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

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.

The Performance, Physiology and Morphology of Female and Male Olympic-Distance Triathletes

Sex differences in triathlon performance have been decreasing in recent decades and little information is available to explain it. Thirty-nine male and eighteen female amateur triathletes were evaluated for fat mass, lean mass, maximal oxygen uptake (VO2 max), ventilatory threshold (VT), respiratory compensation point (RCP), and performance in a national Olympic triathlon race. Female athletes presented higher fat mass (p = 0.02, d = 0.84, power = 0.78) and lower lean mass (p < 0.01, d = 3.11, power = 0.99). VO2 max (p < 0.01, d = 1.46, power = 0.99), maximal aerobic velocity (MAV) (p < 0.01, d = 2.05, power = 0.99), velocities in VT (p < 0.01, d = 1.26, power = 0.97), and RCP (p < 0.01, d = 1.53, power = 0.99) were significantly worse in the female group. VT (%VO2 max) (p = 0.012, d = 0.73, power = 0.58) and RCP (%VO2 max) (p = 0.005, d = 0.85, power = 0.89) were higher in the female group. Female athletes presented lower VO2 max value, lower lean mass, and higher fat mass. However, females presented higher values of aerobic endurance (%VO2 max), which can attenuate sex differences in triathlon performance. Coaches and athletes should consider that female athletes can maintain a higher percentage of MAV values than males during the running split to prescribe individual training.

Impact of training volume and experience on amateur Ironman triathlon performance

PURPOSE

To investigate the association between training volume, sleep time, signs and symptoms of excessive training (overtraining), and  previous triathlon experience with overall and split race times in the Ironman distance triathlon.

METHODS

Ninety-nine triathletes (19 women and 80 men) answered an online survey containing questions about anthropometric characteristics (body mass and height), weekly training volume (hours per day and days per week), previous experience in Ironman distance triathlon race, and signs and symptoms of excessive training. Data of race times of all participants were collected by a single race (the Ironman Brazil 2019 - Florianópolis). All surveys were collected between 28 and 30 days before the race. The athlete was instructed to answer the questions according to what was happening in the week before completing the survey.

RESULTS

Total race time did not differ among those who trained up to 14 h per week (11:28:46±01:54:30 h:min:sec), between 15 and 20 h per week (11:37:31±01:20:26 h:min:sec) or more than 20 h per week (11:30:18±01:31:28 h:min:sec) (p = 0.922). Total race time of the triathletes who presented (12:42:22±01:49:36 h:min:sec) or no (11:23:06±01:29:02 h:min:sec) unintentional body mass loss (p = 0.006), feeling (12:46:17±02:03:13 h:min:sec) or no (11:24:09±01:28:07 h:min:sec) of decreased performance (p = 0.009) or feeling (12:08:58±01:47:12 h:min:sec) or no (11:16:34±01:24:53 h:min:sec) loss of energy (p = 0.011) in the week prior to the race were significantly different. Triathletes who had a previous experience in Ironman races achieved a better performance (11:15:21±01:32:04 h:min:sec) than those without previous experience (12:06:38±01:32:10 h:min:sec) (p = 0.010).

CONCLUSION

In summary, high volumes of training (more than 20 h per week), when performed forty days before a race, may not have a positive impact on performance compared to lower volumes of training (up to 14 h per week). However, athletes who had a previous experience in Ironman race presented better results in swimming splits and overall race time. Moreover, the presence of overtraining symptoms, such as unintentional loss of weight, sensation of fatigue and/or performance decrease impact negatively triathlon performance.

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.

Body composition among long distance runners

OBJECTIVE

The current study aimed to examine the body composition of adult male ultra-trail runners (UTR) according to their level of participation (regional UTR-R, vs. national UTR-N).

METHODS

The sample was composed of 44 adult male UTR (aged 36.5±7.2 years; UTR-R: n=25; UTR-N: n=19). Body composition was assessed by air displacement plethysmography, bioelectrical impedance, and dual-energy X-ray absorptiometry. In addition, the Food Frequency Questionnaire (FFQ) was applied. A comparison between the groups was performed using independent samples t-test.

RESULTS

Significant differences between groups contrasting in the competitive level were found for chronological age (in years; UTR-R: 38.8±8.2 vs. UTR-N: 33.5±4.1); body density (in L.kg-1; UTR-R: 1.062±0.015 vs. UTR-N: 1.074±0.009); and fat mass (in kg; UTR-R: 12.7±6.8 vs. UTR-N: 7.6±2.7).

CONCLUSION

UTR-N were younger, presented higher values for body density, and had less fat mass, although no significant differences were found for fat-free mass. The current study evidenced the profile of long-distance runners and the need for weight management programs to regulate body composition.

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.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Psycho-social factors determining success in high-performance triathlon: compared perception in the coach-athlete pair

High-level sport can be analyzed using the complex system model, in which performance is constrained by many factors. Coaches' and athletes' perceptions of important positive and negative factors affecting performance were compared. Participants were 48 high-level international triathletes (n = 34) and their coaches (n = 14). They were personally interviewed via a questionnaire designed by four accredited experts, who selected groups of both positive and negative factors affecting performance. A list of factors was developed, in order of greater to lesser importance in the opinion of athletes and coaches, for subsequent analysis. Two ranked lists (positive and negative factors) indicated that athletes appear to rate personal environment factors (family, teammates, lack of support from relatives) higher, while the coaches tended to give more importance to technical and institutional aspects (institutional support, coach, medical support). There was complete agreement between coaches and triathletes about the top five positive factors. Negative factor agreement was somewhat lower (agreement on 3/5 factors). The most important positive factor for coaches and athletes was "dedication/engagement," while the most important factor adversely affecting performance was "injuries".

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.

Predictor variables for a half marathon race time in recreational male runners

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r² = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m²) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

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.