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Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2018 The age-related performance decline in marathon cross-country skiing - the Engadin Ski Marathon Nikolaidis, Pantelis Theodoros ; Knechtle, Beat Abstract: Demographic and performance data from 197,825 athletes competing in ”Engadin Ski Marathon” between 1998 and 2016 were analysed. When all fnishers per age group were considered, there was no gender diference in time (2:59:00 in women versus 2:59:09 h:min:s in men; P = 0.914, < 0.001) and the main efect of age group on time was trivial (P < 0.001, = 0.007). When the top 10 fnishers per age group were considered, men were faster than women (1:27:32 versus 1:34:19 h:min:s, respectively; P < 0.001, = 0.373), there was a large efect of age group on time (P < 0.001, = 0.590) and the gender diference was larger in the older than in the younger age groups (P < 0.001, = 0.534). The age of peak performance for all fnishers by 1-year interval age group was 40.3 and 39.6 years in all women and men, respectively. The top 10 fnishers by 1-year interval age group achieved their peak performance in the age of 38.4 and 42.2 years in women and men, respectively. The age of peak performance was older and the age-related performance decline occurred earlier in marathon cross-country skiing, compared to road-based marathon running. DOI: https://doi.org/10.1080/02640414.2017.1325965 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-152628 Journal Article Accepted Version Originally published at: Nikolaidis, Pantelis Theodoros; Knechtle, Beat (2018). The age-related performance decline in marathon cross-country skiing - the Engadin Ski Marathon. Journal of Sports Sciences, 36(6):599-604. DOI: https://doi.org/10.1080/02640414.2017.1325965

Transcript of Nikolaidis The age-related performance decline in marathon …...1 1 The age-related performance...

Page 1: Nikolaidis The age-related performance decline in marathon …...1 1 The age-related performance decline in marathon cross- 2 country skiing – the Engadin Ski Marathon 3 4 Running

Zurich Open Repository andArchiveUniversity of ZurichMain LibraryStrickhofstrasse 39CH-8057 Zurichwww.zora.uzh.ch

Year: 2018

The age-related performance decline in marathon cross-country skiing - theEngadin Ski Marathon

Nikolaidis, Pantelis Theodoros ; Knechtle, Beat

Abstract: Demographic and performance data from 197,825 athletes competing in ”Engadin Ski Marathon”between 1998 and 2016 were analysed. When all finishers per age group were considered, there was nogender difference in time (2:59:00 in women versus 2:59:09 h:min:s in men; P = 0.914, < 0.001) andthe main effect of age group on time was trivial (P < 0.001, = 0.007). When the top 10 finishers perage group were considered, men were faster than women (1:27:32 versus 1:34:19 h:min:s, respectively; P< 0.001, = 0.373), there was a large effect of age group on time (P < 0.001, = 0.590) and the genderdifference was larger in the older than in the younger age groups (P < 0.001, = 0.534). The age ofpeak performance for all finishers by 1-year interval age group was 40.3 and 39.6 years in all women andmen, respectively. The top 10 finishers by 1-year interval age group achieved their peak performance inthe age of 38.4 and 42.2 years in women and men, respectively. The age of peak performance was olderand the age-related performance decline occurred earlier in marathon cross-country skiing, compared toroad-based marathon running.

DOI: https://doi.org/10.1080/02640414.2017.1325965

Posted at the Zurich Open Repository and Archive, University of ZurichZORA URL: https://doi.org/10.5167/uzh-152628Journal ArticleAccepted Version

Originally published at:Nikolaidis, Pantelis Theodoros; Knechtle, Beat (2018). The age-related performance decline in marathoncross-country skiing - the Engadin Ski Marathon. Journal of Sports Sciences, 36(6):599-604.DOI: https://doi.org/10.1080/02640414.2017.1325965

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The age-related performance decline in marathon cross-1 

country skiing – the Engadin Ski Marathon 2  3 

Running head: Age and performance in cross-country skiing 4 

Pantelis Theodoros Nikolaidis1, Beat Knechtle2,3 6  7  8 

1Exercise Physiology Laboratory, Nikaia, Greece 9 2Gesundheitszentrum St. Gallen, St. Gallen, Switzerland 10 

3Institute of Primary Care, University of Zurich, Zurich, Switzerland 11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40 Corresponding author 41 Prof. Dr. med. Beat Knechtle 42 Facharzt FMH für Allgemeinmedizin 43 Gesundheitszentrum St. Gallen 44 Vadianstrasse 26 45 9001 St. Gallen 46 Switzerland 47 Telefon +41 (0) 71 226 93 00 48 Telefax +41 (0) 71 226 93 01 49 E-Mail [email protected] 50 

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Abstract 51  52  53 

The aim of the present study was to determine the age-related performance decline in 54 

the ‘Engadin Ski Marathon’, a cross-country race covering the marathon distance of 55 

42.2km. Demographic and performance data from 197,825 athletes competing in this 56 

race between 1998 and 2016 were analysed. During these 17 years, ~5 times more 57 

men finished than women. When all finishers per age group were considered, there 58 

was no gender difference in race time (2:59:00 in women versus 2:59:09 h:min:s in 59 

men; p=0.914, η2<0.001) and the main effect of age group on race time was trivial 60 

(p<0.001, η2=0.007). When the top 10 finishers per age group were considered, men 61 

were faster than women (1:27:32 versus 1:34:19 h:min:s, respectively; p<0.001, 62 

η2=0.373), there was a large effect of age group on race time (p<0.001, η2=0.590) and 63 

the gender difference was larger in the older than in the younger age groups (p<0.001, 64 

η2=0.534). The age of peak performance for all finishers by 1-year interval age group 65 

was 40.3 and 39.6 years in all women and men, respectively. The top 10 finishers by 66 

1-year interval age group achieved their peak performance in the age of 38.4 and 42.2 67 

years in women and men, respectively. In summary, the age of peak performance was 68 

older and the age-related performance decline occurred earlier in marathon cross-69 

country skiing compared to road-based marathon running. 70 

Key words: athlete, female, male, winter sport 71 

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Introduction 77 

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It is well known that athletic performance decreases with increasing age (Gava, Kern, 79 

& Carraro, 2015; Tanaka & Seals, 2008). The best endurance performances are often 80 

achieved at the age of ~35 years with a small and linear decline until the age of ~50-81 

60 years and a more pronounced decline after that age (Tanaka & Seals, 2008). 82 

The age-related performance decline is also dependent upon the mode of physical 83 

exercise. For example, physical performances involving mostly upper limbs (e.g. shot 84 

put, javelin throw) show a higher rate of decline (1.4% per year) compared to 85 

performances where the lower limbs are mostly involved (e.g. long jump 1.1%, track 86 

events 0.6-0.7% per year) (Gava et al., 2015). Most importantly, the performance 87 

decline with ageing is related to the duration of an event, as it has been observed that 88 

sprint or power sports show higher rates of decline than endurance sports with ageing 89 

(Allen & Hopkins, 2015). 90 

Apart from the mode of physical performance, there is also an influence of gender on 91 

the age-related performance decline. For example, in running there is a difference in 92 

the age-related decline by both distance and gender (Zingg, Rüst, Rosemann, Lepers, 93 

& Knechtle, 2014). In track and field athletes, differences were reported in the rates of 94 

percentage decline in running events among various distances, and significant 95 

differences between women and men were found in a study, where the decline with 96 

increasing age was greater for women and for longer endurance running events 97 

(Baker, Tang, & Turner, 2003). 98 

In running, the age-related performance decline has been well-investigated in female 99 

and male marathoners. Leyk et al. (2007) reported that a significant age-related 100 

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decline in marathon running performance did not occur before the age of ~50 years. 101 

Indeed, they showed that female and male marathoners can achieve a similar race 102 

time between the ages of 20 and 50 years. A more recent study investigating female 103 

and male master marathoners competing in the ‘New York City Marathon’ between 104 

1980 and 2009 showed that male (≥65 years) and female (≥45 years) master runners 105 

may not have reached their limits in marathon running performance (Lepers & 106 

Cattagni, 2012). 107 

These findings for the age-related performance decline in marathon running are most 108 

probably due to the fact that master athletes have significantly improved their 109 

performance over years (Ahmadyar, Rosemann, Rüst, & Knechtle, 2016) whereas the 110 

performance of younger athletes has remained the same (Akkari, Machin, & Tanaka, 111 

2015). In running and swimming, the magnitude of improvements over four decades 112 

was greater in athletes in older age groups gradually closing the gap in athletic 113 

performance between younger and older athletes (Akkari et al., 2015). Recently, it has 114 

been shown that even athletes older than 100 years are able to achieve outstanding 115 

endurance performances (Lepers, Stapley, & Cattagni, 2016). 116 

Although we know the age-related performance decline for shorter (Akkari et al., 117 

2015; Gava et al., 2015) and longer running distances such as the marathon distance 118 

(Leyk et al., 2007), we have no knowledge about the age-related performance decline 119 

in cross-country skiing, and especially on the marathon distance in cross-country 120 

skiing. Cross-country skiing is a very popular sport, especially in Scandinavian and 121 

Central European countries. Consequently, knowledge about the age-related trends in 122 

performance might be of great importance for athletes’ coaches and sport scientists. 123 

Sport scientists might use this knowledge to better understand the variation of age-124 

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related changes among different modes of exercise, whereas coaches might develop 125 

gender- and age-tailored training goals and programs. 126 

Therefore, the aim of the current study was to determine the age-related performance 127 

decline in marathon cross-country skiing in the ‘Engadin Ski Marathon’, a cross-128 

country race covering the distance of a marathon (42.195 km). Based upon findings 129 

for road-based marathon running following Leyk et al. (2007) showing that a 130 

significant age-related decline in marathon running performance did not occur before 131 

the age of ~50 years, we hypothesized that the age-related performance decline in 132 

marathon cross-country skiing would not occur before the age of 50 years for both 133 

women and men. 134 

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Methods 149 

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Ethical approval 151 

All procedures used in the study were approved by the Institutional Review Board of 152 

Kanton St. Gallen, Switzerland with a waiver of the requirement for informed consent 153 

of the participants given the fact that the study involved the analysis of publicly 154 

available data. 155 

The race 156 

All athletes who finished the ‘Engadin Ski Marathon’ between 1998 and 2016 were 157 

considered. Data with name, age and gender of the athletes, i.e. 34,833 women and 158 

162,992 men, were obtained from the publicly available race website of the ‘Engadin 159 

Skin Marathon’ at www.engadin-skimarathon.ch. 160 

The ‘Engadin Ski Marathon’ is an annually held cross-country ski race taking place 161 

on the second Sunday of March in the upper Engadin valley in Switzerland, Europe, 162 

between Maloja and S-chanf. The race started in 1969 and is one of the major cross-163 

country skiing events in the Swiss Alps. Each year, between 11,000 and 13,000 skiers 164 

participate in the race. Since 1998, the total distance covered is 42 km. In that year, 165 

the race was extended by 2 km to match the distance of a full running marathon and 166 

the track was changed slightly resulting in a more difficult topography and longer race 167 

times. While it is a freestyle race, there are separate tracks for skiers practicing classic 168 

style for all but the narrowest parts of the race. Participation is open to anyone from 169 

the age of 16 years and no license is required to enter the event. The start of the race 170 

takes place in Maloja at the Maloja Palace Hotel with an elevation of 1,820 meters 171 

above sea level and the finish at an elevation of 1,670 meters above sea level. 172 

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Statistical analysis 173 

All statistical analyses were performed using the statistical package IBM SPSS v.20.0 174 

(SPSS, Chicago, USA). The figures were created using the software GraphPad Prism 175 

v. 7.0 (GraphPad Software, San Diego, USA). Data were presented as mean±standard 176 

deviation. We examined the association of gender and age group, i.e. whether the 177 

distribution of gender varied by age group, using chi-square (χ2) and Cramer’s phi 178 

(φC) to evaluate the magnitude of association. We used two approaches to classify the 179 

participants into age groups; (a) 5-year age groups from <20 to 75-79 years and (b) 1-180 

year age groups from 17 to 92 years. Furthermore, in each approach we examined 181 

both the top 10 and all skiers. The men-to-women ratio was calculated for the whole 182 

sample and for each age group. A two-way ANOVA examined the main effects of 183 

gender and age group, and the gender × age group interaction on race time, followed 184 

by a Bonferroni post-hoc analysis. The magnitude of differences in the ANOVA was 185 

evaluated using eta-squared (η2) as trivial (η2<0.01), small (0.01≤η2<0.06), moderate 186 

(0.06≤η2<0.14) and large (η2≥0.14) (Cohen, 1988). The above mentioned ANOVA 187 

was run twice, one considering all finishers and one considering only the top 10 188 

skiers. We used a mixed-effects regression model to examine differences in race time 189 

by gender and age group (Tabachnick & Fidell, 2013). In the model, finisher was 190 

inserted as random variable, and gender and age group as fixed variables. In addition, 191 

we examined interaction effects between these fixed variables. Akaike information 192 

criterion (AIC) was used to select the final model. The alpha level was set at 0.05. 193 

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Results 197 

Full data with gender, age and race time from a total of 197,826 female and male 198 

athletes were available. In one athlete an extreme value was found and this athlete was 199 

deleted, leaving 197,825 athletes for data analysis. 200 

Gender × age group chi square 201 

The overall men-to-women ratio was 4.7. However, a gender×age group association 202 

was observed (χ2=6130.13, p<0.001, Cramer’s φ=0.176), where the men-to-women 203 

ratio was ranged from 2.4 (20-24 years age group) to 130.5 (80-84 years age group) 204 

(Figure 1). A trend was shown where the men-to-women ratio was higher in the older 205 

than in the younger age groups. In addition, the distribution of women and men in age 206 

groups followed a different pattern with most women in the age group 30-34 years 207 

whereas most men were in age group 40-44 years. 208 

Gender × age group ANOVA 209 

When all finishers were considered, no main effect of gender on race time was 210 

observed (p=0.914, η2<0.001), where the scores were 2:59:00 and 2:59:09 h:min:s in 211 

men and women, respectively (Figure 2). A trivial main effect of age group on race 212 

time was observed (p<0.001, η2=0.007) with the age group 40-44 years the fastest and 213 

the oldest age group the slowest (Figure 3). No gender × age group interaction on race 214 

time was observed (p=0.773, η2<0.001). The findings of the mixed-effects regression 215 

analysis are presented in Table 1. 216 

When the top 10 finishers were considered for each age group, a large main effect of 217 

gender on race time was observed (p<0.001, η2=0.373) with men significantly faster 218 

than women (1:27:32 and 1:34:19 h:min:s, respectively). Also, a large main effect of 219 

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age group on race time was shown (p<0.001, η2=0.590) with the oldest group the 220 

slowest. A large gender × age group interaction on race time was observed (p<0.001, 221 

η2=0.534) with men significantly faster than women in most age groups and the 222 

gender difference increased with age. The results of the mixed-effects regression 223 

analysis are presented in Table 2. 224 

The age of peak performance 225 

The age of peak performance was 40.3 and 39.6 years in all women and men, 226 

respectively, when they were examined in 1-year intervals (Figure 4). The top 10 227 

finishers by 1-year interval age group achieved their peak performance at the age of 228 

38.4 and 42.2 years in women and men, respectively. 229 

When 5-year intervals were considered, the age group of peak performance was 40-44 230 

years in both all women and men, but it was 40-44 years in women and 55-59 years in 231 

men when the top 10 finishers were considered. 232 

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Discussion 245 

This study determined the age-related performance decline in marathon cross-country 246 

skiing. It was hypothesized that this age-related decline in performance would not 247 

occur before the age of 50 years in contrast to previous findings in marathon running. 248 

The main findings of the present study were that (i) the number of men finishers was 249 

~5 times more than women and the men-to-women ratio was higher in the older than 250 

in the younger age groups; (ii) when all finishers in each age group were considered, 251 

there was no gender difference in race time and the effect of age group on race time 252 

was trivial; (iii) when the top 10 finishers in each age group were considered, men 253 

were faster than women and there was a large effect of age group on race time and the 254 

gender difference increased with age; and (iv) the age of peak performance was ~40 255 

years for both women and men. 256 

Gender difference in performance 257 

A potential explanation for the difference in the fastest women and men cross-country 258 

skiers could be the fact that most women were in the age group 30-34 years and most 259 

men in the age group 40-44 years. Furthermore, the men-to-women ratio was higher 260 

in the older cross-country skiers than in the younger age groups indicating that 261 

relatively more men competed in the older age groups compared to women. Also, the 262 

men-to-women ratio was ~5:1 in these athletes whereas in marathon running, the 263 

men-to-women ratio decreased in the ‘New York City Marathon’ from 5.6:1 in 1983 264 

to 2.5:1 in 1999 indicating an increase in female athletes (Jokl, Sethi, & Cooper, 265 

2004). 266 

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Differences in the age-related performance decline between women and men 268 

A major finding was that the age-related performance declined in these marathon 269 

cross-country skiers after the age of 40-44 years when all women and men were 270 

considered. However, when the 10 top skiers per age group were considered, the 271 

decline started after the age of 40-44 years in women and 50-59 years in men. This 272 

finding is in contrast to marathon running where the age-related decline in 273 

performance did not occur before the age of 50 years for both women and men (Leyk 274 

et al., 2007). 275 

Older age of peak performance compared to marathon running 276 

Another important finding was that the age of peak marathon cross-country 277 

performance was at the age of ~40 years for both women and men. In marathon 278 

running, the age of peak performance is at ~30-35 years, depending upon the sample 279 

and the method of analysis (Hunter, Stevens, Magennis, Skelton, & Fauth, 2011; Lara, 280 

Salinero, & Del Coso, 2014; Lehto, 2016; Nikolaidis, Onywera, & Knechtle, 2016). 281 

With regards to the general physiological functions of humans, the age of 282 

performance peak was 26 years in a study that examined the development of sport and 283 

chess performances across the lifetime (Berthelot et al., 2012). 284 

The difference of ~10 years between marathon cross-country skiing and marathon 285 

running might be explained by differences in analyses, performance level and mode of 286 

movement. Cross-country skiing performance relies on factors limiting endurance 287 

such as maximal oxygen uptake, anaerobic threshold and exercise economy (Tanaka 288 

& Seals, 2008) as in other exercise modes including running, cycling and swimming. 289 

On the other hand, in addition to these physiological factors, cross-country skiers need 290 

to interact efficiently with their equipment as skiing is a sport that relies more in 291 

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technique and recruits the upper and lower body, which might explain why they 292 

maintain peak performance in older age than in other exercise modes such as 293 

marathon running. 294 

A factor explaining the peak performance in older age in cross-country skiing 295 

compared to marathon runners could be fact that studies investigating age of peak 296 

marathon running performance have used data from flat city marathons at close to sea 297 

level (Hunter et al., 2011; Lara et al., 2014; Lehto, 2016; Nikolaidis et al., 2016) while 298 

the cross-country skiing marathon examined in the current study was held in hilly 299 

Alps and at altitude. A recent study investigating age of peak marathon running 300 

performance comparing flat city marathons and a mountain marathon showed 301 

significant differences in the age of peak marathon performance between mountain 302 

and city marathon running (Knechtle, Nikolaidis, Zingg, Rosemann, & Rüst, 2017). In 303 

that study, the age of the top 10 women and men was older in the mountain marathon 304 

compared to the city marathons when the fastest runners were considered in 1-year 305 

age-intervals, but when all finishers were considered in 1-year age-intervals, the age 306 

of the fastest women and men was younger in the mountain marathon compared to the 307 

city marathons. An explanation of a younger age of peak performance in exercise in 308 

mountain compared to flat might be the additional muscle work and muscle power 309 

demands during exercise in mountain. Considering that the age of the peak of muscle 310 

power performance is younger than that of endurance, it is deduced that an the age of 311 

peak performance in an exercise demanding more muscle power would be younger 312 

than an exercise demanding more endurance capacity. 313 

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Limitations, strength, implications for future research and practical applications 316 

A limitation of the present study was that since every cross-country skiing marathon 317 

possesses its own unique characteristics in terms of number participants, 318 

environmental conditions, duration and topography, the current findings should be 319 

generalized with caution. Although the findings were limited to a single skiing 320 

marathon race, they were focused on a major skiing event in Alps with large 321 

international participation. Future studies might investigate other races of the same or 322 

similar distance and environmental conditions. Moreover, the large number of 323 

finishers covering the adult life span was a major strength of the present study that 324 

allowed for the robust examinations of differences among age groups. The gender- 325 

and age-related differences observed add theoretical knowledge that may be useful for 326 

sport scientists studying the variation of performance decline with aging in different 327 

exercise modes. In addition, coaches and fitness trainers working with cross-country 328 

skiers may benefit from the findings by setting gender- and age-tailored goals and 329 

developing optimal training programs. For instance, considering that there was no 330 

gender difference in performance when all finishers were analysed, recreational 331 

athletes should be advised independent of their gender. However, since elite men were 332 

faster than women, this should be taken into account when training more competitive 333 

athletes. 334 

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Conclusions 339 

In marathon cross-country skiers competing in the ‘Engadin Ski Marathon’ between 340 

1998 and 2016, the age of peak performance was 40.3 and 39.6 years in all women 341 

and men, when they were examined in 1-year intervals. The top 10 finishers examined 342 

by 1-year intervals achieved their peak performance at the age of 38.4 and 42.2 years 343 

in women and men, respectively. When the age-related performance decline was 344 

considered in 5-year intervals, the age group of peak performance was 40-44 years in 345 

women and men when all finishers were considered, but it was 40-44 years in women 346 

and 55-59 years in men when the top 10 finishers only were considered. Compared to 347 

existing findings for road-based running marathon, the age of peak performance was 348 

higher and the age-related performance decline occurred earlier in marathon cross-349 

country skiing. 350 

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References 364 

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Akkari, A., Machin, D., & Tanaka, H. (2015). Greater progression of athletic 368 performance in older Masters athletes. Age Ageing, 44, 683-686. doi: 369 10.1093/ageing/afv023 370 

Allen, S.V., & Hopkins, W.G. (2015). Age of peak competitive performance of elite 371 athletes: a systematic review. Sports Med, 45, 1431-1441. doi: 10.1007/s40279-015-372 0354-3 373 

Baker, A.B., Tang, Y.Q., & Turner, M.J. (2003). Percentage decline in masters 374 superathlete track and field performance with aging. Exp Aging Res, 29, 47-65. 375 

Berthelot, G., Len, S., Hellard, P., Tafflet, M., Guillaume, M., Vollmer, J.-C., Gager, 376 B., Quinquis, L., Marc, A., Toussaint, J.-F. Exponential growth combined with 377 exponential decline explains lifetime performance evolution in individual and 378 human species. Age, 34, 1001-9. doi: 10.1007/s11357-011-9274-9 379 

Cohen, J. 1988. Statistical power analysis for the behavioral sciences (2nd ed.). 380 Hillsdale, NJ: Lawrence Erlbaum Associates. 381 

Gava, P., Kern, H., & Carraro, U. (2015). Age-associated power decline from 382 running, jumping, and throwing male masters world records. Exp Aging Res, 41, 115-383 135. doi: 10.1080/0361073X.2015.1001648 384 

Hunter, S.K., Stevens, A.A., Magennis, K., Skelton, K.W., & Fauth, M. (2011). Is 385 there a sex difference in the age of elite marathon runners? Med Sci Sports Exerc, 43, 386 656-664. doi:10.1249/MSS.0b013e3181fb4e00 387 

Jokl, P., Sethi, P.M., & Cooper, A.J. (2004). Master's performance in the New York 388 City Marathon 1983-1999. Br J Sports Med, 38, 408-412. 389 

Knechtle, B., Nikolaidis, P.T., Zingg, M.A., Rosemann, T., & Rüst CA. (2017). 390 Differences in age of peak marathon performance between mountain and city 391 marathon running - The ‘Jungfrau Marathon’ in Switzerland. Chin J Physiol, 60, 11-392 22. doi: 10.4077/CJP.2017.BAE400 393 

Lara, B., Salinero, J.J., & Del Coso, J. (2014). The relationship between age and 394 running time in elite marathoners is U-shaped. Age (Dordr), 36, 1003-1008. 395 doi:10.1007/s11357-013-9614-z 396 

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Nikolaidis, P.T., Onywera, V.O., & Knechtle, B. (2017). Running Performance, 407 Nationality, Sex and Age in 10km, Half-marathon, Marathon and 100km Ultra-408 marathon IAAF 1999-2015. J Strength Cond Res. Epub ahead of print, 409 doi:10.1519/JSC.0000000000001687 410 

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Table 1 Results of the mixed-effects regression analysis for race time considering all 446 finishers in each 5-year age group 447  448 Parameter Estimate SEE P value

Intercept 10379.78 20.43 <0.001

Group 57.16 2.92 <0.001

[Gender=Women] 334.69 45.71 <0.001

[Gender=Men] 0a 0 .

[Gender=Women] ×Age group -60.14 7.66 <0.001

[Gender=Men] ×Age group 0a 0 . aThis parameter is set to zero because it is redundant. 449 SEE=standard error of estimate. 450  451  452  453  454  455  456  457  458  459  460  461  462  463  464  465  466  467  468  469  470  471  472  473  474  475  476  477  478  479  480  481  482  483  484  485  486  487  488 

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Table 2 Results of the mixed-effects regression analysis for race time considering the 489 top 10 finishers in each 5-year age group 490  491 Parameter Estimate SEE P value

Intercept 4246.49 254.81 <0.001

Group 262.57 28.02 <0.001

[Gender=Women] 371.16 375.56 0.324

[Gender=Men] 0a 0 .

[Gender=Women] ×Age group -9.15 44.65 0.838

[Gender =Men] ×Age group 0a 0 . aThis parameter is set to zero because it is redundant. 492 SEE=standard error of estimate. 493  494 

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List of figures 513 

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Figure 1 Distribution of finishers by gender and age group 515 

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Figure 2 Gender difference in race time of all and top 10 finishers. 517 

*=significant gender difference at p≤0.004. 518 

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Figure 3 Race times of all and top 10 finishers in 5-year intervals 520 

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Figure 4 Race times of all and top 10 finishers in 1-year intervals 522 

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Figure 1 540 

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Figure 2 560 

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Figure 3 579 

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Figure 4 595 

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