Running performance, not anthropometric factors, is associated with race success in a Triple Iron Triathlon

Ultra-Cycling- Past, Present, Future: A Narrative Review

BACKGROUND

Ultra-endurance events are gaining popularity in multiple exercise disciplines, including cycling. With increasing numbers of ultra-cycling events, aspects influencing participation and performance are of interest to the cycling community.

MAIN BODY

The aim of this narrative review was, therefore, to assess the types of races offered, the characteristics of the cyclists, the fluid and energy balance during the race, the body mass changes after the race, and the parameters that may enhance performance based on existing literature. A literature search was conducted in PubMed, Scopus, and Google Scholar using the search terms 'ultracycling', 'ultra cycling', 'ultra-cycling', 'ultra-endurance biking', 'ultra-bikers' and 'prolonged cycling'. The search yielded 948 results, of which 111 were relevant for this review. The studies were classified according to their research focus and the results were summarized. The results demonstrated changes in physiological parameters, immunological and oxidative processes, as well as in fluid and energy balance. While the individual race with the most published studies was the Race Across America, most races were conducted in Europe, and a trend for an increase in European participants in international races was observed. Performance seems to be affected by characteristics such as age and sex but not by anthropometric parameters such as skin fold thickness. The optimum age for the top performance was around 40 years. Most participants in ultra-cycling events were male, but the number of female athletes has been increasing over the past years. Female athletes are understudied due to their later entry and less prominent participation in ultra-cycling races. A post-race energy deficit after ultra-cycling events was observed.

CONCLUSION

Future studies need to investigate the causes for the observed optimum race age around 40 years of age as well as the optimum nutritional supply to close the observed energy gap under consideration of the individual race lengths and conditions. Another research gap to be filled by future studies is the development of strategies to tackle inflammatory processes during the race that may persist in the post-race period.

Anthropometrics and Body Composition Predict Physical Performance and Selection to Attend Special Forces Training in United States Army Soldiers

INTRODUCTION

Anthropometrics and body composition characteristics differentiate many types of athletes and are related to performance on fitness tests and tasks in military personnel. Soldiers competing to enter elite units must demonstrate physical fitness and operational competence across multiple events. Therefore, this study determined whether anthropometrics and body composition predicted physical performance and selection for special forces training among soldiers attending the rigorous Special Forces Assessment and Selection (SFAS) course.

MATERIALS AND METHODS

Soldiers attending the SFAS course between May 2015 and March 2017 were enrolled in a longitudinal, observational study. Anthropometrics (height, body mass, and body mass index [BMI]; n = 795) and body composition measured by dual-energy X-ray absorptiometry (percentage body fat, fat mass, lean mass, bone mineral content [BMC], and bone mineral density [BMD]; n = 117) were assessed before the course start. Associations with physical performance were determined with correlation coefficients. Associations with selection were determined with analyses of variance and t-tests; effect sizes were calculated as Cohen's d. The U.S. Army Research Institute of Environmental Medicine Institutional Review Board (IRB) initially approved this study, and the U.S. Army Medical Research and Development Command IRB approved the continuing review.

RESULTS

Lower percentage body fat and fat mass predicted better performance on all assessments: Army Physical Fitness Test (APFT), pull-ups, SFAS run, loaded road march, obstacle course, and land navigation (P ≤ .05). Higher lean mass predicted better performance on the loaded road march (P ≤ .05). Lower body mass and BMI predicted better performance on APFT, pull-ups, run, and obstacle course; higher body mass and BMI predicted better performance on the loaded road march (P ≤ .05). Shorter stature predicted better performance on push-ups (APFT) and pull-ups; taller stature predicted better performance on SFAS run and loaded road march (P ≤ .05). On average, the selected soldiers were taller (179.0 ± 6.6 vs. 176.7 ± 6.7 cm), had higher body mass (85.8 ± 8.8 vs. 82.1 ± 9.6 kg), BMI (26.8 ± 2.2 vs. 26.3 ± 2.6 kg/m2), lean mass (67.2 ± 7.3 vs. 61.9 ± 7.6 kg), BMC (3.47 ± 0.40 vs. 3.29 ± 0.56 kg), and BMD (1.34 ± 0.10 vs. 1.28 ± 0.10 g/cm2), and lower percentage body fat (17.3 ± 3.4 vs. 20.1 ± 4.5%) and fat mass (14.2 ± 3.7 vs. 15.8 ± 4.4 kg) (P ≤ .05). Effect sizes were largest for lean mass (Cohen's d = 0.71) and percentage body fat (d = 0.70), followed by BMD (d = 0.60), body mass (d = 0.40), fat mass (d = 0.39), BMC (d = 0.37), height (d = 0.35), and BMI (d = 0.21). Body mass adjustment attenuated associations between height and selection.

CONCLUSIONS

Anthropometrics and body composition are predictors of physical performance and SFAS success. Since these measures are modifiable (excluding height), they may be the focus of intervention studies aiming to improve performance in arduous military training courses, sports that require competition in multiple events, and occupations that have varied physical demands, such as firefighting, law enforcement, and construction.

Distribution of body fat is associated with physical performance of male amateur triathlon athletes

BACKGROUND

Endurance sports are strongly associated with maximum oxygen uptake, anaerobic threshold, running economy and body fat percentage. Despite the importance for performance of the low-fat mass being a consensus in the literature, there are no data about the importance of the pattern of fat distribution. Therefore, the aim of the present study was to investigate the association between fat mass distribution with triathlon performance and physiological determinants of performance: maximal oxygen consumption (VO2max), ventilatory threshold (AT) and running economy (RE), and to verify the predictive value for performance of gynoid or android fat mass distribution.

METHODS

Thirty-nine triathletes (38.8±6.9 years, 174.8±6.5cm and 74.3±8.8kg) were evaluated for anthropometric (total body mass, fat mass, lean mass, android and gynoid fat mass) and physiological (VO2max, AT and RE) parameters. Split and overall race times were registered.

RESULTS

Overall race time relationship with gynoid fat mass (r=.529, p<.05) was classified as moderate higher than and with android fat mass (r=.416, p<.05) was classified as low. All split times and overall race time presented significant positive correlation with only total fat mass (%) (r =.329 to .574, p<.05) and with gynoid fat mass (%) (r=.359 to .529, p<.05). Overall race time can be better predicted by gynoid fat mass (ß=0.529, t=4.093, p<0.001, r2=0.28) than by android fat mass (ß =0.416, t=2.997, p=0.005, r2=0.17).

CONCLUSIONS

Fat mass distribution is associated with triathlon performance, and the gynoid fat pattern is worse for triathlon performance than the android pattern.

Self-Selected Pacing During a World Record Attempt in 40 Ironman-Distance Triathlons in 40 Days

The present case study analyzed performance, pacing, and potential predictors in a self-paced world record attempt of a professional triathlete to finish 40 Ironman-distance triathlons within 40 days. Split times (i.e., swimming, cycling, running) and overall times, body weight, daily highest temperature, wind speed, energy expenditure, mean heart rate, and sleeping time were recorded. Non-linear regressions were applied to investigate changes in split and overall times across days. Multivariate regression analyses were performed to test which variables showed the greatest influence on the dependent variables cycling, running and overall time. The athlete completed the 40×Ironman distances in a total time of 444:22 h:min. He spent 50:26 h:min in swimming, 245:37 h:min in cycling, 137:17 h:min in running and 11:02 h:min in transition times. Swimming and cycling times became slower across days, whereas running times got faster until the 20th day and, thereafter, became slower until the 40th day. Overall times got slower until the 15th day, became faster to 31st, and started then to get slower until the end. Wind speed, previous day’s race time and average heart race during cycling were significant independent variables influencing cycling time. Body weight and average heart rate during running were significant independent variables influencing running performance. Cycling performance, running performance, and body weight were significant independent variables influencing overall time. In summary, running time was influenced by body weight, cycling by wind speed, and overall time by both running and cycling performances.

Effects of The Performance Level and Race Distance on Pacing in Ultra-Triathlons

The aim of this study was to examine the effects of the performance level and race distance on pacing in ultra-triathlons (Double, Triple, Quintuple and Deca), wherein pacing is defined as the relative time (%) spent in each discipline (swimming, cycling and running). All finishers (n = 3,622) of Double, Triple, Quintuple and Deca Iron ultra-triathlons between 1985 and 2016 were analysed and classified into quartile groups (Q1, Q2, Q3 and Q4) with Q1 being the fastest and Q4 the slowest. Performance of all non-finishers (n = 1,000) during the same period was also examined. Triple and Quintuple triathlons (24.4%) produced the highest rate of non-finishers, and Deca Iron ultra-triathlons produced the lowest rate (18.0%) (χ² = 12.1, p = 0.007, φC = 0.05). For the relative swimming and cycling times (%), Deca triathletes (6.7 ± 1.5% and 48.8 ± 4.9%, respectively) proved the fastest and Double (9.2 ± 1.6% and 49.6 ± 3.6%) Iron ultra-triathletes were the slowest (p < 0.008) with Q4 being the fastest group (8.3 ± 1.6% and 48.8 ± 4.3%) and Q1 the slowest one (9.5 ± 1.5% and 50.9 ± 3.0%) (p < 0.001). In running, Double triathletes were relatively the fastest (41.2 ± 4.0%) and Deca (44.5 ± 5.4%) Iron ultra-triathletes the slowest (p < 0.001) with Q1 being the fastest (39.6 ± 3.3%) and Q4 the slowest group (42.9 ± 4.7%) (p < 0.001). Based on these findings, it was concluded that the fastest ultra-triathletes spent relatively more time swimming and cycling and less time running, highlighting the importance of the role of the latter discipline for the overall ultra-triathlon performance. Furthermore, coaches and ultra-triathletes should be aware of differences in pacing between Double, Triple, Quintuple and Deca Iron triathlons.

Running performance in a timed city run and body composition: A cross-sectional study in more than 3000 runners

OBJECTIVE

The importance of body composition for running performance is unclear in the general population. The aim of this study was to evaluate whether body composition influences running speed and whether it is a better predictor of running speed than body mass index (BMI).

METHODS

The study included 1353 women (38.2 ± 12.1 y of age) and 1771 men (39.6 ± 12.1 y of age) who underwent, for the first time, a measurement of body composition by bioelectrical impedance analysis between 1999 and 2016, before a timed run occurring annually in Geneva. The running distances and times were converted to average speed (km/h). Body composition was expressed as sex-specific quartiles, where quartile 1 (lowest values) was the reference quartile. The relationships between speed and BMI or body composition were analyzed by multivariate linear regressions.

RESULTS

Multivariate regressions showed that the higher the fat mass index (FMI) quartile, the lower the running speed in women and men (all P < 0.001). In men, a fat-free mass index (FFMI) in quartile 4 (>20 kg/m²) was associated with a poor running performance (r = -0.50, P < 0.001), whereas in women, an FFMI in quartile 2 or 3 (15-16.4 kg/m²) was associated with a higher running speed (r = 0.23, P = 0.04; r = 0.28, P = 0.01, respectively). Body composition predicted speed better than BMI in women (R² = 26.8% versus 14.4%) and men (R² = 29.8% versus 25.4%).

CONCLUSIONS

Running speed is negatively associated with BMI and FMI in both sexes. Body composition is a better predictor of running performance than BMI.

The aspect of experience in ultra-triathlon races

Previous experience seems to be an important predictor for endurance and ultra-endurance performance. The present study investigated whether the number of previously completed races and/or the personal best times in shorter races is more predictive for performance in longer non-stop ultra-triathlons such as a Deca Iron ultra-triathlon. All female and male ultra-triathletes who had finished between 1985 and 2014 at least one Double Iron ultra-triathlon (i.e. 7.6 km swimming, 360 km cycling and 84.4 km running), one Triple Iron ultra-triathlon (i.e. 11.4 km swimming, 540 km cycling and 126.6 km running), one Quintuple Iron ultra-triathlon (i.e. 19 km swimming, 900 km cycling and 221 km running) and one Deca Iron ultra-triathlon (i.e. 38 km swimming, 1,800 km cycling and 422 km running) were identified and their best race times for each distance were recorded. Multiple regression analysis (stepwise, forward selection, p of F for inclusion <0.05, p of F for exclusion >0.1, listwise deletion) was used to determine all variables correlating to overall race time and performance in split disciplines for both Quintuple and Deca Iron ultra-triathlon. The number of finished shorter races (i.e. Double and Triple Iron ultra-triathlon) was not associated with the number of finished longer races (i.e. Quintuple and Deca Iron ultra-triathlon) whereas both split and overall race times correlated to split and overall race times of the longer races with the exception of the swimming split times in Double Iron ultra-triathlon showing no correlation with swimming split times in both Quintuple and Deca Iron ultra-triathlon. In summary, previous experience seemed of importance in performance for longer ultra-triathlon races (i.e. Quintuple and Deca Iron ultra-triathlon) where the personal best times of shorter races (i.e. Double and Triple Iron ultra-triathlon) were important, but not the number of previously finished races. For athletes and coaches, fast race times in shorter ultra-triathlon races (i.e. Double and Triple Iron ultra-triathlon) are more important than a large of number finished races in order to achieve a fast race time in a longer ultra-triathlon (i.e. Quintuple and Deca Iron ultra-triathlon).

What predicts performance in ultra-triathlon races? - a comparison between Ironman distance triathlon and ultra-triathlon

Objective

This narrative review summarizes recent intentions to find potential predictor variables for ultra-triathlon race performance (ie, triathlon races longer than the Ironman distance covering 3.8 km swimming, 180 km cycling, and 42.195 km running). Results from studies on ultra-triathletes were compared to results on studies on Ironman triathletes.

Methods

A literature search was performed in PubMed using the terms “ultra”, “triathlon”, and “performance” for the aspects of “ultra-triathlon”, and “Ironman”, “triathlon”, and “performance” for the aspects of “Ironman triathlon”. All resulting papers were searched for related citations. Results for ultra-triathlons were compared to results for Ironman-distance triathlons to find potential differences.

Results

Athletes competing in Ironman and ultra-triathlon differed in anthropometric and training characteristics, where both Ironmen and ultra-triathletes profited from low body fat, but ultra-triathletes relied more on training volume, whereas speed during training was related to Ironman race time. The most important predictive variables for a fast race time in an ultra-triathlon from Double Iron (ie, 7.6 km swimming, 360 km cycling, and 84.4 km running) and longer were male sex, low body fat, age of 35–40 years, extensive previous experience, a fast time in cycling and running but not in swimming, and origins in Central Europe.

Conclusion

Any athlete intending to compete in an ultra-triathlon should be aware that low body fat and high training volumes are highly predictive for overall race time. Little is known about the physiological characteristics of these athletes and about female ultra-triathletes. Future studies need to investigate anthropometric and training characteristics of female ultra-triathletes and what motivates women to compete in these races. Future studies need to correlate physiological characteristics such as maximum oxygen uptake (VO_2max) with ultra-triathlon race performance in order to investigate whether these characteristics are also predictive for ultra-triathlon race performance.

Tracking career performance of successful triathletes

PURPOSE

Tracking athletes' performances over time is important but problematic for sports with large environmental effects. Here we have developed career performance trajectories for elite triathletes, investigating changes in swim, cycle, run stages, and total performance times while accounting for environmental and other external factors.

METHODS

Performance times of 337 female and 427 male triathletes competing in 419 international races between 2000 and 2012 were obtained from triathlon.org. Athletes were categorized according to any top 16 placing at World Championships or Olympics between 2008 and 2012. A mixed linear model accounting for race distance (sprint and Olympic), level of competition, calendar-year trend, athlete's category, and clustering of times within athletes and races was used to derive athletes' individual quadratic performance trajectories. These trajectories provided estimates of age of peak performance and predictions for the 2012 London Olympic Games.

RESULTS

By markedly reducing the scatter of individual race times, the model produced well-fitting trajectories suitable for comparison of triathletes. Trajectories for top 16 triathletes showed different patterns for race stages and differed more among women than among men, but ages of peak total performance were similar for men and women (28 ± 3 yr, mean ± SD). Correlations between observed and predicted placings at Olympics were slightly higher than those provided by placings in races before the Olympics.

CONCLUSIONS

Athletes' trajectories will help identify talented athletes and their weakest and strongest stages. The wider range of trajectories among women should be taken into account when setting talent identification criteria. Trajectories offer a small advantage over usual race placings for predicting men's performance. Further refinements, such as accounting for individual responses to race conditions, may improve utility of performance trajectories.

Sex difference in top performers from Ironman to double deca iron ultra-triathlon

This study investigated changes in performance and sex difference in top performers for ultra-triathlon races held between 1978 and 2013 from Ironman (3.8 km swim, 180 km cycle, and 42 km run) to double deca iron ultra-triathlon distance (76 km swim, 3,600 km cycle, and 844 km run). The fastest men ever were faster than the fastest women ever for split and overall race times, with the exception of the swimming split in the quintuple iron ultra-triathlon (19 km swim, 900 km cycle, and 210.1 km run). Correlation analyses showed an increase in sex difference with increasing length of race distance for swimming (r²=0.67, P=0.023), running (r²=0.77, P=0.009), and overall race time (r²=0.77, P=0.0087), but not for cycling (r²=0.26, P=0.23). For the annual top performers, split and overall race times decreased across years nonlinearly in female and male Ironman triathletes. For longer distances, cycling split times decreased linearly in male triple iron ultra-triathletes, and running split times decreased linearly in male double iron ultra-triathletes but increased linearly in female triple and quintuple iron ultra-triathletes. Overall race times increased nonlinearly in female triple and male quintuple iron ultra-triathletes. The sex difference decreased nonlinearly in swimming, running, and overall race time in Ironman triathletes but increased linearly in cycling and running and nonlinearly in overall race time in triple iron ultra-triathletes. These findings suggest that women reduced the sex difference nonlinearly in shorter ultra-triathlon distances (ie, Ironman), but for longer distances than the Ironman, the sex difference increased or remained unchanged across years. It seems very unlikely that female top performers will ever outrun male top performers in ultratriathlons. The nonlinear change in speed and sex difference in Ironman triathlon suggests that female and male Ironman triathletes have reached their limits in performance.

Performance and sex difference in ultra-triathlon performance from Ironman to Double Deca Iron ultra-triathlon between 1978 and 2013

It was assumed that women would be able to outperform men in ultra-marathon running. The present study investigated the sex difference in performance for all ultra-triathlon distances from the Ironman distance (i.e. 3.8 km swimming, 180 km cycling and 42 km running) in the ‘Ironman Hawaii’ to the Double Deca Iron ultra-triathlon distance (i.e. 76 km swimming, 3,600 km cycling and 840 km running) between 1978 and 2013. The changes in performance and in the sex difference in performance for the annual three fastest finishers were analysed using linear, non-linear and multi-variate regression analyses from 46,123 athletes (i.e. 9,802 women and 46,123 men). Women accounted for 11.9 ± 5.8% of the total field and their percentage was highest in ‘Ironman Hawaii’ (22.1%) and lowest in Deca Iron ultra-triathlon (6.5%). In ‘Ironman Hawaii’, the sex difference decreased non-linearly in swimming, cycling, running and overall race time. In Double Iron ultra-triathlon, the sex difference increased non-linearly in overall race time. In Triple Iron ultra-triathlon, the sex difference increased non-linearly in cycling and overall race time but linearly in running. For the three fastest finishers ever, the sex difference in performance showed no change with increasing race distance with the exception for the swimming split where the sex difference increased with increasing race distance (r² = 0.93, P = 0.001). The sex differences for the three fastest finishers ever for swimming, cycling, running and overall race times for all distances from Ironman to Deca Iron ultra-triathlon were 27.0 ± 17.8%, 24.3 ± 9.9%, 24.5 ± 11.0%, and 24.0 ± 6.7%, respectively. To summarize, these findings showed that women reduced the sex difference in the shorter ultra-triathlon distances (i.e. Ironman distance) but extended the sex difference in longer distances (i.e. Double and Triple Iron ultra-triathlon). It seems very unlikely that women will ever outperform men in ultra-triathlons from Ironman to Double Iron ultra-triathlon.

The age of peak performance in Ironman triathlon: a cross-sectional and longitudinal data analysis

BACKGROUND

The aims of the present study were, firstly, to investigate in a cross-sectional analysis the age of peak Ironman performance within one calendar year in all qualifiers for Ironman Hawaii and Ironman Hawaii; secondly, to determine in a longitudinal analysis on a qualifier for Ironman Hawaii whether the age of peak Ironman performance and Ironman performance itself change across years; and thirdly, to determine the gender difference in performance.

METHODS

In a cross-sectional analysis, the age of the top ten finishers for all qualifier races for Ironman Hawaii and Ironman Hawaii was determined in 2010. For a longitudinal analysis, the age and the performance of the annual top ten female and male finishers in a qualifier for Ironman Hawaii was determined in Ironman Switzerland between 1995 and 2010.

RESULTS

In 19 of the 20 analyzed triathlons held in 2010, there was no difference in the age of peak Ironman performance between women and men (p > 0.05). The only difference in the age of peak Ironman performance between genders was in 'Ironman Canada' where men were older than women (p = 0.023). For all 20 races, the age of peak Ironman performance was 32.2 ± 1.5 years for men and 33.0 ± 1.6 years for women (p > 0.05). In Ironman Switzerland, there was no difference in the age of peak Ironman performance between genders for top ten women and men from 1995 to 2010 (F = 0.06, p = 0.8). The mean age of top ten women and men was 31.4 ± 1.7 and 31.5 ± 1.7 years (Cohen's d = 0.06), respectively. The gender difference in performance in the three disciplines and for overall race time decreased significantly across years. Men and women improved overall race times by approximately 1.2 and 4.2 min/year, respectively.

CONCLUSIONS

Women and men peak at a similar age of 32-33 years in an Ironman triathlon with no gender difference. In a qualifier for Ironman Hawaii, the age of peak Ironman performance remained unchanged across years. In contrast, gender differences in performance in Ironman Switzerland decreased during the studied period, suggesting that elite female Ironman triathletes might still narrow the gender gap in the future.

Sex difference in Double Iron ultra-triathlon performance

BACKGROUND

The present study examined the sex difference in swimming (7.8 km), cycling (360 km), running (84 km), and overall race times for Double Iron ultra-triathletes.

METHODS

Sex differences in split times and overall race times of 1,591 men and 155 women finishing a Double Iron ultra-triathlon between 1985 and 2012 were analyzed.

RESULTS

The annual number of finishes increased linearly for women and exponentially for men. Men achieved race times of 1,716 ± 243 min compared to 1,834 ± 261 min for women and were 118 ± 18 min (6.9%) faster (p < 0.01). Men finished swimming within 156 ± 63 min compared to women with 163 ± 31 min and were 8 ± 32 min (5.1 ± 5.0%) faster (p < 0.01). For cycling, men (852 ± 196 min) were 71 ± 70 min (8.3 ± 3.5%) faster than women (923 ± 126 min) (p < 0.01). Men completed the run split within 710 ± 145 min compared to 739 ± 150 min for women and were 30 ± 5 min (4.2 ± 3.4%) faster (p = 0.03). The annual three fastest men improved race time from 1,650 ± 114 min in 1985 to 1,339 ± 33 min in 2012 (p < 0.01). Overall race time for women remained unchanged at 1,593 ± 173 min with an unchanged sex difference of 27.1 ± 8.6%. In swimming, the split times for the annual three fastest women (148 ± 14 min) and men (127 ± 20 min) remained unchanged with an unchanged sex difference of 26.8 ± 13.5%. In cycling, the annual three fastest men improved the split time from 826 ± 60 min to 666 ± 18 min (p = 0.02). For women, the split time in cycling remained unchanged at 844 ± 54 min with an unchanged sex difference of 25.2 ± 7.3%. In running, the annual fastest three men improved split times from 649 ± 77 min to 532 ± 16 min (p < 0.01). For women, however, the split times remained unchanged at 657 ± 70 min with a stable sex difference of 32.4 ± 12.5%.

CONCLUSIONS

To summarize, the present findings showed that men were faster than women in Double Iron ultra-triathlon, men improved overall race times, cycling and running split times, and the sex difference remained unchanged across years for overall race time and split times. The sex differences for overall race times and split times were higher than reported for Ironman triathlon.

Sex Differences in Ultra-Triathlon Performance at Increasing Race Distance

It has been argued that women should be able to outrun men in ultra-endurance distances. The present study investigated the sex difference in overall race times and split times between elite female and male Ironman Triathletes competing in Ironman Hawaii (3.8 km swimming, 180 km cycling, and 42.195 km running) and Double Iron ultra-triathletes (7.6 km swimming, 360 km cycling, and 84.4 km running). Data from 20,638 athletes, including 5,163 women and 15,475 men competing in Ironman Hawaii and from 143 women and 1,252 men competing in Double Iron ultra-triathlon races held worldwide between 1999 and 2011 were analyzed. In Ironman Hawaii, the sex difference in performance of the top three athletes remained unchanged during the period studied for overall race time. For Double Iron ultra-triathletes, the sex difference for the top three athletes remained unchanged for overall race time. Sex differences increased as endurance race distances increased and showed no changes over time. It appears that women are unlikely to close the gap in ultra-endurance performance with men in ultra-triathlons in the near future. Physiological (e.g., maximum oxygen uptake) and anthropometric characteristics (e.g., skeletal muscle mass) may set biological limits for women.

Participation and performance trends in ultracycling

Background

Participation and performance trends have been investigated in ultramarathons and ultratriathlons but not in ultracycling. The aim of the present study was to investigate (1) participation and performance trends in ultraendurance cyclists, (2) changes in cycling speed over the years, and (3) the age of the fastest male and female ultraendurance cyclists.

Methods

Participation and performance trends in the 5000 km Race Across America (RAAM) and in two RAAM-qualifier races – the 818 km Furnace Creek 508 in the United States and the 715 km Swiss Cycling Marathon in Europe – were investigated using linear regression analyses and analyses of variance.

Results

On average, ~41% of participants did not finish either the RAAM or the Furnace Creek 508, whereas ~26% did not finish the Swiss Cycling Marathon. Female finishers accounted for ~11% in both the RAAM and the Furnace Creek 508 but only ~3% in the Swiss Cycling Marathon. The mean cycling speed of all finishers remained unchanged during the studied periods. The winner’s average speed was faster for men than for women in the RAAM (22.6 ± 1.1 km · h⁻¹ versus 18.4 ± 1.7 km · h⁻¹, respectively; average speed difference between male and female winners, 25.0% ± 11.9%), the Swiss Cycling Marathon (30.8 ± 0.8 km · h⁻¹ versus 24.4 ± 1.9 km · h⁻¹, respectively; average speed difference between male and female winners, 27.8% ± 9.4%), and the Furnace Creek 508 (27.4 ± 1.6 km · h⁻¹ versus 23.4 ± 3.0 km · h⁻¹, respectively; average speed difference between male and female winners, 18.4% ± 13.9%). In both the Furnace Creek 508 and the Swiss Cycling Marathon, ~46% of the finishers were aged between 35 and 49 years. The mean age of winners, both male and female, across the years in the Furnace Creek 508 and in the Swiss Cycling Marathon was 37 ± 10 years.

Conclusion

These findings in ultracycling races showed that (1) ~26%–40% of starters were unable to finish, (2) the percentage of female finishers was ~3%–11%, (3) the gender difference in performance was ~18%–28%, and (4) ~46% of the successful finishers were master athletes. Future studies need to investigate the reasons for the low female participation and focus on the age-related performance decline in other ultraendurance events in order to confirm that master athletes are predisposed to ultraendurance performances.

Age-related changes in ultra-triathlon performances

Background

The age-related decline in performance has been investigated in swimmers, runners and triathletes. No study has investigated the age-related performance decline in ultra-triathletes. The purpose of this study was to analyse the age-related declines in swimming, cycling, running and overall race time for both Triple Iron ultra-triathlon (11.4-km swimming, 540-km cycling and 126.6-km running) and Deca Iron ultra-triathlon (38-km swimming, 1,800-km cycling and 420-km running).

Methods

The age and performances of 423 male Triple Iron ultra-triathletes and 119 male Deca Iron ultra-triathletes were analysed from 1992 to 2010 using regression analyses and ANOVA.

Results

The mean age of the finishers was significantly higher for Deca Iron ultra-triathletes (41.3 ± 3.1 years) compared to a Triple Iron ultra-triathletes (38.5 ± 3.3 years) (P < 0.05). For both ultra-distances, the fastest overall race times were achieved between the ages of 25 and 44 years. Deca Iron ultra-triathletes achieved the same level of performance in swimming and cycling between 25 and 54 years of age.

Conclusions

The magnitudes of age-related declines in performance in the three disciplines of ultra-triathlon differ slightly between Triple and Deca Iron ultra-triathlon. Although the ages of Triple Iron ultra-triathletes were on average younger compared to Deca Iron ultra-triathletes, the fastest race times were achieved between 25 and 44 years for both distances. Further studies should investigate the motivation and training of ultra-triathletes to gain better insights in ultra-triathlon performance.

Participation and Performance Trends in Triple Iron Ultra-triathlon - a Cross-sectional and Longitudinal Data Analysis

Purpose

The aims of the present study were to investigate (i) the changes in participation and performance and (ii) the gender difference in Triple Iron ultra-triathlon (11.4 km swimming, 540 km cycling and 126.6 km running) across years from 1988 to 2011.

Methods

For the cross-sectional data analysis, the association between with overall race times and split times was investigated using simple linear regression analyses and analysis of variance. For the longitudinal data analysis, the changes in race times for the five men and women with the highest number of participations were analysed using simple linear regression analyses.

Results

During the studied period, the number of finishers were 824 (71.4%) for men and 80 (78.4%) for women. Participation increased for men (r²=0.27, P<0.01) while it remained stable for women (8%). Total race times were 2,146 ± 127.3 min for men and 2,615 ± 327.2 min for women (P<0.001). Total race time decreased for men (r²=0.17; P=0.043), while it increased for women (r²=0.49; P=0.001) across years. The gender difference in overall race time for winners increased from 10% in 1992 to 42% in 2011 (r²=0.63; P<0.001). The longitudinal analysis of the five women and five men with the highest number of participations showed that performance decreased in one female (r²=0.45; P=0.01). The four other women as well as all five men showed no change in overall race times across years.

Conclusions

Participation increased and performance improved for male Triple Iron ultra-triathletes while participation remained unchanged and performance decreased for females between 1988 and 2011. The reasons for the increase of the gap between female and male Triple Iron ultra-triathletes need further investigations.

Comparison between Recreational Male Ironman Triathletes and Marathon Runners

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

European athletes dominate performances in Double Iron ultra-triathlons--a retrospective data analysis from 1985 to 2010

We investigated the participation and performance trends of ultra-endurance triathletes from all nationalities competing in a Double Iron ultra-triathlon (7.6-km swim, 360-km cycle and 84.4-km run) from 1985 to 2010. A total of 1854 athletes participated in 92 Double Iron ultra-triathlons. The majority of the winners came from Europe with 72 victories, followed by North America with 17 victories. The race time for the European ultra-triathletes was 1340 (s=95.3) min, decreasing highly significantly (r (2)=0.28; P<0.0001) across the years. North American ultra-triathletes finished the races within 1556 (s=124.5) min; their race time showed no changes across the years (r (2)=0.045; P=0.07). The race time for the Europeans was highly significantly faster compared to the North Americans (P<0.0001). Future studies should investigate each country in Europe and North America in order to find the country with the largest participation of athletes and their best performance.

Analysis of ultra-triathlon performances

Despite increased interest in ultra-endurance events, little research has examined ultra-triathlon performance. The aims of this study were: (i) to compare swimming, cycling, running, and overall performances in three ultra-distance triathlons, double Ironman distance triathlon (2IMT) (7.6 km swimming, 360 km cycling, and 84.4 km running), triple Ironman distance triathlon (3IMT) (11.4 km, 540 km, and 126.6 km), and deca Ironman distance triathlon (10IMT) (38 km, 1800 km, and 420 km) and (ii) to examine the relationships between the 2IMT, 3IMT, and 10IMT performances to create predicted equations of the 10IMT performances. Race results from 1985 through 2009 were examined to identify triathletes who performed the three considered ultra-distances. In total, 73 triathletes (68 men and 5 women) were identified. The contribution of swimming to overall ultra-triathlon performance was lower than for cycling and running. Running performance was more important to overall performance for 2IMT and 3IMT compared with 10IMT The 2IMT and 3IMT performances were significantly correlated with 10IMT performances for swimming and cycling, but not for running. 10IMT total time performance might be predicted by the following equation: 10IMT race time (minutes) = 5885 + 3.69 × 3IMT race time (minutes). This analysis of human performance during ultra-distance triathlons represents a unique data set in the field of ultra-endurance events. Additional studies are required to determine the physiological and psychological factors associated with ultra-triathlon performance.

Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes

Background

The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes.

Methods

Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis.

Results

A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001) and in an Olympic distance triathlon (P < 0.0001) were the best variables related to Ironman race time. Ironman race time (minutes) might be partially predicted by the following equation: (r² = 0.65, standard error of estimate = 56.8) = 152.1 + 1.332 × (personal best time in a marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes).

Conclusion

These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes.

Age and gender interactions in ultraendurance performance: insight from the triathlon

PURPOSE

The purposes of this study were (i) to investigate the effect of age on gender difference in Hawaii Ironman triathlon performance time and (ii) to compare the gender difference among swimming (3.8 km), cycling (180 km), and running (42 km) performances as a function of age.

METHODS

Gender difference in performance times and estimated power output in the three modes of locomotion were analyzed for the top 10 men and women amateur triathletes between the ages of 18 and 64 yr for three consecutive years (2006-2008).

RESULTS

The gender difference in total performance time was stable until 55 yr and then significantly increased. Mean gender difference in performance time was significantly (P < 0.01) smaller for swimming (mean ± 95% confidence interval = 12.1% ± 1.9%) compared with cycling (15.4% ± 0.7%) and running (18.2% ± 1.3%). In contrast, mean gender difference in cycling estimated power output (38.6% ± 1.1%) was significantly (P < 0.01) greater compared with swimming (27.5% ± 3.8%) and running (32.6% ± 0.7%).

CONCLUSIONS

This cross-sectional study provides evidence that gender difference in ultraendurance performance such as an Ironman triathlon was stable until 55 yr and then increased thereafter and differed between the locomotion modes. Further studies examining the changes in training volume and physiological characteristics with advanced age for men and women are required to better understand the age-associated changes in ultraendurance performance.

Personal best time, not anthropometry or training volume, is associated with total race time in a triple iron triathlon

The purpose of this study was to investigate in 81 male recreational ultratriathletes (64 finishers and 17 nonfinishers) the relationship of anthropometry, prerace experience, and training with race outcome in a Triple Iron triathlon, using bi and multivariate analyses. In the bivariate analysis, the sum of 8 skinfolds (r = 0.38) and the sum of upper body skinfolds (r = 0.37) were positively related to total race time. None of the anthropometric variables was related to the swim or bike split. Circumference of upper arm (r = 0.42), percent body fat (r = 0.43), the sum of 8 skinfolds (r = 0.47), and the sum of upper body skinfolds (r = 0.45) were positively associated with the time in the run split. None of the training variables was related to total race time or split times. Personal best time in an Ironman triathlon (r = 0.59) and a Triple Iron triathlon (r = 0.82) were positively and highly significantly related to total race time. When all significant variables after bivariate analysis were included in a regression model, personal best time in a Triple Iron triathlon (p < 0.0001) remained the single predictor variable. For practical considerations, athletes with a background as an ultrarunner might have an advantage in successfully finishing a Triple Iron triathlon. However, ultrarunners should also have enough prerace experience in competing in Ironman and Triple Iron triathlons to successfully finish such a race.

Similarity of anthropometric measures for male ultra-triathletes and ultra-runners

Previous research concluded that Triple Iron ultra-triathletes were close to runners in anthropometry. We assessed similarities in anthropometry between 64 Triple Iron triathletes who competed over 11.4 km swimming, 540 km cycling, and 126 km running versus 95 100-km ultra-marathoners. Variables of anthropometry such as body mass, body height, length and circumferences of limbs, skin-folds and body fat, and training such as volume and speed were compared between ultra-triathletes and ultra-runners. The Triple Iron triathletes completed their race distance within 2811 min. (SD=379) and the 100-km ultra-marathoners within 691 min. (SD=117). Triathletes were younger, had higher body mass, shorter legs, higher circumference of upper arm and thigh, lower sum of skin-folds, and lower percent body fat compared to runners. Weekly training volume was higher for triathletes, and weekly hours in running and weekly kilometres in running were higher for runners. In the Triple Iron ultra-triathletes, the sum of eight skin-folds correlated to total race time. The circumference of upper arm, the sum of eight skin-folds, and percent body fat correlated with time in the running section .42, .47, and .43, respectively. In the 100-km ultra-marathoners, the sum of eight skin-folds, the skin-fold thickness of thigh, percent body fat, weekly running hours, and weekly running kilometres correlated with race time .55, .40, .56, -.50, and -.51, respectively. However, in the triathletes, none of these training variables was significantly correlated with race time. In the ultra-marathoners, the sum of eight skin-folds, the skin-fold thickness of thigh, percent body fat, weekly running kilometres, and speed in running during training were related to race time (correlations of .55, .40, -.28, and -.51, respectively). Overall, the ultra-triathletes were not similar to ultra-runners in their anthropometric measures and training variables.

Pre- and postmarathon training habits of nonelite runners

Background

Despite the increasing popularity of marathons, little research has examined the training habits of nonelite marathon runners. Given that nonelite runners, particularly those with a competitive motive, have a higher risk for injury than experienced elite runners, it is important for physicians to understand the training program and features that might distinguish running performance and injury rates in this population.

Hypothesis:

We hypothesized that nonelite runners who qualify for the Boston Marathon (“qualifers”) would have higher running volumes, more running sessions per week, lower injury rates, and lower body mass index (BMI) than nonqualifying runners.

Study design:

A cross-sectional Web-based survey of runners (convenience sample) at 1 month (n = 50) and 6 months (n = 41) after participation in the 2008 Twin Cities Marathon (TCM) that acquired data on anthropometric measures, demographic data, finishing time, premarathon/current training program, and self-reported injury.

Results

Thirteen of 50 initial survey respondents were classified as a “qualifier” based on their finishing time. Mean BMI was significantly lower in the qualifiers at 1 month (22.0 versus 23.9 kg/m², P = 0.0267) but not 6 months postmarathon. There were no significant differences in training volume (running frequency, run length, or cross-training volume) or injury rates between qualifiers and nonqualifiers. Prior to the 2008 TCM, 54% of runners included cross-training in their exercise program, which increased significantly to 74% 1 month postmarathon (P = 0.0039) and 71% 6 months postmarathon (P = 0.0325). There was no association between cross-training and injury rates.

Conclusions

Nonelite marathon runners had a high degree of cross-training in their training program. Qualifiers for the Boston Marathon did not significantly differ in running frequency, run length, or cross-training volume compared with nonqualifiers. Whether changes in the training program at an individual level might facilitate a change in qualifying status remains to be determined.

The Relationship between Anthropometry and Split Performance in Recreational Male Ironman Triathletes

PURPOSE

The aim of this study was to investigate the relation between anthropometric variables and total race time including split times in 184 recreational male Ironman triathletes.

METHODS

Body mass, body height, body mass index, lengths and circumferences of imbs, thicknesses of skin-folds, sum of skin-fold thicknesses, and percent body fat were related to total race time including split times using correlation analysis and effect size.

RESULTS

A large effect size (r>0.37) was found for the association between body mass index and time in the run split and between both the sum of skin-folds and percent body fat with total race time. A medium effect size (r=0.24-0.36) was observed in the association between body mass and both the split time in running and total race time, between body mass index and total race time, between both the circumferences of upper arm and thigh with split time in the run and between both the sum of skin-folds and percent body fat with split times in swimming, cycling and running.

CONCLUSIONS

The results of this study showed that lower body mass, lower body mass index and lower body fat were associated with both a faster Ironman race and a faster run split; lower circumferences of upper arm and thigh were also related with a faster run split.

Differential correlations between anthropometry, training volume, and performance in male and female Ironman triathletes

We investigated in 27 male Ironman triathletes aged 30.3 (9.1) years, with 77.7- (9.8) kg body mass, 1.78- (0.06) m body height, 24.3- (2.2) kg·m⁻² body mass index (BMI), and 14.4 (4.8) % body fat and in 16 female Ironman triathletes aged 36.6 (7.0) years, with 59.7- (6.1) kg body mass, 1.66- (0.06) m body height, 21.5 (1.0) kg·m⁻² BMI, and 22.8 (4.8) % body fat to ascertain whether anthropometric or training variables were related to total race time. The male athletes were training 14.8 (3.2) h·wk⁻¹ with a speed of 2.7 (0.6) km·h⁻¹ in swimming, 27.3 (3.0) in cycling, and 10.6 (1.4) in running. The female athletes trained for 13.9 (3.4) h·wk⁻¹ at 2.1 (0.8) km·h⁻¹h in swimming, 23.7 (7.6) km·h⁻¹ in cycling, and 9.0 (3.7) km·h⁻¹ in running, respectively. For male athletes, percent body fat was highly significantly (r² = 0.583; p < 0.001) associated with total race time. In female triathletes, training volume showed a relationship to total race time (r² = 0.466; p < 0.01). Percent body fat was unrelated to training volume for both men (r² = 0.001; p > 0.05) and women (r² = 0.007; p > 0.05). We conclude that percent body fat showed a relationship to total race time in male triathletes, and training volume showed an association with total race time in female triathletes. Presumably, the relationship between percent body fat, training volume, and race performance is genetically determined.

Predictors of Race Time in Male Ironman Triathletes: Physical Characteristics, Training, or Prerace Experience?

The aim of the present study was to assess whether physical characteristics, training, or prerace experience were related to performance in recreational male Ironman triathletes using bi- and multivariate analysis. 83 male recreational triathletes who volunteered to participate in the study ( M age 41.5 yr., SD = 8.9) had a mean body height of 1.80 m ( SD = 0.06), mean body mass of 77.3 kg ( SD = 8.9), and mean Body Mass Index of 23.7 kg/m2 ( SD = 2.1) at the 2009 IRONMAN SWITZERLAND competition. Speed in running during training, personal best marathon time, and personal best time in an Olympic distance triathlon were related to the Ironman race time. These three variables explained 64% of the variance in Ironman race time. Personal best marathon time was significantly and positively related to the run split time in the Ironman race. Faster running while training and both a fast personal best time in a marathon and in an Olympic distance triathlon were associated with a fast Ironman race time.

Personal best time, percent body fat, and training are differently associated with race time for male and female ironman triathletes

We studied male and female nonprofessional Ironman triathletes to determine whether percent body fat, training, and/or previous race experience were associated with race performance. We used simple linear regression analysis, with total race time as the dependent variable, to investigate the relationship among athletes' percent body fat, average amount of weekly training, and best time in an Ironman triathlon. For male athletes, percent body fat (r2 = 0.57, p < .001) was related to total race time but not average weekly training. For women, percent body fat showed no association with total race time; howeven average weekly training volume was related to total race time (r = .43, p < .01). Percent body fat and average weekly training were not correlated in either gender Speed in training was not associated with race performance in either gender. For men (r2 = .56, p < .001) and women (r2 = .45, p < .05), personal best time in an Ironman triathlon was related to total race time. We concluded that percent body fat was related to race performance in male athletes and to average weekly training in female athletes. Personal best time in an Ironman triathlon was associated with total race time for both male and female athletes.