Predictor variables for a 100-km race time in male ultra-marathoners

Limits of Ultra: Towards an Interdisciplinary Understanding of Ultra-Endurance Running Performance

Ultra-endurance running (UER) poses extreme mental and physical challenges that present many barriers to completion, let alone performance. Despite these challenges, participation in UER events continues to increase. With the relative paucity of research into UER training and racing compared with traditional endurance running distance (e.g., marathon), it follows that there are sizable improvements still to be made in UER if the limitations of the sport are sufficiently understood. The purpose of this review is to summarise our current understanding of the major limitations in UER. We begin with an evolutionary perspective that provides the critical background for understanding how our capacities, abilities and limitations have come to be. Although we show that humans display evolutionary adaptations that may bestow an advantage for covering large distances on a daily basis, these often far exceed the levels of our ancestors, which exposes relative limitations. From that framework, we explore the physiological and psychological systems required for running UER events. In each system, the factors that limit performance are highlighted and some guidance for practitioners and future research are shared. Examined systems include thermoregulation, oxygen delivery and utilisation, running economy and biomechanics, fatigue, the digestive system, nutritional and psychological strategies. We show that minimising the cost of running, damage to lower limb tissue and muscle fatigability may become crucial in UER events. Maintaining a sustainable core body temperature is critical to performance, and an even pacing strategy, strategic heat acclimation and individually calculated hydration all contribute to sustained performance. Gastrointestinal issues affect almost every UER participant and can be due to a variety of factors. We present nutritional strategies for different event lengths and types, such as personalised and evidence-based approaches for varying types of carbohydrate, protein and fat intake in fluid or solid form, and how to avoid flavour fatigue. Psychology plays a vital role in UER performance, and we highlight the need to be able to cope with complex situations, and that specific long and short-term goal setting improves performance. Fatigue in UER is multi-factorial, both physical and mental, and the perceived effort or level of fatigue have a major impact on the ability to continue at a given pace. Understanding the complex interplay of these limitations will help prepare UER competitors for the different scenarios they are likely to face. Therefore, this review takes an interdisciplinary approach to synthesising and illuminating limitations in UER performance to assist practitioners and scientists in making informed decisions in practice and applicable research.

Prediction of performance in a 100-km run from a simple equation

This study aimed to identify predictive variables of performance for a 100-km race (Perf100-km) and develop an equation for predicting this performance using individual data, recent marathon performance (Perfmarathon), and environmental conditions at the start of the 100-km race. All runners who had performed official Perfmarathon and Perf100-km in France, both in 2019, were recruited. For each runner, gender, weight, height, body mass index (BMI), age, the personal marathon record (PRmarathon), date of the Perfmarathon and Perf100-km, and environmental conditions during the 100-km race (i.e., minimal and maximal air temperatures, wind speed, total amount of precipitation, relative humidity and barometric pressure) were collected. Correlations between the data were examined, and prediction equations were then developed using stepwise multiple linear regression analyses. Significant bivariate correlations were found between Perfmarathon (p<0.001, r = 0.838), wind speed (p<0.001, r = -0.545), barometric pressure (p<0.001, r = 0.535), age (p = 0.034, r = 0.246), BMI (p = 0.034, r = 0.245), PRmarathon (p = 0.065, r = 0.204) and Perf100-km in 56 athletes The, 2 prediction equations with larger sample (n = 591) were developed to predict Perf100-km, one including Perfmarathon, wind speed and PRmarathon (model 1, r² = 0.549; standard errors of the estimate, SEE = 13.2%), and the other including only Perfmarathon and PRmarathon (model 2, r² = 0.494; SEE = 14.0%). Perf100-km can be predicted with an acceptable level of accuracy from only recent Perfmarathon and PRmarathon, in amateur athletes who want to perform a 100 km for the first time.

The Sex Difference in 6-h Ultra-Marathon Running-The Worldwide Trends from 1982 to 2020

Background and Objectives: The 6-h ultra-marathon is the shortest time-limited ultra-marathon race, but little has been investigated regarding this race format. Previously, only the age of peak performance in the context of longer time-limited ultra-marathons was determined. The purpose of this study was to investigate the trends in 6-h ultra-marathon races from 1982 to 2020 for female and male ultra-runners, the participation and performance by countries, the age of peak performance, and the differences in performance regarding countries. Materials and Methods: The sample included 23,203 female ultra-runners, aged 18-83 years, and 87,264 male ultra-runners, aged 18-85 years, who were finishers in a 6-h ultra-marathon held between 1982 and 2020. The age of peak performance was tested using the Kruskal-Wallis test, followed by the Bonferroni Correction. The difference in performance by countries was verified using a linear regression model with the fastest runners from Russia in women, and Tunisia in men, used as reference. Results: Over the years, the men-to-women ratio decreased. The mean age was 43.20 ± 9.30 years for female and 46.09 ± 10.17 years for male runners. Athletes in younger age groups were faster than athletes in older age groups. Most female and male participants originated from Germany. Women from Russia (10.01 ± 1.28 km/h) and men from Tunisia (12.16 ± 1.46 km/h) were the fastest. Conclusions: In summary, in 6-h ultra-marathons held between 1982 and 2020, the participation for both women and men increased, while the men-to-women ratio decreased. The mean age was higher in men compared to women. Most female and male runners originated from Germany, but the fastest women were from Russia, and the fastest men from Tunisia. Future studies need to investigate whether Russian women and Tunisian men are also the best in other distance-limited ultra-marathon races, such as 12-h and 24-h.

Potential Long-Term Health Problems Associated with Ultra-Endurance Running: A Narrative Review

It is well established that physical activity reduces all-cause mortality and can prolong life. Ultra-endurance running (UER) is an extreme sport that is becoming increasingly popular, and comprises running races above marathon distance, exceeding 6 h, and/or running fixed distances on multiple days. Serious acute adverse events are rare, but there is mounting evidence that UER may lead to long-term health problems. The purpose of this review is to present the current state of knowledge regarding the potential long-term health problems derived from UER, specifically potential maladaptation in key organ systems, including cardiovascular, respiratory, musculoskeletal, renal, immunological, gastrointestinal, neurological, and integumentary systems. Special consideration is given to youth, masters, and female athletes, all of whom may be more susceptible to certain long-term health issues. We present directions for future research into the pathophysiological mechanisms that underpin athlete susceptibility to long-term issues. Although all body systems can be affected by UER, one of the clearest effects of endurance exercise is on the cardiovascular system, including right ventricular dysfunction and potential increased risk of arrhythmias and hypertension. There is also evidence that rare cases of acute renal injury in UER could lead to progressive renal scarring and chronic kidney disease. There are limited data specific to female athletes, who may be at greater risk of certain UER-related health issues due to interactions between energy availability and sex-hormone concentrations. Indeed, failure to consider sex differences in the design of female-specific UER training programs may have a negative impact on athlete longevity. It is hoped that this review will inform risk stratification and stimulate further research about UER and the implications for long-term health.

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.

Predictors of Athlete's Performance in Ultra-Endurance Mountain Races

Background: In previous studies, ultra-endurance performance has been associated with training and psychological variables. However, performance under extreme conditions is understudied, mainly due to difficulties in making field measures. Aim: The aim of this study was to analyze the role of training, hydration, nutrition, oral health status, and stress-related psychological factors in athletes’ performance in ultra-endurance mountain events. Methods: We analyzed the variables of race time and training, hydration state, nutrition, oral health status, and stress-related psychological factors in 448 ultra-endurance mountain race finishers divided into three groups according to race length (less than 45 km, 45–90 km, and greater than 90 km), using a questionnaire. Results: Higher performance in ultra-endurance mountain races was associated with better oral health status and higher accumulative altitude covered per week as well as higher positive accumulative change of altitude per week during training. In longer distance races, experience, a larger volume of training, and better hydration/nutrition prior to the competition were associated with better performance. Conclusions: Ultra-endurance mountain athletes competing in longer races (>90 km) have more experience and follow harder training schedules compared with athletes competing in shorter distances. In longer races, a larger fluid intake before the competition was the single best predictor of performance. For races between 45 and 90 km, training intensity and volume were key predictors of performance, and for races below 45 km, oral health status was a key predictor of performance. Psychological factors previously reported as ultra-endurance mountain race performance predictors were inconsistent or failed to predict the performance of athletes in the present research.

The Advantage of Supine and Standing Heart Rate Variability Analysis to Assess Training Status and Performance in a Walking Ultramarathon

Cardiac autonomic modulation of heart rate, assessed by heart rate variability (HRV), is commonly used to monitor training status. HRV is usually measured in athletes after awakening in the morning in the supine position. Whether recording during standing reveals additional information compared to supine remains unclear. We aimed to evaluate the association between short-duration HRV, assessed both in the supine and standing position, and a low-intensity long-duration performance (walking ultramarathon), as well as training experience. Twenty-five competitors in a 100 km walking ultramarathon underwent pre-race supine (12 min) and standing (6 min) HR recordings, whereas performance and subjective training experience were assessed post-race. There were no significant differences in both supine and standing HRV between finishers (n = 14) and non-finishers (n = 11, mean distance 67 km). In finishers, a slower race velocity was significantly correlated with a higher decrease in parasympathetic drive during position change [larger decrease in High Frequency power normalized units (HF_nu: r = −0.7, p = 0.01) and higher increase in the detrended fluctuation analysis alpha 1 index (DFA1: r = 0.6, p = 0.04)]. Highly trained athletes accounted for higher HF_nu during standing compared to poorly trained competitors (+11.5, p = 0.01). Similarly, greater training volume (total km/week) would predict higher HF_nu during standing (r = 0.5, p = 0.01). HRV assessment in both supine and standing position may provide additional information on the dynamic adaptability of cardiac autonomic modulation to physiologic challenges and therefore be more valuable for performance prediction than a simple assessment of supine HRV. Self-reported training experience may reliably associate with parasympathetic drive, therefore indirectly predicting long-term aerobic performance in ultramarathon walking races.

Multidisciplinary Analysis of Differences Between Finisher and Non-finisher Ultra-Endurance Mountain Athletes

Ultra-endurance races are one of the most physically and psychologically demanding sports, depending performance on several elements. The aims of the present study were (i) to analyze differences in selected psychophysiological parameters between finisher and non-finisher ultra-endurance mountain athletes, and (ii) to analyze modifications in psychophysiological parameters before and after an ultra-endurance mountain event. Selected psychophysiological variables were assessed in 46 finishers and 24 non-finishers in two over 100 km ultra-endurance races were examined. We found how an ultra-endurance mountain race produced dehydration, a decrease in systolic blood pressure, weight and leg strength muscle values, as well as an increase in heart rate and rate of perceived exertion values. Finishers presented lower systolic blood pressure, weight, body mass index, half marathon time and fluid intake before competition day compared to non-finishers. In addition, body mass index, pre-race hydration, and performance in lower distance races were predictors of performance in these ultra-endurance mountain races.

Predicting Competition Performance in Short Trail Running Races with Lactate Thresholds

Trail running is a popular sport, yet factors related to performance are still not fully understood. Lactate thresholds have been thoroughly investigated in road running and correlate strongly with race performance, but to date few data are available about the value in trail running performance prediction. We examined 25 trail runners (age 31.2 ± 5.1 years, BMI 22.2 ± 1.82 kg/m²) with an initial graded exercise test for measurement of VO_2max (59.5 ± 5.2 ml.kg^‐1.min^{‐ 1}) and lactate thresholds (LT): LTAET (LT aerobic) 1.03 ± 0.59 mmol/l; 11.2 ± 1.1 km/h), IAT (individual lactate threshold) (2.53 ± 0.59 mmol/l; 15.4 ± 1.6 km/h) and LT4 (lactate threshold at 4 mmol/l) (16.2 ± 1.9 km/h). All runners subsequently participated in a 31.1 km XS trail race and 9 runners in a 21 km XXS trail race. Race performance times correlated negatively with the XS trail run (LTAET: r = ‐0.65, p < 0.01; LT4: r = ‐0.87, p < 0.01; IAT: r = ‐0.84, p < 0.01) and regression analysis showed that race performance could be predicted by: LT4: ‐324.15×LT4+13195.23 (R² = .753, F_1,23 = 70.02, p < 0.01). A subgroup analysis showed higher correlations with race performance for slower than faster runners. No correlations were found with the XXS race. Lactate thresholds can be of value in predicting trail race performance and help in designing training plans.

Pre-race determinants influencing performance and finishing of a transcontinental 4486-km ultramarathon

BACKGROUND

Pre-race determinants influencing performance and finishing of one of the largest transcontinental multistage ultramarathons were investigated.

METHODS

Forty-four participants of the TransEurope FootRace 2009 (TEFR), running 4486 km in 64 stages (average 70.1 km daily) were analyzed regarding training and running history. This included years of regular endurance running (PRY), number of finished marathons, ultramarathons (UM) and multistage ultramarathons (MSUM), pre-race records (PRR) for marathon and specific UM races and the extent of pre-race training (PRT) in the last year before TEFR: volume (km/week), duration (h/week) and intensity (km/h).

RESULTS

Mean total running speed during TEFR was 8.25 km/h.Seventy-one percent of subjects finished the race. The mean PRT-volume extends 5500 km. Finishers and non-finishers of the TEFR did not show significant difference in any tested pre-race determinants. There was no association between PRY, number of finished marathons, UM, and MSUM and TEFR performance. There was very strong positive correlation between PRT-intensity and TEFR performance. PRT volume correlated with a medium effect size to TEFR performance. PRR in specific ultra-races (6-hour, 50-km, 100-km races) showed a high correlation to TEFR performance. Performance in ultramarathon correlates inversely with age.

CONCLUSIONS

Like in other endurance disciplines with shorter distances, in ultra-long multistage endurance running the athletes also need a stage-specific pre-race experience, training and adaptation if he wants to end up with a good performance. But dropping out of a MSUM seems not to be consistent with regard to specific pre-race experience. Further research results of TEFR project may reveal potential risk factors for non-finishing a transcontinental footrace.

Different Predictor Variables for Women and Men in Ultra-Marathon Running-The Wellington Urban Ultramarathon 2018

Ultra-marathon races are increasing in popularity. Women are now 20% of all finishers, and this number is growing. Predictors of performance have been examined rarely for women in ultra-marathon running. This study aimed to examine the predictors of performance for women and men in the 62 km Wellington Urban Ultramarathon 2018 (WUU2K) and create an equation to predict ultra-marathon race time. For women, volume of running during training per week (km) and personal best time (PBT) in 5 km, 10 km, and half-marathon (min) were all associated with race time. For men, age, body mass index (BMI), years running, running speed during training (min/km), marathon PBT, and 5 km PBT (min) were all associated with race time. For men, ultra-marathon race time might be predicted by the following equation: (r² = 0.44, adjusted r² = 0.35, SE = 78.15, degrees of freedom (df) = 18) ultra-marathon race time (min) = −30.85 ± 0.2352 × marathon PBT + 25.37 × 5 km PBT + 17.20 × running speed of training (min/km). For women, ultra-marathon race time might be predicted by the following equation: (r² = 0.83, adjusted r² = 0.75, SE = 42.53, df = 6) ultra-marathon race time (min) = −148.83 + 3.824 × (half-marathon PBT) + 9.76 × (10 km PBT) − 6.899 × (5 km PBT). This study should help women in their preparation for performance in ultra-marathon and adds to the bulk of knowledge for ultra-marathon preparation available to men.

Revisiting the United States Army body composition standards: a receiver operating characteristic analysis

BACKGROUND

The objective for percent body fat standards in the United States Army Body Composition Program (ABCP) is to ensure soldiers maintain optimal well-being and performance under all conditions. However, conducting large-scale experiments within the United States Army to evaluate the efficacy of the thresholds is challenging.

METHODS

A receiver operating characteristic (ROC) analysis with corresponding area under the curve (AUC) was performed on body mass index (BMI) and waist circumference to determine optimal gender-specific age cohort thresholds that meet ABCP percent body fat standards in the National Health and Nutrition Examination Survey (NHANES) III. A second dataset consisting of a cohort of basic training recruits (N = 20,896 soldiers, 28% female) with BMI and waist circumference measured using a 3D body image scanner was applied to calculate what percent of basic training recruits meet the ABCP percent body fat standards. Regression models to determine the contribution of different circumference sites to the predictions of percent body fat were developed using a database compiled at the New York Obesity Research Center (N = 500).

RESULTS

Optimal BMI thresholds ranged from 23.65 kg/m² (17-21-year-old cohort) to 26.55 kg/m² (40 and over age cohort) for males and 21.75 to 24.85 kg/m² for females. The AUC values were between 0.86 and 0.92. The waist circumference thresholds ranged 81.35 to 97.55 cm for males and 77.05 to 89.35 cm for females with AUC values between 0.90 and 0.91. These BMI thresholds were exceeded by 65% of male and 74% of female basic training recruits and waist circumference thresholds were exceeded by 73% of male and 85% of female recruits. The single circumference that contributed most to prediction of body fat was waist circumference in males and mid-thigh circumference in females.

CONCLUSIONS

The ABCP percent body fat thresholds yield BMI thresholds that are below the United States Army BMI standards, especially in females which suggests the ABCP percent body fat standards may be too restrictive. The United States Army percent body fat standards could instead be matched to existing national health guidelines.

How accurate are runners' prospective predictions of their race times?

Metacognition is a domain which has illuminated our understanding of the regulation of cognition, but has yet to be applied in detail to more physical activities. We used half marathon finish time predictions from 7211 runners to investigate the factors that influence running performance metacognitive accuracy. In particular, we were concerned with the effects of experience, gender, and age on calibration. We expected more experienced runners to be better calibrated than less experienced ones. Given analogous findings in the domain of metacognition, we expected women to be less overconfident in their predictions, and better calibrated than male runners. Based on the metacognition literature, we expected that if older runners have effectively learned from previous experience, they would be as well-calibrated as younger runners. In contrast, uninformed inferences not based on performance feedback would lead to overestimating performance for older compared to younger runners. As expected, experience in terms of both club membership and previous race completion improved calibration. Unexpectedly though, females were more overconfident than males, overestimating their performance and demonstrating poorer calibration. A positive relationship was observed between age and prediction accuracy, with older runners showing better calibration. The present study demonstrates that data, collected before a test of physical activity, can inform our understanding of how participants anticipate their performance, and how this ability is affected by a number of demographic and situational variables. Athletes and coaches alike should be aware of these variables to better understand, organise, plan, and predict running performance, potentially leading to more appropriate training sessions and faster race finish times.

Russians are the fastest 100-km ultra-marathoners in the world

Objectives

A recent study investigating the top 10 100-km ultra-marathoners by nationality showed that Japanese runners were the fastest worldwide. This selection to top athletes may lead to a selection bias and the aim of this study was to investigate from where the fastest 100-km ultra-marathoners originate by considering all finishers in 100-km ultra-marathons since 1959.

Methods

We analysed data from 150,710 athletes who finished a 100-km ultra-marathon between 1959 and 2016. To get precise estimates and stable density plots we selected only those nationalities with 900 and more finishes resulting in 24 nationalities. Histograms and density plots were performed to study the distribution of race time. Crude mean, standard deviation, median, interquartile range (IQR), mode, skewness and excess of time for each nationality were computed. A linear regression analysis adjusted by sex, age and year was performed to study the race time between the nationalities. Histograms, density and scatter plots showed that some races seemed to have a time limit of 14 hours. From the complete dataset the finishes with more than 14 hours were removed (truncated dataset) and the same descriptive plots and analysis as for the complete dataset were performed again. In addition to the linear regression a truncated regression was performed with the truncated dataset to allow conclusion for the whole sample. To study a potential difference between races at home and races abroad, an interaction term race site home/abroad with nationality was included in the model.

Results

Most of the finishes were achieved by runners from Japan, Germany, Switzerland, France, Italy and USA with more than 260’000 (85%) finishes. Runners from Russia and Hungary were the fastest and runners from Hong Kong and China were the slowest finishers.

Conclusion

In contrast to existing findings investigating the top 10 by nationality, this analysis showed that ultra-marathoners from Russia, not Japan, were the fastest 100-km ultra-marathoners worldwide when considering all races held since 1959.

Physiology and Pathophysiology in Ultra-Marathon Running

In this overview, we summarize the findings of the literature with regards to physiology and pathophysiology of ultra-marathon running. The number of ultra-marathon races and the number of official finishers considerably increased in the last decades especially due to the increased number of female and age-group runners. A typical ultra-marathoner is male, married, well-educated, and ~45 years old. Female ultra-marathoners account for ~20% of the total number of finishers. Ultra-marathoners are older and have a larger weekly training volume, but run more slowly during training compared to marathoners. Previous experience (e.g., number of finishes in ultra-marathon races and personal best marathon time) is the most important predictor variable for a successful ultra-marathon performance followed by specific anthropometric (e.g., low body mass index, BMI, and low body fat) and training (e.g., high volume and running speed during training) characteristics. Women are slower than men, but the sex difference in performance decreased in recent years to ~10–20% depending upon the length of the ultra-marathon. The fastest ultra-marathon race times are generally achieved at the age of 35–45 years or older for both women and men, and the age of peak performance increases with increasing race distance or duration. An ultra-marathon leads to an energy deficit resulting in a reduction of both body fat and skeletal muscle mass. An ultra-marathon in combination with other risk factors, such as extreme weather conditions (either heat or cold) or the country where the race is held, can lead to exercise-associated hyponatremia. An ultra-marathon can also lead to changes in biomarkers indicating a pathological process in specific organs or organ systems such as skeletal muscles, heart, liver, kidney, immune and endocrine system. These changes are usually temporary, depending on intensity and duration of the performance, and usually normalize after the race. In longer ultra-marathons, ~50–60% of the participants experience musculoskeletal problems. The most common injuries in ultra-marathoners involve the lower limb, such as the ankle and the knee. An ultra-marathon can lead to an increase in creatine-kinase to values of 100,000–200,000 U/l depending upon the fitness level of the athlete and the length of the race. Furthermore, an ultra-marathon can lead to changes in the heart as shown by changes in cardiac biomarkers, electro- and echocardiography. Ultra-marathoners often suffer from digestive problems and gastrointestinal bleeding after an ultra-marathon is not uncommon. Liver enzymes can also considerably increase during an ultra-marathon. An ultra-marathon often leads to a temporary reduction in renal function. Ultra-marathoners often suffer from upper respiratory infections after an ultra-marathon. Considering the increased number of participants in ultra-marathons, the findings of the present review would have practical applications for a large number of sports scientists and sports medicine practitioners working in this field.

Use of Bioimpedianciometer as Predictor of Mountain Marathon Performance

This study aimed to examine the relation among body composition, training experience and race time during a mountain marathon. Body composition and training pre-race experience analyses were conducted previous to a mountain marathon in 52 male athletes. A significant correlation between race time and mountain marathon with chronological age, body fat mass, percentage of body fat (BF), level of abdominal obesity, sport experience and daily training volume was revealed. In addition, BF and athlete's chronological age were negatively associated with race performance. In contrast, the daily training volume was positively associated with mountain marathon time. A regression analysis showed that race time could be predicted (R² = .948) by the daily training load, sports experience, age, body fat mass, BF and level of abdominal obesity. The comparison between performance groups regarding to body composition and training characteristics showed that the higher performance group was lighter with lower BF, fat mass and level of abdominal obesity, and with more days of training per week compared with the lower performance group (p < .05). Therefore, coaches and fitness trainers working with mountain marathon runners should develop exercise and nutritional strategies to reduce BF and consider increasing mean daily training volume to improve performance.

Similarities and Differences in Pacing Patterns in a 161-km and 101-km Ultra-Distance Road Race

Tan, PLS, Tan, FHY, and Bosch, AN. Similarities and differences in pacing patterns in a 161-km and 101-km ultra-distance road race. J Strength Cond Res 30(8): 2145-2155, 2016-The purpose of this study was to establish and compare the pacing patterns of fast and slow finishers in a tropical ultra-marathon. Data were collected from the Craze Ultra-marathon held on the 22nd and 21st of September in 2012 and 2013, respectively. Finishers of the 161-km (N = 47) and 101-km (N = 120) categories of the race were divided into thirds (groups A-C) by merit of finishing time. Altogether, 17 and 11 split times were recorded for the 161-km and 101-km finishers, respectively, and used to calculate the mean running speed for each distance segment. Running speed for the first segment was normalized to 100, with all subsequent splits adjusted accordingly. Running speed during the last 5 km was calculated against the mean race pace to establish the existence of an end spurt. A reverse J-shaped pacing profile was demonstrated in all groups for both distance categories and only 38% of the finishers executed an end spurt. In the 101-km category, in comparison with groups B and C, group A maintained a significantly more even pace (p = 0.013 and 0.001, respectively) and completed the race at a significantly higher percent of initial starting speed (p = 0.001 and 0.001, respectively). Descriptive data also revealed that the top 5 finishers displayed a "herd-behavior" by staying close to the lead runner in the initial portion of the race. These findings demonstrate that to achieve a more even pace, recreational ultra-runners should adopt a patient sustainable starting speed, with less competitive runners setting realistic performance goals whereas competitive runners with a specific time goal to consider running in packs of similar pace.

Do women reduce the gap to men in ultra-marathon running?

The aim of the present study was to examine sex differences across years in performance of runners in ultra-marathons lasting from 6 h to 10 days (i.e. 6, 12, 24, 48, 72, 144, and 240 h). Data of 32,187 finishers competing between 1975 and 2013 with 93,109 finishes were analysed using multiple linear regression analyses. With increasing age, the sex gap for all race durations increased. Across calendar years, the gap between women and men decreased in 6, 72, 144 and 240 h, but increased in 24 and 48 h. The men-to-women ratio differed among age groups, where a higher ratio was observed in the older age groups, and this relationship varied by distance. In all durations of ultra-marathon, the participation of women and men varied by age (p < 0.001), indicating a relatively low participation of women in the older age groups. In summary, between 1975 and 2013, women were able to reduce the gap to men for most of timed ultra-marathons and for those age groups where they had relatively high participation.

Performance and Age of the Fastest Female and Male 100-KM Ultramarathoners Worldwide From 1960 to 2012

The purpose of this cross-sectional study was to investigate the change in 100-km running performance and in the age of peak performance for 100-km ultramarathoners. Age and running speed of the annual fastest women and men in all 100-km ultramarathons held worldwide between 1960 and 2012 were analyzed in 148,017 finishes with 18,998 women and 129,019 men using single, multivariate, and nonlinear regressions. Running speed of the annual fastest men increased from 8.67 to 15.65 km.h(-1) and from 8.06 to 13.22 km.h(-1) for the annual fastest women. For the annual 10 fastest men, running speed increased from 10.23 ± 1.22 to 15.05 ± 0.29 km.h(-1) (p < 0.0001) and for the annual 10 fastest women from 7.18 ± 1.54 to 13.03 ± 0.18 km.h(-1) (p < 0.0001). The sex difference decreased from 56.1 to 16.3% for the annual fastest finishers (p < 0.0001) and from 46.7 ± 8.7% to 14.0 ± 1.2% for the annual 10 fastest finishers (p < 0.0001). The age of the annual fastest men increased from 29 to 40 years (p = 0.025). For the annual fastest women, the age remained unchanged at 35.0 ± 9.7 years (p = 0.469). For the annual 10 fastest women and men, the age remained unchanged at 34.9 ± 3.2 (p = 0.902) and 34.5 ± 2.5 years (p = 0.064), respectively. To summarize, 100-km ultramarathoners became faster, the sex difference in performance decreased but the age of the fastest finishers remained unchanged at ∼ 35 years. For athletes and coaches to plan a career as 100-km ultramarathoner, the age of the fastest female and male 100-km ultramarathoners remained unchanged at ∼ 35 years between 1960 and 2012 although the runners improved their performance over time.

Effects of training and anthropometric factors on marathon and 100 km ultramarathon race performance

Background

Marathon (42 km) and 100 km ultramarathon races are increasing in popularity. The aim of the present study was to investigate the potential associations of anthropometric and training variables with performance in these long-distance running competitions.

Methods

Training and anthropometric data from a large cohort of marathoners and 100 km ultramarathoners provided the basis of this work. Correlations between training and anthropometric indices of subjects and race performance were assessed using bivariate and multiple regression analyses.

Results

A combination of volume and intensity in training was found to be suitable for prediction of marathon and 100 km ultramarathon race pace. The relative role played by these two variables was different, in that training volume was more important than training pace for the prediction of 100 km ultramarathon performance, while the opposite was found for marathon performance. Anthropometric characteristics in terms of body fat percentage negatively affected 42 km and 100 km race performance. However, when this factor was relatively low (ie, less than 15% body fat), the performance of 42 km and 100 km races could be predicted solely on the basis of training indices.

Conclusion

Mean weekly training distance run and mean training pace were key predictor variables for both marathon and 100 km ultramarathon race performance. Predictive correlations for race performance are provided for runners with a relatively low body fat percentage.

Pacing strategy in male elite and age group 100 km ultra-marathoners

Pacing strategy has been investigated in elite 100 km and elite 161 km (100 mile) ultra-marathoners, but not in age group ultra-marathoners. This study investigated changes in running speed over segments in male elite and age group 100 km ultra-marathoners with the assumption that running speed would decrease over segments with increasing age of the athlete. Running speed during segments in male elite and age group finishers for 5-year age groups (ie, 18-24 to 65-69 years) in the 100 km Lauf Biel in Switzerland was investigated during the 2000-2009 period. Average running speed over segment time station (TS) TS1-TS2 (56.1 km) was compared with running speed Start-TS1 (38 km) and Start-TS3 (76.7 km) and running speed TS2-TS3 was compared with running speed Start-Finish. For the top ten athletes in each edition, running speed decreased from 2000 to 2009 for TS1-TS2 and TS2-TS3 (P<0.0001) but not in TS3-Finish (P>0.05). During TS1-TS2, athletes were running at 98.0%±2.1% of the running speed of Start-TS1. In TS2-TS3, they were running at 94.6%±3.4% of the running speed of TS1-TS2. In TS3-Finish, they were running at 95.5%±3.8% of running speed in TS2-TS3. For age group athletes, running speed decreased in TS1-TS2 and TS2-TS3. In TS3-Finish, running speed remained unchanged with the exception of the age group 40-44 years for which running speed increased. Running speed showed the largest decrease in the age group 18-24 years. To summarize, the top ten athletes in each edition maintained their running speed in the last segment (TS3-Finish) although running speed decreased over the first two segments (TS1-TS2 and TS2-TS3). The best pacers were athletes in the age group 40-44 years, who were able to achieve negative pacing in the last segment (TS3-Finish) of the race. The negative pacing in the last segment (TS3-Finish) was likely due to environmental conditions, such as early dawn and the flat circuit in segment TS3-Finish of the race.

Do non-elite older runners slow down more than younger runners in a 100 km ultra-marathon?

Background

This study investigated changes in normalised running speed as a proxy for effort distribution over segments in male elite and age group 100 km ultra-marathoners with the assumption that older runners would slow down more than younger runners.

Methods

The annual ten fastest finishers (i.e. elite and age group runners) competing between 2000 and 2009 in the ‘100 km Lauf Biel’ were identified. Normalised average running speed (i.e. relative to segment 1 of the race corrected for gradient) was analysed as a proxy for pacing in elite and age group finishers. For each year, the ratio of the running speed from the final to the first segment for each age cohort was determined. These ratios were combined across years with the assumption that there were no ‘extreme’ wind events etc. which may have impacted the final relative to the first segment across years. The ratios between the age cohorts were compared using one-way ANOVA and Tukey’s post-hoc test. The ratios between elite and age group runners were investigated using one-way ANOVA with Dunnett’s multiple comparison post-hoc tests. The trend across age groups was investigated using simple regression analysis with age as the dependent variable.

Results

Normalised average running speed was different between age group 18–24 years and age groups 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59 and 65–69 years. Regression analysis showed no trend across age groups (r² = 0.003, p > 0.05).

Conclusion

To summarize, (i) athletes in age group 18–24 years were slower than athletes in most other age groups and (ii) there was no trend of slowing down for older athletes.

Age and ultra-marathon performance - 50 to 1,000 km distances from 1969 - 2012

We investigated age and performance in distance-limited ultra-marathons held from 50 km to 1,000 km. Age of peak running speed and running speed of the fastest competitors from 1969 to 2012 in 50 km, 100 km, 200 km and 1,000 km ultra-marathons were analyzed using analysis of variance and multi-level regression analyses. The ages of the ten fastest women ever were 40 ± 4 yrs (50 km), 34 ± 7 yrs (100 km), 42 ± 6 yrs (200 km), and 41 ± 5 yrs (1,000 km). The ages were significantly different between 100 km and 200 km and between 100 km and 1,000 km. For men, the ages of the ten fastest ever were 34 ± 6 yrs (50 km), 32 ± 4 yrs (100 km), 44 ± 4 yrs (200 km), and 47 ± 9 yrs (1,000 km). The ages were significantly younger in 50 km compared to 100 km and 200 km and also significantly younger in 100 km compared to 200 km and 1,000 km. The age of the annual ten fastest women decreased in 50 km from 39 ± 8 yrs (1988) to 32 ± 4 yrs (2012) and in men from 35 ± 5 yrs (1977) to 33 ± 5 yrs (2012). In 100 km events, the age of peak running speed of the annual ten fastest women and men remained stable at 34.9 ± 3.2 and 34.5 ± 2.5 yrs, respectively. Peak running speed of top ten runners increased in 50 km and 100 km in women (10.6 ± 1.0 to 15.3 ± 0.7 km/h and 7.3 ± 1.5 to 13.0 ± 0.2 km/h, respectively) and men (14.3 ± 1.2 to 17.5 ± 0.6 km/h and 10.2 ± 1.2 to 15.1 ± 0.2 km/h, respectively). In 200 km and 1,000 km, running speed remained unchanged. In summary, the best male 1,000 km ultra-marathoners were ~15 yrs older than the best male 100 km ultra-marathoners and the best female 1,000 km ultra-marathoners were ~7 yrs older than the best female 100 km ultra-marathoners. The age of the fastest 50 km ultra-marathoners decreased across years whereas it remained unchanged in 100 km ultra-marathoners. These findings may help athletes and coaches to plan an ultra-marathoner's career. Future studies are needed on the mechanisms by which the fastest runners in the long ultra-marathons tend to be older than those in shorter ultra-marathons.

What is the age for the fastest ultra-marathon performance in time-limited races from 6 h to 10 days?

Recent findings suggested that the age of peak ultra-marathon performance seemed to increase with increasing race distance. The present study investigated the age of peak ultra-marathon performance for runners competing in time-limited ultra-marathons held from 6 to 240 h (i.e. 10 days) during 1975-2013. Age and running performance in 20,238 (21%) female and 76,888 (79%) male finishes (6,863 women and 24,725 men, 22 and 78%, respectively) were analysed using mixed-effects regression analyses. The annual number of finishes increased for both women and men in all races. About one half of the finishers completed at least one race and the other half completed more than one race. Most of the finishes were achieved in the fourth decade of life. The age of the best ultra-marathon performance increased with increasing race duration, also when only one or at least five successful finishes were considered. The lowest age of peak ultra-marathon performance was in 6 h (33.7 years, 95% CI 32.5-34.9 years) and the highest in 48 h (46.8 years, 95% CI 46.1-47.5). With increasing number of finishes, the athletes improved performance. Across years, performance decreased, the age of peak performance increased, and the age of peak ultra-marathon performance increased with increasing number of finishes. In summary, the age of peak ultra-marathon performance increased and performance decreased in time-limited ultra-marathons. The age of peak ultra-marathon performance increased with increasing race duration and with increasing number of finishes. These athletes improved race performance with increasing number of finishes.

The best triathletes are older in longer race distances - a comparison between Olympic, Half-Ironman and Ironman distance triathlon

The purpose of this study was (i) to determine the age of peak triathlon performance for world class athletes competing in Olympic, Half-Ironman and Ironman distance races and (ii) to investigate a potential change in the age of the annual fastest athletes across years. Data of ages and race times of all finishers in the international top races over the three distances between 2003 and 2013 were collected and the annual top ten women and men were analysed using linear, non-linear and hierarchical multivariate regression analyses. The age of peak male performance was 27.1 ± 4.9 years in the Olympic, 28.0 ± 3.8 years in the Half-Ironman and 35.1 ± 3.6 years in the Ironman distance and the age of peak male performance was higher in the Ironman compared to the Olympic (p < 0.05) and the Half-Ironman distance (p < 0.05) triathlon. The age of peak female performance was 26.6 ± 4.4 years in the Olympic, 31.6 ± 3.4 years in the Half-Ironman and 34.4 ± 4.4 years in the Ironman distance and the age of peak female performance was lower in the Olympic compared to the Half-Ironman (p < 0.05) and Ironman distance (p < 0.05) triathlon. The age of the annual top ten women and men remained unchanged over the last decade in the Half-Ironman and the Ironman distance. In the Olympic distance, however, the age of the annual top ten men decreased slightly. To summarize, the age of peak triathlon performance was higher in the longer triathlon race distances (i.e. Ironman) and the age of the annual top triathletes remained mainly stable over the last decade. With these findings top athletes competing at world class level can plan their career more precisely as they are able to determine the right time in life to switch from the shorter (i.e. Olympic distance) to the longer triathlon race distances (i.e. Half-Ironman and Ironman) in order to continuously compete in triathlon races at world class level.

Age group performances in 100 km and 100 miles ultra-marathons

Improved performance has been reported for master runners (i.e. athletes older than 40 years) in both single marathons and single ultra-marathons. This study investigated performance trends of age group ultra-marathoners competing in all 100 km and 100 miles races held worldwide between 1971 and 2013. Changes in running speeds across years were investigated for the annual ten fastest 5-year age group finishers using linear, non-linear and multi-level regression analyses. In 100 km, running speed remained unchanged in women in 25–29 years, increased non-linearly in 30–34 to 55–59 years, and linearly in 60–64 years. In men, running speed increased non-linearly in 18–24 to 60–64 years and linearly in 65–69 to 75–79 years. In 100 miles, running speed increased in women linearly in 25–29 and 30–34 years, non-linearly in 35–39 to 45–49 years, and linearly in 50–54 and 55–59 years. For men, running speed increased linearly in 18–24 years, non-linearly in 25–29 to 45–49 years, and linearly in 50–54 to 65–69 years. Overall, the faster race times over the last 30 years are a result of all top ten finishers getting faster. These findings suggest that athletes in younger to middle age groups (i.e. 25–35 to 50–65 years depending upon sex and distance) have reached their limits due to a non-linear increase in running speed whereas runners in very young (i.e. younger than 25–35 years) and older age groups (i.e. older than 50–65 years) depending upon sex and distance might still improve their performance due to a linear increase in running speed.

Prediction of half-marathon race time in recreational female and male runners

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r² = 0.42, adjusted r² = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) – 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r² = 0.68, adjusted r² = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) – 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the models.

Sex and age-related differences in performance in a 24-hour ultra-cycling draft-legal event - a cross-sectional data analysis

Background

The purpose of this study was to examine the sex and age-related differences in performance in a draft-legal ultra-cycling event.

Methods

Age-related changes in performance across years were investigated in the 24-hour draft-legal cycling event held in Schötz, Switzerland, between 2000 and 2011 using multi-level regression analyses including age, repeated participation and environmental temperatures as co-variables.

Results

For all finishers, the age of peak cycling performance decreased significantly (β = −0.273, p = 0.036) from 38 ± 10 to 35 ± 6 years in females but remained unchanged (β = −0.035, p = 0.906) at 41.0 ± 10.3 years in males. For the annual fastest females and males, the age of peak cycling performance remained unchanged at 37.3 ± 8.5 and 38.3 ± 5.4 years, respectively. For all female and male finishers, males improved significantly (β = 7.010, p = 0.006) the cycling distance from 497.8 ± 219.6 km to 546.7 ± 205.0 km whereas females (β = −0.085, p = 0.987) showed an unchanged performance of 593.7 ± 132.3 km. The mean cycling distance achieved by the male winners of 960.5 ± 51.9 km was significantly (p < 0.001) greater than the distance covered by the female winners with 769.7 ± 65.7 km but was not different between the sexes (p > 0.05). The sex difference in performance for the annual winners of 19.7 ± 7.8% remained unchanged across years (p > 0.05). The achieved cycling distance decreased in a curvilinear manner with advancing age. There was a significant age effect (F = 28.4, p < 0.0001) for cycling performance where the fastest cyclists were in age group 35–39 years.

Conclusion

In this 24-h cycling draft-legal event, performance in females remained unchanged while their age of peak cycling performance decreased and performance in males improved while their age of peak cycling performance remained unchanged. The annual fastest females and males were 37.3 ± 8.5 and 38.3 ± 5.4 years old, respectively. The sex difference for the fastest finishers was ~20%. It seems that women were not able to profit from drafting to improve their ultra-cycling performance.

Runners in their forties dominate ultra-marathons from 50 to 3,100 miles

OBJECTIVES

This study investigated performance trends and the age of peak running speed in ultra-marathons from 50 to 3,100 miles.

METHODS

The running speed and age of the fastest competitors in 50-, 100-, 200-, 1,000- and 3,100-mile events held worldwide from 1971 to 2012 were analyzed using single- and multi-level regression analyses.

RESULTS

The number of events and competitors increased exponentially in 50- and 100-mile events. For the annual fastest runners, women improved in 50-mile events, but not men. In 100-mile events, both women and men improved their performance. In 1,000-mile events, men became slower. For the annual top ten runners, women improved in 50- and 100-mile events, whereas the performance of men remained unchanged in 50- and 3,100-mile events but improved in 100-mile events. The age of the annual fastest runners was approximately 35 years for both women and men in 50-mile events and approximately 35 years for women in 100-mile events. For men, the age of the annual fastest runners in 100-mile events was higher at 38 years. For the annual fastest runners of 1,000-mile events, the women were approximately 43 years of age, whereas for men, the age increased to 48 years of age. For the annual fastest runners of 3,100-mile events, the age in women decreased to 35 years and was approximately 39 years in men.

CONCLUSION

The running speed of the fastest competitors increased for both women and men in 100-mile events but only for women in 50-mile events. The age of peak running speed increased in men with increasing race distance to approximately 45 years in 1,000-mile events, whereas it decreased to approximately 39 years in 3,100-mile events. In women, the upper age of peak running speed increased to approximately 51 years in 3,100-mile events.

A Comparison of Anthropometric and Training Characteristics between Female and Male Half-Marathoners and the Relationship to Race Time

Purpose

Lower limb skin-fold thicknesses have been differentially associated with sex in elite runners. Front thigh and medial calf skin-fold appear to be related to 1,500m and 10,000m time in men but 400m time in women. The aim of the present study was to compare anthropometric and training characteristics in recreational female and male half-marathoners.

Methods

The association between both anthropometry and training characteristics and race time was investigated in 83 female and 147 male recreational half marathoners using bi- and multi-variate analyses.

Results

In men, body fat percentage (β=0.6), running speed during training (β=-3.7), and body mass index (β=1.9) were related to half-marathon race time after multi-variate analysis. After exclusion of body mass index, r² decreased from 0.51 to 0.49, but body fat percentage (β=0.8) and running speed during training (β=-4.1) remained predictive. In women, body fat percentage (β=0.75) and speed during training (β=-6.5) were related to race time (r²=0.73). For women, the exclusion of body mass index had no consequence on the predictive variables for half-marathon race time.

Conclusion

To summarize, in both female and male recreational half-marathoners, both body fat percentage and running speed during training sessions were related to half-marathon race times when corrected with co-variates after multi-variate regression analyses.

Finisher and performance trends in female and male mountain ultramarathoners by age group

Background

This study examined changes according to age group in the number of finishers and running times for athletes in female and male mountain ultramarathoners competing in the 78 km Swiss Alpine Marathon, the largest mountain ultramarathon in Europe and held in high alpine terrain.

Methods

The association between age and performance was investigated using analysis of variance and both single and multilevel regression analyses.

Results

Between 1998 and 2011, a total of 1,781 women and 12,198 men finished the Swiss Alpine Marathon. The number of female finishers increased (r² = 0.64, P = 0.001), whereas the number of male finishers (r² = 0.18, P = 0.15) showed no change. The annual top ten men became older and slower, whereas the annual top ten women became older but not slower. Regarding the number of finishers in the age groups, the number of female finishers decreased in the age group 18–24 years, whereas the number of finishers increased in the age groups 30–34, 40–44, 45–49, 50–54, 55–59, 60–64, and 70–74 years. In the age groups 25–29 and 35–39 years, the number of finishers showed no changes across the years. In the age group 70–74 years, the increase in number of finishers was linear. For all other age groups, the increase was exponential. For men, the number of finishers decreased in the age groups 18–24, 25–29, 30–34, and 35–39 years. In the age groups 40–44, 45–49, 50–54, 55–59, 60–64, 70–74, and 75–79 years, the number of finishers increased. In the age group 40–44 years, the increase was linear. For all other age groups, the increase was exponential. Female finishers in the age group 40–44 years became faster over time. For men, finishers in the age groups 18–24, 25–29, 30–34, 40–44, and 45–49 years became slower.

Conclusion

The number of women older than 30 years and men older than 40 years increased in the Swiss Alpine Marathon. Performance improved in women aged 40–44 years but decreased in male runners aged 18–49 years.

Age and gender difference in non-drafting ultra-endurance cycling performance - the 'Swiss Cycling Marathon'

Background

In recent years, there was an increased interest in investigating the gender difference in performance and the age of peak performance in ultra-endurance performances such as ultra-triathlon, ultra-running, and ultra-swimming, but not in ultra-cycling. The aim of the present study was to analyze the gender difference in ultra-cycling performance and the age of peak ultra-cycling performance in the 720-km ‘Swiss Cycling Marathon’, the largest European qualifier for the ‘Race Across America’.

Methods

Changes in the cycling speed and age of 985 finishers including 38 women and 947 men competing in the Swiss Cycling Marathon from 2001 to 2012 covering a distance of 720 km with a change of altitude of 4,993 m were analyzed using linear regression.

Results

The gender difference in performance was 13.6% for the fastest cyclists ever, 13.9% ± 0.5% for the three fastest cyclists ever and 19.1% ± 3.7% for the ten fastest cyclists ever. The gender difference in performance for the annual top three women and men decreased from 35.0% ± 9.5% in 2001 to 20.4% ± 7.7% in 2012 (r² = 0.72, p = 0.01). The annual top three women improved cycling speed from 20.3 ± 3.1 km h⁻¹ in 2003 to 24.8 ± 2.4 km h⁻¹ in 2012 (r² = 0.79, p < 0.01). The cycling speed of the annual top three men remained unchanged at 30.2 ± 0.6 km h⁻¹ (p > 0.05). The age of peak performance for the ten fastest finishers ever was 35.9 ± 9.6 years for men and 38.7 ± 7.8 years for women, respectively (p = 0.47).

Conclusions

The gender difference in ultra-cycling performance decreased over the 2001 to 2012 period in the 720-km Swiss Cycling Marathon for the annual top three cyclists and reached approximately 14%. Both women and men achieved peak performance at the age of approximately 36 to 39 years. Women might close the gender gap in ultra-endurance cycling in longer cycling distances. Future studies need to investigate the gender difference in performance in the Race Across America, the longest nonstop and non-drafting ultra-cycling race in the world.

Participation and performance trends in ultra-endurance running races under extreme conditions - 'Spartathlon' versus 'Badwater'

BACKGROUND

The aim of the present study was to compare the trends in participation, performance and age of finishers in 'Badwater' and 'Spartathlon' as two of the toughest ultramarathons in the world of more than 200 km of distance.

METHODS

Running speed and age of male and female finishers in Badwater and Spartathlon were analyzed from 2000 to 2012. Age of peak performance and sex difference in running speed were investigated during the studied period.

RESULTS

The number of female and male finishes increased in Badwater and Spartathlon. Women accounted on average for 21.5% ± 6.9% in Badwater and 10.8% ± 2.3% in Spartathlon. There was a significant increase in female participation in Badwater from 18.4% to 19.1% (p < 0.01) and in Spartathlon from 11.9% to 12.5% (p = 0.02). In men, the age of finishers was higher in Badwater (46.5 ± 9.3 years) compared to Spartathlon (44.8 ± 8.2 years) (p < 0.01). The age of female finishers of both races was similar with 43.0 ± 7.5 years in Badwater and 44.5 ± 7.8 years in Spartathlon (p > 0.05). Over the years, the age of the annual five fastest men decreased in Badwater from 42.4 ± 4.2 to 39.8 ± 5.7 years (p < 0.05). For women, the age remained unchanged at 42.3 ± 3.8 years in Badwater (p > 0.05). In Spartathlon, the age was unchanged at 39.7 ± 2.4 years for men and 44.6 ± 3.2 years for women (p > 0.05). In Badwater, women and men became faster over the years. The running speed increased from 7.9 ± 0.7 to 8.7 ± 0.6 km/h (p < 0.01) in men and from 5.4 ± 1.1 to 6.6 ± 0.5 km/h (p < 0.01) in women. The sex difference in running speed remained unchanged at 19.8% ± 4.8% (p > 0.05). In Spartathlon, the running speed was stable over time at 10.8 ± 0.7 km/h for men and 8.7 ± 0.5 km/h for women (p > 0.05). The sex difference remained unchanged at 19.6% ± 2.5% (p > 0.05).

CONCLUSIONS

These results suggest that for both Badwater and Spartathlon, (a) female participation increased, (b) the fastest finishers were approximately 40 to 45 years, and (c) the sex difference was at approximately 20%. Women will not outrun men in both Badwater and Spartathlon races. Master ultramarathoners can achieve a high level of performance in ultramarathons greater than 200 km under extreme conditions.

Estimation bias: body mass and body height in endurance athletes

Body Mass Index is associated with endurance performance in athletes. Reported and measured values of body mass and body height in 1,618 endurance athletes (1,358 men, 260 women) showed that men and women both underestimated their body mass and overestimated their body height, leading to an underestimation of Body Mass Index. There were age and sex differences in estimates of height and weight; for both women and men, underestimation of Body Mass Index amounted to 0.4 kg/m2. Master athletes tended to underestimate their body mass and overestimate their body height thus leading to significant differences between estimated and measured Body Mass Index. However, the magnitude of underestimation of BMI probably has a negligible influence on performance predictions. The differences between measured and estimated body mass, height, and BMI were within the range of normal daily variation, and for body height even within the precision of the measurement (0.5 cm).

Does Muscle Mass Affect Running Times in Male Long-distance Master Runners?

Purpose

The aim of the present study was to investigate associations between skeletal muscle mass, body fat and training characteristics with running times in master athletes (age > 35 years) in half-marathon, marathon and ultra-marathon.

Methods

We compared skeletal muscle mass, body fat and training characteristics in master half-marathoners (n=103), master marathoners (n=91) and master ultra-marathoners (n=155) and investigated associations between body composition and training characteristics with race times using bi- and multi-variate analyses.

Results

After multi-variate analysis, body fat was related to half-marathon (β=0.9, P=0.0003), marathon (β=2.2, P<0.0001), and ultra-marathon (β=10.5, P<0.0001) race times. In master half-marathoners (β=-4.3, P<0.0001) and master marathoners (β=-11.9, P<0.0001), speed during training was related to race times. In master ultra-marathoners, however, weekly running kilometers (β=-1.6, P<0.0001) were related to running times.

Conclusions

To summarize, body fat and training characteristics, not skeletal muscle mass, were associated with running times in master half-marathoners, master marathoners, and master ultra-marathoners. Master half-marathoners and master marathoners rather rely on a high running speed during training whereas master ultra-marathoners rely on a high running volume during training. The common opinion that skeletal muscle mass affects running performance in master runners needs to be questioned.

Ultramarathon runners: nature or nurture?

Ultramarathon running is increasingly popular. An ultramarathon is defined as a running event involving distances longer than the length of a traditional marathon of 42.195 km. In ultramarathon races, ~80% of the finishers are men. Ultramarathoners are typically ~45 y old and achieve their fastest running times between 30 and 49 y for men, and between 30 and 54 y for women. Most probably, ultrarunners start with a marathon before competing in an ultramarathon. In ultramarathoners, the number of previously completed marathons is significantly higher than the number of completed marathons in marathoners. However, recreational marathoners have a faster personal-best marathon time than ultramarathoners. Successful ultramarathoners have 7.6 ± 6.3 y of experience in ultrarunning. Ultramarathoners complete more running kilometers in training than marathoners do, but they run more slowly during training than marathoners. To summarize, ultramarathoners are master runners, have a broad experience in running, and prepare differently for an ultramarathon than marathoners do. However, it is not known what motivates male ultramarathoners and where ultramarathoners mainly originate. Future studies need to investigate the motivation of male ultramarathoners, where the best ultramarathoners originate, and whether they prepare by competing in marathons before entering ultramarathons.

Body mass change and ultraendurance performance: a decrease in body mass is associated with an increased running speed in male 100-km ultramarathoners

We investigated, in 50 recreational male ultrarunners, the changes in body mass, selected hematological and urine parameters, and fluid intake during a 100-km ultramarathon. The athletes lost (mean and SD) 2.6 (1.8) % in body mass (p < 0.0001). Running speed was significantly and negatively related to the change in body mass (p < 0.05). Serum sodium concentration ([Na⁺]) and the concentration of aldosterone increased with increasing loss in body mass (p < 0.05). Urine-specific gravity increased (p < 0.0001). The change in body mass was significantly and negatively related to postrace serum [Na⁺] (p < 0.05). Fluid intake was significantly and positively related to both running speed (r = 0.33, p = 0.0182) and the change in body mass (r = 0.44, p = 0.0014) and significantly and negatively to both postrace serum [Na⁺] (r = -0.42, p = 0.0022) and the change in serum [Na⁺] (r = -0.38, p = 0.0072). This field study showed that recreational, male, 100-km ultramarathoners dehydrated as evidenced by the decrease in >2 % body mass and the increase in urine-specific gravity. Race performance, however, was not impaired because of the loss in body mass. In contrast, faster athletes lost more body mass compared with slower athletes while also drinking more. The concept that a loss of >2% in body mass leads to dehydration and consequently impairs endurance performance must be questioned for ultraendurance athletes competing in the field. For practical applications, a loss in body mass during a 100-km ultramarathon was associated with a faster running speed.

Comparison of anthropometric and training characteristics between recreational male marathoners and 24-hour ultramarathoners

Background

Of the anthropometry and training variables used to predict race performance in a 24-hour ultrarun, the personal best marathon time is the strongest predictor in recreational male 24-hour ultramarathoners. This finding raises the question of whether similarities exist between male recreational 24-hour ultramarathoners and male recreational marathoners.

Methods

The association between age, anthropometric variables (ie, body mass, body height, body mass index, percent body fat, skeletal muscle mass, limb circumference, and skinfold thickness at the pectoral, mid axillary, triceps, subscapular, abdominal, suprailiac, front thigh, and medial calf sites), previous experience and training characteristics (ie, volume, speed, and personal best time), and race time for 79 male recreational 24-hour ultramarathoners and 126 male recreational marathoners was investigated using bivariate and multivariate analysis.

Results

The 24-hour ultramarathoners were older (P < 0.05), had a lower circumference at both the upper arm (P < 0.05) and thigh (P < 0.01), and a lower skinfold thickness at the pectoral, axillary, and suprailiac sites (P < 0.05) compared with the marathoners. During training, the 24-hour ultramarathoners were running for more hours per week (P < 0.001) and completed more kilometers (P < 0.001), but were running slower (P < 0.01) compared with the marathoners. In the 24-hour ultramarathoners, neither anthropometric nor training variables were associated with kilometers completed in the race (P > 0.05). In the marathoners, percent body fat (P < 0.001) and running speed during training (P < 0.0001) were related to marathon race times.

Conclusion

In summary, differences in anthropometric and training predictor variables do exist between male recreational 24-hour ultramarathoners and male recreational marathoners for race performance.

Effects of individual aerobic performance on finish time in mountain running

It was hypothesized that for each mountain running competition, there is a certain individual performance level below which running times increase dramatically. The running times of 869 finishers of 3 international mountain running competitions have been analysed. A hyperbolic association was demonstrated between finish times in mountain running competitions and individual performance at the anaerobic threshold (VO2AT(Race)). Due to the non-linear association, there is an increasing effect on both the finish time and the change of finish time with decreasing aerobic performance. In all three competitions, the change of finish time is about 7 times more pronounced in mountain runners with the lowest VO2ATL,, compared to those with the highest values of VO2AT(Race). Both athletes and organizers should keep in mind these effects of decreasing aerobic performance on running times and potentially associated risks.

Age-related changes in 100-km ultra-marathon running performance

The aims of this study were (1) to investigate the participation and performance trends at the '100 km Lauf Biel' in Switzerland from 1998 to 2010, and (2) to compare the age-related changes in 100-km running performance between males and females. For both sexes, the percent of finishers significantly (P < 0.01) decreased for the 18-29 and the 30-39-year age groups, while it significantly (P < 0.01) increased for the 40-49 and the 50-59-year age groups over the studied period. From 1998 to 2010, the mean age of the top ten finishers increased by 0.4 years per annum for both females (P = 0.02) and males (P = 0.003). The running time for the top ten finishers remained stable for females, while it significantly (P = 0.001) increased by 2.4 min per annum for males. There was a significant (P < 0.001) age effect on running times for both sexes. The best 100-km running times was observed for the age comprised between 30 and 49 years for males, and between 30 and 54 years for females, respectively. The age-related decline in running performance was similar until 60-64 years between males and females, but was greater for females compared to males after 65 years. Future studies should investigate the lifespan from 65 to 75 years to better understand the performance difference between male and female master ultra-marathoners.

Comparison between Recreational Male Ironman Triathletes and Marathon Runners

Recent investigations described a personal best marathon time as a predictor variable for an Ironman race time in recreational male Ironman triathletes. Similarities and differences in anthropometry and training were investigated between 83 recreational male Ironman triathletes and 81 recreational male marathoners. Ironman triathletes were significantly taller and had a higher body mass and a higher skin-fold thickness of the calf compared to the marathoners. Weekly training volume in hours was higher in Ironman triathletes. In the Ironman triathletes, percent body fat was related to overall race time and both the split time in cycling and running. The weekly swim kilometres were related to the split time in swimming, and the speed in cycling was related to the bike split time. For the marathoners, the calf skin-fold thickness and running speed during training were related to marathon race time. Although personal best marathon time was a predictor of Ironman race time in male triathletes, anthropometric and training characteristics of male marathoners were different from those of male Ironman triathletes, probably due to training of different muscle groups and metabolic endurance beyond marathon running, as the triathletes are also training for high-level performance in swimming and cycling. Future studies should compare Olympic distance triathletes and road cyclists with Ironman triathletes.

Age and sex interactions in mountain ultramarathon running - the Swiss Alpine Marathon

Background

The aims of the study were to examine the (a) participation, (b) difference in running times between the sexes, and (c) age-related decline in the running times of ultramarathoner women and men competing in the Swiss Alpine Marathon from 1998 to 2011.

Methods

The ultramarathoners competing in the Swiss Alpine Marathon were analyzed in terms of participation, difference in running times between the sexes, age of the fastest runners, and age-related decline in the fastest running times. The race covers a distance of 78 km, with a total altitude change of approximately 2260 m. A total of 12,194 men and 1781 women finished the race between 1998 and 2011.

Results

Women’s participation increased from approximately 10% in 1998 to approximately 16% in 2011 (r² = 0.57; P = 0.001), but participation remained unchanged in men (r² = 0.17; P > 0.05). Over the years, the top ten women showed no change in running times (r² = 0.02; P > 0.05), whereas the top ten men’s running times increased (r² = 0.46; P < 0.01). The age for peak running times increased over time both for the top ten women (r² = 0.58; P < 0.01) and for the top ten men (r² = 0.40; P = 0.01).

Conclusion

Among the top women, participation increased, the age for peak running times increased, and the running times remained unchanged. Among the men, however, the participation remained steady, and both the peak running-time age and the running times increased.

Similarities and differences in anthropometry and training between recreational male 100-km ultra-marathoners and marathoners

Several recent investigations showed that the best marathon time of an individual athlete is also a strong predictor variable for the race time in a 100-km ultra-marathon. We investigated similarities and differences in anthropometry and training characteristics between 166 100-km ultra-marathoners and 126 marathoners in recreational male athletes. The association of anthropometric variables and training characteristics with race time was assessed by using bi- and multi-variate analysis. Regarding anthropometry, the marathoners had a significantly lower calf circumference (P < 0.05) and a significantly thicker skinfold at pectoral (P < 0.01), axilla (P < 0.05), and suprailiacal sites (P < 0.05) compared to the ultra-marathoners. Considering training characteristics, the marathoners completed significantly fewer hours (P < 0.001) and significantly fewer kilometres (P < 0.001) during the week, but they were running significantly faster during training (P < 0.001). The multi-variate analysis showed that age (P < 0.0001), body mass (P = 0.011), and percent body fat (P = 0.019) were positively and weekly running kilometres (P < 0.0001) were negatively related to 100-km race times in the ultra-marathoners. In the marathoners, percent body fat (P = 0.002) was positively and speed in running training (P < 0.0001) was negatively associated with marathon race times. In conclusion, these data suggest that performance in both marathoners and 100-km ultra-marathoners is inversely related to body fat. Moreover, marathoners rely more on speed in running during training whereas ultra-marathoners rely on volume in running training.

Age, Training, and Previous Experience Predict Race Performance in Long-Distance Inline Skaters, Not Anthropometry

The association of characteristics of anthropometry, training, and previous experience with race time in 84 recreational, long-distance, inline skaters at the longest inline marathon in Europe (111 km), the Inline One-eleven in Switzerland, was investigated to identify predictor variables for performance. Age, duration per training unit, and personal best time were the only three variables related to race time in a multiple regression, while none of the 16 anthropometric variables were related. Anthropometric characteristics seem to be of no importance for a fast race time in a long-distance inline skating race in contrast to training volume and previous experience, when controlled with covariates. Improving performance in a long-distance inline skating race might be related to a high training volume and previous race experience. Also, doing such a race requires a parallel psychological effort, mental stamina, focus, and persistence. This may be reflected in the preparation and training for the event. Future studies should investigate what motivates these athletes to train and compete.