In recent years, several studies investigating ultraendurance cyclists have been published. In case reports and field studies, the changes in body mass (3,14,25), the intensity and energy turnover (3,6,14,33,34,38), and the nutrition (5,11,29,30) in ultraendurance cycling events were investigated. Others tried to find an association between anthropometry and training with race performance (16,22,27,28).
The most famous nonstop ultracycling race in the World is the ‘Race Across America,’ called the ‘RAAM.’ In the ‘RAAM,’ the cyclists have to cover about 5,000 km, with around 30,000 m of altitude, depending upon the course (11,14,29,34). To become an official finisher in the ‘RAAM,’ the competitors have to complete the total distance within 12 days. During the whole race, the athletes must be followed by a support crew consisting of 2 cars providing equipment and nutrition (11,14,29,34). To enter the ‘RAAM,’ the cyclists have to qualify in an ultracycling race over 720 km (444 miles), and they must finish within 115% of the winner to get the qualification for a start in the ‘RAAM.’
In this study, we investigated ultraendurance cyclists intending to qualify for the ‘RAAM’ by taking part in the ‘Swiss Cycling Marathon.’ In the ‘Swiss Cycling Marathon’ over 720 km with 5,580 m of altitude, nonprofessional athletes are competing, and they have the unique opportunity of testing their physical fitness, their equipment, and their support crew in the same manner they would use in the ‘RAAM.’ We assumed that, apart from anthropometry, training and nutrition, other factors such as support during the race would also show an association with race performance.
The aim of this study was to investigate the association between training, anthropometry, and race support with race time in qualifiers for the ‘RAAM’ in a 720-km ultracycling race. Because ‘RAAM’ qualifiers must finish within 115% of the winner, we assumed we would find associations with intensity during training, and support during the race, for a faster race time.
Experimental Approach to the Problem
The ‘Swiss Cycling Marathon’ takes place every year and offers the opportunity to qualify for the ‘RAAM.’ To increase the sample size, we collected data in the 2008, 2009, and 2010 events. The organizer of the ‘Swiss Cycling Marathon’ contacted all the race participants via monthly newsletters and provided information about the planned investigation. Interested athletes contacted the investigator by e-mail and were provided with the study documentation. The ‘Swiss Cycling Marathon’ takes place each year at the end of either in late June or early July. The weather conditions were comparable in all 3 years. In the 720-km ‘RAAM’ qualifier, the cyclists must complete a 600-km loop first and then an additional loop of 120 km. In total, they have 11 check points to pass and 5,580 m of altitude to cover during the 720 km. The 600-km loop starts from the outskirts of Berne (Switzerland) over the border to Germany, then along Lake Constance into the Alps of Eastern Switzerland and back to Berne. Then, they must add the 120-km loop.
All interested starters were included. No criteria for an inclusion or exclusion of subjects were used. Any athlete intending to qualify for the ‘RAAM’ must finish the ‘Swiss Cycling Marathon’ within 115% of the winner's time. Athletes who failed in a previous year of the ‘Swiss Cycling Marathon’ can start as many times as they want in future years to finally get the qualification to enter the ‘RAAM.’ A total of 76 male athletes volunteered to participate in the study; no female athlete started. The study was approved by the Institutional Review Board for the use of Human Subjects of the Canton of St. Gallen, Switzerland. The athletes were informed of the experimental procedures and gave their informed written consent.
For each year, upon entering the study via the inscription, the athletes kept a comprehensive training diary recording their training units in cycling, showing the distance (km), the duration (h), and the speed (km·h−1) for each training session, until the start of the race. For each year, before the start of the race, body mass, body height, the length of the arm and right leg, the circumferences of the limbs and the thicknesses of 8 skinfolds were measured on the right side of the body. One trained investigator took all the measurements because intertester variability is a major source of error in anthropometric measurements. With these data, we calculated body mass index, percent body fat, skeletal muscle mass, and the sum of skinfolds using anthropometric methods. Body mass was measured using a commercial scale (Beurer BF 15, Beurer, Ulm, Germany) to the nearest 0.1 kg. Body height was measured using a stadiometer to the nearest 1.0 cm. The circumferences and lengths of limbs were measured using a nonelastic tape measure (cm) (KaWe CE, Kirchner & Welhelm, Asperg, Germany). The length of the right arm was measured, from the acromion to the tip of the third finger, to the nearest 0.1 cm on the right side; the length of the right leg from the trochanter major to the malleolus lateralis, to the nearest 0.1 cm again on the right side. The circumference of the upper arm was measured in the middle of the right upper arm (between the acromion and olecranon) to the nearest 0.1 cm; the circumference of the right thigh was taken at the level where the skinfold thickness of the thigh was measured (20 cm above the upper margin of the patella), and the circumference of the right calf was measured at the maximum circumference of the calf. The skinfold data were obtained using a skinfold calliper (GPM-Hautfaltenmessgerät, Siber & Hegner, Zurich, Switzerland) and recorded to the nearest 0.2 mm. The skinfold measurements were taken once for all 8 skinfolds (chest, midaxilla, triceps, subscapular, abdomen, suprailiac, front thigh, and medial calf), and then, the procedure was repeated twice more by the same investigator; the mean of the 3 times was then used for the analyses. The timing of the taking of the skinfold measurements was standardized to ensure reliability. According to Becque et al., readings were taken 4 seconds after applying the calliper (2). An intratester reliability check was conducted on 27 male athletes before testing. Intraclass correlation (ICC) within the 2 judges was excellent for all anatomical measurement sites and various summary measurements of skinfold thicknesses (ICC > 0.9). Agreement tended to be higher within measurers than between measurers but still reached excellent reliability (ICC > 0.9) for the summary measurements of skinfold thicknesses (15). Percent body fat was calculated using the anthropometric formula following Ball et al. (1): Percent body fat = 0.465 + 0.180(Σ7SF) − 0.0002406(Σ7SF)2 + 0.0661(age), where Σ7SF = sum of skinfold thickness of chest, midaxilla, triceps, subscapular, abdomen, suprailiac, and thigh. Skeletal muscle mass was calculated using the anthropometric formula: Skeletal muscle mass = Ht × (0.00744 × CAG2 + 0.00088 × CTG2 + 0.00441 × CCG2) + 2.4 × sex − 0.048 × age + race + 7.8, where Ht = height, CAG = skinfold-corrected upper arm girth, CTG = skinfold-corrected thigh girth, CCG = skinfold-corrected calf girth, sex = 1 for male and 0 for female, race = 0 for white, according to Lee et al. (28). For each year, prerace, the weight of the race bike was determined without additional equipment. After the race, the athletes were asked whether they had completed the race alone or with the help of a support crew, whether they had followed the signposts set by the organizer or used global positioning system (GPS), whether they carried their own equipment to mend a flat tire or whether they had spare parts or a complete replacement bike with them in their support car, and whether they used the nutrition provided by the organizer at the check points or whether they used their own nutrition.
Data are presented as mean and SD. For finishers and nonfinishers, the results of prerace anthropometry and training were compared using Kruskal–Wallis equality-of-populations rank test. The results of categorical data such as racing with or without support crew, racing using GPS, racing using a complete spare bike and racing using their own nutrition, were compared using Fisher's exact test. Pearson correlation analysis with the independent variables of anthropometry, training and race support, and race time as the dependent variable, was performed. A probability value of <0.05 was accepted as significant. To achieve a power of 80% (2-sided type 1 error of 5%) to detect a minimal association between race time and anthropometric characteristics of 20% (i.e., coefficient of determination r2 = 0.2) a sample of 40 participants was required. An alpha level of 0.05 was used to indicate significance.
Seventy-six male subjects entered the investigation, 39 cyclists (51%) finished the ‘Swiss Cycling Marathon’ within the time limit. The ‘RAAM’ qualifiers completed the 720 km within 26.6 (2.5) hours and 27.4 (15.5) minutes, cycling at 26.8 (2.2) km·h−1. Race time was also expressed as a percentage of the course record (22:24 hours:minutes for the 720 km); the athletes finished within 120 (10) % of the course record. Among the 37 unsuccessful ‘RAAM’ qualifiers, the athletes complained about exhaustion (9 athletes), loss of orientation (6 athletes), problems with the locomotor system (4 athletes), tiredness (4 athletes), too much traffic (4 athletes), arriving after closure of the race (3 athletes), problems with digestion (2 athletes), loss of motivation (3 athletes), and technical problems with the bike (2 athletes).
The finishers had a lower body mass, a lower body mass index, a lower circumference of upper arm and thigh, and a lower percent body fat compared to the nonfinishers (Table 1). None of the anthropometric characteristics was related to race time in the finishers. Considering previous experience, training and race bike, the finishers 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 finishes previously in the ‘Swiss Cycling Marathon’ and had a lighter race bike compared to the nonfinishers (Table 2). Distance (Figure 1), duration (Figure 2), and speed (Figure 3) per training unit were associated with race time. Distance (r = −0.47, p = 0.0030) and duration (r = −0.55, p = 0.0005) per training unit were significantly and negatively related to speed during training. No differences were found between finishers and nonfinishers regarding support and nutrition during the race (Table 3). Racing using their own nutrition (r = 0.50) and racing using nutrition provided by the organizer (r = 0.49) were both related to race time in finishers.
The aim of this study was to investigate the association of anthropometry, training and race support with race time in male athletes in the ‘Swiss Cycling Marathon,’ a qualifier for the ‘RAAM.’ Because ‘RAAM’ qualifiers must finish within 115% of the winner, we assumed that we would find associations with intensity during training and support during the race, to ride a faster race. The main finding was that training variables such as distance, duration, and speed per training unit and nutrition during the race were associated with race time after multivariate analysis.
Because 49% of the participants were not able to finish the 720 km within the time limit, we thought we might find important differences between finishers and nonfinishers. Regarding anthropometry, the finishers had a lower body mass, a lower body mass index, a lower circumference of upper arm and thigh and lower percent body fat than the nonfinishers. Respecting existing literature, we would expect that anthropometric variables such as body mass (13), body mass index (8,12), circumference of upper arm (13,21), body fat (9,23,24), or skinfold thicknesses (17,19) would be related to race time. Regarding especially cyclists, one might assume that body mass (36,37) would also be related to race time in ultraendurance cyclists. However, anthropometric variables showed no association with race time in these ultraendurance athletes as has already been found in ultratriathletes (20), ultracyclists (16,28), and ultrarunners in both a single stage ultramarathon (26) and a multistage ultramarathon (18). Presumably, ultraendurance athletes do not seem to profit from specific anthropometric characteristics for a fast race time.
Considering training, the finishers completed more weekly training units, covered more kilometers in the longest training ride, rode at a faster speed during training, and rode more kilometers per week and for more hours. The cycling distance per training unit, the duration per training unit, and the speed per training unit were associated with race time in the bivariate analysis. This finding is in line with previous findings of training in cyclists where long-term training programs seem to be of importance for cycling performance (35). The literature regarding the association between training and race performance in ultraendurance athletes is, however, rather scarce (10,20,23,26). In long-distance triathletes, training distances seem to be more important than training paces in the preparation for an Ironman triathlon (7,31). Obviously, training seems to be of higher importance than particular anthropometric characteristics for ultraendurance cyclists.
When we examine the training variables in details, we see that speed in cycling during training was the single variable that was both different between nonfinishers and finishers and related to race time in the finishers. Variables of training volume such as maximal cycling distance per training unit, mean weekly distance in cycling, and mean weekly duration of training were different between nonfinishers and finishers but not related to race time for finishers. Distance and duration per training unit were significantly and positively associated with race time in finishers where finishers training fewer kilometers per unit and lesser minutes per unit were riding faster in the race. This supports the finding that speed during training was significantly and negatively related to race time and that both distance and duration per training unit were significantly and negatively related to speed during training. Therefore, neither large training volumes in kilometers nor long training rides seem to be related to performance in these ultraendurance cyclists but rather short training units at high intensity although the successful finishers were investing more kilometers and more hours in the training than the nonfinishers. We would therefore assume that intensity in training seems more important than volume in these ultraendurance cyclists. This presumption might also be supported by the finding that the coefficient of correlation for mean speed per training unit was higher (r = −0.59) than the coefficients for distance (r = 0.37) and duration (r = 0.44) per training unit, respectively. Likewise, in other ultradistance athletes such as ultrarunners (18), Ironman triathletes (27), and ultraswimmers (12), speed during training was associated with race performance.
A further hypothesis was that support during the race would be related to race time. During the ‘Swiss Cycling Marathon,’ the riders' own support crew can supply equipment, or a complete bike, in the case of technical problems. In the ‘RAAM,’ riders must have a support crew with 2 cars. Considering equipment and nutrition, the finishers rode a lighter race bike compared to the nonfinishers; however, the weight of the race bike was not related to race time. During such a race, athletes change their clothing and may put drinking bottles with different weights in their bottle cages. Accordingly, the weight of the race bike will change during the race. Adequate nutrition is important for ultraendurance performance (4,32,39). We found no differences for nutrition between finishers and nonfinishers. In the bivariate analysis, however, racing using their own nutrition and racing using nutrition provided by the organizer were both related to race time in finishers. The finding that both using own nutrition and using the provided nutrition were associated with race time might be because several athletes used both their own nutrition and nutrition provided by the organizer. Of the 39 finishers, 14 athletes indicated to have used nutrition provided by the organizer and 31 athletes relied on their own nutrition. Six athletes used both the nutrition provided by the organizer and their own nutrition. This was obviously the reason for this finding.
This study is limited that the subjects finished within 120% of the course record. The findings therefore do not apply to cyclists who finished faster or slower than 120% of the course record. A further limitation of this investigation is the fact that we did not determine energy turnover (3,6,14), change in body composition (3,14), and energy intake (30,33), because an energy deficit may limit ultraendurance performance.
It is noteworthy that ∼50% of the starters were not able to finish the 720 km of the ‘Swiss Cycling Marathon’ within the time limit and so could not qualify for the ‘RAAM.’ Although successful finishers had a lower body mass, a lower body mass index, a lower circumference of upper arm and thigh, and a lower percent body fat compared to nonfinishers, anthropometric characteristics showed no association with race time in the finishers. In the bivariate analysis, training variables such as cycling distance per training unit, duration per training unit, and speed per training unit and nutrition during the race were 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 such as high intensity 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.
1. Ball, SD, Altena, T, and Swan, PD. Comparison of anthropometry to DXA: A new prediction equation for men. Eur J Clin Nutr
58: 1525–1531, 2004.
2. Becque, MD, Katch, VL, and Moffatt, J. Time course of skin-plus-fat compression in males and females. Hum Biol
58: 33–42, 1986.
3. Bircher, S, Enggist, A, Jehle, T, and Knechtle, B. Effects of an extreme endurance race on energy balance and body composition—A case study. J Sports Sci Med
5: 154–162, 2006.
4. Burke, LM, Millet, G, and Tarnopolsky, MA. International Association of Athletics Federations. Nutrition for distance events. J Sports Sci
25: S29–S38, 2007.
5. Ebert, TR, Martin, DT, Stephens, B, McDonald, W, and Withers, RT. Fluid and food intake during professional men's and women's road-cycling tours. Int J Sports Physiol Perform
2: 58–71, 2007.
6. Francescato, MP and Di Prampero, PE. Energy expenditure during an ultra-endurance cycling race. J Sports Med Phys Fitness
42: 1–7, 2002.
7. Gulbin, JP and Gaffney, PT. Ultraendurance triathlon participation: Typical race preparation of lower level triathletes. J Sports Med Phys Fitness
39: 12–15, 1999.
8. Hoffman, MD. Anthropometric characteristics of ultramarathoners. Int J Sports Med
29: 808–811, 2008.
9. Hoffman, MD, Lebus, DK, Ganong, AC, Casazza, GA, and Van Loan, M. Body composition of 161-km ultramarathoners. Int J Sports Med
31: 106–109, 2010.
10. Hopker, J, Coleman, D, and Passfield, L. Changes in cycling efficiency during a competitive season. Med Sci Sports Exerc
41: 912–919, 2009.
11. Hulton, AT, Lahart, I, Williams, KL, Godfrey, R, Charlesworth, S, Wilson, M, Pedlar, C, and Whyte, G. Energy expenditure in the Race Across America (RAAM). Int J Sports Med
31: 463–467, 2010.
12. Knechtle, B, Baumann, B, Knechtle, P, and Rosemann, T. Speed during training and anthropometric measures in relation to race performance by male and female open-water ultra-endurance swimmers. Percept Mot Skills
111: 463–474, 2010.
13. Knechtle, B, Duff, B, Welzel, U, and Kohler, G. Body mass and circumference of upper arm are associated with race performance in ultraendurance runners in a multistage race—The Isarrun 2006. Res Q Exerc Sport
80: 262–268, 2009.
14. Knechtle, B, Enggist, A, and Jehle, T. Energy turnover at the Race Across America (RAAM)—A case report. Int J Sports Med
26: 499–503, 2005.
15. Knechtle, B, Joleska, I, Wirth, A, Knechtle, P, Rosemann, T, and Senn, O. Intra-investigator and inter-investigator variability in measuring skin-fold thicknesses and body fat in ultra-runners under field conditions. Percept Mot Skills
111: 105–106, 2010.
16. Knechtle, B, Knechtle, P, and Rosemann, T. No association between skinfold thicknesses and race performance in male ultra-endurance cyclists in a 600 km ultra-cycling marathon. Hum Mov
10: 91–95, 2009.
17. Knechtle, B, Knechtle, P, and Rosemann, T. Skin-fold thickness and training volume in ultra-triathletes. Int J Sports Med
30: 343–347, 2009.
18. Knechtle, B, Knechtle, P, and Rosemann, T. Race performance in male mountain ultra-marathoners: Anthropometry or training? Percept Mot Skills
110: 721–735, 2010.
19. Knechtle, B, Knechtle, P, and Rosemann, T. Upper body skinfold thickness is related to race performance in male Ironman triathletes. Int J Sports Med
32: 20–27, 2011.
20. Knechtle, B, Knechtle, P, Rosemann, T, and Senn, O. Personal best time, not anthropometry or training volume is associated with total race time in a Triple Iron triathlon. J Strength Cond Res
[Epub ahead of print]
21. Knechtle, B, Knechtle, P, Schulze, I, and Kohler, G. Upper arm circumference is associated with race performance in ultra-endurance runners. Br J Sports Med
42: 295–299, 2007.
22. Knechtle, B and Rosemann, T. No correlation of skin-fold thickness with race performance in male recreational mountain bike ultra-marathoners. Med Sport
13: 152–156, 2009.
23. Knechtle, B, Wirth, A, Baumann, B, Knechtle, P, Rosemann, T, and Oliver, S. Differential correlations between anthropometry, training volume, and performance in male and female Ironman triathletes. J Strength Cond Res
24: 2785–2793, 2010.
24. Knechtle, B, Wirth, A, Knechtle, P, and Rosemann, T. Moderate association of anthropometry, but not training volume, with race performance in male ultraendurance cyclists. Res Q Exerc Sport
80: 563–568, 2009.
25. Knechtle, B, Wirth, A, Knechtle, P, and Rosemann, T. An ultra-cycling race leads to no decrease in skeletal muscle mass. Int J Sports Med
30: 163–167, 2009.
26. 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: 604–609, 2010.
27. Knechtle, B, Wirth, A, and Rosemann, T. Predictors of race time in male Ironman triathletes: Physical characteristics, training, or prerace experience? Percept Mot Skills
111: 437–446, 2010.
28. Lee, RC, Wang, Z, Heo, M, Ross, R, Janssen, I, and Heymsfield, SB. Total-body skeletal muscle mass: Development and cross-validation of anthropometric prediction models. Am J Clin Nutr
72: 796–803, 2000.
29. Lindeman, AK. Nutrient intake of an ultraendurance cyclist. Int J Sport Nutr
1: 79–85, 1991.
30. Martin, MK, Martin, DT, Collier, GR, and Burke, LM. Voluntary food intake by elite female cyclists during training and racing: influence of daily energy expenditure and body composition. Int J Sport Nutr Exerc Metab
12: 249–267, 2002.
31. O'Toole, ML. Training for ultraendurance triathlons. Med Sci Sports Exerc
21: S209–S213, 1989.
32. Peters, EM. Nutritional aspects in ultra-endurance exercise. Curr Opin Clin Nutr Metab Care
6: 427–434, 2003.
33. Rehrer, NJ, Hellemans, IJ, Rolleston, AK, Rush, E, and Miller, BF. Energy intake and expenditure during a 6-day cycling stage race. Scand J Med Sci Sports
20: 609–618, 2010.
34. Schumacher, YO, Ahlgrim, C, Prettin, S, and Pottgiesser, T. Physiology, power output and racing strategy of a Race across America (RAAM) finisher. Med Sci Sports Exerc
[Epub ahead of print].
35. Schumacher, YO, Mroz, R, Mueller, P, Schmid, A, and Ruecker, G. Success in elite cycling: A prospective and retrospective analysis of race results. J Sports Sci
24: 1149–1156, 2006.
36. Swain, DP. The influence of body mass in endurance bicycling. Med Sci Sports Exerc
26: 58–63, 1994.
37. Swain, DP, Coast, JR, Clifford, PS, Milliken, MC, and Stray-Gundersen, J. Influence of body size on oxygen consumption during bicycling. J Appl Physiol
62: 668–672, 1987.
38. Wirnitzer, KC and Kornexl, E.Exercise intensity during an 8-day mountain bike marathon race. Eur J Appl Physiol
104: 999–1005, 2008.
39. Zaryski, C and Smith, DJ. Training principles and issues for ultra-endurance athletes. Curr Sports Med Rep
4: 165–170, 2005.