Age Differences in Pacing in Endurance Running: Comparison between Marathon and Half-Marathon Men and Women

ackground and Objective: The increased popularity of marathons and half-marathons has led to a signi cant increase in the number of master runners worldwide. Since the age-related decrease in performance is dependent on race duration, pacing in long distance running might also vary by race distance in both men and women. Therefore, the main aim of this study was to assess pacing di erences between marathon and half-marathon runners with regard to the runners' age group, and independently for men and women.Materials and Methods: In total, 17,465 participants in the Vienna City marathon in 2017 were considered for this study (marathon, N=6081; half-marathon,N=11,384). Pacing was expressed as two variables (i.e., pace range and end spurt).Results: All runners showed positive pacing strategies (i.e., a fast start with gradual decrease of speed). However, marathon runners showed greater variability in pacing than half-marathon runners. Furthermore, women showed no di erences in pace variability in regard to the age group, whereas men younger than 30 years of age, as well as older men (over the age of 60), showed a greater variability in pace than other age groups. Finally, younger half-marathon men and women showed the fastest end spurt compared to older age groups and marathon runners.Conclusions: The presented ndings could help sports and medicine practitioners to create age speci c training plans and pacing strategies. This approach could help long distance runners to improve their physical tness, achieve better race times, reduce the potential risk of musculoskeletal

Finally, younger half-marathon men and women showed the fastest end spurt compared to older age groups and marathon runners.Conclusions: The presented ndings could help sports and medicine practitioners to create age speci c training plans and pacing strategies. This approach could help long distance runners to improve their physical tness, achieve better race times, reduce the potential risk of musculoskeletal injuries and increase the overall pleasure of long distance running. Keywords:aerobic endurance; running; pacing strategy; aging; health 1. Introduction Pacing in long distance sport events, such as cycling, running, cross-country skiing or triathlon races, can be de ned as the e cient distribution of energetic reserves, power output, and speed through the entire race, without a signi cant slowdown [1]. For successful long distance pacing, it is necessary to select a suitable pacing strategy. This can be de ned as a self-selected strategy that long distance athletes adopt from the beginning of a race [2]. Several pacing strategies have been previously observed by researchers [3,4]. These can be broadly categorized as: negative pacing (slow start with gradual increase of speed), positive pacing (fast start with gradual decrease of speed), even pacing (without signi cant speed changes) and variable pacing (with signi cant speed changes). Selecting an optimal pacing strategy can be a crucial aspect in the successful completion of a long distance event [5,6]. Additionally, the best pacing strategies can decrease the potential risk of musculoskeletal injuries [4], Medicina2019,55, 479; doi:10.3390 /medicina55080479 /journal/medicina

Medicina2019,55, 479 2 of 11 improve the performance of an athlete [7] as well as increase the overall pleasure of event participation for recreational runners [6]. The most popular long distance events in recent decades are running events, such as the marathon and half-marathon. Pacing in marathons is, so far, rather well documented in regards to the age, sex, performance, physiological, psychological and neurological aspects [2,8–11]. In general, even pacing and negative pacing strategies have proved to be the best strategies for maximizing performance in prolonged activities [12,13]; however, studies have shown that marathon runners seem to prefer positive pacing, regardless of age, sex or performance. Positive pacing strategies are the result of psychological factors (e.g., fast start due to competitiveness) [8] as well as physiological factors, such as neurological fatigue and muscle glycogen depletion [1]. Physiological factors particularly in uence men's pacing strategies in long distance running in comparison to women's pacing strategies. Men are more likely to exhaust muscle glycogen, since they have weaker fat utilization systems than women [13,14]. Therefore, future studies on pacing in long distance running (with regard to age or performance di erences) should assess pacing independently in men and women. Furthermore, substantial muscle fatigue, in ammation and ber damage are more signi cant in marathons compared to half-marathons [15,16]. As a result, in recent years, the half-marathon has become the preferred long distance event for runners. The increasing popularity of half-marathon running can be observed in the United States [17], Europe [18] and worldwide [19]. Besides the previously mentioned medical bene ts of half-marathon running in comparison to marathon running, half-marathons require less time to prepare for and less time to complete, thus its popularity [6]. Even so, pacing in long distance running is not as well documented as marathon pacing, particularly in recreational athletes of all ages. However, pacing in half-marathon events, has shown less variability than pacing in marathons, with regards to the participants' ages [19]. Since only one event/race was analyzed, with a somewhat limited number of participants, these results cannot be generalized. The increased popularity of both marathon and

is not as well documented as marathon pacing, particularly in recreational athletes of all ages. However, pacing in half-marathon events, has shown less variability than pacing in marathons, with regards to the participants' ages [19]. Since only one event/race was analyzed, with a somewhat limited number of participants, these results cannot be generalized. The increased popularity of both marathon and half-marathon events has led to a significant increase in participants, particularly master athletes, in both the USA and Europe [17,18,20]. Long distance running could provide considerable health benefits for older runners, such as: risk reduction of cardiovascular diseases, cancer, diabetes, depression, and falls [21]. Therefore, insight into pacing strategies for age group runners could be an important scientific breakthrough for sports and medicine practitioners specializing in treating master runners as well as younger runners. The currently available studies regarding pacing in age group endurance runners prove to be somewhat inconsistent in their findings. There are some indications that older marathon runners are more likely to utilize even or negative pacing strategies compared to younger runners [9,13]. Other studies have shown rather similar pacing profiles in age group marathon runners [19,22]. Differences in pacing might also vary by race distance, since age-related decrease in performance is dependent on race duration [23]. Finally, only one study has investigated pacing between age groups of half-marathon runners [19]. Half-marathoners in all age groups had more even pacing in comparison to marathoners, whereas age did not play an important role in the pacing. However, a reduced number of participants, as well as lack of information on weather conditions (e.g., wind, humidity, temperature) limits the findings in this study. Considering all previously mentioned di erences between marathon and half-marathon running (e.g., performance, physiological, psychological and neurological aspects), further studies on pacing in master runners for these long distance events are needed. Therefore, the main aim of this study was to assess pacing di erences between marathon and half-marathon events in regards to the runners' age group, independently for men and women. 2. Materials and Methods 2.1. Participants and Race Details This study was approved by the

aspects), further studies on pacing in master runners for these long distance events are needed. Therefore, the main aim of this study was to assess pacing di erences between marathon and half-marathon events in regards to the runners' age group, independently for men and women. 2. Materials and Methods 2.1. Participants and Race Details This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland (Approval number EKSG 01-06-2010), with a waiver of the requirement for informed consent of the

Medicina2019,55, 479 3 of 11 participants as the study involved the analysis of publicly available data. The study was conducted in accordance with recognized ethical standards according to the Declaration of Helsinki adopted in 1964 and revised in 2013. For the purpose of this study, we included o cial results and split times from the 2017 Vienna City Marathon [24], i.e., the initial sample. Participants who did not nish any of the races, or did not have record of any of the split times were excluded from the initial sample. Moreover, participants who did not provide information on their age were also excluded from the initial sample. In total, 17,465 participants were considered for this study (marathon,N=6081; half-marathon,N=11,384). Further information regarding the 2017 Vienna City Marathon is as follows: Both the marathon and the half-marathon were held on the same day, on an o cially certi ed and fairly at track (the elevation di erence was only 50 m; ranging from 154 to 210 m). For comparison, the Berlin Marathon, considered to be “the fastest marathon” has an elevation di erence of 21 m [19]. During the race day, the weather was cloudy, with temperatures ranging from 7.8 C at 9 am to 11.8 C at 2 pm, without excess humidity or strong wind [24]. No additional information on humidity grade or wind speed was available on the o cial race website. The half marathon race was entirely contained within the marathon race. 2.2. Data Analysis In the rst step of data analysis, we calculated the average speed for the entire race for each participant in both the half-marathon and the marathon. Additionally, we calculated the average running speed in ve race sections, for both the marathon and the half-marathon [6,25]. The race sections were divided as follows: Section from start to the 5th km of the half-marathon race. Both distances correspond to the rst 23.7% of the marathon and half-marathon races. Section race and from the 5th km to the 10th km of the half-marathon race. These distances represent a section of 23.7–47.4% of the marathon and half-marathon races.

[6,25]. The race sections were divided as follows: Section from start to the 5th km of the half-marathon race. Both distances correspond to the rst 23.7% of the marathon and half-marathon races. Section race and from the 5th km to the 10th km of the half-marathon race. These distances represent a section of 23.7–47.4% of the marathon and half-marathon races. Section race and from the 10th km to the 15th km of the half-marathon race. Both distances represent a section of 47.4–71.1% of the marathon and half-marathon races. Section race and from the 15th km to the 20th km of the half-marathon race. These distances represent a section of 71.1–94.8 % of the marathon and half-marathon races. Section race nish (42.195 km) in the marathon as well as from the 20th kilometer to the race nish (21.0975 km) in the half-marathon. The end spurt represents a section of94.8% to the nish line of the marathon and half-marathon races. Furthermore, considering the methodology of Breen et al. [26], we calculated the variable of interest, called the “pace range”. Five race sections were subsequently expressed as a percentage faster or slower than the average section speed. The fastest section for each individual was then named the “positive range” (PR), while the slowest segment was named the “negative range” (NR). In addition, the absolute sum of the positive range and negative range was calculated and named the “pace range” (PaceR). This method allowed for normalized speed comparisons between all athletes as well as between the marathon and half-marathon. Finally, to examine the nal 2.195 km of the marathon and the nal 1.0975 km of the half-marathon, running speed for this segment was expressed as a percentage faster or slower than the running speed

Medicina2019,55, 479 4 of 11 during Section aimed to examine the “end spurt” (ES) and was named as such. 2.3. Statistical Analysis Prior to all statistical tests, descriptive statistics were calculated as the mean and standard deviation. Moreover, data distribution normality was veri ed by visual inspection of histograms and QQ plots [6]. To assess sex and age group distribution among the participants in the half-marathon and marathon, we used a chi-square test ( 2). Speci cally, we examined the association between participants' sex and age group separately for each race, as well as between their sex and the race they participated in. The magnitude of these associations was tested by Cramer's phi ('), while the results were presented as a men-to-women ratio (MWR). To test di erences in PaceR between marathon and half-marathon runners in 9 age groups, 2 two-way analyses of variance (ANOVA) were performed (separately for men and women). Main e ects of the race (marathon and half-marathon), age group (18–24; 25–29; 30–34; 35–39; 40–44; 45–49; 50–54; 55–59; 60+years) and their interaction race x age group were performed. Additionally, 2 two-way analyses of variance (ANOVA) were performed (separately for men and women) to test di erences in ES between marathon and half-marathon runners in 9 age groups. Main e ects of race (marathon and half-marathon), age group (18–24; 25–29; 30–34; 35–39; 40–44; 45–49; 50–54; 55–59; 60+years) and their interaction race x age group were performed. For all ANOVAs, the post-hoc Bonferroni test was performed. E ect size was presented via eta squared (­ 2 ), where the values of 0.01, 0.06 and above 0.14 were considered small, medium, and large, respectively [27]. Alpha level was set atp<0.05. All statistical tests were performed using Microsoft O ce Excel 2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS 20 (IBM, Armonk, NY, USA). 3. Results The number of men and women in each race (i.e., men-to-women ratio – MWR) and their age group are presented in Table. As expected, more men participated in the 2017 Vienna Marathon than women (Total MWR was 2.44). A sex x race association was shown,

2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS 20 (IBM, Armonk, NY, USA). 3. Results The number of men and women in each race (i.e., men-to-women ratio – MWR) and their age group are presented in Table. As expected, more men participated in the 2017 Vienna Marathon than women (Total MWR was 2.44). A sex x race association was shown, ( 2=293.6,p<0.01,'=0.13), where the men-to-women ratio was smaller in the half-marathon (1.99) in comparison to the marathon (3.77). Furthermore, sex x age group association was also shown in both the marathon ( 2=72.4, p<0.01,'=0.11) and half-marathon ( 2=263.6,p<0.01,'=0.15). In the marathon, the smallest MWR of 2.45 was observed in the 25–29 years age group, whereas the largest MWR of 8.35 was observed in the oldest age group. In the half-marathon, the smallest MWR of 1.14 was observed in the youngest age group, whereas the largest MWR of 3.96 was observed in the oldest age group. Table 1.Number of participants and men-to-women ration in each race and age group. Age Groups Marathon Half-Marathon Men Women Total MWR Men Women Total MWR 18–24 146 45 191 3.24 434 380 814 1.14 25–29 404 165 569 2.45 886 713 1599 1.24 30–34 618 194 812 3.19 1206 661 1867 1.82 35–39 694 214 908 3.24 1070 502 1572 2.13 40–44 798 237 1035 3.37 1032 496 1528 2.08 45–49 797 173 970 4.61 1128 433 1561 2.61 50–54 716 146 862 4.90 930 362 1292 2.57 55–59 373 71 444 5.25 513 162 675 3.17 60+ 259 31 290 8.35 380 96 476 3.96 Total 4805 1276 6081 3.77 7579 3805 11,384 1.99 MWR=men-to-women ratio.

Medicina2019,55, 479 5 of 11 The average running speeds for four sections and ES are presented in Table. From the descriptive data in Table, we can observe a gradual decrease in average speed throughout the race segments for both sexes in both the marathon and half-marathon, among all age groups. Furthermore, we can observe that ES are typically faster than Section, which has often been noted in marathon related studies. Table 2. Segments and race speed (m/s) for men and women, marathon and half marathon runners for each age group. Men Women Marathon Half-Marathon Marathon Half-Marathon Segment speed (m/s) Mean SD Mean SD Mean SD Mean SD Age 18–24 Segment 1 3.18 0.50 3.20 0.49 2.94 0.41 2.87 0.35 Segment 2 3.12 0.47 3.19 0.46 2.87 0.45 2.84 0.35 Segment 3 3.05 0.51 3.17 0.48 2.88 0.51 2.80 0.37 Segment 4 2.76 0.55 3.02 0.52 2.67 0.49 2.68 0.40 End spurt 2.86 0.52 3.13 0.53 2.76 0.49 2.79 0.41 Age 25–29 Segment 1 3.28 0.56 3.20 0.48 3.00 0.41 2.88 0.33 Segment 2 3.23 0.56 3.21 0.46 2.93 0.42 2.87 0.33 Segment 3 3.18 0.61 3.19 0.48 2.91 0.44 2.84 0.36 Segment 4 2.90 0.65 3.04 0.52 2.71 0.48 2.71 0.38 End spurt 2.94 0.57 3.13 0.51 2.79 0.43 2.80 0.38 Age 30–34 Segment 1 3.27 0.52 3.22 0.47 2.99 0.40 2.89 0.31 Segment 2 3.22 0.52 3.22 0.45 2.93 0.41 2.87 0.32 Segment 3 3.19 0.54 3.21 0.47 2.91 0.43 2.85 0.34 Segment 4 2.92 0.58 3.07 0.51 2.75 0.44 2.72 0.38 End spurt 2.96 0.53 3.13 0.51 2.83 0.40 2.81 0.37 Age 35–39 Segment 1 3.30 0.47 3.18 0.46 2.99 0.43 2.89 0.36 Segment 2 3.25 0.47 3.17 0.45 2.92 0.44 2.88 0.36 Segment 3 3.22 0.49 3.15 0.46 2.90 0.46 2.85 0.39 Segment 4 2.95 0.54 3.01 0.50 2.71 0.47 2.72 0.41 End spurt 2.97 0.50 3.06 0.51 2.78 0.42 2.78 0.39 Age 40–44 Segment 1 3.26 0.42 3.17 0.44 2.88 0.29 2.86 0.33 Segment 2 3.20 0.43 3.15 0.43 2.81 0.30 2.84 0.34 Segment 3 3.18 0.45 3.14 0.44 2.79 0.33 2.82 0.35 Segment 4 2.92

0.46 2.90 0.46 2.85 0.39 Segment 4 2.95 0.54 3.01 0.50 2.71 0.47 2.72 0.41 End spurt 2.97 0.50 3.06 0.51 2.78 0.42 2.78 0.39 Age 40–44 Segment 1 3.26 0.42 3.17 0.44 2.88 0.29 2.86 0.33 Segment 2 3.20 0.43 3.15 0.43 2.81 0.30 2.84 0.34 Segment 3 3.18 0.45 3.14 0.44 2.79 0.33 2.82 0.35 Segment 4 2.92 0.50 2.99 0.48 2.63 0.36 2.69 0.37 End spurt 2.94 0.47 3.04 0.48 2.71 0.35 2.76 0.37 Age 45–49 Segment 1 3.21 0.40 3.15 0.42 2.88 0.31 2.84 0.29 Segment 2 3.15 0.40 3.13 0.41 2.81 0.32 2.82 0.30 Segment 3 3.12 0.43 3.11 0.43 2.80 0.35 2.79 0.32 Segment 4 2.87 0.49 2.96 0.46 2.65 0.37 2.66 0.34 End spurt 2.88 0.46 2.99 0.46 2.72 0.35 2.71 0.34 Age 50–54 Segment 1 3.13 0.39 3.10 0.41 2.85 0.28 2.81 0.28 Segment 2 3.06 0.39 3.07 0.40 2.77 0.28 2.78 0.29 Segment 3 3.02 0.42 3.04 0.43 2.75 0.31 2.74 0.31 Segment 4 2.77 0.47 2.90 0.47 2.58 0.34 2.60 0.34 End spurt 2.80 0.44 2.93 0.46 2.65 0.31 2.65 0.34 Age 55–59 Segment 1 3.10 0.35 3.01 0.39 2.84 0.27 2.77 0.26 Segment 2 3.02 0.35 2.99 0.38 2.76 0.28 2.72 0.27 Segment 3 2.99 0.37 2.95 0.40 2.75 0.29 2.68 0.28 Segment 4 2.74 0.40 2.80 0.44 2.55 0.33 2.54 0.32 End spurt 2.76 0.40 2.84 0.44 2.57 0.32 2.58 0.31 Age 60+ Segment 1 2.95 0.34 2.94 0.38 2.73 0.29 2.66 0.22 Segment 2 2.87 0.36 2.91 0.39 2.63 0.31 2.62 0.25 Segment 3 2.81 0.40 2.88 0.41 2.62 0.33 2.58 0.28 Segment 4 2.57 0.43 2.74 0.43 2.41 0.36 2.45 0.29 End spurt 2.58 0.41 2.75 0.43 2.45 0.34 2.46 0.30 SD=standard deviation.

Medicina2019,55, 479 6 of 11 When PaceR in men runners was assessed (Figure), the results showed signi cant main e ects of race [F(17,12366)=849.8,­ 2 =0.06,p<0.01], age group [F(17,12366)=14.3,­ 2 =0.01,p<0.01] as well as race x age group interaction [F(17,12366)=3.5,­ 2 <0.01,p<0.01].Medicina 2019, 55, x FOR PEER REVIEW 6 of 12 Age 55–59 Segment 1 3.10 0.35 3.01 0.39 2.84 0.27 2.77 0.26 Segment 2 3.02 0.35 2.99 0.38 2.76 0.28 2.72 0.27 Segment 3 2.99 0.37 2.95 0.40 2.75 0.29 2.68 0.28 Segment 4 2.74 0.40 2.80 0.44 2.55 0.33 2.54 0.32 End spurt 2.76 0.40 2.84 0.44 2.57 0.32 2.58 0.31 Age 60+ Segment 1 2.95 0.34 2.94 0.38 2.73 0.29 2.66 0.22 Segment 2 2.87 0.36 2.91 0.39 2.63 0.31 2.62 0.25 Segment 3 2.81 0.40 2.88 0.41 2.62 0.33 2.58 0.28 Segment 4 2.57 0.43 2.74 0.43 2.41 0.36 2.45 0.29 End spurt 2.58 0.41 2.75 0.43 2.45 0.34 2.46 0.30 SD = standard deviation. When PaceR in men runners was assessed (Figure 1), the results showed significant main effects of race [F (17, 12366) = 849.8, ŋ 2 = 0.06, p < 0.01], age group [F (17, 12366) = 14.3, ŋ 2 = 0.01, p < 0.01] as well as race x age group interaction [F (17,12366) = 3.5, ŋ 2 < 0.01, p < 0.01]. Figure 1. Men’s pace range (%) by age group for the marathon and the half-marathon. Error bars represent 95% confidence intervals. Bonferroni post-hoc test results for each race showed higher PaceR in marathon runners in comparison to half-marathon runners for all age groups (p < 0.01). Regarding age groups, all marathon runners younger than 30 years of age showed significantly higher PaceR than all runners from 30 to 59 years of age (p < 0.01). Moreover, marathon runners from the 60+ years group had higher PaceR than runners from 35 to 49 years of age (p < 0.01). Finally, half-marathon runners younger than 24 had higher PaceR than all runners from 30 to 54 years of age (p< 0.01). When PaceR in women runners was assessed (Figure 2), the

This research analyzes pacing strategies in marathon and half-marathon runners based on age and gender.