International Journal of Environmental Research and Public Health

Article The Age-Related Performance Decline in Running: The Paradigm of the Marathon

Pantelis T. Nikolaidis 1,2 , José Ramón Alvero-Cruz 3 , Elias Villiger 4 , Thomas Rosemann 4 and Beat Knechtle 4,5,*

1 Exercise Physiology Laboratory, 18450 Nikaia, ; [email protected] 2 School of Health and Caring Sciences, University of West Attica, 12243 , Greece 3 Faculty of Medicine, University of Malaga—Andalucia Tech, 29071 Malaga, ; [email protected] 4 Institute of Primary Care, University of Zurich, 8091 Zurich, ; [email protected] (E.V.); [email protected] (T.R.) 5 Medbase St. Gallen Am Vadianplatz, 9001 St. Gallen, Switzerland * Correspondence: [email protected]; Tel.: +41-(0)-71-226-9300

 Received: 12 May 2019; Accepted: 3 June 2019; Published: 6 June 2019 

Abstract: The variation of marathon race time by age group has been used recently to model the decline of endurance with aging; however, paradigms of races (i.e., marathon running) examined so far have mostly been from the United States. Therefore, the aim of the present study was to examine the age of peak performance (APP) in a European race, the “”. Race times of 387,222 finishers (women, n = 93,022; men, n = 294,200) in this marathon race from 2008 to 2018 were examined. Men were faster by +1.10 km.h 1 (10.74 1.84 km.h 1 versus 9.64 1.46 km.h 1, p <0.001, − ± − ± − η2 = 0.065, medium effect size) and older by +2.1 years (43.1 10.0 years versus 41.0 9.8 years, ± ± p < 0.001, η2 = 0.008, trivial effect size) than women. APP was 32 years in women and 34 years in men using 1-year age groups, and 30–34 years in women and 35–39 years in men using 5-year age groups. Women’s and men’s performance at 60–64 and 55–59 age groups, respectively, corresponded to ~90% of the running speed at APP. Based on these findings, it was concluded that although APP occurred earlier in women than men, the observed age-related differences indicated that the decline of endurance with aging might differ by sex.

Keywords: aerobic capacity; ageing; age of peak performance; exercise; gender

1. Introduction Recently, a dramatic increase has been observed in the number of outdoors running races—such as —and the number of those participating in them [1,2]. For marathon running, mainly races and events held in the United States have been investigated more deeply [3–5]. An important performance characteristic in marathons, similarly to other sporting events, has been the age of peak performance (APP), i.e., the age of the best performance during the human lifetime [6]. The information about when APP occurs would be beneficial for coaches and athletes to set long-term training goals. In addition, the knowledge of APP might assist exercise physiologists and gerontologists in the study of the decline of endurance performance across the human life. APP has been well studied in marathon running using different sampling approaches (e.g., top athletes, all finishers) and statistical methods (e.g., multiple linear regression models, non-linear regression analyses, mixed-effects regression analyses) [7–11]. Independent of the methodological approaches, APP in this endurance sport has been estimated ~25–35 years; however, the precise APP might vary by sex [7–11]. With regards to the role of sex, it has been suggested that APP was older in women than in men [7,9–11], with an exception [8] that showed the opposite trend.

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It should be acknowledged that these studies have enhanced our comprehension of APP in marathon running; however, it should be highlighted that most of the existing research has been conducted by using data from races held in the United States [6,7,11], and limited information was available with regards to European marathons [12]. Filling this gap in the existing literature might be of practical importance considering that the world record was achieved recently in a European marathon (Berlin). The course in Berlin seems to be the fastest marathon course in the world, since seven of the 10 fastest male marathon times were set in the Berlin Marathon, including the actual world record of 2:01:39 h:min:s set by in 2018 [13]. Therefore, the aim of the present study was to assess APP in the Berlin Marathon. We hypothesized that the age of peak marathon performance would be different in women and men based on data analyzed from marathon races held in the United States.

2. Materials and Methods

2.1. Ethics Approval The institutional review board of St Gallen, Switzerland, approved this study. Since the study involved analysis of publicly available data, the requirement for informed consent was waived.

2.2. Methodology Data (i.e., sex, age, calendar year, and running speed) on finishers in the Berlin Marathon from 2008 to 2018 were examined. Initially, 389,958 finishers were considered. These data were acquired from the official website of the race [13]. Race time in h:min:s was converted to running speed in km/h. Cases with missing age (n = 133) or race time slower than the official time limit of 6:15 h:min (n = 2603) were excluded, resulting in a final sample of 387,222 that was entered in the analysis (women, n = 93,022; men, n = 294,200). Considering their age, finishers were classified in 1-year (e.g., 42 years, 43 years) and 5-year age groups (e.g., 40–44 years, 45–49 years). Both classifications had practical applications; 1-year intervals would aid examining age-related decline in performance and study of the effect of age with more detail, whereas 5-year intervals followed the official system of the race.

2.3. Statistical and Data Analysis The statistical package IBM Statistical Package for the Social Sciences (SPSS) v.20.0 (SPSS, Chicago, USA) and GraphPad Prism (Version 5, GraphPad Software, La Jolla, USA) were used to analyze the data. Mean standard deviation described age and speed. Normality of age and speed was tested by ± the Kolmogorov-Smirnoff test and visual inspection of normal Q-Q plots. The main effects of sex, age group, and calendar year and their interactions on running speed were tested by a two-way analysis of variance (ANOVA) followed by a post-hoc Bonferroni test for differences among calendar years or age groups. Eta square (η2) examined the magnitude of the differences among age groups or calendar years with the following criteria: small (0.010 < η2 0.059), moderate (0.059 < η2 0.138), and large ≤ ≤ (η2 > 0.138) [14]. The men-to-women ratio (MWR) was calculated as the ratio of men versus women finishers. The associations between calendar year and sex, and between age group (with at least 10 cases per sex) and sex were tested using chi-square test (χ2), and their magnitude was tested by Cramer’s phi (ϕ). APP (i.e., age of fastest running speed)—considering age groups in 1-year and 5-year intervals—was calculated by a non-linear regression model with a second order (quadratic) polynomial function (y=ax2+bx+c) that fitted the data. The vertex of the quadratic function was calculated as p(x|y) = b/2a | c (b2/4a). Non-linear regression analysis was used instead of linear regression analysis, since − − it was previously observed that maximal oxygen uptake—a main correlate of running speed—varied in an inverse U trend across life-time [15]. Alpha level was set at 0.05. Int. J. Environ. Res. Public Health 2019, 16, 2022 3 of 12 Int. J. Environ. Res. Public Health 2019, 16, x 3 of 12

3. ResultsThe overall MWR was 3.16. Men (race speed 10.74 ± 1.84 km.h-1, race time 4:02:41 ± 0:41:42 h:min:s,The age overall 43.1MWR ± 10.0 wasyears) 3.16. were Men faster (race by speed +1.10 10.74km.h−1 1.84(p <0.001, km.h η12, = race 0.065, time medium 4:02:41 effect)0:41:42 and ± − ± h:min:s,older by age +2.1 43.1 years10.0 (p <0.001, years) η were2 = 0.008, faster trivial by +1.10 ES) km.hthan women1 (p < 0.001, (9.64 η±2 1.46= 0.065, km.h medium−1, 4:28:37 eff ect)± 0:39:41 and ± − olderh:min:s, by +41.02.1 ± years 9.8 years (p < 0.001,). η2 = 0.008, trivial ES) than women (9.64 1.46 km.h 1, 4:28:37 0:39:41 ± − ± h:min:s, 41.0 9.8 years). 3.1. Trends of± Participation, Running Speed, and Age across Calendar Years 3.1. Trends of Participation, Running Speed, and Age across Calendar Years A sex × calendar year association was observed (χ2 = 1813.69, p <0.001, φ = 0.068; Figure 1), whereA sex the mencalendar-to-women year association ratio was thewas smallest observed in ( χ 20182 = 1813.69, (2.36) andp < 0.001, the largestϕ = 0.068; in 2009 Figure (3.97).1), × whereCompared the men-to-women to 2009, the rationumber was of the women smallest finishers in 2018 (2.36) increased and the by largest +69.9% in 2009 in 2018, (3.97). whereas Compared the torespective 2009, the change number in of men women was finishers+0.9%. Accordingly, increased by the+69.9% number in 2018,of women whereas finishers the respective increased change across incalendar men was years,+0.9%. MWR Accordingly, decreased, and the the number number of womenof male finishers finishers remained increased stable. across A calendar trivial effect years, of MWRcalendar decreased, year on and running the number speedof was male observe finishersd ( remainedp <0.001, ηstable.2 = 0.003) A trivial, with e ff 2018ect of being calendar the year slowest on running(10.21 ± speed1.94 km.h was−1 observed) and 2013 (p the< 0.001, fastestη 2(10.62= 0.003), ± 1.75 with) (Figure 2018 being 2) years the.slowest A trivial (10.21 sex × calendar1.94 km.h year1) ± − andinteraction 2013 the on fastest running (10.62 speed1.75) was (Figure shown2 )(p years. <0.001, A η trivial2 <0.001) sex, withcalendar the smallest year interaction sex difference on running in 2009 ± 2 × 1 speed(+1.02 was km.h shown−1) and (p < the0.001, largestη < in0.001), 2010 with (+1.17 the km.h smallest−1). The sex di overallfference trend in 2009 in women,(+1.02 km.h men− ,) and the sex 1 largestdifference in 2010 across (+1.17 calendar km.h −years). The was overall that the trend running in women, speed men, remained and sex stable difference during across this period. calendar A yearstrivial was effect that of the calendar running year speed on age remained was found stable (p during <0.001, this η2 = period. 0.001), A with trivial the e ffyoungestect of calendar finishers year in on2009 age (41.74 was found± 9.87 years) (p < 0.001, and theη2 = oldest0.001), finishers with the in youngest 2014 (42.94 finishers ± 10.02 inyears) 2009 ( (41.74Figure 39.87). A trivial years) sex and × ± thecalendar oldest year finishers interaction in 2014 on (42.94 running10.02 speed years) was (Figure observed3). A (p trivial <0.001, sex η2 = calendar0.001), with year the interaction smallest sex on ± × runningdifference speed in was2013 observed (+1.4 years) (p < 0.001, and theη2 = largest0.001), within 2017 the smallest (+2.7 years). sex di Duringfference thein 2013 calendar (+1.4 years) years and2008 the–2018, largest the in age 2017 of (+ men2.7 years). and the During sex difference the calendar in ageyears increased, 2008–2018, whereas the age theof men age and of women the sex diremainedfference instable. age increased, whereas the age of women remained stable.

Figure 1. Number of finishers by sex and calendar year. Note: MWR = men-to-women ratio; shadowed areasFigure denote 1. Number 95% confidence of finishers intervals; by sex = andwomen, calendar = men.year. Note: MWR = men-to-women ratio; shadowed areas denote 95% confidence# intervals;  = women, ⚫ = men.

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Figure 2. Running speed of finishers by calendar year. Note: Δ = sex difference; error bars denote standardFigure 2. deviation;Running speedshadowed of finishers areas denote by calendar 95% confidence year. Note: intervals;∆ = sex  di =ff women,erence; error⚫ = men. bars denote standardFigure 2. deviation; Running shadowedspeed of finishers areas denote by calendar 95% confidence year. Note: intervals; Δ = sex difference;= women, error= men. bars denote standard deviation; shadowed areas denote 95% confidence intervals;#  = women, ⚫ = men.

Figure 3. Age of finishers by calendar year. Note: ∆ = sex difference; error bars denote standard Figure 3. Age of finishers by calendar year. Note: Δ = sex difference; error bars denote standard deviation; shadowed areas denote 95% confidence intervals; = women,  = men. deviation; shadowed areas denote 95% confidence intervals; # = women, ⚫ = men. 3.2. TrendsFigure in 3. Participation Age of finishers by Age by Groupcalendar year. Note: Δ = sex difference; error bars denote standard 3.2. Trendsdeviation; in Participation shadowed areas by Age denote Group 95% confidence intervals;  = women, ⚫ = men. A sex age association was shown when age groups were considered in 5-year groups 2 × 2 (3.2.χ = TrendsA3304.46 sex ×in ,age Participationp < association0.001, ϕ =by0.092; Agewas Group Figureshown 4 ),when with age the lowestgroups MWR were (2.06)considered in age groupin 5-year 25–29 groups years (χ and= 3304.46,the highest p <0.001, MWR φ (8.66) = 0.092; in the Figure age group4), with 75–79 the lowest years. MWR Also, a(2.06) sex inage age association group 25– was29 years found and when the A sex × age association was shown when age groups were× considered in 5-year groups (χ2 = highestage groups MWR were (8.66) considered in the age in group 1-year 75 groups–79 years. (χ2 =Also,3483.08, a sexp ×< age0.001, associationϕ = 0.095; was Figure found5), when with theage 3304.46, p <0.001, φ = 0.092; Figure 4), with the lowest MWR (2.06) in age group 25–29 years and the groupslowest MWRwere considered (1.96) in the in 26 1- yearsyear groups age group (χ2 = and 3483.08, the highest p <0.001, MWR φ = (11.16)0.095; Figure in the 715), years with agethe lowest group. MWRhighest (1.96) MWR in (8.66)the 26 inyears the ageage group and75–79 the years. highest Also, MWR a sex (11.16) × age inassociation the 71 years was age found group. when age groups were considered in 1-year groups (χ2 = 3483.08, p <0.001, φ = 0.095; Figure 5), with the lowest MWR (1.96) in the 26 years age group and the highest MWR (11.16) in the 71 years age group.

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Figure 4. Finishers by 5-year age groups. MWR = men-to-women ratio. The horizontal dashed line shows the overall MWR. Figure 4. Finishers by 5-year age groups. MWR = men-to-women ratio. The horizontal dashed line shows the overall MWR. Figure 4. Finishers by 5-year age groups. MWR = men-to-women ratio. The horizontal dashed line shows the overall MWR.

Figure 5. Finishers by 1-year age groups. MWR = men-to-women ratio. The horizontal dashed line showsFigure the 5. Finishers overall MWR. by 1-year age groups. MWR = men-to-women ratio. The horizontal dashed line shows the overall MWR.

3.3. AgeFigure of Peak 5. Finishers Performance by 1- year age groups. MWR = men-to-women ratio. The horizontal dashed line shows the overall MWR. When the age was considered in 1-year age intervals, APP was observed at 32 years in women 3.3.and Age34 years of Peak in Performance men (Table 1, Figure 6, Figure 7); a small main effect of age group on running speed was shown (p <0.001, η2 = 0.027), with the fastest running speed at 33 years (10.79 ± 1.97 km.h−1) and the slowestWhen theat 80 age–84 was years considered (7.26 ± 1.82 in km.h1-year−1 ).age A trivialintervals, sex ×APP age wasgroup observed interaction at 32 on years running in women speed and 34 years in men (Table 1, Figure 6, Figure 7); a small main effect of age group on running speed 2 1 was shown (p <0.001, η = 0.027), with the fastest running speed at 33 years (10.79 ± 1.97 km.h− ) and the slowest at 80–84 years (7.26 ± 1.82 km.h−1). A trivial sex × age group interaction on running speed

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3.3. Age of Peak Performance When the age was considered in 1-year age intervals, APP was observed at 32 years in women andInt. J. 34Environ. years Res. in Public men (TableHealth 20191, Figures, 16, x 6 and7); a small main e ffect of age group on running speed6 of was 12 2 1 shown (p < 0.001, η = 0.027), with the fastest running speed at 33 years (10.79 1.97 km.h− ) and the 1 ± slowestwas found at 80–84(p <0.001, years η2 (7.26 = 0.001);1.82 considering km.h− ). A1-year trivial age sex groupsage with group at interaction least 10 finishers on running in each speed sex, ± × wasthe smallest found (p sex< 0.001, differenceη2 = 0.001);was observed considering at 73 1-yearyears (1.0%) age groups and the with largest at least at 38 10 years finishers (13.7%). in each sex, the smallest sex difference was observed at 73 years (1.0%) and the largest at 38 years (13.7%). Table 1. Parameters in the second-order polynomial regression running speed (km.h−1) = a + bx + cx2, 1 2 Tableusing 1.theParameters running speed in the of second-order all runners in polynomial 1-year age regression and 5-year running age groups speed by (km.h sex. − ) = a + bx + cx , using the running speed of all runners in 1-year age and 5-year age groups by sex. 1-year age groups 5-year age groups Parameter 1-YearWomen Age Groups Men Women 5-Year AgeMen Groups −1 Parametera (km.h ) Women−0.00117 − Men0.001928 −0.02773 Women −0.04594 Men b (km.h−1.years−1) 0.07472 0.131 0.218 0.399 a (km.h 1) 0.00117 0.001928 0.02773 0.04594 −c (km.h−1.years−−2) 8.650 − 8.868 9.413− 10.230 − 1 1 0.07472 0.131 0.218 0.399 b (km.h− .yearsAge− ) (years) 31.93 33.97 30-34 35-39 c (km.h 1.years 2) 8.650 8.868 9.413 10.230 −Running− speed (km.h−1) 9.84 11.09 9.84 11.10 Age (years) 31.93 33.97 30–34 35–39 The parameters a, b, and c1 are coefficients in the polynomial equation that shows the relationship Running speed (km.h− ) 9.84 11.09 9.84 11.10 between running speed and age. Based on the regression analysis, we calculated the parameters Age The parameters a, b, and c are coefficients in the polynomial equation that shows the relationship between running speed(the ageand age. or Based age group on the regression of peak analysis, performance) we calculated and theRunning parameters speed Age (the age performance or age group of peak the performance)corresponding and Age). Running speed (the performance of the corresponding Age).

Figure 6. Running speed in 5-year age groups. Arrows denote age of peak performance. Error bars Figure 6. Running speed in 5-year age groups. Arrows denote age of peak performance. Error bars represent standard deviations; = women,  = men. represent standard deviations;  = women, ⚫ = men. #

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FigureFigure 7. 7. Race time time in in 5-year 5-year age age groups. groups. Arrows Arrows denote denote age of age peak of performance. peak performance. Error bars Error represent bars representFigurestandard 7. standard deviations;Race time deviations in = 5-women,year;  age = women, groups.= men. ⚫ Arrows= men. denote age of peak performance. Error bars represent standard deviations# ;  = women, ⚫ = men. WhenWhen the the age age was was considered considered in in 5 5-year-year age age intervals, intervals, APP APP was was shown shown in in age age group group 30 30–34–34 years years inin women womenWhen and the and inage in age agewas group groupconsidered 35 35–39–39 years in years 5- yearin inmen age men (Figure intervals, (Figures 8, Figure 8APP and was9 9);); a ashown smallsmall maininmain age eeffect groupffect of of30 age age–34 group groupyears oninon womenrunning running and speed speed in age was was group shown shown 35 –( (39pp <0.001,

Figure 8. Running speed in 1-year age groups. Arrows denote age of peak performance. Error bars representFigureFigure 8. 8. standardRunningRunning deviationsspeed speed in in 1 1-year;- year = women,age age groups. groups. ⚫ = men.Arrows Arrows denote denote age age of of peak peak performance. performance. Error Error bars bars representrepresent standard standard deviations deviations;;  = women,women, ⚫ == men.men. #

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Figure 9. Race time in 1-year age groups. Arrows denote age of peak performance. Error bars representFigure 9. 9. standardRaceRace time time deviations in in 1-year 1-year age;  age groups.= women, groups. Arrows ⚫ Arrows= men denote. denote age of agepeak of performance. peak performance. Error bars Error represent bars representstandard deviations;standard deviations= women,;  = women,= men. ⚫ = men. #

Figure 10. Running speed expressed as percentage of the speed at the age of peak performance. Figure 10. Running speed expressed as percentage of the speed at the age of peak performance. 4. DiscussionFigure 10. Running speed expressed as percentage of the speed at the age of peak performance. 4. Discussion 4. DiscussionThe aim of the present study was to assess APP in the Berlin Marathon. We hypothesized that the age ofThe peak aim marathon of the present performance study was would to assess be diff APPerent in in the women Berlin and Marathon men compared. We hypothesized to data analyzed that thfrome ageThe marathon of aim peak of racesmarathonthe present held inperformance study the United was to would States. assess ThebeAPP different main in the findings Berlinin women ofMarathon the and present men. We study comparedhypothesized were to that: data that (i ) analyzedthwomene age of were from peak slower marathon marathon and racesyounger performance held than in the men;would United (ii )be the differentStates number. The in of mainwomen women findings and finishers men of the compared during present 2008–2018 to study data wereanalyzedincreased, that: from( thei) women number marathon were of men racesslower finishers held and inyounger remained the United than stable, men;States whereas (.ii The) the MWR mainnumber decreased; findings of women of (iii) the finishers MWR present increased during study 2008wereand– sex 2018that di: ff(increased,ierence) women inrunning werethe number slower speed ofand decreased men younger finishers in than the remained older men; age (ii) groups;thestable, number whereas (iv) APP of women wasMWR 32 finishersdecreased years in womenduring; (iii) MWR2008and 34– 2018increased years increased, in menand usingsex the difference number 1-year age ofin groups,runningmen finishers and speed 30-34 remaineddecreased years in stable, in women the olderwhereas and age 35–39 MWRgroups; years decreased ( iniv) men APP using ;was (iii) 32MWR5-year years increased age in groups;women and andand sex ( v34difference) smalleryears in diin menff runningerences using amongspeed 1-year decreased age age groups groups, in were the and older shown 30 -age34 in yearsgroups; women in (womeniv than) APP in men.andwas 3532– 39years years in womenin men usingand 34 5 -yearsyear agein men groups; using and 1- year(v) smaller age groups, differences and 30 among-34 years age in groups women were and shown354.1.–39 The yearsin Age women within men thethan Fastestusing in men 5 Race-year. Time age was groups; Younger and in ( Womenv) smaller than differences in Men among age groups were shown in women than in men. The most important result was that independently from the approach of the age intervals (i.e., 4.1. The Age with the Fastest Race Time was Younger in Women than in Men 1-year versus 5-year interval), the age with the fastest race time was younger in women than in men. 4.1. The Age with the Fastest Race Time was Younger in Women than in Men BothThe methodological most important approaches result was off thatered independently information about from the the age approach of peak of performance the age intervals of practical (i.e., 1value-yearThe forversus marathonmost 5 -importantyear runners interval), result and the theirwas age coaches.that with independently the When fastest the race age from time groups the was approach were younger considered of in thewomen age in 1-year intervals than intervals,in men. (i.e., Both1this-year dimethodological ffversuserence 5- wasyear ~2 interval),approaches years (i.e., the offered age 32 years with information the in women fastest aboutrace and time 34the years agewas of inyounger peak men). performance in When women 5-year than of practical intervals in men. valueBoth methodological for marathon runners approaches and offered their coaches. information When about the the age age groups of peak were performance considered of in practical 1-year value for marathon runners and their coaches. When the age groups were considered in 1-year

Int. J. Environ. Res. Public Health 2019, 16, 2022 9 of 12 were considered, the difference was ~5 years (i.e., age group 30–34 years in women and age group 35–39 years in men). This finding confirms a recent observation for the , where the age of peak marathon performance in 1-year and 5-year age intervals of 451,637 runners (168,702 women and 282,935 men) who finished the race between 2006 and 2016 were analyzed. The fastest race time was shown at 29.7 years in women and 34.8 years in men in the 1-year age intervals, and in age group 30–34 years in women and 35–39 years in men in the 5-year age intervals [6]. The most likely explanation for the fastest women in the Berlin Marathon also being younger than the fastest men was the approach, which included all women and men in the data analysis, since most studies investigating the age of peak marathon performance used only a limited sample of athletes [7,9,11]. Therefore, the location where the race is held is not the reason that a sex difference in the age of peak marathon performance has been found, but the statistical approach of the data analysis. A further potential explanation of this difference might be the sex difference in the age of all participants. It was shown that women were younger than men by 2.1 years; therefore, a corresponding difference in the age of fastest race time should be expected. Indeed, a difference of 3.3 years has been found for the New York City Marathon [6]. In addition, the participation of women during the last decade increased disproportionately to that of men, resulting in a decrease of the men-to-women ratio. General health orientation and psychological coping were the two strongest motivational factors to understand the growth of female participation [16]. In general, it has been shown that people participate in running events not only for physical activity, but also for mental well-being and socio-psychological effects [17]. Relatively more women participated in the younger age groups, with the lowest men-to-women ratio in age group 25–29 years and the highest men-to-women ratio in the age group 75–79 years. Similarly, in the New York City Marathon, women were competing between the ages of 23 and 28 years more often than men; more women were in age group 30–34 years and more men were in age group 40–44 years [6]. Also, when all races of the New York City Marathon were considered, the men-to-women ratio decreased over years [1]. Based on the above mentioned analysis, it was concluded that the younger age of peak performance in women than in men should be attributed mostly to the younger age of women participants compared to men. Particularly, relatively fewer women than men participated in the older age groups, which could be explained by women engaging in regular training and sport more recently compared to men. We observed that the men-to-women ratio was higher in the older than in the younger age groups. An increase in female participation in marathon running has occurred since the first women were officially allowed to compete in marathon races, such as the [18].

4.2. Differences between Women and Men for Age of Peak Running Performance The finding that the age with the fastest marathon race time was younger in women than in men confirms a recent finding, where the relationship between race times and age in 1-year intervals by using the world single age records from 5 km to marathon running (i.e., 5 km, 4 miles, 8, 10, 12, 15 km, 10 miles, 20 km, half-marathon, 25 km, 30 km, and marathon) was investigated. Women achieved their best half-marathon and marathon race time, respectively, 1-year and 3-years earlier in life than men. On the contrary, in the other races, the best female performances were achieved later in life than men [19]. In half-marathon running and 100-km ultra-marathon running, a similar trend for a younger age in female peak performance has been found. When data from 138,616 runners (48,148 women and 90,469 men) competing between 2014 and 2016 in GöteborgsVarvet—the world’s largest half-marathon—were analyzed, the fastest race times were observed in age groups <35 and 35–39 years in women and in age group 35–39 years in men [20]. When 370,051 athletes (i.e., 44,601 women and 325,450 men) who finished a 100-km ultra-marathon between 1959 and 2016 were analyzed in 1- and 5-year age group intervals, the age of peak performance was younger in women than in men [21]. Int. J. Environ. Res. Public Health 2019, 16, 2022 10 of 12

The sex difference in the age of peak running performance might also be explained by the kind of the sports discipline. When the peak age in elite athletic contestants according to athlete performance level, sex, and discipline for individual season bests for world-ranked top 100 athletes from 2002 to 2016 was analyzed from the International Association of Athletics Federations (IAAF), women reached greater peak age than men in the hurdles and middle- and long-distance running events. However, male throwers had greater peak age than corresponding women [22]. The relatively lower number of women finishers in the older than in the younger age groups was in line with existing literature [23], and might be partially explained by the variation in exercise participation by sex [24–26]. For instance, it has been observed previously that more older men than women participated in sport activities [24–26], and more opportunities for exercise were identified for men than women [27]. Consequently, it would be reasonable to attribute the relatively lower number of female marathon finishers in the older age to their rates of overall sports participation. In addition to the abovementioned aspect of overall sports participation, the variation in the number of finishers by sex in marathons might be explained by psychological factors (e.g., motivation). Women have been observed to set goals in task-oriented domains (win, compete, fun, and health) [16] and were interested in aspects such as meeting people, relief from depression, and feeling less shy [28] to a larger degree than men. Motivation for participation in a marathon race might vary by sex [29]. Participating in an endurance run could be characterized as a “challenge” that could aid previously inactive women to improve their physical activity levels [30]. Furthermore, the relatively smaller number of women in the older than in the younger age groups might be explained by a social discrimination, such as when medical attention for participating in endurance races was requested to larger degrees for women than in men [31]. A limitation of the present study was that the Berlin Marathon was unique in terms of topographical characteristics (a “flat” marathon); thus, the findings should be generalized with caution to other marathon races. Moreover, it should be highlighted that the findings referred to all marathon runners, the large majority of whom might be characterized as recreational rather than competitive. It was acknowledged that if selected runners were analyzed (e.g., the fastest, top 10, or top 100), different findings would be expected. For instance, when the world records in marathon were considered by age, APP was also younger in women than in men (25 versus 28 years, respectively); however, APP was younger in both sexes—and closer to the age of peak maximal oxygen uptake—than in the present study [19]. Accordingly, it might be assumed that compared to elite runners, physiological characteristics had a smaller role on performance in recreational runners [32]. On the other hand, the strength of the study was its novelty, asto the best of our knowledge, this was the first one to examine APP in the fastest marathon in the world. Surprisingly, in contrast to other marathon races that have attracted scientific interest, such as the New York City Marathon [4–6], the Berlin Marathon has received less scientific attention, although seven of the 10 world fastest men marathon race times—including the actual world record—were set in this marathon. The results concerning APP and its variation by sex would be of great practical value for runners and coaches to set long-term training goals.

5. Conclusions In summary, we found that women achieved their best marathon race time in the Berlin Marathon earlier in life compared to men. This sex difference might be associated with the overall younger age of female finishers. In contrast to other reports, the finding is most likely explained by the fact that all women and men finishers over a long period of time have been included in the data analysis.

Author Contributions: Conceptualization, P.T.N. and B.K.; methodology, P.T.N. and B.K.; software, P.T.N. and B.K.; validation, P.T.N. and B.K.; formal analysis, P.T.N. and B.K.; investigation, P.T.N. and B.K.; resources, P.T.N. and B.K.; data curation, P.T.N. and B.K.; writing—original draft preparation, P.T.N. and B.K.; writing—review and editing, P.T.N., J.R.A.C., E.V., T.R., and B.K.; visualization, P.T.N. and B.K.; supervision, B.K.; project administration, B.K.; funding acquisition, T.R. Funding: This research received no external funding. Int. J. Environ. Res. Public Health 2019, 16, 2022 11 of 12

Conflicts of Interest: The authors declare no conflict of interest.

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