majority of HM runners are amateur runners in need of sex-, age- and performance-tailored exercise prescription.

Analysis of IGHA1 and other salivary proteins post half marathon in female participants

Background

High-intensity exercise (HIE), such as that in marathons and triathlons, suppresses transient local and systemic immunity. Serum and salivary immunoglobulin heavy constant alpha 1 (IGHA1) are major markers of immunosuppression by HIE. Although much is known about the systemic immunosuppressive response, little is known about its local response in the oral cavity, lungs, bronchial tubes, and skin. The oral cavity allows bacteria or viruses to enter the body. Saliva covers the epidermis of the oral cavity and plays an important role in the local stress response by preventing infection. In this study, we examined the properties of saliva secreted during the local stress response for half-marathon (HM) induced IGHA1 protein expression using quantitative proteomics.

Methods

The Exercise Group (ExG) (19 healthy female university students) participated in a HM race. The Non-Exercise Group (NExG) (16 healthy female university students) did not participate in the ExG. The ExG saliva samples were collected 1 h pre and 2 h and 4 h post-HM. The NExG saliva samples were collected at the same time intervals. The saliva volume, protein concentration, and relative IGHA1 expression were analyzed. In addition, 1 h pre and 2 h post- HM saliva samples were analyzed by iTRAQ. The identified factors in iTRAQ were analyzed for the ExG and the NExG using western blotting.

Results

We identified kallikrein 1 (KLK1), immunoglobulin kappa chain (IgK), and cystatin S (CST4) as suppression factors, as well as IGHA1, which has been reported to be an immunological stress marker. IGHA1 (p = 0.003), KLK1 (p = 0.011), IGK (p = 0.002), and CST4 (p = 0.003) were suppressed 2 h post-HM compared with their levels pre HM, and IGHA1 (p < 0.001), KLK1 (p = 0.004), and CST4 (p = 0.006) were suppressed 4 h post-HM. There was also a positive correlation between IGHA1, IGK, and CST4 levels at 2 and 4 h post-HM. In addition, KLK1 and IGK levels at 2 h post-HM were positively correlated.

Conclusion

Our study demonstrated that the salivary proteome is regulated, and antimicrobial proteins are suppressed post-HM. These results suggest that oral immunity was transiently suppressed post-HM. The positive correlation of each protein at 2 and 4 h post-HM suggests that the suppressed state was similarly regulated up to 4 h after a HM. The proteins identified in this study may have applications as stress markers for recreational runners and individuals who perform moderate to HIE on a regular basis.

Sex Differences in Racing History of Recreational 10 km to Ultra Runners (Part B)-Results from the NURMI Study (Step 2)

Sex differences in anatomy and physiology are the primary underlying factor for distinctions in running performance. Overall participation in recreational running events has been dominated by males, although increasing female participation has been reported in recent years. The NURMI study participants filled in a survey following the cross-sectional study design with questions on sociodemographic data, running and racing motivations, training behaviors, and racing history and experience. Data analysis included 141 female and 104 male participants aged 39 (IQR 17) with a healthy median BMI (21.7 kg/m²; IQR 3.5). Statistical analyses revealed sex differences with the males performing faster at half-marathon (p < 0.001) and marathon (p < 0.001) events but no difference at ultra-marathons (p = 0.760). Mediation analyses revealed no significant sex differences in the performance of half-marathon and marathon when considering training behaviors (p > 0.05), racing history (p > 0.05), or racing experience (p > 0.05). Differences in recreational performance may be more closely related to social constraints and expectations of females rather than the physiological advantages of the male athlete. Health professionals who guide and support recreational runners as well as the runners themselves and their coaches may benefit from this study's results in order to improve the best time performance through a deeper understanding of the areas that mediate sex differences.

Consistency of pacing profile according to performance level in three different editions of the Chicago, London, and Tokyo marathons

Running pacing has become a focus of interest over recent years due to its relationship with performance, however, it is still unknown the consistency of each race in different editions. The aim of this study is to analyze the consistency of pacing profile in three consecutive editions of three marathon races. A database of 282,808 runners, compiled from three different races (Chicago, London, and Tokyo Marathon) and three editions (2017, 2018, and 2019) was analyzed. Participants were categorized according to their time performance in the marathon, every 30 min from 2:30 h to sub-6 h. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. The intraclass correlation coefficients (ICC) of relative speed at the different pacing section, taking into account the runner time categories, was excellent over the three marathon editions (ICC > 0.93). The artificial intelligence model showed an accuracy of 86.8% to classify the runners' data in three marathons, suggesting a consistency between editions with identifiable differences between races. In conclusion, although some differences have been observed between editions in certain sections and marathon runner categories, excellent consistency of the pacing profile was observed. The study of pacing profile in a specific marathon can, therefore, be helpful for runners, coaches and marathon organizers for planning the race and improving its organization.

The effect of mental fatigue on half-marathon performance: a pragmatic trial

Purpose

It is well established that mental fatigue impairs performance during lab-based endurance tests lasting less than 45 min. However, the effects of mental fatigue on longer duration endurance events and in field settings are unknown. The aim of this study was to investigate the effect of mental fatigue on performance during a half-marathon race.

Methods

Forty-six male amateur runners (means ± SD: age 43.8 ± 8.6 years, $$\dot V{O_{2peak}}$$ V ˙ O 2 p e a k 46.0 ± 4.1 ml/kg/min) completed a half-marathon after being randomly allocated to performing a 50-min mentally fatiguing task (mental fatigue group) or reading magazines for 50 min (control group). Running speed, heart rate, and perceived effort were measured during the race.

Results

Runners in the mental fatigue group completed the half-marathon approximately 4 min slower (106.2 ± 12.4 min) than those in the control group (102.4 ± 10.2 min), but this difference was not statistically significant (Cohen’s d = 0.333; p = 0.265). However, equivalence was not established [t(40.88) = 0.239, p = 0.594] and equivalence testing analysis excluded a beneficial effect of mental fatigue on half-marathon performance.

Conclusion

Due to its posttest-only design and the achievable sample size, the study did not have enough power to provide evidence that the observed 4-min increase in half-marathon time is statistically significant. However, equivalence testing suggests that mental fatigue has no beneficial effect on half-marathon performance in male amateur runners, and a harmful effect cannot be excluded. Overall, it seems prudent for endurance athletes to avoid mentally fatiguing tasks before competitions.

Pacing in Long-Distance Running: Sex and Age Differences in 10-km Race and Marathon

Background and objective: The recent availability of data from mass-participation running events has allowed researchers to examine pacing from the perspective of non-elite distance runners. Based on an extensive analysis of the literature, we concluded that no study utilizing mass-participation events data has ever directly compared pacing in the 10-km race, with other long-distance races. Therefore, the main aim of this study was to assess and compare pacing between 10-km runners and marathoners, in regards to their sex and age. Materials and methods: For the purpose of this study, official results from the Oslo marathon (n = 8828) and 10-km race (n = 16,315) held from 2015 to 2018 were included. Results: Both 10-km runners and marathoners showed positive pacing strategies. Moreover, two-way analysis of variance showed that women were less likely to slow in the marathon than men (9.85% in comparison to 12.70%) however, not in the 10-km race (3.99% in comparison to 3.38%). Finally, pace changing is more prominent in youngest and oldest marathoners comparing to the other age groups (12.55% in comparison to 10.96%). Conclusions: Based on these findings, practitioners should adopt different training programmes for marathoners in comparison to shorter long-distance runners.

Training, Anthropometric, and Physiological Characteristics in Men Recreational Marathon Runners: The Role of Sport Experience

The aim of the present study was to examine the physiological and training characteristics in marathon runners with different sport experiences (defined as the number of finishes in marathon races). The anthropometry and physiological characteristics of men recreational endurance runners with three or less finishes in marathon races (novice group, NOV; n = 69, age 43.5 ± 8.0 years) and four or more finishes (experienced group, EXP; n = 66, 45.2 ± 9.4 years) were compared. EXP had faster personal best marathon time (3:44 ± 0:36 vs. 4:20 ± 0:44 h:min, p < 0.001, respectively); lower flexibility (15.9 ± 9.3 vs. 19.3 ± 15.9 cm, p = 0.022), abdominal (20.6 ± 7.9 vs. 23.8 ± 9.0 mm, p = 0.030) and iliac crest skinfold thickness (16.7 ± 6.7 vs. 19.9 ± 7.9 mm, p = 0.013), and body fat assessed by bioimpedance analysis (13.0 ± 4.4 vs. 14.6 ± 4.7%, p = 0.047); more weekly training days (4.6 ± 1.4 vs. 4.1 ± 1.0 days, p = 0.038); and longer weekly running distance (58.8 ± 24.0 vs. 47.2 ± 16.1 km, p = 0.001) than NOV. The findings indicated that long-term marathon training might induce adaptations in endurance performance, body composition, and flexibility.

Physiological responses during the long-distance race in the warm environment in runners: a pilot-study

BACKGROUND

This study investigated the effects of warm temperature in the external environment on physiological response in self- pace during the long-distance race in runners and the association between the physiological index of endurance performance (i.e., speeds at ventilatory anaerobic threshold [VVAT], respiratory compensation [VRCP], maximum oxygen uptake [<inf>V</inf>VO2max], and running economy) and average pace for each 3-km during the 21-km race.

METHODS

Five male recreational runners (mean±SE age 36.6±6.1 years; VO<inf>2</inf> max: 59.2±7.9) were submitted to a 21-kilometers race in the outdoor environment using a portable metabolic analyzer.

RESULTS

Our results showed a reduction in speed to kilometers 12, 15, 18 and 21 than kilometer 3 (P<0.05). The runners showed a decrease in both VO<inf>2</inf> (mL/kg/min) and RER from kilometer 15 (P=0.001 and P=0.003, respectively). Regarding cardiovascular response, our data demonstrated a steady HR response from kilometer 6 to 21 (P=0.99). Otherwise, the runners showed a decrease in oxygen pulse from kilometer 9 than both kilometers 3 and 6 (P=0.001). During the race, the runners demonstrated a significant increase in body temperature compared to rest (P=0.001). The results of the correlation analysis between physiological index of endurance performance and average pace for each 3-km during the 21-km race showed significant correlation between VVAT and average pace for: 12-km (r=0.95; P=0.01), 15-km (r=0.89; P=0.05) and 21-km (r=0.86; P=0.04); VRCP and average pace for 3-km (r=0.88; P=0.05).

CONCLUSIONS

Our findings demonstrated that the increase in body temperature in a warm environment during the 21-km race is associated with both cardiovascular and metabolic strain in runners. Concerning physiological markers of endurance performance, VVAT appears to be the best predictor of the average pace throughout the 21-km race in a warm environment in recreational runners.

Do Sex Differences in Physiology Confer a Female Advantage in Ultra-Endurance Sport?

Ultra-endurance has been defined as any exercise bout that exceeds 6 h. A number of exceptional, record-breaking performances by female athletes in ultra-endurance sport have roused speculation that they might be predisposed to success in such events. Indeed, while the male-to-female performance gap in traditional endurance sport (e.g., marathon) remains at ~ 10%, the disparity in ultra-endurance competition has been reported as low as 4% despite the markedly lower number of female participants. Moreover, females generally outperform males in extreme-distance swimming. The issue is complex, however, with many sports-specific considerations and caveats. This review summarizes the sex-based differences in physiological functions and draws attention to those which likely determine success in extreme exercise endeavors. The aim is to provide a balanced discussion of the female versus male predisposition to ultra-endurance sport. Herein, we discuss sex-based differences in muscle morphology and fatigability, respiratory-neuromechanical function, substrate utilization, oxygen utilization, gastrointestinal structure and function, and hormonal control. The literature indicates that while females exhibit numerous phenotypes that would be expected to confer an advantage in ultra-endurance competition (e.g., greater fatigue resistance, greater substrate efficiency, and lower energetic demands), they also exhibit several characteristics that unequivocally impinge on performance (e.g., lower O₂-carrying capacity, increased prevalence of GI distress, and sex-hormone effects on cellular function/injury risk). Crucially, the advantageous traits may only manifest as ergogenic in the extreme endurance events which, paradoxically, are those that females less often contest. The title question should be revisited in the coming years, when/if the number of female participants increases.

Effect of marathon characteristics and runners' time category on pacing profile

This study aimed to analyse differences in pacing profiles in four marathon competitions and to explore that pacing per time category. A database of 91,493 runners gathered from 4 different races was analysed (Valencia, Chicago, London and Tokyo Marathon). Participants were categorized in accordance with their completion time. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. In the four marathons studied, the first 5 km differed widely, presenting London the highest relative speeds (5 km: CI95% London vs. Valencia [12.1, 13.6%], p < 0.001 and ES = 2.1; London vs. Chicago [5.5, 7.1%], p < 0.001 and ES = 1.1; London vs. Tokyo [15.2, 16.8%], p < 0.001 and ES = 2.3). Races did not differ at each section for high-performance runners (sub-2:30), but differences between races increased as the time category increases (e.g. 35 km and sub-3:00: CI95% London vs. Tokyo [-3.1, -1.8%], p < 0.001 and ES = 0.7; 35 km and sub-5:00: London vs. Tokyo [-9.8, -9.2%], p < 0.001 and ES = 1.3). The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons (e.g. CI95% London vs. Valencia [10.3, 10.8%], p < 0.001 and ES = 1.3). In conclusion, although race characteristics affect pacing, this effect was higher as the category time increases. Race pacing characteristics should be taken into consideration for runners and coaches choosing the race and working on pacing strategies, for researches to extrapolate or interpret results, or for race organizations to improve its pacing characteristics.Highlights The first 5 km differed widely on pacing profiles between the four marathons assessed.London had the highest relative speeds in the first 5 km.Race characteristics affect pacing, but this effect was higher as the category time increases.The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons.

Using Accelerometry for Evaluating Energy Consumption and Running Intensity Distribution Throughout a Marathon According to Sex

The proportion of females participating in long-distance races has been increasing in the last years. Although it is well-known that there are differences in how females and males face a marathon, higher research may be done to fully understand the intrinsic and extrinsic factors affecting sex differences in endurance performance. In this work, we used triaxial accelerometer devices to monitor 74 males and 14 females, aged 30 to 45 years, who finished the Valencia Marathon in 2016. Moreover, marathon split times were provided by organizers. Several physiological traits and training habits were collected from each participant. Then, we evaluated several accelerometry- and pace-estimated parameters (pacing, average change of speed, energy consumption, oxygen uptake, running intensity distribution and running economy) in female and male amateur runners. In general, our results showed that females maintained a more stable pacing and ran at less demanding intensity throughout the marathon, limiting the decay of running pace in the last part of the race. In fact, females ran at 4.5% faster pace than males in the last kilometers. Besides, their running economy was higher than males (consumed nearly 19% less relative energy per distance) in the last section of the marathon. Our results may reflect well-known sex differences in physiology (i.e., muscle strength, fat metabolism, VO_2max), and in running strategy approach (i.e., females run at a more conservative intensity level in the first part of the marathon compared to males). The use of accelerometer devices allows coaches and scientific community to constantly monitor a runner throughout the marathon, as well as during training sessions.

Different race pacing strategies among runners covering the 2017 Berlin Marathon under 3 hours and 30 minutes

The purposes of this study were 1) to analyse the different pacing behaviours based on athlete's performance and 2) to determine whether significant differences in each race split and the runner's performance implied different race profiles. A total of 2295 runners, which took part in Berlin's marathon (2017), met the inclusion criteria. 4 different groups were created based on sex and performance. Men: Elite (<02:19:00 h), Top 1 (<02:30:00 h), Top 2 (<02:45:00 h) and Top 3 (<03:00:00 h); women: Elite (02:45:00 h), Top 1 (<03:00:00 h), Top 2 (<03:15:00 h), Top 3 (<03:30:00 h). With the aim of comparing the pacing between sex and performance the average speed was normalized. In men, no statistically significant changes were found between performance group and splits. A large number of significant differences between splits and groups were found amongst women: 5-10 km Top 2 vs Top 3 (P = 0.0178), 10-15 km Top1 vs Top 2 (P = 0.0211), 15-20 km Top1 vs Top 2 (P = 0.0382), 20-21.1 km Elite vs Top 2 (P = 0.0129); Elite vs Top 3 (P = 0.0020); Top1 vs Top 2 (P = 0.0233); Top 1 vs Top 3 (P = 0.0007), 25-30 km Elite vs Top 2 (P = 0.0273); Elite vs Top 3 (P = 0.0156), 30-35 km Elite vs Top 2 (P = 0.0096); Top 1 vs Top 2 (P = 0.0198); Top2 vs Top3 (P = 0.0069). In men there were little significant differences based on athletes' performance which implied a similar pacing behaviour. Women presented numerous differences based on their performance which suggested different pacing behaviours.

Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices

As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. Triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30–45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. Energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors.

Age Differences in Pacing in Endurance Running: Comparison between Marathon and Half-MarathonMen and Women

Background and Objective: The increased popularity of marathons and half-marathons has led to a significant increase in the number of master runners worldwide. Since the age-related decrease in performance is dependent on race duration, pacing in long distance running might also vary by race distance in both men and women. Therefore, the main aim of this study was to assess pacing differences between marathon and half-marathon runners with regard to the runners’ age group, and independently for men and women. Materials and Methods: In total, 17,465 participants in the Vienna City marathon in 2017 were considered for this study (marathon, N = 6081; half-marathon, N = 11,384). Pacing was expressed as two variables (i.e., pace range and end spurt). Results: All runners showed positive pacing strategies (i.e., a fast start with gradual decrease of speed). However, marathon runners showed greater variability in pacing than half-marathon runners. Furthermore, women showed no differences in pace variability in regard to the age group, whereas men younger than 30 years of age, as well as older men (over the age of 60), showed a greater variability in pace than other age groups. Finally, younger half-marathon men and women showed the fastest end spurt compared to older age groups and marathon runners. Conclusions: The presented findings could help sports and medicine practitioners to create age specific training plans and pacing strategies. This approach could help long distance runners to improve their physical fitness, achieve better race times, reduce the potential risk of musculoskeletal injuries and increase the overall pleasure of long distance running.