Recent findings suggested that the age of peak ultra-marathon performance seemed to increase with increasing race distance. The present study investigated the age of peak ultra-marathon performance for runners competing in time-limited ultra-marathons held from 6 to 240 h (i.e. 10 days) during 1975–2013. Age and running performance in 20,238 (21 %) female and 76,888 (79 %) male finishes (6,863 women and 24,725 men, 22 and 78 %, respectively) were analysed using mixed-effects regression analyses. The annual number of finishes increased for both women and men in all races. About one half of the finishers completed at least one race and the other half completed more than one race. Most of the finishes were achieved in the fourth decade of life. The age of the best ultra-marathon performance increased with increasing race duration, also when only one or at least five successful finishes were considered. The lowest age of peak ultra-marathon performance was in 6 h (33.7 years, 95 % CI 32.5–34.9 years) and the highest in 48 h (46.8 years, 95 % CI 46.1–47.5). With increasing number of finishes, the athletes improved performance. Across years, performance decreased, the age of peak performance increased, and the age of peak ultra-marathon performance increased with increasing number of finishes. In summary, the age of peak ultra-marathon performance increased and performance decreased in time-limited ultra-marathons. The age of peak ultra-marathon performance increased with increasing race duration and with increasing number of finishes. These athletes improved race performance with increasing number of finishes.
Master athlete; Ultra-running; Sex; Endurance performance
Participation and performance trends for age group marathoners have been investigated for large city marathons such as the ‘New York City Marathon’ but not for mountain marathons. This study investigated participation and trends in performance and sex difference in the mountain marathon ‘Jungfrau Marathon’ held in Switzerland from 2000 to 2014 using single and mixed effects regression analyses. Results were compared to a city marathon (Lausanne Marathon) also held in Switzerland during the same period. Sex difference was calculated using the equation ([race time in women] − [race time in men]/[race time in men] × 100). Changes in sex differences across calendar years and were investigated using linear regression models. In ‘Jungfrau Marathon’, participation in all female and male age groups increased with exception of women in age groups 18–24 and men in age groups 30–34, 40–44 and 60–64 years where participation remained unchanged. In ‘Lausanne Marathon’, participation increased in women in age groups 30–34 to 40–44 years. In men, participation increased in age groups 25–29 to 44–44 years and 50–54 years. In ‘Jungfrau Marathon’ runners became slower across years in age groups 18–24 to 70–74 years. In ‘Lausanne Marathon’, runners became slower across years in age groups 18–24 and 30–34 to 65–69 years, but not for 25–29, 70–74 and 75–79 years. In ‘Jungfrau Marathon’, sex difference increased in age groups 25–29 (from 4 to 10 %) and 60–64 years (from 3 to 8 %) but decreased in age group 40–44 years (from 12 to 6 %). In ‘Lausanne Marathon’, the sex difference showed no changes. In summary, participation increased in most female and male age groups but performance decreased in most age groups for both the mountain marathon ‘Jungfrau Marathon’ and the city marathon ‘Lausanne Marathon’. The sex differences were lower in the ‘Jungfrau Marathon’ (~6–7 %) compared to the ‘Lausanne Marathon’ where the sex difference was ~10–12 % from age groups 18–24 to 55–59 years. These unexpected findings might be a typical Swiss phenomenon. Future studies need to investigate whether this trend can also be found in other endurance sports events held in Switzerland and other mountain marathons held in other countries.
Female; Male; Runner; Master
We investigated the nation related participation and performance trends in triathletes competing in ‘Norseman Xtreme Triathlon’ between 2006 and 2014 using mixed models, one-way analysis of variance and multi-variate regression analyses. A total of 1594 athletes (139 women and 1455 men) originating from 34 different countries finished the race. Most of the athletes originated from Norway, Germany, Great Britain, Sweden, USA and France. In the mixed model analysis considering all finishers (n = 1594), with calendar year, sex and country as independent and overall race time as dependent variable, calendar year (p < 0.0001), sex (p < 0.0001), country (p < 0.0001) and the interaction sex × calendar year (p = 0.012) were significant. In the model where overall race time was separated in the three disciplines, we found interactions such as country × discipline (p < 0.0001), year × discipline (p < 0.0001), sex × discipline (p < 0.0001), calendar year × sex (p = 0.044), calendar year × sex × discipline (p = 0.031). Overall race time decreased every year, above all in the year 2012. Women were slower than men, but women reduced this gender gap year after year and above all in the year 2007 (p = 0.001). Athletes from Norway and Germany were faster than those from Great Britain and other countries. Split times of the discipline decreased throughout the years. In particular, the discipline having more impact on overall race time was cycling. Most of the podiums were achieved by Norwegian women and men. For women, the fastest split and transition times were achieved by Norwegian women with exception of the run where German women were faster. Norwegian men were the fastest in split and transition times although French athletes were the fastest in swimming. Across years, the annual three fastest Norwegian women improved in cycling, running, overall race time and transition times but not Norwegian and German men. British men, however, improved running split times and transition times. To summarize, most of the finishers in ‘Norseman Xtreme Triathlon’ originated from Norway and the fastest race times were achieved by Norwegian women and men. Norwegian women improved race times across years but not Norwegian men.
Swimming; Cycling; Running; Ultra-endurance; Mixed model
Performance and age of elite marathoners is well known. Participation and performance trends of elderly marathoners (75 years and older) are not well investigated. This study investigated participation and performance
trends in elderly marathoners older than 75 years competing during 2004–2011 in four races (Berlin, New York, Chicago and Boston) of the ‘World Marathon Majors’ using mixed-effects regression models. Participation for women and men remained unchanged at 17 and 114, respectively, during the investigated period. For all finishers, marathon race times showed a significant and positive trend for gender, calendar year and age. For the annual fastest, calendar year and age showed a significant and positive trend. For the annual three fastest, gender, calendar year and age showed a significant and positive trend. The gender difference for the annual fastest and the annual three fastest showed no change across years. For the annual fastest and the annual three fastest, race times were fastest in the youngest age group (75–79 years) and slowest in the oldest age group (85–89 and 80–84 years, respectively). The gender difference in marathon race times remained unchanged across years at 19.7 ± 11.2, 28.1 ± 23.8 and 41.9 ± 22.6 % for the annual fastest in age groups 75–79, 80–84 and 85–89 years, respectively. For the annual three fastest men and women in age groups 75–79 and 80–84 years, the values were 23.7 ± 3.2 and 30.0 ± 13.2 %, respectively. In summary, for marathoners older than 75 years participating during 2004–2011 in four of the largest marathons in the world, participation for female and male runners remained unchanged, the fastest women and men became slower across years and the gender difference in performance remained unchanged. These findings might be the results of the relatively short period of time of 8 years. Future studies might investigate the performance trends in a large city marathon across a longer period of time.
Master athlete; Running; Age group; Gender
This narrative review summarizes findings for Ironman triathlon performance and intends to determine potential predictor variables for Ironman race performance in female and male triathletes.
A literature search was performed in PubMed using the terms “Ironman”, “triathlon”, and “performance”. All resulting articles were searched for related citations.
Age, previous experience, sex, training, origin, anthropometric and physiological characteristics, pacing, and performance in split disciplines were predictive. Differences exist between the sexes for anthropometric characteristics. The most important predictive variables for a fast Ironman race time were age of 30–35 years (women and men), a fast personal best time in Olympic distance triathlon (women and men), a fast personal best time in marathon (women and men), high volume and high speed in training where high volume was more important than high speed (women and men), low body fat, low skin-fold thicknesses and low circumference of upper arm (only men), and origin from the United States of America (women and men).
These findings may help athletes and coaches to plan an Ironman triathlon career. Age and previous experience are important to find the right point in the life of a triathlete to switch from the shorter triathlon distances to the Ironman distance. Future studies need to correlate physiological characteristics such as maximum oxygen uptake with Ironman race time to investigate their potential predictive value and to investigate socio-economic aspects in Ironman triathlon.
swimming; cycling; running; age; body fat; sex
‘Ice Mile’ swimming is a new discipline in open-water swimming introduced in 2009. This case study investigated changes in body core temperature during preparation for and completion of two official ‘Ice Miles’, defined as swimming 1.609 km in water of 5°C or colder, in one swimmer.
One experienced ice swimmer (56 years old, 110.2 kg body mass, 1.76 m body height, BMI of 35.6 kg/m2, 44.8% body fat) recorded data including time, distance and body core temperature from 65 training units and two ‘Ice Miles’.
Discussion and evaluation
During training and the ‘Ice Miles’, body core temperature was measured using a thermoelectric probe before, during and after swimming. During trainings and the ‘Ice Miles’, body core temperature increased after start, dropped during swimming but was lowest during recovery. During training, body core temperature at start was the only predictor (ß = −0.233, p = 0.025) for the increase in body core temperature. Water temperature (ß = 0.07, p = 0.006) and body core temperature at start (ß = −0.90, p = 0.006) explained 61% of the variance for the non-significant decrease in body core temperature. Water temperature (ß = 0.077, p = 0.0059) and body core temperature at finish (ß = 0.444, p = 0.02) were the most important predictors for the lowest body core temperature. In ‘Ice Miles’, body core temperature was highest ~6–18 min after the start (38.3–38.4°C), dropped during swimming by 1.7°C to ~36.5°C and was lowest ~40–56 min after finish. The lowest body core temperature (34.5–35.0°C) was achieved ~100 min after start.
In an experienced ice swimmer with a high BMI (>35 kg/m2) and a high percent body fat (~45%), body core temperature decreased by 1.7°C while swimming and by 3.2–3.7°C after the swim to reach the lowest temperature in an official ‘Ice Mile’. The swimmer suffered no hypothermia during ice swimming, but body core temperature dropped to <36°C after ice swimming. Future athletes intending to swim an ‘Ice Mile’ should be aware that a large body fat prevents from suffering hypothermia during ice swimming, but not after ice swimming.
Open-water swimming; Ice swimming; Body fat
Open-water ultra-distance swimming has a long history where the ‘English Channel’ (~33 km) was crossed in 1875 for the first time. Nowadays, the three most challenging open-water swims worldwide are the 21-miles (34 km) ‘English Channel Swim’, the 20.1-miles (32.2 km) ‘Catalina Channel Swim’ and the 28.5-miles (45.9 km) ‘Manhattan Island Marathon Swim’, also called the ‘Triple Crown of Open Water Swimming’. Recent studies showed that women were able to achieve men’s performance in the ‘English Channel Swim’ or to even outperform men in the ‘Manhattan Island Marathon Swim’. However, the analysis of the ‘Catalina Channel Swim’ as part of the ‘Triple Crown of Open Water Swimming’ is missing. We investigated performance and sex difference in performance for successful women and men crossing the ‘Catalina Channel’ between 1927 and 2014. The fastest woman ever was ~22 min faster than the fastest man ever. Although the three fastest women ever were ~20 min faster than the three fastest men ever, the difference reached not statistical significance (p > 0.05). Similarly for the ten fastest ever, the ~1 min difference for women was not significant (p > 0.05). However, when the swimming times of the annual fastest women (n = 39) and the annual fastest men (n = 50) competing between 1927 and 2014 were compared, women (651 ± 173 min) were 52.9 min (16 ± 12%) faster than men (704 ± 279 min) (p < 0.0001). Across years, swimming times decreased non-linearly in the annual fastest men (polynomial 2nd degree) and women (polynomial 3rd degree) whereas the sex difference decreased linearly from 52.4% (1927) to 7.1% (2014). In summary, the annual fastest women crossed the ‘Catalina Channel’ faster than the annual fastest men. The non-linear decrease in swimming times suggests that female and male swimmers have reached a limit in this event. However, the linear decrease in the sex difference may indicate that women continuously narrow the gap to men.
Ultra-endurance; Women; Men; Sex difference
Exercise-associated hyponatremia (EAH), rhabdomyolysis and renal failure appear to be a unique problem in ultra-endurance racers.
We investigated the combined occurrence of EAH and rhabdomyolysis in seven different ultra-endurance races and disciplines (i.e. multi-stage mountain biking, 24-h mountain biking, 24-h ultra-running and 100-km ultra-running).
Two (15.4 %) ultra-runners (man and woman) from hyponatremic ultra-athletes (n = 13) and four (4 %) ultra-runners (four men) from the normonatremic group (n = 100) showed rhabdomyolysis following elevated blood creatine kinase (CK) levels > 10,000 U/L without the development of renal failure and the necessity of a medical treatment. Post-race creatine kinase, plasma and urine creatinine significantly increased, while plasma [Na+] and creatine clearance decreased in hyponatremic and normonatremic athletes, respectively. The percentage increase of CK was higher in the hyponatremic compared to the normonatremic group (P < 0.05). Post-race CK levels were higher in ultra-runners compared to mountain bikers (P < 0.01), in faster normonatremic (P < 0.05) and older and more experienced hyponatremic ultra-athletes (P < 0.05). In all finishers, pre-race plasma [K+] was related to post-race CK (P < 0.05).
Hyponatremic ultra-athletes tended to develop exercise-induced rhabdomyolysis more frequently than normonatremic ultra-athletes. Ultra-runners tended to develop rhabdomyolysis more frequently than mountain bikers. We found no association between post-race plasma [Na+] and CK concentration in both hypo- and normonatremic ultra-athletes.
Mountain biking; Running; Long distances
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).
Swimming; Cycling; Running; Personal best time
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.
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.
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.
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 (VO2max) with ultra-triathlon race performance in order to investigate whether these characteristics are also predictive for ultra-triathlon race performance.
swimming; cycling; running; age; experience; ultra-endurance
Marathon (42 km) and 100 km ultramarathon races are increasing in popularity. The aim of the present study was to investigate the potential associations of anthropometric and training variables with performance in these long-distance running competitions.
Training and anthropometric data from a large cohort of marathoners and 100 km ultramarathoners provided the basis of this work. Correlations between training and anthropometric indices of subjects and race performance were assessed using bivariate and multiple regression analyses.
A combination of volume and intensity in training was found to be suitable for prediction of marathon and 100 km ultramarathon race pace. The relative role played by these two variables was different, in that training volume was more important than training pace for the prediction of 100 km ultramarathon performance, while the opposite was found for marathon performance. Anthropometric characteristics in terms of body fat percentage negatively affected 42 km and 100 km race performance. However, when this factor was relatively low (ie, less than 15% body fat), the performance of 42 km and 100 km races could be predicted solely on the basis of training indices.
Mean weekly training distance run and mean training pace were key predictor variables for both marathon and 100 km ultramarathon race performance. Predictive correlations for race performance are provided for runners with a relatively low body fat percentage.
running; performance; training indices; body fat; sports training
Pacing strategy has been investigated in elite 100 km and elite 161 km (100 mile) ultra-marathoners, but not in age group ultra-marathoners. This study investigated changes in running speed over segments in male elite and age group 100 km ultra-marathoners with the assumption that running speed would decrease over segments with increasing age of the athlete. Running speed during segments in male elite and age group finishers for 5-year age groups (ie, 18–24 to 65–69 years) in the 100 km Lauf Biel in Switzerland was investigated during the 2000–2009 period. Average running speed over segment time station (TS) TS1–TS2 (56.1 km) was compared with running speed Start–TS1 (38 km) and Start–TS3 (76.7 km) and running speed TS2–TS3 was compared with running speed Start–Finish. For the top ten athletes in each edition, running speed decreased from 2000 to 2009 for TS1–TS2 and TS2–TS3 (P<0.0001) but not in TS3–Finish (P>0.05). During TS1–TS2, athletes were running at 98.0%±2.1% of the running speed of Start–TS1. In TS2–TS3, they were running at 94.6%±3.4% of the running speed of TS1–TS2. In TS3–Finish, they were running at 95.5%±3.8% of running speed in TS2–TS3. For age group athletes, running speed decreased in TS1–TS2 and TS2–TS3. In TS3–Finish, running speed remained unchanged with the exception of the age group 40–44 years for which running speed increased. Running speed showed the largest decrease in the age group 18–24 years. To summarize, the top ten athletes in each edition maintained their running speed in the last segment (TS3–Finish) although running speed decreased over the first two segments (TS1–TS2 and TS2–TS3). The best pacers were athletes in the age group 40–44 years, who were able to achieve negative pacing in the last segment (TS3–Finish) of the race. The negative pacing in the last segment (TS3–Finish) was likely due to environmental conditions, such as early dawn and the flat circuit in segment TS3–Finish of the race.
running; men; long distance; master athlete
Anecdotal reports have assumed that women would be able to outrun men in long-distance running. The aim of this study was to test this assumption by investigating the changes in performance difference between sexes in the best ultramarathoners in 50-mile, 100-mile, 200-mile, 1,000-mile, and 3,100-mile events held worldwide between 1971 and 2012. The sex differences in running speed for the fastest runners ever were analyzed using one-way analysis of variance with subsequent Tukey–Kramer posthoc analysis. Changes in sex difference in running speed of the annual fastest were analyzed using linear and nonlinear regression analyses, correlation analyses, and mixed-effects regression analyses. The fastest men ever were faster than the fastest women ever in 50-mile (17.5%), 100-mile (17.4%), 200-mile (9.7%), 1,000-mile (20.2%), and 3,100-mile (18.6%) events. For the ten fastest finishers ever, men were faster than women in 50-mile (17.1%±1.9%), 100-mile (19.2%±1.5%), and 1,000-mile (16.7%±1.6%) events. No correlation existed between sex difference and running speed for the fastest ever (r2=0.0039, P=0.91) and the ten fastest ever (r2=0.15, P=0.74) for all distances. For the annual fastest, the sex difference in running speed decreased linearly in 50-mile events from 14.6% to 8.9%, remained unchanged in 100-mile (18.0%±8.4%) and 1,000-mile (13.7%±9.1%) events, and increased in 3,100-mile events from 12.5% to 16.9%. For the annual ten fastest runners, the performance difference between sexes decreased linearly in 50-mile events from 31.6%±3.6% to 8.9%±1.8% and in 100-mile events from 26.0%±4.4% to 24.7%±0.9%. To summarize, the fastest men were ~17%–20% faster than the fastest women for all distances from 50 miles to 3,100 miles. The linear decrease in sex difference for 50-mile and 100-mile events may suggest that women are reducing the sex gap for these distances.
running; sex difference; running speed; ultraendurance
This study investigated changes in normalised running speed as a proxy for effort distribution over segments in male elite and age group 100 km ultra-marathoners with the assumption that older runners would slow down more than younger runners.
The annual ten fastest finishers (i.e. elite and age group runners) competing between 2000 and 2009 in the ‘100 km Lauf Biel’ were identified. Normalised average running speed (i.e. relative to segment 1 of the race corrected for gradient) was analysed as a proxy for pacing in elite and age group finishers. For each year, the ratio of the running speed from the final to the first segment for each age cohort was determined. These ratios were combined across years with the assumption that there were no ‘extreme’ wind events etc. which may have impacted the final relative to the first segment across years. The ratios between the age cohorts were compared using one-way ANOVA and Tukey’s post-hoc test. The ratios between elite and age group runners were investigated using one-way ANOVA with Dunnett’s multiple comparison post-hoc tests. The trend across age groups was investigated using simple regression analysis with age as the dependent variable.
Normalised average running speed was different between age group 18–24 years and age groups 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59 and 65–69 years. Regression analysis showed no trend across age groups (r2 = 0.003, p > 0.05).
To summarize, (i) athletes in age group 18–24 years were slower than athletes in most other age groups and (ii) there was no trend of slowing down for older athletes.
Running; Men; Long-distance; Master athlete
We investigated seventy-four ultra-mountain bikers (MTBers) competing in the solo category in the first descriptive field study to detail nutrition habits and the most common food before during and after the 24 hour race using questionnaires. During the race, bananas (86.5%), energy bars (50.0%), apples (43.2%) and cheese (43.2%) were the most commonly consumed food, followed by bread (44.6%), rice (33.8%) and bananas (33.8%) after the race. Average fluid intake was 0.5 ± 0.2 l/h. The main beverage was isotonic sports drink (82.4%) during and pure water (66.2%) after the race. The most preferred four supplements in the four weeks before, the day before, during and after the race were vitamin C (35.1%), magnesium (44.6%), magnesium (43.2%) and branched-chain amino acids (24.3%), respectively. Total frequency of food intake (30.6 ± 10.5 times/24 hrs) was associated with fluid intake (r = 0.43, P = 0.04) and both were highest at the beginning of the race and lower during the night hours and the last race segment in a subgroup of twenty-three ultra-MTBers. Supplement intake frequency (6.8 ± 8.4 times/24 hrs) was highest during the night hours and lower at the beginning and end of the race. Elevated food and fluid intake among participants tracked across all race segments (P < 0.001). In conclusion, the nutrition strategy employed by ultra-MTBers was similar to those demonstrated in previous studies of ultra-cyclists with some exceptions among selected individuals.
Race nutrition; 24-hour race; Ultra-cycling
We investigated age and performance in distance-limited ultra-marathons held from 50 km to 1,000 km. Age of peak running speed and running speed of the fastest competitors from 1969 to 2012 in 50 km, 100 km, 200 km and 1,000 km ultra-marathons were analyzed using analysis of variance and multi-level regression analyses. The ages of the ten fastest women ever were 40 ± 4 yrs (50 km), 34 ± 7 yrs (100 km), 42 ± 6 yrs (200 km), and 41 ± 5 yrs (1,000 km). The ages were significantly different between 100 km and 200 km and between 100 km and 1,000 km. For men, the ages of the ten fastest ever were 34 ± 6 yrs (50 km), 32 ± 4 yrs (100 km), 44 ± 4 yrs (200 km), and 47 ± 9 yrs (1,000 km). The ages were significantly younger in 50 km compared to 100 km and 200 km and also significantly younger in 100 km compared to 200 km and 1,000 km. The age of the annual ten fastest women decreased in 50 km from 39 ± 8 yrs (1988) to 32 ± 4 yrs (2012) and in men from 35 ± 5 yrs (1977) to 33 ± 5 yrs (2012). In 100 km events, the age of peak running speed of the annual ten fastest women and men remained stable at 34.9 ± 3.2 and 34.5 ± 2.5 yrs, respectively. Peak running speed of top ten runners increased in 50 km and 100 km in women (10.6 ± 1.0 to 15.3 ± 0.7 km/h and 7.3 ± 1.5 to 13.0 ± 0.2 km/h, respectively) and men (14.3 ± 1.2 to 17.5 ± 0.6 km/h and 10.2 ± 1.2 to 15.1 ± 0.2 km/h, respectively). In 200 km and 1,000 km, running speed remained unchanged. In summary, the best male 1,000 km ultra-marathoners were ~15 yrs older than the best male 100 km ultra-marathoners and the best female 1,000 km ultra-marathoners were ~7 yrs older than the best female 100 km ultra-marathoners. The age of the fastest 50 km ultra-marathoners decreased across years whereas it remained unchanged in 100 km ultra-marathoners. These findings may help athletes and coaches to plan an ultra-marathoner’s career. Future studies are needed on the mechanisms by which the fastest runners in the long ultra-marathons tend to be older than those in shorter ultra-marathons.
Ultra-marathon; Age of peak running speed; Running speed
The aims of the present study were to investigate the changes in the age and in swimming performance of finalists in World Championships (1994–2013) and Olympic Games (1992–2012) competing in all events/races (stroke and distance). Data of 3,295 performances from 1,615 women and 1,680 men were analysed using correlation analyses and magnitudes of effect sizes. In the World Championships, the age of the finalists increased for all strokes and distances with exception of 200 m backstroke in women, and 400 m freestyle and 200 m breaststroke in men where the age of the finalists decreased. The magnitudes of the effects were small to very large (mean ± SD 2.8 ± 2.7), but extremely large (13.38) for 1,500 m freestyle in women. In the Olympic Games, the age of the finalists increased for all strokes and distances with exception of 800 m freestyle in women and 400 m individual medley in men. The magnitudes of the effects were small to very large (mean ± SD 4.1 ± 7.1), but extremely large for 50 m freestyle in women (10.5) and 200 m butterfly in men (38.0). Swimming performance increased across years in both women and men for all strokes and distances in both the World Championships and the Olympic Games. The magnitudes of the effects were all extremely large in World Championships (mean ± SD 20.1 ± 8.4) and Olympic Games (mean ± SD 52.1 ± 47.6); especially for 100 m and 200 m breaststroke (198) in women in the Olympic Games. To summarize, in the last ~20 years the age of the finalists increased in both the World Championships and the Olympic Games with some minor exceptions (200 m backstroke in women, 400 m freestyle and 200 m breaststroke in men in World Championships and 800 m freestyle in women and 400 m individual medley in men in Olympic Games) and performance of the finalists improved.
Elite swimmers; Age; Performance; Sex difference
Endurance performance decreases during ageing due to alterations in physiological characteristics, energy stores, and psychological factors. To investigate alterations in physiological characteristics and body composition of elderly master athletes in response to an extreme endurance event, we present the case of the first ninety-year-old official male marathon finisher.
Before and directly after the marathon, a treadmill incremental test, dual-energy X-ray absorptiometry, peripheral quantitative computed tomography, mechanography, and dynamometry measurements were conducted. The athlete finished the marathon in 6 h 48 min 55 s, which corresponds to an average competition speed of 6.19 km h-1.
Discussion and Evaluation
Before the marathon,
was 31.5 ml min-1 kg-1 body mass and peak heart rate was 140 beats min-1. Total fat mass increased in the final preparation phase (+3.4%), while leg fat mass and leg lean mass were slightly reduced after the marathon (-3.7 and -1.6%, respectively). Countermovement jump (CMJ) peak power and peak velocity decreased after the marathon (-16.5 and -14.7%, respectively). Total impulse during CMJ and energy cost of running were not altered by the marathon. In the left leg, maximal voluntary ground reaction force (Fm1LH) and maximal isometric voluntary torque (MIVT) were impaired after the marathon (-12.2 and -14.5%, respectively).
Side differences in Fm1LH and MIVT could be attributed to the distinct non-symmetrical running pattern of the athlete. Similarities in alterations in leg composition and CMJ performance existed between the nonagenarian athlete and young marathon runners. In contrast, alterations in total body composition and m1LH performance were markedly different in the nonagenarian athlete when compared to his younger counterparts.
Dual-energy X-ray absorptiometry; Peripheral quantitative computed tomography; Countermovement jump; Multiple one-legged hopping; Impulse
Effects of course length (25 m versus 50 m) and advances in performance of individual medley swimming were examined for men and women in Swiss national competitions and FINA World Championships during 2000–2011. Linear regression and analysis of variance (ANOVA) were used to analyse 200 m and 400 m race results for 26,081 swims on the Swiss high score list and 382 FINA finalists. Swiss and FINA swimmers of both sexes were, on average, 4.3±3.2% faster on short courses for both race distances. Sex-related differences in swim speed were significantly greater for FINA swimmers competing in short-course events than in long-course events (10.3±0.2% versus 9.7±0.3%, p<0.01), but did not differ for Swiss swimmers (p>0.05). Sex-related differences in swimming speed decreased with increasing race distance for both short- and long-course events for Swiss athletes, and for FINA athletes in long-course events. Performance improved significantly (p<0.05) during 2000–2011 for FINA men competing in either course length and FINA females competing in short-course events, but not for Swiss swimmers. Overall, the results showed that men and women individual medley swimmers, competing at both national and international levels, have faster average swimming speeds on short courses than on long courses, for both 200 m and 400 m distances. FINA athletes demonstrate an improving performance in the vast majority of individual medley events, while performance at national level seems to have reached a plateau during 2000–2011.
swim speed; pool length; sex-related difference; temporal trends
Recent findings showed that elite Ironman triathletes competing in ‘Ironman Hawaii’ improved both split and overall race times. The present study investigated whether elite athletes also improved in transition time (i.e. time needed between disciplines for changing clothes and equipment).
Changes in split times, overall race times and transition times (i.e. expressed in absolute and relative terms) in the annual fastest competing in ‘Ironman Hawaii’ were investigated using linear, non-linear and multi-level regression analyses. To detect a potential difference in transition times between different race distances, we compared transition times in ‘Ironman Hawaii’ to transition times in the World Championships ‘Ironman 70.3’ covering the half distance of the Ironman distance triathlon.
In ‘Ironman Hawaii’, transition times remained unchanged for the annual fastest women but increased linearly for the annual fastest men. For the annual ten fastest, transition times increased linearly for women and men in both absolute and relative terms. The sex difference in transition times remained unchanged for the annual fastest, but decreased linearly for the annual ten fastest. In ‘Ironman 70.3’, transition times remained unchanged for the annual fastest. For the annual ten fastest, transition times decreased linearly for both women and men in absolute and relative terms. The sex difference in transition times remained unchanged for both the annual fastest and the annual ten fastest. Transition times were faster in ‘Ironman 70.3’ for women in 2011 and for men in 2006, 2007, and 2010-2013. In relative terms, transition times were faster in ‘Ironman 70.3’compared to ‘Ironman Hawaii’ during 2006-2013. The sex difference in transition times remained unchanged.
In ‘Ironman Hawaii’, transition times increased for both women and men whereas the sex difference decreased. In ‘Ironman 70.3’, transition times decreased for both women and men whereas the sex difference remained unchanged. Generally, transition times were slower in ‘Ironman Hawaii’ compared to ‘Ironman 70.3’.
Swimming; Cycling; Running; Sex difference; Endurance
Recent studies found that the athlete’s age of the best ultra-marathon performance was higher than the athlete’s age of the best marathon performance and it seemed that the athlete’s age of peak ultra-marathon performance increased in distance-limited races with rising distance.
We investigated the athlete’s age of peak ultra-marathon performance in the fastest finishers in time-limited ultra-marathons from 6 hrs to 10 d. Running performance and athlete’s age of the fastest women and men competing in 6 hrs, 12 hrs, 24 hrs, 48 hrs, 72 hrs, 144 hrs (6 d) and 240 hrs (10 d) were analysed for races held between 1975 and 2012 using analysis of variance and multi-level regression analysis.
The athlete’s ages of the ten fastest women ever in 6 hrs, 12 hrs, 24 hrs, 48 hrs, 72 hrs, 6 d and 10 d were 41 ± 9, 41 ± 6, 42 ± 5, 46 ± 5, 44 ± 6, 42 ± 4, and 37 ± 4 yrs, respectively. The athlete’s age of the ten fastest women was different between 48 hrs and 10 d. For men, the athlete’s ages were 35 ± 6, 37 ± 9, 39 ± 8, 44 ± 7, 48 ± 3, 48 ± 8 and 48 ± 6 yrs, respectively. The athlete’s age of the ten fastest men in 6 hrs and 12 hrs was lower than the athlete’s age of the ten fastest men in 72 hrs, 6 d and 10 d, respectively.
The athlete’s age of peak ultra-marathon performance did not increase with rising race duration in the best ultra-marathoners. For the fastest women ever in time-limited races, the athlete’s age was lowest in 10 d (~37 yrs) and highest in 48 hrs (~46 yrs). For men, the athlete’s age of the fastest ever in 6 hrs (~35 yrs) and 12 hrs (~37 yrs) was lower than the athlete’s age of the ten fastest in 72 hrs (~48 yrs), 6 d (~48 yrs) and 10 d (~48 yrs). The differences in the athlete’s age of peak performance between female and male ultra-marathoners for the different race durations need further investigations.
Master athlete; Female; Male; Ultra-endurance
The purpose of this study was (i) to determine the age of peak triathlon performance for world class athletes competing in Olympic, Half-Ironman and Ironman distance races and (ii) to investigate a potential change in the age of the annual fastest athletes across years. Data of ages and race times of all finishers in the international top races over the three distances between 2003 and 2013 were collected and the annual top ten women and men were analysed using linear, non-linear and hierarchical multivariate regression analyses. The age of peak male performance was 27.1 ± 4.9 years in the Olympic, 28.0 ± 3.8 years in the Half-Ironman and 35.1 ± 3.6 years in the Ironman distance and the age of peak male performance was higher in the Ironman compared to the Olympic (p < 0.05) and the Half-Ironman distance (p < 0.05) triathlon. The age of peak female performance was 26.6 ± 4.4 years in the Olympic, 31.6 ± 3.4 years in the Half-Ironman and 34.4 ± 4.4 years in the Ironman distance and the age of peak female performance was lower in the Olympic compared to the Half-Ironman (p < 0.05) and Ironman distance (p < 0.05) triathlon. The age of the annual top ten women and men remained unchanged over the last decade in the Half-Ironman and the Ironman distance. In the Olympic distance, however, the age of the annual top ten men decreased slightly. To summarize, the age of peak triathlon performance was higher in the longer triathlon race distances (i.e. Ironman) and the age of the annual top triathletes remained mainly stable over the last decade. With these findings top athletes competing at world class level can plan their career more precisely as they are able to determine the right time in life to switch from the shorter (i.e. Olympic distance) to the longer triathlon race distances (i.e. Half-Ironman and Ironman) in order to continuously compete in triathlon races at world class level.
Age trends; Endurance; Swimming; Cycling; Running
The aim of the present study was to investigate participation and performance trends regarding the nationality of successful solo swimmers in the ‘English Channel Swim’.
The nationality and swim times for all swimmers who successfully crossed the 33.8-km ‘English Channel’ from 1875 to 2013 were analysed.
Between 1875 and 2013, the number of successful female (571, 31.4%) and male (1,246, 68.6%) solo swimmers increased exponentially; especially for female British and American swimmers and male British, US-American and Australian swimmers. Most of the swimmers were crossing the ‘English Channel’ from England to France and most of the competitors were from Great Britain, the United States of America, Australia and Ireland. For women, athletes from the United States of America, Australia and Great Britain achieved the fastest swim times. For men, the fastest swim times were achieved by athletes from the United States of America, Great Britain and Australia. Swim times of the annual fastest women from Great Britain and the United States of America decreased across years. For men, swim times decreased across years in the annual fastest swimmers from Australia, Great Britain, Ireland, South Africa and the United States of America. Men were swimming faster from England to France than from France to England compared to women. Swim times became faster across years for both women and men for both directions.
Between 1875 and 2013, the most representative nations in the ‘English Channel Swim’ were Great Britain, the United States of America, Australia and Ireland. The fastest swim times were achieved by athletes from the United States of America, Australia and Great Britain.
Swimmer; Ultra endurance; Origin; Country
This study intended to compare the performance of ultra-triathletes competing in a Deca Iron ultra-triathlon (i.e. 10 times 3.8 km swimming, 180 km cycling, and 42.2 km running) with the performance of athletes competing in a Triple Deca Iron ultra-triathlon (i.e. 30 times 3.8 km swimming, 180 km cycling, and 42.2 km running). Split and overall race times of six male finishers in a Deca Iron ultra-triathlon and eight male finishers in a Triple Deca Iron ultra-triathlon were analysed using multiple t-tests, linear and non-linear regression analyses, and analysis of variance. Among the 19 starters (i.e. 17 men and two women) in the Deca Iron ultra-triathlon, six men (i.e. 35.3% of all starters) finished the race. The mean swimming, cycling, running and overall race times of the six finishers across the ten days were 1:19 ± 0:09 h:min, 6:36 ± 0:19 h:min, 6:03 ± 0:47 h:min and 14:44 ± 1:17 h:min, respectively. The times of the split disciplines and overall race time increased linearly across the ten days. Total transition times did not change significantly across the days and were equals to 48 ± 8 min. Among the 22 starters (i.e. 20 men and two women) in the Triple Deca Iron ultra-triathlon, eight men (i.e. 36.4% of all starters) finished. The mean swimming, cycling, running and overall race times of the eight finishers across the 30 days were 1:11 ± 0:07 h:min, 6:19 ± 0:32 h:min, 5:34 ± 1:15 h:min and 13:44 ± 1:50 h:min, respectively. Split and overall race times showed no change across the 30 days. Total transition times showed no change across the days and were equal to 41 ± 11 min. To summarize, the daily performance decreased across the ten days for the Deca Iron ultra-triathletes (i.e. positive pacing) while it remained unchanged across the 30 days for the Triple Deca Iron ultra-triathletes (i.e. even pacing).
Triathlon; Swimming; Cycling; Running; Ultra-endurance
The aims of the study were (i) to investigate the relationship between elite marathon race times and age in 1-year intervals by using the world single age records in marathon running from 5 to 93 years and (ii) to evaluate the sex difference in elite marathon running performance with advancing age.
World single age records in marathon running in 1-year intervals for women and men were analysed regarding changes across age for both men and women using linear and non-linear regression analyses for each age for women and men.
The relationship between elite marathon race time and age was non-linear (i.e. polynomial regression 4th degree) for women and men. The curve was U-shaped where performance improved from 5 to ~20 years. From 5 years to ~15 years, boys and girls performed very similar. Between ~20 and ~35 years, performance was quite linear, but started to decrease at the age of ~35 years in a curvilinear manner with increasing age in both women and men. The sex difference increased non-linearly (i.e. polynomial regression 7th degree) from 5 to ~20 years, remained unchanged at ~20 min from ~20 to ~50 years and increased thereafter. The sex difference was lowest (7.5%, 10.5 min) at the age of 49 years.
Elite marathon race times improved from 5 to ~20 years, remained linear between ~20 and ~35 years, and started to increase at the age of ~35 years in a curvilinear manner with increasing age in both women and men. The sex difference in elite marathon race time increased non-linearly and was lowest at the age of ~49 years.
Running; Sex difference; Performance; Boys; Girls; Master runner