Race performance in male mountain ultra-marathoners: anthropometry or training?

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.

Risk factors for not finishing an ultramarathon: 4-year study in 23996 race starters, SAFER XXI

BACKGROUND

Limited data support pre-race medical screening to identify risk factors for not finishing an endurance running race. The aim of the study was to determine risk factors associated with not finishing an ultramarathon.

METHODS

A prospective, cross-sectional study of Two Oceans ultramarathon (56km) race starters who completed a pre-race medical screening questionnaire. Race day environmental conditions were recorded on race day. Univariate analyses of risk factors associated with the did-not-finish (DNF) included race day factors and pre-race medical screening history.

RESULTS

Risk factors for DNF amongst 23996 starters during the 56km race included older age and being female (p<0.0001). After adjusting for age and sex, the following were significant univariate risk factors: fewer years of running (p<0.0001), less previous race experience (p<0.0001), less training / racing per week (p=0.0002), lower average weekly training distance (p=0.0016), slower race vs. training speed (p<0.0001), lack of allergies (p=0.0100) and average wet-bulb globe temperature (p<0.0001).

CONCLUSIONS

Females, older age, training-related factors (less training / racing, average weekly training distance, race vs. training speed) and average wet-bulb temperature, were risk factors for not finishing an ultramarathon. The results may not only assist runners and coaches in race preparation, but also have clinical implications for the medical planning prior to races.

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 Determinants of Ultramarathon Trail-Running Performance

CONTEXT

The physiological determinants of ultramarathon success have rarely been assessed and likely differ in their contributions to performance as race distance increases.

PURPOSE

To examine predictors of performance in athletes who completed either a 50-, 80-, or 160-km trail race over a 20-km loop course on the same day.

METHODS

Measures of running history, aerobic fitness, running economy, body mass loss, hematocrit alterations, age, and cardiovascular health were examined in relation to race-day performance. Performance was defined as the percentage difference from the winning time at a given race distance, with 0% representing the fastest possible time.

RESULTS

In the 50-km race, training volumes, cardiovascular health, aerobic fitness, and a greater loss of body mass during the race were all related to better performance (all P < .05). Using multiple linear regression, peak velocity achieved in the maximal oxygen uptake test (β = -11.7, P = .002) and baseline blood pressure (β = 3.1, P = .007) were the best performance predictors for the men's 50-km race (r = .98, r2 = .96, P < .001), while peak velocity achieved in the maximal oxygen uptake test (β = -13.6, P = .001) and loss of body mass (β = 12.8, P = .03) were the best predictors for women (r = .94, r2 = .87, P = .001). In the 80-km race, only peak velocity achieved in the maximal oxygen uptake test predicted performance (β = -20.3, r = .88, r2 = .78, P < .001). In the 160-km race, there were no significant performance determinants.

CONCLUSIONS

While classic determinants of running performance, including cardiovascular health and running fitness, predict 50-km trail-running success, performance in longer-distance races appears to be less influenced by such physiological parameters.

To Be a Champion of the 24-h Ultramarathon Race. If Not the Heart ... Mosaic Theory?

This comprehensive case analysis aimed to identify the features enabling a runner to achieve championship in 24-h ultramarathon (UM) races. A 36-year-old, multiple medalist of the World Championships in 24-h running, was assessed before, one and 10 days after a 24-h run. Results of his extensive laboratory and cardiological diagnostics with transthoracic echocardiography (TTE) and a one-time cardiopulmonary exercise test (CPET) were analyzed. After 12 h of running (approximately 130 km), the athlete experienced an increasing pain in the right knee. His baseline clinical data were within the normal range. High physical efficiency in CPET (VO₂max 63 mL/kg/min) was similar to the average achieved by other ultramarathoners who had significantly worse results. Thus, we also performed genetic tests and assessed his psychological profile, body composition, and markers of physical and mental stress (serotonin, cortisol, epinephrine, prolactin, testosterone, and luteinizing hormone). The athlete had a mtDNA haplogroup H (HV0a1 subgroup, belonging to the HV cluster), characteristic of athletes with the highest endurance. Psychological studies have shown high and very high intensity of the properties of individual scales of the tools used mental resilience (62–100% depending on the scale), openness to experience (10th sten), coherence (10th sten), positive perfectionism (100%) and overall hope for success score (10th sten). The athlete himself considers the commitment and mental support of his team to be a significant factor of his success. Body composition assessment (%fat 13.9) and the level of stress markers were unremarkable. The tested athlete showed a number of features of the champions of ultramarathon runs, such as: inborn predispositions, mental traits, level of training, and resistance to pain. However, none of these features are reserved exclusively for “champions”. Team support’s participation cannot be underestimated. The factors that guarantee the success of this elite 24-h UM runner go far beyond physiological and psychological explanations. Further studies are needed to identify individual elements of the putative “mosaic theory of being a champion”.

Muscle Tone and Body Weight Predict Uphill Race Time in Amateur Trail Runners

Background: Vertical kilometer is an emerging sport where athletes continuously run uphill. The aims of this study were to assess changes in vertical impacts caused by uphill running (UR) and the relation between the anthropometric and lower limb muscular characteristics with speed. Methods: Ten male experienced runners (35 ± 7 years old) participated in this study. In the racetrack (4.2 km long, 565 m high), seven sections were stablished. Mean speed and impact value of sections with similar slope (≈21%) were calculated. The gastrocnemius stiffness (GS) and tone (GT); and the vastus lateralis stiffness (VS) and tone (VT) were assessed before the race. Results: Pearson’s correlation showed a linear relationship between vs. and VT (r = 0.829; p = 0.000), GT and GS (r = 0.792; p = 0.001). Mean speed is correlated with weight (r = −0.619; p = 0.024) and GT (r = 0.739; p = 0.004). Multiple linear regressions showed a model with weight and GT as dependent variables of mean speed. Mean impacts decreased significantly between sections along the race. Conclusions: The vertical impacts during UR were attenuated during the race. Moreover, body weight and GT were associated with the time-to-finish, which supports that low weight alone could not be enough to be faster, and strength training of plantar flexors may be a determinant in UR.

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.

Long distance running - Can bioprofiling predict success in endurance athletes?

The TransEuropeFootRace (TEFR) was one of the most extreme multistage competitions worldwide. The ultramarathon took the runners over a distance of 4487 km, from Bari, Italy, to the North Cape, Norway, in 64 days. The participating ultra-long-distance runners had to complete almost two marathons per day (~70 km). The race was accompanied by a research team analysing adaptations of different organ systems of the human body that were exposed to a chronic lack of regeneration time. Here, we analyzed runner's urine using mass spectrometric profiling of thousands of low-molecular weight compounds. The results indicated that pre-race molecular factors can predict finishers and separate them from nonfinishers already before the race. These observations were related to the training volume as finishers ran about twice as many kilometers per week before TEFR than nonfinishers, thus apparently achieving a higher performance level and resistance against overuse. While this hypothesis needs to be validated in future long-distance races, the bioprofiling experiments suggest that the competition readiness of the runners is measurable and might be adjustable.

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.

Training and Body Composition during Preparation for a 48-Hour Ultra-Marathon Race: A Case Study of a Master Athlete

Although the acute effects of ultra-endurance exercise on body composition have been well studied, limited information exists about the chronic adaptations of body composition to ultra-endurance training. The aim of the present study was to examine the day-by-day variation of training and body composition of a master athlete during the preparation for a 48-hour ultra-marathon race. For all training sessions (n = 73) before the race, the running distance, duration, and pace were recorded, and body mass, body fat (BF), body water (%), visceral fat, fat-free mass (FFM), four circumferences (i.e., waist, upper arm, thigh and calf), and eight skinfolds (i.e., chest, mid-axilla, triceps, subscapular, abdomen, iliac crest, thigh and calf) were measured accordingly in a 53-year-old experienced ultra-endurance athlete (body mass 80.1 kg, body height 177 cm, body mass index 25.6 kg·m⁻²). The main findings of the present study were that (a) the training plan of the ultra-endurance master athlete followed a periodization pattern with regard to exercise intensity and training volume, which increased over time, (b) the body mass, BF, and FFM decreased largely during the first 30 training sessions, and (c) the circumferences and skinfolds reflected the respective decrease in BF. The findings of this case study provided useful information about the variation of training and body composition during the preparation for an ultra-marathon race in a male master ultra-marathoner. The preparation for an ultra-endurance race seems to induce pronounced changes in body mass and body composition.

Motivation in ultra-marathon runners

Background

In ultra-marathon running the proper motivation of the athlete is one of the milestones, not only during the races, but also during the practice sessions, which are long and very exhausting.

Purpose

The aim of this study was to examine the relationship of sport experience (expressed as number of finishes in ultra-marathons) with motivation characteristics of ultra-marathon runners.

Subjects and methods

The Motivation of Marathoners Scale examined the motivation of ultra-marathon runners compared to endurance runners of shorter distances (control group). Participants were 1,539 Polish runners, 382 women (24.7%) and 1,157 men (75.3%). Ultra-marathoners (N=425; 26.7%) finished at least one ultra-marathon, whereas the control group consisted of runners of shorter distances (N=1,114, 72.3%).

Results

Ultra-marathoners had higher scores in affiliation (3.55±1.60 vs 3.34±1.62, P<0.05), life meaning (4.20±1.40 vs 4.03±1.44, P<0.05) and lower in the areas of weight concern (4.33±1.68 vs 4.64±1.65, P<0.01), personal goal achievement (5.09±1.25 vs 4.64±1.65, P<0.001) and self-esteem (4.44±1.36 vs 4.68±1.38, P<0.01), than runners in the control group. The number of completed ultra-marathons was negatively related to the personal goal achievement, competition and recognition scale. The level of training experience was negatively correlated with the personal goal achievement scale in all participants, and with the self-esteem scale in the control group. In summary, ultra-marathoners had different motivations compared to runners of shorter race distance.

Conclusions

These findings should be considered by sport psychologists and other professionals to develop performance-tailored interventions for ultra-marathoners.

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.

Anthropometric characteristics of top-class Olympic race walkers

BACKGROUND

Typical training programmes in elite race walkers involve high training volumes at low and moderate intensities, which have been reported to induce functional and structural adaptations at an anthropometric level. Since anthropometrical variables are closely related to movement efficiency and performance in endurance events, the aim of this study was to describe the anthropometric profile of world-class race walkers.

METHODS

Twenty-nine world-class race walkers (21 men and 8 women) participated in this study. Anthropometric characteristics, including height, body mass, eight skinfolds, five girths and four bone breadths were measured. Body composition, somatotype, somatotype dispersion mean, somatotype attitudinal mean and height to weight ratio, as well as skinfolds extremity to trunk ratio were also calculated.

RESULTS

Mean height, body mass and body mass index were 177.1±7.1 cm, 66.4±5.8 kg, and 21.2±1.3 kg·m2 for men and 165.6±4.5 cm, 53.6±3.7 kg, and 19.6±1.6 kg· m2 for women, respectively. Women presented greater body fat content (6.7±0.6 vs. 12.2±0.8%; very large effect), less muscle mass (65.6±4.6 vs. 61.6±2.6 kg; large effect), and were more endomorphic (large effect) than men. Men specialists in 20-km showed greater muscle mass (66.7±4.9 vs. 64.4±4.3 kg; moderate effect), and slightly higher skinfolds, girths, body fat content and were more mesomorphic than 50-km specialists (moderate effect).

CONCLUSIONS

The present study expands the limited knowledge on the anthropometric characteristics and somatotype elements of elite top-class race walkers. The characterisation of the morphology of elite race walkers provides coaches a reference values to control the training development of the race walker, as well as providing reference values to improve talent identification.

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.

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.

Anthropometry and Dietary Intake before and during a Competition in Mountain Runners

Mountain running is a non-Olympic sport consisting of uphill or up- and downhill races at moderate-to-high altitude. Special nutritional requirements are anticipated, but no nutritional data of mountain runners are available. In three studies, physique of elite and recreational athletes (N = 62), maximum oxygen uptake (N = 3), and prerace and race day dietary intake (N = 6) were measured (mean ± SD). Mean oxygen uptake was 68.7 ± 5.2 mL/kg/min. Energy and carbohydrate intake before a race (29 ± 15 km, 1596 ± 556 m HD) was 3199 ± 701 kcal/d (13.4 ± 2.9 MJ/d) and 497 ± 128 g/d (8.3 ± 1.8 g/kg/d) in German national team members. Fluid intake was calculated as 2783 ± 1543 mL/d. During the race, athletes consumed 336 ± 364 kcal and 927 ± 705 mL of fluids. Substrate intake per hour was calculated as 23 ± 22 g of carbohydrates and 4.0 ± 3.2 g of proteins. In conclusion, anthropometric and oxygen uptake characteristics of mountain runners were similar to those reported for elite distance runners. Carbohydrate intake before and during the race was below recommendations for endurance athletes. This is of concern when considering the increased reliance on carbohydrates at altitude.

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.

Master runners dominate 24-h ultramarathons worldwide-a retrospective data analysis from 1998 to 2011

BACKGROUND

The aims of the present study were to examine (a) participation and performance trends and (b) the age of peak running performance in master athletes competing in 24-h ultra-marathons held worldwide between 1998 and 2011.

METHODS

Changes in both running speed and the age of peak running speed in 24-h master ultra-marathoners (39,664 finishers, including 8,013 women and 31,651 men) were analyzed.

RESULTS

The number of 24-h ultra-marathoners increased for both women and men across years (P < 0.01). The age of the annual fastest woman decreased from 48 years in 1998 to 35 years in 2011. The age of peaking running speed remained unchanged across time at 42.5 ± 5.2 years for the annual fastest men (P > 0.05). The age of the annual top ten women decreased from 42.6 ± 5.9 years (1998) to 40.1 ± 7.0 years (2011) (P < 0.01). For the annual top ten men, the age of peak running speed remained unchanged at 42 ± 2 years (P > 0.05). Running speed remained unchanged over time at 11.4 ± 0.4 km h-1 for the annual fastest men and 10.0 ± 0.2 km/h for the annual fastest women, respectively (P > 0.05). For the annual ten fastest women, running speed increased over time by 3.2% from 9.3 ± 0.3 to 9.6 ± 0.3 km/h (P < 0.01). Running speed of the annual top ten men remained unchanged at 10.8 ± 0.3 km/h (P > 0.05). Women in age groups 25-29 (r2 = 0.61, P < 0.01), 30-34 (r2 = 0.48, P < 0.01), 35-39 (r2 = 0.42, P = 0.01), 40-44 (r2 = 0.46, P < 0.01), 55-59 (r2 = 0.41, P = 0.03), and 60-64 (r2 = 0.57, P < 0.01) improved running speed; while women in age groups 45-49 and 50-54 maintained running speed (P > 0.05). Men improved running speed in age groups 25-29 (r2 = 0.48, P = 0.02), 45-49 (r2 = 0.34, P = 0.03), 50-54 (r2 = 0.50, P < 0.01), 55-59 (r2 = 0.70, P < 0.01), and 60-64 (r2 = 0.44, P = 0.03); while runners in age groups 30-34, 35-39, and 40-44 maintained running speed (P > 0.05).

CONCLUSIONS

Female and male age group runners improved running speed. Runners aged >40 years achieved the fastest running speeds. By definition, runners aged >35 are master runners. The definition of master runners aged >35 years needs to be questioned for ultra-marathoners competing in 24-h ultra-marathons.

Reduced performance difference between sexes in master mountain and city marathon running

Background

The performance in master marathoners has been investigated in flat city marathons but not in mountain marathons. This study examined changes in the sex differences in performance across time in female and male master runners competing in a mountain marathon compared to a flat city marathon.

Methods

The association between age and performance of finishers in the Jungfrau Marathon, Switzerland, with 1830 meter changes in altitude and a flat city marathon (Lausanne Marathon), Switzerland, were analyzed from 2000 to 2011.

Results

In both events, athletes in the 35–44 years age group showed the highest number of finishers. In the mountain marathon, the number of female master runners aged > 35 years increased in contrast to female finishers aged < 35 years, while the number of male finishers was unchanged in all age groups. In the city marathon, the number of female finishers was unchanged while the number of male finishers in the age groups for 25–34-year-olds and 35–44-year-olds decreased. In female marathoners, performance improved in athletes aged 35–44 and 55–64 years in the city marathon. Male marathoners improved race time in age group 45–54 years in both the city marathon and the mountain marathon. Female master runners reduced the sex difference in performance in the 45–54-year age group in both competitions and in the 35–44-year age group in the mountain marathon. The sex difference in performance decreased in the 35–44-year age group from 19.1% ± 4.7% to 16.6% ± 1.9% in the mountain marathon (r² = 0.39, P = 0.03). In age groups 45–54 years, the sex difference decreased from 23.4% ± 1.9% to 15.9% ± 6.1% in the mountain marathon (r² = 0.39, P < 0.01) and from 34.7% ± 4.6% to 11.8% ± 6.2% in the city marathon (r² = 0.39, P < 0.01).

Conclusion

These findings suggest that female master runners aged 35–54 years reduced sex differences in their performance in both mountain and city marathon running.

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.

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.

The TransEurope FootRace Project: longitudinal data acquisition in a cluster randomized mobile MRI observational cohort study on 44 endurance runners at a 64-stage 4,486 km transcontinental ultramarathon

BACKGROUND

The TransEurope FootRace 2009 (TEFR09) was one of the longest transcontinental ultramarathons with an extreme endurance physical load of running nearly 4,500 km in 64 days. The aim of this study was to assess the wide spectrum of adaptive responses in humans regarding the different tissues, organs and functional systems being exposed to such chronic physical endurance load with limited time for regeneration and resulting negative energy balance. A detailed description of the TEFR project and its implemented measuring methods in relation to the hypotheses are presented.

METHODS

The most important research tool was a 1.5 Tesla magnetic resonance imaging (MRI) scanner mounted on a mobile unit following the ultra runners from stage to stage each day. Forty-four study volunteers (67% of the participants) were cluster randomized into two groups for MRI measurements (22 subjects each) according to the project protocol with its different research modules: musculoskeletal system, brain and pain perception, cardiovascular system, body composition, and oxidative stress and inflammation. Complementary to the diverse daily mobile MR-measurements on different topics (muscle and joint MRI, T2*-mapping of cartilage, MR-spectroscopy of muscles, functional MRI of the brain, cardiac and vascular cine MRI, whole body MRI) other methods were also used: ice-water pain test, psychometric questionnaires, bioelectrical impedance analysis (BIA), skinfold thickness and limb circumference measurements, daily urine samples, periodic blood samples and electrocardiograms (ECG).

RESULTS

Thirty volunteers (68%) reached the finish line at North Cape. The mean total race speed was 8.35 km/hour. Finishers invested 552 hours in total. The completion rate for planned MRI investigations was more than 95%: 741 MR-examinations with 2,637 MRI sequences (more than 200,000 picture data), 5,720 urine samples, 244 blood samples, 205 ECG, 1,018 BIA, 539 anthropological measurements and 150 psychological questionnaires.

CONCLUSIONS

This study demonstrates the feasibility of conducting a trial based centrally on mobile MR-measurements which were performed during ten weeks while crossing an entire continent. This article is the reference for contemporary result reports on the different scientific topics of the TEFR project, which may reveal additional new knowledge on the physiological and pathological processes of the functional systems on the organ, cellular and sub-cellular level at the limits of stress and strain of the human body. Please see related articles: http://www.biomedcentral.com/1741-7015/10/76 and http://www.biomedcentral.com/1741-7015/10/77.

Running speed during training and percent body fat predict race time in recreational male marathoners

BACKGROUND

Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners.

METHODS

Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times.

RESULTS

After multivariate regression, running speed of the training units (β = -0.52, P < 0.0001) and percent body fat (β = 0.27, P < 0.0001) were the two variables most strongly correlated with marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r (2) = 0.44): race time ( minutes) = 326.3 + 2.394 × (percent body fat, %) - 12.06 × (speed in training, km/hours). Running speed during training sessions correlated with prerace percent body fat (r = 0.33, P = 0.0002). The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics.

CONCLUSION

The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour) are two key factors for a fast marathon race time in recreational male marathoner runners.

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.

Finishers and nonfinishers in the 'Swiss Cycling Marathon ' to qualify for the 'Race Across America '

Knechtle, B, Knechtle, P, Rüst, CA, Rosemann, T, and Lepers, R. Finishers and nonfinishers in the 'Swiss Cycling Marathon' to qualify for the 'Race across America.' J Strength Cond Res 25(12): 3257-3263, 2011-We compared the characteristics of prerace anthropometry, previous experience, and training and support during the race in 39 finishers and 37 nonfinishers in the 'Swiss Cycling Marathon,' over 720 km. In this race, the cyclists intended to qualify for the 'Race across America,' the longest nonstop cycling race in the World from the West to the East of the USA. Finishers in the 'Swiss Cycling Marathon' had a lower body mass, a lower body mass index, lower circumferences of upper arm and thigh, a lower percent body fat, completed more weekly training units, covered more kilometers in the longest training ride, rode at a faster speed during training, rode more kilometers per week and for more hours, had more previous finishes in the 'Swiss Cycling Marathon' and a lighter race bike compared to the nonfinishers. In the bivariate analysis, the cycling distance per training unit (r = 0.37), the duration per training unit (r = 0.44), the speed per training unit (r = -0.59), using nutrition provided by the organizer (r = 0.50), and using own nutrition (r = 0.49) during the race were significantly and positively associated with race time. For practical applications, anthropometric characteristics such as a low body mass or low body fat were not related to race time, whereas training characteristics and nutrition during the race were associated with race time. The key to a successful finish in an ultraendurance cycling race such as the 'Swiss Cycling Marathon' seems a high speed in training and an appropriate nutrition during the race.

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.

Validation of a German version of the Sport Motivation Scale (SMS28) and motivation analysis in competitive mountain runners

This study validated a German version of the Sport Motivation Scale (SMS28) and investigated the sex-specific and age-related differences in motivation of competitive mountain runners. Translation and cross-cultural adaptation of the SMS28 was based on translation and back-translation methodology. Acceptable validity of the German version of the SMS28 was indicated by the high correlations (.81 to .98) of scores on the seven subscales for the English and German versions completed by 15 subjects. Motivation analysis was performed with 127 competitive male and female mountain runners. The seven subscales of the German version showed good internal consistency (Cronbach's coefficient alphas .70 to .85). Findings on motivation of competitive mountain runners were a decline across age groups of Intrinsic motivation toward accomplishment for both sexes and an age-related decline of External regulation only for females. These motivational changes might well be associated with the observed diminishing numbers of older participants in mountain running competitions.

Leg skinfold thicknesses and race performance in male 24-hour ultra-marathoners

The association of skinfold thicknesses with race performance has been investigated in runners competing over distances of ≤50 km. This study investigated a potential relation between skinfold thicknesses and race performance in male ultra-marathoners completing >50 km in 24 hours. Variables of anthropometry, training, and previous performance were related to race performance in 63 male ultra-marathoners aged 46.9 (standard deviation [SD] 10.3) years, standing 1.78 (SD 0.07) m in height, and weighing 73.3 (SD 7.6) kg. The runners clocked 146.1 (SD 43.1) km during the 24 hours. In the bivariate analysis, several variables were associated with race performance: body mass (r = -0.25); skinfold thickness at axilla (r = -0.37), subscapula (r = -0.28), abdomen (r = -0.31), and suprailiaca (r = -0.30); the sum of skinfold thicknesses (r = -0.32); percentage body fat (r = -0.32); weekly kilometers run (r = 0.31); personal best time in a marathon (r = -0.58); personal best time in a 100-km ultra-run (r = -0.31); and personal best performance in a 24-hour run (r = 0.46). In the multivariate analysis, no anthropometric or training variable was related to race performance. In conclusion, in contrast to runners up to distances of 50 km, skinfold thicknesses of the lower limbs were not related to race performance in 24-hour ultra-marathoners.

What is associated with race performance in male 100-km ultra-marathoners--anthropometry, training or marathon best time?

We investigated the associations of anthropometry, training, and pre-race experience with race time in 93 recreational male ultra-marathoners (mean age 44.6 years, s = 10.0; body mass 74.0 kg, s = 9.0; height 1.77 m, s = 0.06; body mass index 23.4 kg · m(-2), s = 2.0) in a 100-km ultra-marathon using bivariate and multivariate analysis. In the bivariate analysis, body mass index (r = 0.24), the sum of eight skinfolds (r = 0.55), percent body fat (r = 0.57), weekly running hours (r = -0.29), weekly running kilometres (r = -0.49), running speed during training (r = -0.50), and personal best time in a marathon (r = 0.72) were associated with race time. Results of the multiple regression analysis revealed an independent and negative association of weekly running kilometres and average speed in training with race time, as well as a significant positive association between the sum of eight skinfold thicknesses and race time. There was a significant positive association between 100-km race time and personal best time in a marathon. We conclude that both training and anthropometry were independently associated with race performance. These characteristics remained relevant even when controlling for personal best time in a marathon.

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

In 169 male 100-km ultra-marathoners, the variables of anthropometry, training, and prerace experience, in order to predict race time, were investigated. In the bivariate analysis, age (r = .24), body mass (r = .20), Body Mass Index (r = .29), circumference of upper arm (r = .26), percent body fat (r = .45), mean weekly running hours (r = -.21), mean weekly running kilometers (r = -.43), mean speed in training (r=-.56), personal best time in a marathon (r = .65), the number of finished 100-km ultra-runs (r = .24), and the personal best time in a 100-km ultra-run (r = .72) were associated with race time. Stepwise multiple regression showed that training speed (p < .0001), mean weekly running kilometers (p < .0001), and age (p < .0001) were the best correlations for a 100-km race time. Performance may be predicted (n=169, r2 = .43) by the following equation: 100-km race time (min) = 1085.60 - 36.26 x (training speed, km/hr.) - 1.43 x (training volume, km/wk.) + 2.50 x (age, yr.). Overall, intensity of training might be more important for a successful outcome in a 100-km race than anthropometric attributes. Motivation to train intensely for such an ultra-endurance run should be explored as this might be the key for a successful finish.