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Training Volume and Personal Best Time in Marathon, Not Anthropometric Parameters, are Associated with Performance in Male 100-KM Ultrarunners

Knechtle, Beat1,2; Wirth, Andrea1; Knechtle, Patrizia1; Rosemann, Thomas2

Journal of Strength and Conditioning Research: March 2010 - Volume 24 - Issue 3 - p 604-609
doi: 10.1519/JSC.0b013e3181c7b406
Original Research

Knechtle, B, Wirth, A, Knechtle, P, and Rosemann, T. Training volume and personal best time in marathon, not anthropometric parameters, are associated with performance in male 100-km ultrarunners. J Strength Cond Res 24(3): 604-609, 2010-We investigated the relation between selected anthropometric and training variables and the personal best time in a marathon with total race time in 66 Caucasian male nonprofessional ultrarunners in a 100-km run. In the multiple linear regression analysis, the average weekly training volume in kilometers (r2 = 0.224, p < 0.01) and the personal best time in a marathon (r2 = 0.334, p < 0.01) were significantly associated with total race time, whereas no anthropometric variable was related to race performance (p > 0.05). We conclude that high training volume and a fast time in a marathon were more important for a fast race time in male 100-km runners than any of the determined anthropometric variables.

1Gesundheitszentrum St. Gallen, St. Gallen, Switzerland; and 2Department of General Practice, University of Zurich, Zurich, Switzerland

Address correspondence to Dr. Beat Knechtle,

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In runners, different factors with an effect on performance are described. Apart from physiological parameters, several different anthropometric parameters depending upon the duration of running are related to performance such as body mass (2,20), body height (2,30,32), body mass index (BMI) (15,31), body fat (15), total skin-fold thickness (2), skin-fold thickness of the lower limb (1,2,28,29), length of legs (24,39), and circumferences of limbs (20,21,31,39).

These anthropometric factors may have different effects over different distances. Body height seems to be associated with performance in running 10 km (2) and marathons (30); BMI is related to marathon (15) and ultramarathon performances (19). In addition to BMI, body fat seems to have an effect on running time and is positively associated with marathon performance times (15). In some studies, a relation between skin-fold thicknesses and performance has been described. Lower skin folds are positively associated with improved running times up to 10,000 m (1,2,28,29), and skin-fold thicknesses in the lower limbs are positively associated with running times over 1,500 and 10,000 m (1,2) and marathons (3). The length of the upper leg has a positive association with running times over 800, 1,500, and 5,000 m (39). Circumferences of chest and thigh are positively associated with running times over 800 m, and 1,500 and 5,000 m, whereas upper arm circumference has shown a positive association with 10,000-m running times (39) and in ultrarunners (20,21).

Anthropometric properties and their effect on exercise performance during short and middle distance running and marathon running have been investigated previously (1,28); however, there is very little scientific data about the effect of anthropometry on race performance in ultramarathon running (3,17,20,21,22). Ultrarunning means distances longer than the classic marathon distance of 42.195 km. Ultramarathon runners seem to have a lower BMI than sedentary people (40) and have low amounts of fat at the abdomen and legs (17). These low amounts of body fat are supposed to be the result of intense training in ultrarunners (17), and this intense training may lead to an improved performance (3). Probably a thinner upper body with low circumferences of the upper arm is advantageous for ultrarunners of distances of more than 300 km (20) or even 1,200 km (21).

The question is whether anthropometry or training or both have a relationship with race performance in ultrarunners. There are a lot of data for runners up to the marathon distance, but very little data are available for ultrarunners. Bale et al. found that elite runners running over 10 km both trained more often and for more miles per week (2), and the same group found that for female marathon runners, the number of training sessions per week and the number of years of training were the best predictors of competitive performance over 10 miles and marathons (3). In addition, Leake and Carter (26) concluded that training parameters were more important than anthropometric measurements in the prediction of performance of female triathletes in short-distance races, and Laurenson et al. (25) concluded from their study of female short-distance triathletes that no ideal or unique anthropometric profile with influence on overall performance can be established. Presumably, gender was of importance in these 2 studies.

In addition to training volume, previous race experience might also be of importance because in a recent study of male ultrarunners the positive association of a personal best time in marathon running on performance in a 24-hour run was demonstrated, whereas anthropometry and training volume showed no relation (22). According to Gulbin and Gaffney, previous best performances in short-distance triathlon coupled with weekly cycling distances and longest training ride could partially predict overall performance in an Ironman race with male and female triathletes (13).

The aim of this present investigation was therefore to determine in male 100-km ultrarunners whether variables of anthropometry and training or personal best times in marathon and 100-km running showed a relation with race performance.

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Experimental Approach to the Problem

The organizer of the 50th edition of the 100-km run in Biel, Switzerland, contacted all participants of the race in 2008 by a separate newsletter, 3 months before the race, in which they were asked to participate in the study. About 2,000 male Caucasian runners started in the race, and 70 male ultrarunners were interested in our investigation. The study was approved by the Institutional Review Board for use of Human subjects of the Canton of St. Gallen, Switzerland. The athletes were informed of the experimental risks and gave informed written consent. No criteria for inclusion/exclusion were used. Sixty-six athletes out of our study group finished the race within the time limit, whereas one subject finished in the top 3 position. Four runners had to retire because of medical problems. The anthropometric variables of our athletes are summarized in Table 1, and their training variables are given in Table 2. The 50th edition of the 100-km run in Biel, Berne, Switzerland, took place during the night of June 13/14, 2008. The runners started the 100-km run at 10:00 pm, 13 June. There was an altitude difference of 645 m. During this 100-km run, they had 17 aid stations offering food and beverages. The athletes were allowed to be supported by a cyclist to have additional food and clothing, if necessary. At the 10:00 pm start, the temperature was 15° C, and it was dry. During the night, the temperature dropped to 8° C and then rose to 18° C the next morning by 10:00 am.

Table 1

Table 1

Table 2

Table 2

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Immediately before the start of the race, body mass; circumferences of upper arm, thigh, and calf; length of leg and skin-fold thicknesses at 8 sites were measured in our subjects to calculate BMI, sum of skin folds, and percent body fat using the anthropometric method. Body mass was determined using a commercial scale (Beurer BF 15, Beurer GmbH, Ulm, Germany) to the nearest 0.1 kg. Skin-fold thicknesses of chest, midaxillary (vertical), triceps, subscapular, abdominal (vertical), suprailiac (at anterior axillary), thigh, and calf were measured using a skin-fold calliper (GPM-skin-fold calliper, Siber & Hegner, Zurich, Switzerland) to the nearest 0.2 mm. Skin-fold thicknesses and circumferences of the extremities were measured on the right side of the body. Circumferences of the upper arm and calf were measured at the largest circumference of the limb. Circumference of the thigh was determined 20 cm above the upper pole of the patella. Length of the leg was measured from the trochanter major to the malleolus lateralis. Every measurement was taken 3 times by the same person to the nearest 0.1 cm, and then the mean value was used for calculation. Percent body fat was calculated using the following formula:

Percent body fat = 0.465 + 0.180(∑7SF) − 0.0002406 (∑7SF)2 + 0.0661(age),

where ∑7SF = sum of skin-fold thickness of chest, midaxillary, triceps, subscapular, abdomen, suprailiac, and thigh mean, according to Ball et al. (4). In addition to the determination of the anthropometric parameters, athletes were asked pre race for their average weekly training volume in hours, and the kilometers run, over the last 3 months before the race. During the 3 months before the race, each athlete maintained a comprehensive training diary consisting of daily workouts showing distance and duration. Furthermore, they listed their previous race experience including the number of finished marathons and 100-km runs, and their personal best time over these distances.

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Statistical Analyses

Results are presented as mean (SD). A multiple linear regression analysis (stepwise, p of F for inclusion <0.05, p of F for exclusion >0.1) using race performance as the dependent variable (Y) was assessed for the prerace measured and calculated anthropometric predictor variables (body mass, body height, BMI, percent body fat, sum of 8 skin-fold thicknesses, length of leg, and circumference of upper arm and calf), the training variables (average weekly training volume in hours and kilometers run and speed in running during training) and previous race experience (years as active runner and personal best time in a marathon). Multicollinearity between predictor variables was excluded with R > 0.9.

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Table 1 shows the measured and calculated anthropometric variables of the athletes, Table 2 the training variables and previous race experience. Athletes were running for 1-25 years, during training, they were running for 2-12 h·wk−1 and completing 25-180 km weekly. Their running speed in training was from 8.3 to 15.0 km·h−1. The successful runners finished the 100-km run in 701 (123) minutes. Our fastest runner finished in the top 3 position. The average weekly running volume in kilometers (Figure 1) (r2 = 0.224, p < 0.01) and the personal best time in a marathon (Figure 2) (r2 = 0.334, p < 0.01) were associated with race performance. All investigated anthropometric parameters showed no relation to total race time (p > 0.05).

Figure 1

Figure 1

Figure 2

Figure 2

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The main finding of this present investigation was the fact that selected anthropometric variables having a well-known effect on performance in runners-up to the marathon distance were not associated with total race time in these male 100-km ultrarunners, whereas the average weekly training volume in kilometers and the personal best time in a marathon showed an association with race performance.

We found a correlation between the personal best time in a marathon and total race time (Figure 2). An association between personal best times over shorter distances and performances in a longer race has been shown in 3 studies involving marathoners, ultrarunners, and triathletes. In a recent study of male ultrarunners in a 24-hour run, the personal best time in marathon running was associated with race performance, but anthropometry and training volume showed no relation (22) and, according to Gulbin and Gaffney, previous best performances in short-distance triathlons coupled with weekly cycling distances and the longest training ride could partially predict the overall performance in an Ironman race involving male and female triathletes (13). McKelvie et al. found that the final race time in a marathon was positively related to the best 10-km race time in the previous 12 months before the marathon (34). For those runners who had already performed a 100-km run, their personal best time in a 100-km run showed no association with actual race performance. Presumably, the number of finishers (n = 42) was too small compared with the athletes who had already finished a marathon (n = 63) (Table 2).

In addition to the personal best time in a marathon, the average weekly training volume in kilometers (Figure 1) was associated with total race time. According to the literature, training parameters seem to be of more importance than anthropometric measurements in the prediction of performance in runners (2,3,6,7,8,11,12,14,36). In marathon finishers, the longest mileage covered per training session is the best predictor for a successful completion of a marathon (42). In female marathon runners, the number of training sessions per week and the number of years training were the best predictors of competitive performance at the marathon distance (3), and Scrimgeour et al. found that runners training more than 100 km per week have significantly faster race times over 10-90 km than athletes covering less than 100 km (37). According to Billat al., top class marathon runners train for more total kilometers per week and at a higher velocity than runners at a lower level (6).

However, training volume seems to have clear limits. There exists an upper limit in training volume above which there are no more improvements (38). When training in runners is analyzed in detail, parameters such as previously completed marathons (15), workout days (15), total workouts (15), total kilometers (14), total workout days (14), mean kilometers per workout (14,15), total training minutes (15), maximal kilometers of running per week (15), mean kilometers per week (15), and mean kilometers per day (15) seem to have an effect on a marathon performance. Probably, gender had an influence when the effect of training volume on performance was studied. In one study, Hagan al. investigated female runners (15) and in another male runners (14).

As Bale et al. could demonstrate in 60 male runners, those elite runners with a higher training frequency, higher weekly training volume, and longer running experience had a better 10-km performance (2). According to Hewson and Hopkins, a correlation exists between seasonal weekly duration of moderate continuous running for runners specializing in longer distances (18). In our multiple linear regression analysis, only average kilometers per week were related to race performance, but speed in running during training and average hours of running per week were not related.

A large number of different anthropometric factors are described that have an influence on race performance in runners, dependent on the distance. In this present study, we investigated male ultrarunners beyond the marathon distance, where presumably other factors are of importance. However, in contrast to a previous study of ultrarunners where body mass was associated with race performance (20), in this present investigation, body mass showed no effect on performance. Hagan et al. found that in female marathon runners marathon performance time was positively correlated to BMI, but not to body mass (15). However, body fat was also positively correlated to marathon performance time. Bale et al. described in female marathon runners that elite runners had a lower percentage of fat (3), and Hetland et al. could demonstrate that regional and total body fat correlated inversely to the performance in an incremental treadmill test in long-distance runners (17). In contrast, we could not find an association of percent body fat with race performance in these 100-km runners. Probably, this was because of to gender. Although we investigated male ultrarunners, Hagan et al. (15) and Bale et al. (3) studied female runners.

Low amounts of body fat seem to be advantageous for ultrarunners. In the literature, there are several studies showing an effect of thin skin-fold thicknesses on running performance, especially up to 10,000 m. The amount of fat and the thickness of skin folds seem to be of importance for performance in runners. It has been shown that physical performance is negatively related to body fat and positively related to skeletal muscle mass (27). In runners, a high amount of adipose tissue leads to a higher body mass and an impairment of performance because more weight has to be moved, which does not contribute to power development. The study of Hetland et al. demonstrated that regional and total body fat was negatively correlated with performance in a treadmill test (17). In runners, decreased skin-fold thicknesses in the lower limb were measured after a longer training period; this might be particularly useful in predicting running performance (28). In the study of Legaz and Eston, 3 years of training reduced skin-fold thickness, and the change in performance was related to the change in skin-fold thickness of triceps, front thigh, and medial calf (28).

The lower skin-fold values found in runners might be because of the high performance (29). However, in nonrunners also, fat percentage is significantly associated with 12-minute running performance (33). A low skin-fold thickness seems to be associated with performance. Bale et al. found that total skin-fold, the type and frequency of training, and the number of years of running were the best predictors of running performance and success for the 10-km distance (2). In our male ultrarunners, anthropometric parameters, such as skin-fold thicknesses and the total sum of skin-fold thickness, did not correlate with total race time. Presumably, distances up to the marathon and 100-km runs are not comparable races.

In addition to skin folds, circumferences of extremities seem to have an influence on performance. In 2 studies of ultrarunners in a multistage ultraendurance run over 333 km (20) and over 1,200 km (21), the upper arm circumference was associated with race performance. In one study of Eritrean and Spanish runners, the Eritrean runners had a lower maximal calf circumference compared with the Spaniards (31). However, in our 100-km runners, neither upper arm circumference nor calf circumference had an effect on race performance (Table 1). Probably, a thin upper body is only advantageous in races longer than 100 km.

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Practical Applications

We have now the dilemma, that in these 100-km runners, both a high training volume (average weekly running kilometers) and a high intensity (personal best marathon time) are correlated to total race time. This means, that a fast race time over 100 km is obviously dependent on both a high mileage in running and a fast running speed in the marathon. However, a high training volume and a high intensity in running are both potentially associated with serious problems: An ultraendurance performance can suppress the hypopituitary-gonadal axis and lead to a decreased testosterone level (23). However, this decrease could be prevented by resistance exercise, thus leading to an increase in testosterone (41). A higher running speed could lead to more forefoot strike (16), thus potentially leading to more injuries of the lower limbs. Fortunately, anthropometry showed no relation to race performance. In case of a low body fat would have been correlated with total race time, anorexia and drive for thinness (10) could also become a problem in ultrarunning. To solve the mentioned dilemma, in future studies with ultrarunners over the 100-km distance, the training should be analyzed in detail and correlated with race performance to find out whether rather a high mileage at low intensity or a low mileage at rather high intensity would be better for a fast race time over 100 km. Furthermore, a training study with 2 groups of ultrarunners where 1 group is performing prerace resistance exercise in addition to the running training and the other group is not could clarify about a potential benefit of resistance exercise during high-volume running. A beneficial effect of resistance exercise on running speed (higher muscle mass) and testosterone (increase despite a higher training mileage) could be expected.

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The authors had no conflict of interest and no financial support for the study. For their help in translation, we thank Matthias Knechtle, Lausanne, Switzerland, and Mary Miller from Stockton-on-Tees, Cleveland in England, crew member of an ultraendurance support crew. Special thanks go to Markus Gnädinger, MD, Steinach, Switzerland, and Hans Drexler, MD, PhD, Braunschweig, Germany, for their constructive criticism.

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    ultraendurance; skin-fold thickness; anthropometry; percent body fat

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