Wunderlich Marathon Abe Download VERIFIED

A luxury touring windshield for your Tourer: The original windshield dimensions were enlarged in all directions to create a windshield that really let you relax and transforms even long marathon tours into a pleasure.

The question of a relationship between performance and origin in marathon runners has also been raised [8,9,20-23]. In addition, the top list of the International Association of Athletics Federations (IAAF) in half-marathon and marathon runners [24] in 2011, especially for men, indicated that all the top 20 performances in marathons and half-marathons were achieved by East African runners originating from Kenya, Ethiopia and Eritrea. The dominance of these East African athletes in long-distance running is a well-known phenomenon [9,25]. Specific advantageous factors such as favourable genetic endowment [9] and a better running economy [8,21,22] have been suggested for the success of East African runners. The anecdotal finding of travelling a long way to school each day was postulated as another important factor for the great success of East African runners. Both Onywera et al. [26] and Scott et al. [25] reported that most of the national and international elite runners from Kenya and Ethiopia run every day more than five kilometres to school. They further showed an association between the distance travelled to school and the performance in long-distance running [25,26].

Wunderlich Marathon Abe Download

Download 🔥 https://urlca.com/2y5IH3 🔥

Change in participation for female and male East African runners in specific half-marathons in Switzerland for men in Genve Marathon for UNICEF (Panel A), Greifenseelauf (Panel B) and Hallwilerseelauf (Panel C) and for women in Genve Marathon for UNICEF (Panel D), Greifenseelauf (Panel E) and Hallwilerseelauf (Panel F).

Change in participation in specific marathons in Switzerland for female and male East African runners for men in Genve Marathon for UNICEF (Panel A), Lausanne Marathon (Panel B) and Zrich Marathon (Panel C) and for women in Genve Marathon for UNICEF (Panel D), Lausanne Marathon (Panel E) and Zrich Marathon (Panel F).

More interesting seems the fact of the difference in participation in male Ethiopian and Kenyan athletes in both half-marathons and marathons. Compared to their Ethiopian counterparts, more Kenyan men were participating in marathons and fewer in half-marathons. A possible explanation for this might be differences in anthropometry between Kenyan and Ethiopian runners. Ethiopians are more mesomorphic in somatotype, which includes more muscle mass especially expressed in a high thigh circumference [32]. Kenyans in contrast are more ectomorphic with slender legs [32]. Zillmann et al.[33] showed differences in anthropometric characteristics between recreational marathoners and recreational half-marathoners competing in Switzerland. They reported for half-marathoners a thicker thigh circumference compared to marathoners [33]. To the best of our knowledge anthropometric characteristics of Ethiopian runners have not been analysed yet. Based on reported differences in anthropometry between Ethiopians and Kenyans [32] we can only speculate that Ethiopian runners were more predestined for running half-marathons than marathons. Therefore, we had a higher number of Ethiopian participants in half-marathons than in marathons. Also in the IAAF top list from 2011 in marathons and half-marathons there was a significant difference between the number of Ethiopian and Kenyan runners for the top 20 race times [24]. In the top 20 marathoners there were 20 Kenyans [24]. In half-marathons there were 13 Kenyans and 9 Ethiopians ranked [24]. Future studies need to analyse anthropometric differences between Ethiopian and Kenyan long-distance runners.

The aims of the study were (i) to investigate the relationship betweenelite marathon race times and age in 1-year intervals by using the worldsingle age records in marathon running from 5 to 93 years and (ii) toevaluate the sex difference in elite marathon running performance withadvancing age.

World single age records in marathon running in 1-year intervals for womenand men were analysed regarding changes across age for both men and womenusing linear and non-linear regression analyses for each age for women andmen.

The relationship between elite marathon race time and age was non-linear(i.e. polynomial regression 4.sup.th degree) for women and men. The curve wasU-shaped where performance improved from 5 to ~20 years. From 5 years to ~15years, boys and girls performed very similar. Between ~20 and ~35 years,performance was quite linear, but started to decrease at the age of ~35 yearsin a curvilinear manner with increasing age in both women and men. The sexdifference increased non-linearly (i.e. polynomial regression 7.sup.thdegree) from 5 to ~20 years, remained unchanged at ~20 min from ~20 to ~50years and increased thereafter. The sex difference was lowest (7.5%, 10.5min) at the age of 49 years.

Elite marathon race times improved from 5 to ~20 years, remained linearbetween ~20 and ~35 years, and started to increase at the age of ~35 years ina curvilinear manner with increasing age in both women and men. The sexdifference in elite marathon race time increased non-linearly and was lowestat the age of ~49 years.

In recent years, the number of successful marathoners increased continuously.For example, in the USA, the number of successful marathon finishersincreased from 25,000 in 1976 to the all-time high in 2011 with 518,000successful finishers [1]. Recent studies investigating participation andperformance trends in a large city marathon in the USA such as the 'NewYork City Marathon' showed that the increase in participants was mainlydue to an increase in master runners (i.e. finishers of > 40 years of age) and women [2, 3]. In the 'New YorkCity Marathon', the number of men > 40 years increased three-foldfrom the 1980s to the 2000-2009, whereas the number of women increased evenseven-fold [3].

Although the fastest elite marathon race times were achieved at the age of~30 years in both female and male elite runners [4, 5], it has been reportedfor both recreational marathoners [6] and ultra-marathoners [7] that thefastest race times can be achieved during a considerably long life span. Formarathoners, the age-related loss in running performance did not occur beforethe age of ~50 years [6]. Mean marathons race times were nearly identical forage group runners from 20 to 49 years [6]. Also for 100-km ultra-marathoners,the fastest race times were observed during the age span of 30-49 years formen and 30-54 years for women, respectively [7].

In a study by Lara et al.[5], the association between elite marathon race time and age in 1-yearintervals from 18 to 75 years in elite women and men competing in the'New York City Marathon' in 2010 and 2011 was investigated. Incontrast to previous findings, the relationship between elite marathon racetime and age was U-shaped [5]. The first aim of the present study was toinvestigate the relationship between elite marathon race times and age in1-year intervals by using the world single age records in marathon runningfor each age from 5 to 93 years. A second aim of the present study was tofurther investigate the relationship between sex difference in elite marathonrunning performance and advancing age. Based upon the findings in Lara et al.[5], we hypothesized to confirm the U-shaped relationship between elitemarathon race times and age also for world single age records in marathonrunning.

The data set for this study was obtained from the website of the'Association of Road Racing Statisticians' (ARRS) [13]. Thiswebsite records the world single age records in marathon running in 1-yearintervals from the age of 5 to 93 years for men and 5 to 92 years for women.Elite marathon race times achieved from 5 to 93 years were analysed regardingchanges across age for both men and women using linear and non-linearregression analyses since the change in endurance performance and sexdifference in endurance performance is assumed to be non-linear [14]. Inmarathons, the lowest age to officially enter the race is 18 years and wetherefore started our analysis at the age of 18 years. The comparison ofraces times for athletes older than 80 years showed large differences inmarathon race performance and we therefore performed a second analysis withrace times of athletes aged 18-80 years. When the best-fit model was anon-linear (i.e. polynomial) regression, we compared the best-fit non-linear model to thelinear model using Akaike's Information Criteria (AICc) and F-test inorder to show which model would be the most appropriate to explain the trendof the data.

For men, the fastest elite marathon race time of 2:03:23 h:min:sec wasachieved by Wilson Kipsang Kiprotich, Kenia, at the age of 31 years and 198days on September 29, 2013, in Berlin, Germany. However, Geoffrey KipronoMutai, Kenia, ran the fastest marathon ever on April 18, 2011, at the'Boston Marathon' in a time of 2:03:02 h:min:sec. However, thistime was not recognized as an official world record in marathon running bythe International Association of Athletics Federations (IAAF). The course ofthe 'Boston Marathon' does not meet the criteria to be eligible forthe mark since the race is a point-to-point course. For women, PaulaRadcliffe, Great Britain, achieved the fastest elite marathon race time of2:15:24.6 h:min:sec on April 13, 2003 in London, England, at the age of 29years and 117 days. Table 1 presents the athletes who were able to achievemore than one world single age record. In men, 14 athletes reached two ormore records where Ed Whitlock, Canada, achieved the highest number with 11records. In women, 16 runners attained two or more records where TatyanaPozdniakova, Ukrainia, holds six records.

Figure 1 presents the relationship between elite marathon race time and agefor women and men from 5 to 93 years (Figure 1A) and from 18 to 80 years(Figure 1B). From 5 to 93 years, the relationship was non-linear for bothwomen and men (i.e. polynomial regression 4th degree). Also for 18-80 years, the relationship was non-linear (i.e. polynomial regression 5th degree) (Table 2). Regarding the group 5 to 93 years (Figure 1A), the curvewas 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 very linear (Figure 1A and B), but started to increase at theage of ~35 years in a curvilinear manner for both men and women withincreasing age in both women and men. 17dc91bb1f