Differences in age of peak marathon performance between mountain and city marathon running - The 'Jungfrau Marathon' in Switzerland.

The age of the best marathon performance has been well investigated for flat city marathon running, but not for mountain marathon running. The aim of this study was to determine the age of the best mountain marathon performance and to compare to results of a flat city marathon. Race times and ages of finishers of a mountain marathon with 1,830 m of altitude change (Jungfrau Marathon, Switzerland) and two flat city marathons (Lausanne Marathon and Zurich Marathon, Switzerland) were analysed using linear, non-linear and mixed-effects regression analyses. Race times were slower in the mountain compared to the city marathon. In both the mountain marathon and the city marathons, women and men improved performance and men were faster than women when the fastest per year and all per year were considered. When the fastest runners in 1-year age intervals were considered in the mountain marathon, the fastest man (3:01 h:min) was ~35.6 years and the fastest women (3:28 h:min) ~34.5 years old. When all finishers were considered in 1-year age intervals, the fastest men (4:59 h:min) were ~29.1 years old and the fastest women (5:16 h:min) were ~25.6 years old. In the city marathons in 1-year age intervals, the fastest man (2:10 h:min) was ~23.7 years old and the fastest woman (2:36 h:min) ~32.2 years old. When all finishers were considered in 1-year age intervals, the fastest men (3:41 h:min) were ~35.0 years old and the fastest women (4:00 h:min) ~33.8 years old. In summary, the age of the fastest women and men was higher in the mountain marathon compared to the city marathons when the fastest runners were considered. However, when all finishers were considered the age of the fastest women and men was lower in the mountain marathon compared to the city marathons.

[Running and the association with anthropometric and training characteristics]. / Laufsport und der Zusammenhang zwischen Training und Körperbau.

Running can be performed as a sprint discipline on the track over a few meters up to 10 km to the marathon and ultramarathon running distances over hundreds to thousands of kilometers. Running performance is influenced by a variety of anthropometric and training factors. Morphological features such as skin fold thickness, body fat percentage, circumferences and length of limbs, body weight, body height and body mass index (BMI) seem to have an influence on the running performance. The training volume and running speed during training are also correlated with running performance. When all variables were investigated comparatively, body fat and running speed during training were usually the most important influencing factors. For longer running performances (over 6 hours or 100 km, respectively), the aspects of experience (number of successfully finished races) and personal best times were, however, far more important than training volume or morphological characteristics such as body fat. It was also shown that ultra runners prepare differently (lower running speed and higher running volume) as runners competing over shorter distances such as half-marathon and marathon.

No damage of joint cartilage of the lower limbs in an ultra-endurance athlete--an MRI-study.

BACKGROUND: Osteoarthritis is an increasing burden in an ageing population. Sports, especially when leading to an overstress of joints, is under suspicion to provoke or at least accelerate the genesis of osteoarthritis. We present the radiologic findings of a 49-years old ultra-endurance athlete with 35 years of training and competing, whose joints of the lower limbs were examined using three different types of magnetic resonance imaging, including a microscopic magnetic resonance imaging coil. To date no case report exists where an ultra-endurance athlete was examined such detailed regarding overuse-injuries of his joints. CASE PRESENTATION: A 49 years old, white, male ultra-endurance athlete reporting no pain during training and racing and with no significant injuries of the lower limbs in his medical history was investigated regarding signs of chronic damage or overuse injuries of the joints of his lower limbs. CONCLUSION: Despite the age of nearly 50 years and a training history of over 35 years, the athlete showed no signs of chronic damage or overuse injuries in the joints of his lower limbs. This leads to the conclusion that extensive sports and training does not compulsory lead to damages of the musculoskeletal system. This is a very important finding for all endurance-athletes as well as for their physicians.

Do women reduce the gap to men in ultra-marathon running?

The aim of the present study was to examine sex differences across years in performance of runners in ultra-marathons lasting from 6 h to 10 days (i.e. 6, 12, 24, 48, 72, 144, and 240 h). Data of 32,187 finishers competing between 1975 and 2013 with 93,109 finishes were analysed using multiple linear regression analyses. With increasing age, the sex gap for all race durations increased. Across calendar years, the gap between women and men decreased in 6, 72, 144 and 240 h, but increased in 24 and 48 h. The men-to-women ratio differed among age groups, where a higher ratio was observed in the older age groups, and this relationship varied by distance. In all durations of ultra-marathon, the participation of women and men varied by age (p < 0.001), indicating a relatively low participation of women in the older age groups. In summary, between 1975 and 2013, women were able to reduce the gap to men for most of timed ultra-marathons and for those age groups where they had relatively high participation.

Half-marathoners are younger and slower than marathoners.

Age and performance trends of elite and recreational marathoners are well investigated, but not for half-marathoners. We analysed age and performance trends in 508,108 age group runners (125,894 female and 328,430 male half-marathoners and 10,205 female and 43,489 male marathoners) competing between 1999 and 2014 in all flat half-marathons and marathons held in Switzerland using single linear regression analyses, mixed-effects regression analyses and analyses of variance. The number of women and men increased across years in both half-marathons and marathons. There were 12.3 times more female half-marathoners than female marathoners and 7.5 times more male half-marathoners than male marathoners. For both half-marathons and marathons, most of the female and male finishers were recorded in age group 40-44 years. In half-marathons, women (10.29 ± 3.03 km/h) were running 0.07 ± 0.06 km/h faster (p < 0.001) than men (10.22 ± 3.06 km/h). Also in marathon, women (14.77 ± 4.13 km/h) were running 0.28 ± 0.16 km/h faster (p < 0.001) than men (14.48 ± 4.07 km/h). In marathon, women (42.18 ± 10.63 years) were at the same age than men (42.06 ± 10.45 years) (p > 0.05). Also in half-marathon, women (41.40 ± 10.63 years) were at the same age than men (41.31 ± 10.30 years) (p > 0.05). However, women and men marathon runners were older than their counterpart half-marathon runners (p < 0.001). In summary, (1) more athletes competed in half-marathons than in marathons, (2) women were running faster than men, (3) half-marathoners were running slower than marathoners, and (4) half-marathoners were younger than marathoners.

Male and female Ethiopian and Kenyan runners are the fastest and the youngest in both half and full marathon.

In major marathon races such as the 'World Marathon Majors', female and male East African runners particularly from Ethiopia and Kenya are the fastest. However, whether this trend appears for female and male Ethiopians and Kenyans at recreational level runners (i.e. races at national level) and in shorter road races (e.g. in half-marathon races) has not been studied yet. Thus, the aim of the present study was to examine differences in the performance and the age of female and male runners from East Africa (i.e. Ethiopians and Kenyans) between half- and full marathons. Data from 508,108 athletes (125,894 female and 328,430 male half-marathoners and 10,205 female and 43,489 male marathoners) originating from 126 countries and competing between 1999 and 2014 in all road-based half-marathons and marathons held in one country (Switzerland) were analysed using Chi square (χ(2)) tests, mixed-effects regression analyses and one-way analyses of variance. In half-marathons, 48 women (0.038 %) and 63 men (0.019 %) were from Ethiopia and 80 women (0.063 %) and 134 men (0.040 %) from Kenya. In marathons, three women (0.029 %) and 15 men (0.034 %) were from Ethiopia and two women (0.019 %) and 33 men (0.075 %) from Kenya. There was no statistically significant association between the nationality of East Africans and the format of a race. In both women and men, the fastest race times in half-marathons and marathons were achieved by East African runners (p < 0.001). Ethiopian and Kenyan runners were the youngest in both sexes and formats of race (p < 0.001). In summary, women and men from Ethiopia and Kenya, despite they accounted for <0.1 % in half-marathons and marathons, achieved the fastest race times and were the youngest in both half-marathons and marathons. These findings confirmed in the case of half-marathon the trend previously observed in marathon races for a better performance and a younger age in East African runners from Ethiopia and Kenya.

Performance differences between sexes in 50-mile to 3,100-mile ultramarathons.

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 (r (2)=0.0039, P=0.91) and the ten fastest ever (r (2)=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.

Increase in participation but decrease in performance in age group mountain marathoners in the 'Jungfrau Marathon': a Swiss phenomenon?

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.

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).

Do non-elite older runners slow down more than younger runners in a 100 km ultra-marathon?

BACKGROUND: 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. METHODS: 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. RESULTS: 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 (r(2) = 0.003, p > 0.05). CONCLUSION: 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.

Pacing strategy in male elite and age group 100 km ultra-marathoners.

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.

Variables that influence Ironman triathlon performance - what changed in the last 35 years?

OBJECTIVE: 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. METHODS: A literature search was performed in PubMed using the terms "Ironman", "triathlon", and "performance". All resulting articles were searched for related citations. RESULTS: 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). CONCLUSION: 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.

Feet swelling in a multistage ultraendurance triathlete: a case study.

Recent studies investigating ultraendurance athletes showed an association between excessive fluid intake and swelling of the lower limbs such as the feet. To date, this association has been investigated in single-stage ultraendurance races, but not in multistage ultraendurance races. In this case study, we investigated a potential association between fluid intake and feet swelling in a multistage ultraendurance race such as a Deca Iron ultratriathlon with ten Ironman triathlons within 10 consecutive days. A 49-year-old well-experienced ultratriathlete competed in autumn 2013 in the Deca Iron ultratriathlon held in Lonata del Garda, Italy, and finished the race as winner within 129:33 hours:minutes. Changes in body mass (including body fat and lean body mass), foot volume, total body water, and laboratory measurements were assessed. Food and fluid intake during rest and competing were recorded, and energy and fluid turnovers were estimated. During the ten stages, the volume of the feet increased, percentage body fat decreased, creatinine and urea levels increased, hematocrit and hemoglobin values decreased, and plasma [Na(+)] remained unchanged. The increase in foot volume was significantly and positively related to fluid intake during the stages. The poststage volume of the foot was related to poststage total body water, poststage creatinine, and poststage urea. This case report shows that the volume of the foot increased during the ten stages, and the increase in volume was significantly and positively related to fluid intake during the stages. Furthermore, the poststage volume of the foot was related to poststage total body water, poststage creatinine, and poststage urea. The continuous feet swelling during the race was most probably due to a combination of a high fluid intake and a progressive decline in renal function (ie, continuous increase in creatinine and urea), leading to body fluid retention (ie, increase in total body water).

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 (VO2max) with ultra-triathlon race performance in order to investigate whether these characteristics are also predictive for ultra-triathlon race performance.

Will the age of peak ultra-marathon performance increase with increasing race duration?

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Age group performances in 100 km and 100 miles ultra-marathons.

Improved performance has been reported for master runners (i.e. athletes older than 40 years) in both single marathons and single ultra-marathons. This study investigated performance trends of age group ultra-marathoners competing in all 100 km and 100 miles races held worldwide between 1971 and 2013. Changes in running speeds across years were investigated for the annual ten fastest 5-year age group finishers using linear, non-linear and multi-level regression analyses. In 100 km, running speed remained unchanged in women in 25-29 years, increased non-linearly in 30-34 to 55-59 years, and linearly in 60-64 years. In men, running speed increased non-linearly in 18-24 to 60-64 years and linearly in 65-69 to 75-79 years. In 100 miles, running speed increased in women linearly in 25-29 and 30-34 years, non-linearly in 35-39 to 45-49 years, and linearly in 50-54 and 55-59 years. For men, running speed increased linearly in 18-24 years, non-linearly in 25-29 to 45-49 years, and linearly in 50-54 to 65-69 years. Overall, the faster race times over the last 30 years are a result of all top ten finishers getting faster. These findings suggest that athletes in younger to middle age groups (i.e. 25-35 to 50-65 years depending upon sex and distance) have reached their limits due to a non-linear increase in running speed whereas runners in very young (i.e. younger than 25-35 years) and older age groups (i.e. older than 50-65 years) depending upon sex and distance might still improve their performance due to a linear increase in running speed.

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 (r (2)=0.67, P=0.023), running (r (2)=0.77, P=0.009), and overall race time (r (2)=0.77, P=0.0087), but not for cycling (r (2)=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.

Analysis of swimming performance in FINA World Cup long-distance open water races.

BACKGROUND: Age and peak performance in ultra-endurance athletes have been mainly investigated in long-distance runners and triathletes, but not for long-distance swimmers. The present study investigated the age and swimming performance of elite ultra-distance swimmers competing in the 5-, 10- and 25-km Fédération Internationale de Natation (FINA) World Cup swimming events. METHODS: The associations of age and swimming speed in elite male and female swimmers competing in World Cup events of 5-, 10- and 25-km events from 2000 to 2012 were analysed using single and multi-level regression analyses. RESULTS: During the studied period, the swimming speed of the annual top ten women decreased significantly from 4.94 ± 0.20 to 4.77 ± 0.09 km/h in 5 km and from 4.60 ± 0.04 to 4.44 ± 0.08 km/h in 25 km, while it significantly increased from 4.57 ± 0.01 to 5.75 ± 0.01 km/h in 10 km. For the annual top ten men, peak swimming speed decreased significantly from 5.42 ± 0.04 to 5.39 ± 0.02 km/h in 5 km, while it remained unchanged at 5.03 ± 0.32 km/h in 10 km and at 4.94 ± 0.35 km/h in 25 km. The age of peak swimming speed for the annual top ten women remained stable at 22.5 ± 1.2 years in 5 km, at 23.4 ± 0.9 years in 10 km and at 23.8 ± 0.9 years in 25 km. For the annual top ten men, the age of peak swimming speed increased from 23.7 ± 2.8 to 28.0 ± 5.1 years in 10 km but remained stable at 24.8 ± 1.0 years in 5 km and at 27.2 ± 1.1 years in 25 km. CONCLUSION: Female long-distance swimmers competing in FINA World Cup races between 2000 and 2012 improved in 10 km but impaired in 5 and 25 km, whereas men only impaired in 5 km. The age of peak performance was younger in women (approximately 23 years) compared to men (about 25-27 years).

Prediction of half-marathon race time in recreational female and male runners.

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r(2) = 0.42, adjusted r(2) = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) - 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p < 0.0001) to the achieved race time. For women, half-marathon race time might be predicted by the equation (r(2) = 0.68, adjusted r(2) = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) - 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the models.

Age and ultra-marathon performance - 50 to 1,000 km distances from 1969 - 2012.

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.