Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch
Year: 2018
World Records in Half-Marathon Running by Sex and Age
Nikolaidis, Pantelis T ; Di Gangi, Stefania ; Knechtle, Beat
Abstract: The relationship between age and elite marathon race times is well investigated, but little is known for half-marathon running. This study investigated the relationship between half-marathon race times and age in 1-year intervals by using the world singe age records in half-marathon running and the sex difference in performance from 5-91 years in men and 5-93 years in women. Wefounda 4 order-polynomial relationship between age and race time for both women and men. Women achieve their best half-marathon race time earlier in life than men, 23.89 years compared with 28.13, but when using a non-linear regression analysis, the age of the fastest race time does not differ between men and women with 26.62 years in women and 26.80 years in men. Moreover, women increase the sex difference in half-marathon running performance to men with advancing age.
DOI: https://doi.org/10.1123/japa.2017-0394
Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-152631 Journal Article Accepted Version
Originally published at: Nikolaidis, Pantelis T; Di Gangi, Stefania; Knechtle, Beat (2018). World Records in Half-Marathon Running by Sex and Age. Journal of aging and physical activity, 26(4):629-636. DOI: https://doi.org/10.1123/japa.2017-0394 1 World records in half-marathon running by sex and age
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3 World records in half-marathon
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5 Pantelis T. Nikolaidis 1, Stefania Di Gangi,2, Beat Knechtle 2,3 6 7 8 9 1Exercise Physiology Laboratory, Nikaia, Greece 10 2Institute of Primary Care, University of Zurich, Zurich, Switzerland 11 3Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland 12
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23 24 25 26 27 28 29 Corresponding author: 30 Prof. Dr. med. Beat Knechtle 31 Facharzt FMH für Allgemeinmedizin 32 Medbase St. Gallen Am Vadianplatz 33 Vadianstrasse 26 34 9001 St. Gallen 35 Switzerland 36 Telefon +41 (0) 71 226 93 00 37 Telefax +41 (0) 71 226 93 01 38 E-Mail [email protected]
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39 Abstract
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41 The relationship between age and elite marathon race times is well investigated, but
42 little is known for half-marathon running. This study investigated the relationship
43 between half-marathon race times and age in 1-year intervals by using the world singe
44 age records in half-marathon running and the sex difference in performance from 5-91
45 years in men and 5-93 years in women. We found a 4th order-polynomial relationship
46 between age and race time for both women and men. Women achieve their best half-
47 marathon race time earlier in life than men, 23.89 years compared with 28.13, but
48 when using a non-linear regression analysis, the age of the fastest race time does not
49 differ between men and women, 26.62 years in women and 26.80 years in men.
50 Moreover, women increase the sex difference in half-marathon running performance
51 to men with advancing age.
52 Key words: female, male, aging, youth, performance
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65 Introduction
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67 It is well known that marathon running still has an unbroken high level of popularity.
68 However, more people run a half-marathon than a marathon in the USA. In 2016, a
69 total of 1,900,000 runners finished a half-marathon which is a little bit less than the
70 1,986,600 finisher in 2015 and the record of more than 2,000,000 finishers in 2014. In
71 contrast to marathon races, the number of half-marathons increased to 2,800 races in
72 2016 from 2,700 races in 2015 (www.runningusa.org/half-marathon-report-2017).
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74 Little is known for the age-related performance decline in both marathon and half-
75 marathon running. When 300,757 runners were analyzed in 10-year age group
76 intervals (i.e. six decades, 20-79 years), mean marathon and half-marathon times were
77 virtually identical for the age groups from 20-49 years (Leyk et al., 2007). However,
78 most probably, the age-related performance decline is different between female and
79 male half-marathoners. A very recent study investigated the age-related decline in 5-
80 year age group intervals in half-marathoners competing in the ‘GöteborgsVarvet’, in
81 the largest half-marathon in the world. In that race, women achieved the fastest race
82 time at an earlier age compared to men where the fastest race times were observed in
83 women in age groups ˂35 and 35-39 years and in men in age group 35-39 years
84 (Knechtle & Nikolaidis, 2018).
85 However, the age of peak performance has been well investigated in marathon
86 running and has been reported to lie at ~25-35 years for both women and men
87 depending upon nationality and performance level of the athletes (Aschmann et al.,
88 2013; Hunter, Stevens, Magennis, Skelton, & Fauth, 2011; Knechtle, Aschmann, et
89 al., 2017; Knechtle, Assadi, Lepers, Rosemann, & Rust, 2014; Lara, Salinero, & Del
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90 Coso, 2014; Lehto, 2016; Nikolaidis, Onywera, & Knechtle, 2016). It has been shown
91 that East-African runners were the youngest and the fastest (Aschmann et al., 2013;
92 Knechtle, Nikolaidis, Onywera, et al., 2016) and their age is at (mean±sd) 29.7±4.0
93 years for Kenyan women, 28.9±4.2 years for Kenyan men, 25.7±3.8 years for
94 Ethiopian women and 26.7±3.8 years for Ethiopian men (Nikolaidis et al., 2016).
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96 Men seem to achieve their best marathon race time at an earlier age than women.
97 When running times of the top ten women and men at 1-year intervals from 18 to 75
98 years in the ‘New York City Marathon’ were analyzed for the 2010 and 2011 races
99 the fastest race times were obtained at 27 years in men and at 29 years in women
100 (Lara et al., 2014). However, when the performance of the world's best runners in the
101 10-km, half-marathon, marathon, and 100-km races by age, sex, and nationality
102 during the period 1999-2015 were analysed, women (29.5±5.5 years) were
103 significantly older than men (29.1±4.3 years) in marathon running (Knechtle,
104 Nikolaidis, Onywera, et al., 2016).
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106 In half-marathon running, little is known for the age of peak performance. A study
107 determined in 125,894 female and 328,430 male half-marathoners competing between
108 1999 and 2014 in all half-marathons held in Switzerland the age of the finishers and
109 found that women (41.4±10.6 years) were at the same age than men (41.3±10.3 years)
110 (Knechtle, Nikolaidis, Zingg, Rosemann, & Rust, 2016). However, when the
111 performance of the world's best runners in the 10-km, half-marathon, marathon, and
112 100-km races by age, sex, and nationality during 1999-2015 were analysed, women
113 (27.5±4.7 years) were older than men (25.9±4.1 years) in half-marathon (Nikolaidis et
114 al., 2016). The ages were for East-African runners 27.2±3.9 years for Kenyan women
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115 and 25.8±6.1 years for Kenyan men and 24.5±3.6 years for Ethiopian women and
116 24.9±4.2 years for Ethiopian men (Nikolaidis et al., 2016).
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118 These findings let assume that for half-marathon running a difference in the age of
119 peak performance compared to marathon running exists and also a difference in the
120 age of peak performance between women and men. In order to elucidate these
121 situation, we investigated the relationship between half-marathon race times and age
122 in 1-year intervals by using the world singe age records in half-marathon running and
123 the sex difference in performance from 5-91 years in men and 5-93 years in women.
124 Based upon existing findings we hypothesized to find a U-shaped quartic polynomial
125 relationship between age and half-marathon running performance with an increasing
126 difference between women and men.
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140 Methods
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142 Ethics
143 This study was approved by the Institutional Review Board of St. Gallen,
144 Switzerland, with a waiver of the requirement for informed consent given that the
145 study involved the analysis of publicly available data.
146 Data sampling and data analysis
147 The data set for this study was obtained from the website of the ‘Association of Road
148 Racing Statisticians’ (ARRS) www.arrs.net/SA_HMar.htm. We analysed world single
149 age records in half-marathon running, during 1977 – 2017, for women and men from
150 the age of 5 to 91 years for men and from the age of 5 to 93 years for women,
151 respectively. Criteria for inclusion are described in the ARRS website www.arrs.net.
152 In particular, performances were subject to the same standards as listing for national
153 records plus the additional requirement that the runner's date of birth (as well as the
154 race date) must be known. Therefore we have the runner's exact age at the time of the
155 performance. Single age records that meet the ARRS qualifying standards may be
156 expected to be fairly reliable. At older (and younger) ages, the best marks known to
157 ARRS are listed.
158 Statistical analyses
159 The acceptable type I error was set at p<0.05. All data are presented as means and
160 standard deviations. Time was recorded in the format “hours:minutes:seconds”. All
161 statistical analyses were carried out using statistical package R, R Core Team (2016).
162 R: A language and environment for statistical computing. R Foundation for Statistical
163 Computing, Vienna, Austria. URL https://www.R-project.org/.
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164 For data visualization, we use ggplot2 package. Percentage sex difference in race time
165 was calculated using the formula 100×(women’s race time-men’s race time)/men’s
166 race time. The age of peak performance was the age at which we have a minimum on
167 the time-fitted value from a non-linear regression model with a fourth order (i.e.
168 quartic) polynomial function, y=ax4+bx3+cx2+dx+e. Since we have repeated
169 measurements within runners, we performed a mixed model, using R packages lme4
170 and lmerTest. Age, gender and their interaction, were considered as fixed effects,
171 runners as random effects of intercepts. In this way, we corrected for correlated
172 observations of the same runner.
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173 Results
174 Overall, we had 170 observations, 87 (51%) men records and 83 (49%) women
175 records. Runners were 109, 53 (49%) men and 56 (51%) women. Therefore we have
176 repeated measurements within runners recorded at different age. Overall the average
177 was 1.56 per runner, but in fact only 27 (25%) runners have more than one record.
178 Martin Rees (WAL) had 11 reported records, the maximum number per runner, as
179 shown in Appendix (Figure A.1) where we reported trend performance for runners
180 with more than one record. In Table 1 we reported participation and performance,
181 overall by gender and age. P-value of T-test for performance between genders is
182 reported. Age was grouped in 7 categories. We observe that gender was quite equally
183 represented overall and in each age group, except age [75, 94) where women were
184 43% of the total age group. Instead age [30-40) had a number of women greater than
185 the number of men, that is women were 53% of the total age group. Overall, men
186 performance was 01.21.46 ± 00.29.32, not significantly different (p=0.103) from
187 women performance 01.29.32 ± 00.32. Instead, performance at different ages was
188 significantly different between gender, except for extreme ages, youngers 5-19,
189 p=0.922, and olders 75-93, p=0.240. If we consider overall performance by sex,
190 corrected for repeated measurement, not shown in Table 1, we have 01:22:33 ±
191 00:31:10 for men and 01:27:24 ± 00:34:33 for women, p=0.444. In men, the
192 youngest half-marathoner was Matthews Feibush (USA) who completed in May 2013
193 at the age of 5.88 years a half-marathon in 03:02:57.5 h:min:s. The oldest half-
194 marathoner was Mike Fremont who finished in April 2013 at the age of 91.18 years in
195 03:06:23 h:min:s. In women, Nasiy Jobe (USA) completed in October 1984 a half-
196 marathon in 01:51:31 h:min:s at the age of 5.90 years as the youngest female half-
197 marathoner. The oldest female half-marathoner is Gladys Burrill (CAN) who finished
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198 in March 2012 at the age of 93.36 years the half-marathon in Honolulu in 04:49:05
199 h:min:s.
200 The fastest half-marathon race times were achieved by Zersenay Tadese (ERI) in
201 2010 in 00:58:23 h:min:s at the age of 28.13 years in men and Joyciline Jepkosgei
202 (KEN) very recently in 2017 in 01:04:51 h:min:s at the age of 23.89 years in women.
203 We found a U-shaped quartic order relationship between age, as a continuous
204 variable, and race time for both women and men (Figure 1). In Table 2 we reported
205 fixed effect estimate coefficients of our mixed effect model. All effects were
206 significant, even interaction term between age and sex. Intraclass Correlation
207 Coefficient (ICC) which is a measure of how strongly units in the same group
208 resemble each other was high: 0.96. This justify the use of a mixed model with one
209 random effect of intercept for each runner. In Appendix, Figure A.2, random effects
210 of intercepts are plotted for each runner. From Figure 1, we can see that the age of the
211 fastest race time was 26.62 years in women and 26.8 years in men. To see how the
212 "tails" of the age distribution influence the estimated regression parameters, we
213 considered a separate regression analysis for age 20-60. Results are shown in
214 Appendix, Table A.1 and Figure A.3. According to this model, the age at the best
215 performance was 26.44 for men and 27.17 for women. The sex difference in
216 performance increased largely across years (Figure 2). We also found, but we did not
217 show in table, that the fastest top ten results occurred in the last ten years, 2007-2017.
218 Their range was 01:04:51 - 01:06:07, mean = 01:05:34 for women and 00:58:23 -
219 00:58:58, mean = 00:58:43 for men.
220 It is beyond our main aim to investigate performance at the time it was set and
221 nationality of runners. However, we reported in Table 3 participation and
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222 performance, overall by period and age. P value of T-test for performance between
223 sex is reported. We noticed that performance is significantly different, p=0.004, only
224 during period 2007-2012, with 01.09.15 ± 00.13.41 for men and 01.26.59 ± 00.20.25
225 for women. We found, but we did not show in tables, that we have a relevant
226 difference, between gender, for runners coming from Canada, 9 women and 10 men.
227 This was due mostly for a high variability within women. In fact we have 02.26.20 ±
228 00.56.01 for women and 01.32.52 ± 00.10.38 for men.
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247 Discussion
248 This study investigated the relationship between half-marathon race times and age in
249 1-year intervals by using the world singe age records in half-marathon running with
250 the hypothesis to find a U-shaped relationship between age and half-marathon running
251 performance with a difference between women and men. Indeed, we found a U-
252 shaped relationship between age and race time for both women and men where the
253 age of the fastest race time was 26.62 years in women and 26.8 years in men. A
254 second important finding was that the sex difference in performance increased largely
255 across years.
256 Women achieve peak half-marathon performance at near the same age as men
257 An earlier age of peak performance in half-marathon running could be assumed by
258 very recent findings of the age-related performance decline in half-marathon running.
259 In the ‘GöteborgsVarvet’, in the largest half-marathon in the world, women achieved
260 the fastest race time in age groups ˂35 and 35-39 years and men in age group 35-
261 39 years (Knechtle & Nikolaidis, 2017). We found, however, that women achieve
262 their best half-marathon race time at a similar age like men.
263 Different findings have been reported for marathon running for the age of peak
264 performance in female and male marathoners. When the age at time of competition
265 and the running times of the first five placed male and female runners who competed
266 in the seven marathons of the World Marathon Majors Series were analyzed, women
267 (29.8±4.2 years) were older than men (28.9±3.8 years), but for only two of the seven
268 marathons, the Chicago and the London marathons. There was no sex difference in
269 age for the Berlin, Boston, New York City, World Championship, and Olympic
270 marathons (Hunter et al., 2011). When the age of peak marathon performance was
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271 investigated in female and male marathoners competing in two large city marathons
272 held in Switzerland, the fastest man was ~23.7 years old and the fastest woman ~32.2
273 years old. When all finishers were considered in 1-year age intervals, the fastest men
274 were ~35.0 years old and the fastest women ~33.8 years old (Knechtle, Nikolaidis,
275 Zingg, Rosemann, & Rust, 2017). These disparate findings for female and male
276 marathoners are most likely explained by the different performance levels of the
277 athletes and the kind of the statistical analysis.
278 Women increase the sex difference to men with increasing age
279 The second important finding was that the sex difference in performance increased
280 largely across years. To date, it is well known that the sex difference in marathon
281 running increases with increasing age (Senefeld, Joyner, Stevens, & Hunter, 2016).
282 When running times of the first 10 placed men and women competing in the ‘New
283 York City Marathon’ between 1980 and 2010 in 5-year age group intervals between
284 20 and 79 years and the number of men and women were analyzed, the sex difference
285 increased with advanced age and decreased across the 31 years, but more for the older
286 age groups (Hunter & Stevens, 2013).
287 However, there seem to exist differences between sports disciplines such as running
288 and swimming where the sex difference in swimming increased with world record
289 place and age, but was less than for marathon running (Senefeld et al., 2016). Recent
290 studies investigating age group swimmers in pool swimming in freestyle (Knechtle,
291 Nikolaidis, Konig, Rosemann, & Rust, 2016), butterfly (Knechtle, Nikolaidis,
292 Rosemann, & Rust, 2017b), individual medley (Nikolaidis & Knechtle, 2017),
293 backstroke (Unterweger, Knechtle, Nikolaidis, Rosemann, & Rust, 2016), and
294 breaststroke (Knechtle, Nikolaidis, Rosemann, & Rust, 2016) as well as open-water
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295 swimmers in 3,000 m (Knechtle, Nikolaidis, Rosemann, & Rust, 2017a) showed that
296 men were not faster than women in all age groups. Regarding elite age group
297 swimmers competing in the FINA World Championships, the distance and the
298 discipline seems to influence the sex difference. In backstroke swimmers from 50 m
299 to 200 m, men were faster than women in age groups 25-29 to 80-84 years, but not in
300 age groups 85-89 to 95-99 years (Unterweger et al., 2016). In freestyle swimming
301 from 50 m to 200 m, men were faster than women in age groups 25-29 to 80-84 years,
302 but not in age groups 85-89 to 95-99 years (Knechtle, Nikolaidis, Konig, et al., 2016).
303 In breaststroke swimming from 50 m to 200 m, women reduced the gap to men
304 between ~40 and ~75 years, but not in younger (<40 years) or older (>75 years) age
305 groups (Knechtle, Nikolaidis, Rosemann, et al., 2016). In 3,000 m freestyle in age
306 groups swimmers in age groups 25-29 to 85-89 years, women were slower in age
307 groups 25-29 to 70-74 years. In age groups 75-79 and 85-89 years, however, race
308 times were similar for both women and men (Knechtle, Nikolaidis, Rosemann, et al.,
309 2017a). In individual 200m and 400m medley, men were faster than women from 25-
310 29 to 80-84 years, but not from 85-89 to 90-94 years (Nikolaidis & Knechtle, 2017).
311 In butterfly swimming from 50 m to 200 m, women were not slower compared to men
312 in the master group 90-94 years; moreover, women reduced the gap to men between
313 ~30 and ~60 years, although not in younger or older master groups (Knechtle,
314 Nikolaidis, Rosemann, et al., 2017b).
315 Interpretation of findings by a physiological point of view
316 The increase of race time in half-marathon records with aging was in agreement with
317 the changes of physiological correlates of this sport across life-span. Actually, in
318 addition to anthropometry and training characteristics (Rust et al., 2011), the race time
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319 in half-marathon has been shown to relate with maximal oxygen uptake (VO2max),
320 speed at the anaerobic threshold and maximal aerobic speed (Gomez-Molina et al.,
321 2017). VO2max in endurance exercise trained athletes was 25-32% lower than their
322 counterparts younger by three-to-four decades (Ogawa et al., 1992). Furthermore, the
323 sex difference in performance in half-marathon should be attributed partially to
324 differences in VO2max, e.g. men endurance runners had 10% higher value than their
325 women counterparts (Helgerud, 1994). With regards to the increase of sex difference
326 in half-marathon performance with aging, this observation should be interpreted in
327 terms of participation (increase of the men-to-women ratio with increasing age)
328 (Knechtle, Nikolaidis, Zingg, et al., 2016) rather than sex difference in changes of
329 physiological characteristics across the life-span.
330 Conclusions
331 In the world singe age records in half-marathon running performance from 5-91 years
332 in men and 5-93 years in women, the age of the fastest race time is 26.62 years in
333 women and 26.8 years in men. The sex difference in performance increased largely
334 across years. Women seem to achieve the best half-marathon race time at the same
335 age as men and increase the sex difference in performance with advancing age.
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424 Unterweger, C. M., Knechtle, B., Nikolaidis, P. T., Rosemann, T., & Rust, C. A.
425 (2016). Increased participation and improved performance in age group
426 backstroke master swimmers from 25-29 to 100-104 years at the FINA World
427 Masters Championships from 1986 to 2014. Springerplus, 5, 645. doi:
428 10.1186/s40064-016-2209-2
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441 Table 1. Participation and performance overall, by gender and age.
P (t.test Gender N (%) Mean (time) Sd mean)
Overall Men 87 (51%) 01.21.46 00.29.32 0.103
N=170 Women 83 (49%) 01.29.32 00.32.06
Age
[5,20) Men 15 (50%) 01.26.54 00.32.50 0.922
Women 15 (50%) 01.27.56 00.23.50
[20,30) Men 10 (50%) 00.58.46 00.00.15 <0.001
Women 10 (50%) 01.05.53 00.00.45
[30,40) Men 9 (47%) 00.59.38 00.00.38 <0.001
Women 10 (53%) 01.07.05 00.01.18
[40,50) Men 11 (52%) 01.04.16 00.02.05 <0.001
Women 10 (48%) 01.12.08 00.02.07
[50,60) Men 10 (50%) 01.10.37 00.02.14 <0.001
Women 10 (50%) 01.20.42 00.03.35
[60,75) Men 15 (50%) 01.19.44 00.06.01 <0.001
Women 15 (50%) 01.35.06 00.07.16
[75,94) Men 17 (57%) 02.02.09 00.32.27 0.240
Women 13 (43%) 02.20.38 00.47.10
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448 Table 2. Non-linear regression analysis (mixed model) of world records by sex, age 449 and their interaction. Men is the reference gender group.
Fixed effects Estimate Std. Error p
intercept 0.05641 0.00112 < 0.001
gender=Women 0.00767 0.00155 < 0.001
a(age) 0.09678 0.01126 < 0.001
b(age2) 0.15832 0.00927 < 0.001
c(age3) -0.03585 0.00797 < 0.001
d(age4) 0.08633 0.00893 < 0.001
Women*a(age) 0.07756 0.01789 < 0.001
Women*b(age2) 0.03810 0.01517 0.013
Women*c(age3) 0.03620 0.01266 0.005
Women*d(age4) -0.00741 0.01299 0.570
450 ICC=0.96
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464 Table 3. Participation and performance overall, by sex and period when record was 465 set.
Year Gender N (%) Mean Sd p
[1977,1997) Men 13 (59%) 01.19.14 00.17.28 0.617
N=22 Women 9 (41%) 01.22.39 00.13.59
[1997,2007) Men 19 (64%) 01.14.29 00.12.19 0.123
N=29 Women 10 (34%) 01.26.09 00.20.27
[2007,2012) Men 18 (49%) 01.09.15 00.13.41 0.004
N=37 Women 19 (51%) 01.26.59 00.20.25
[2012,2017] Men 37 (45%) 01.32.29 00.39.54 0.976
N=82 Women 45 (55%) 01.32.45 00.40.02
466
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467 Table A.1 Non-linear regression analysis (mixed model) of world records by sex, age, 468 20-60, and their interaction. Men is the reference sex group. We have n=80 469 observations and 53 runners.
Fixed effects Estimate Std. Error p
intercept 0.044056 0.000106 < 0.001
gender=Women 0.005604 0.000146 < 0.001
a(age) 0.028610 0.000926 < 0.001
b(age2) 0.008929 0.000857 < 0.001
c(age3) 0.001954 0.000825 0.020
d(age4) 0.001330 0.000811 0.105
Women*a(age) 0.006911 0.001274 < 0.001
Women*b(age2) 0.005355 0.001215 < 0.001
Women*c(age3) 0.001891 0.001212 0.124
Women*d(age4) 0.001932 0.001185 0.107
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482 Legends of figures
483
484 Figure 1 World record in half-marathon by sex and age, in years. Points are 485 observations. Curves represent quartic degree polynomial regression. 486 Vertical dashed line is the age at peak performance.
487 Figure 2 Sex difference by age in years. For each age group, sex difference was 488 calculated as 100 × (women’s mean race time - men’s mean race time) 489 / men’s mean race time. The line represents fitted value from model in 490 Figure 1.
491 Figure A.1 Trend performance for runners with more than one record.
492 Figure A.2 Mixed model: random effects of intercepts, for each runner.
493 Figure A.3 World record in half-marathon by sex and age, 20 - 60. Points are 494 observations. Curves represent quartic degree polynomial regression. 495 Vertical dashed line are the ages at peak performance.
496
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499 Figure 1
500
05:00
04:00
03:00 Sex Men Women Time (h:min) Time
02:00
01:00
25 50 75 Age (years) 501
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502 Figure 2
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506 Figure A.1
20 40 60 80
Tabitha Francks (USA) Warren Utes (USA) Zersenay Tadese (ERI) 03:00 02:30 02:00 01:30 01:00 Norman Green (USA) Patrick Roussel (FRA) Rae Baymiller (USA) Samuel Wanjiru Kamau (KEN) Silke Schmidt (GER) Stefica Gajic (SUI) 03:00 02:30 02:00 01:30 01:00 Linda Somers (USA) Martin Rees (WAL) Mary Keitany Chepkosgei (KEN) Mike Fremont (USA) Mira Preradovic (BIH) Nicole Leveque (FRA) 03:00 02:30 02:00 01:30 01:00
Time (h:min) Time Evy Palm (SWE) Florence Chebet Kiplagat (KEN) Haile Gebreselasie (ETH) Jack Foster (NZL) Knut Angstrom (SWE) Lavinia Petrie (AUS) 03:00 02:30 02:00 01:30 01:00 Alexander Korio Oloitiptip (KEN) Angela Copson (ENG) Bernardine Portenski (NZL) Betty Jean McHugh (CAN) Deidre Larkin (GBR) Ed Whitlock (CAN) 03:00 02:30 02:00 01:30 01:00
20 40 60 80 20 40 60 80 20 40 60 80 507 Age (years)
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524 Figure A.2
(Intercept) Gladys Burrill (CAN) Matthew Feibush (USA) Rosie Dunham (USA) Mike Fremont (USA) Eddie Naisby (WAL) Knut Angstrom (SWE) Johnny A Kelley (USA) Charles Bancarel (FRA) Paul Kosgei Malakwen (KEN) Steven Kibet Kosgei (KEN) Abraham Cheroben Naibei (BRN) Patrick Makau Musyoki (KEN) Eunice Chebichii Chumba (BRN) Kenneth Kiprop Kipkemoi (KEN) Lucy Kabuu Wangui (KEN) Viola Chepchumba (KEN) Alexander Korio Oloitiptip (KEN) Melitta Czerwenka (GER) Zersenay Tadese (ERI) Florence Chebet Kiplagat (KEN) Geoffrey Mutai Kiprono (KEN) Esther Wanjiru Maina (KEN) Rimma Vasina (RUS) Warren Utes (USA) Edith Chelimo (KEN) Mathew Kipkoech Kisorio (KEN) Joyciline Jepkosgei (KEN) Fancy Chemutai (KEN) Mary Keitany Chepkosgei (KEN) Ezekiel Chebii Kiptoo (KEN) Gladys Cherono (KEN) Lydia Cheromei Kogo (KEN) Mare Dibaba Hurssa (ETH) Edna Kiplagat Ngeringwony (KEN) Emmi Luthi (SUI) Robert Newell (ENG) Karl-Walter Trumper (GER) Rae Baymiller (USA) Gilbert Masai (KEN) Abebu Gelan Adugna (ETH) Valeria Straneo (ITA) Joop Ruter (NED) Heather Welsch (USA) Gwen McFarlan (CAN) Mame Feyisa (ETH) Valentine Kipketer Chepkorir (KEN) Jeff Taylor (USA) Heidrun Besler (GER) Christine Kennedy (USA) Jaouad Gharib (MAR) Christelle Daunay (FRA) Jorum Lumbasi Okombo (KEN) Irina Permitina (RUS) John Keston (USA) Houria Frechou (FRA) Zhu-ang Liu (CHN) Angela Copson (ENG) Stefica Gajic (SUI)
Runners Samuel Wanjiru Kamau (KEN) Chantal Langlace (FRA) Lavinia Petrie (AUS) Sally Gibbs (NZL) Bernardine Portenski (NZL) Haile Gebreselasie (ETH) Eyerusalem Kuma Mutal (ETH) Edith Masai (KEN) Jacob Bragg (AUS) Deena Kastor (USA) Luciano Acquarone (ITA) Joanne Pavey (ENG) Nicole Leveque (FRA) Ed Whitlock (CAN) Betty Jean McHugh (CAN) Antonio Salvador (POR) Deidre Larkin (GBR) Charlie Westrip (USA) Miyuki Oka (JPN) John Campbell (NZL) Evy Palm (SWE) Patrick Roussel (FRA) Silke Schmidt (GER) James Moiben Kibitok (KEN) Tatyana Pozdniakova (UKR) Mira Preradovic (BIH) Jackson Kipngok Yegon (KEN) Linda Somers (USA) Jack Foster (NZL) Michael Hager (ENG) Wil vanderLee (NED) Norman Green (USA) Eric Ndiema (KEN) Sergio Fernandez Infestus (ESP) Wesley Paul (USA) Faustin Baha Sulle (TAN) Martin Rees (WAL) Libby James (USA) Titus Mamabolo (RSA) Hunter Perez (USA) Ed Benham (USA) Grattan O'Neill (USA) Alene Emere Reta (ETH) Jerome-Dino Daniels (USA) Makenzie Kolvoord (USA) Tabitha Francks (USA) Jaden Merrick (USA) Stephanie Paparelli (USA) Nathaniel Lee (USA) Nasiya Jobe (USA) Deidre Larkin (RSA)
-0.02 0.00 0.02 0.04 525
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526 Figure A.3
527
Sex Men Women Time (h:min) Time
01:00
20 30 40 50 60 Age (years) 528
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