Pacing in Long-Distance Running: Sex and Age Differences in 10-km Race and Marathon

ackground and objective: The recent availability of data from mass-participation run- ning events has allowed researchers to examine pacing from the perspective of non-elite distance runners. Based on an extensive analysis of the literature, we concluded that no study utilizing mass-participation events data has ever directly compared pacing in the 10-km race, with other long-distance races. Therefore, the main aim of this study was to assess and compare pacing between 10-km runners and marathoners, in regards to their sex and age.Materials and methods: For the purpose of this study, of cial results from the Oslo marathon (n= 8828) and 10-km race (n= 16,315) held from 2015 to 2018 were included.Results: Both 10-km runners and marathoners showed positive pacing strategies. Moreover, two-way analysis of variance showed that women were less likely to slow in the marathon than men (9.85% in comparison to 12.70%) however, not in the 10-km race (3.99% in comparison to 3.38%). Finally, pace changing is more prominent in youngest and oldest marathoners

from 2015 to 2018 were included.Results: Both 10-km runners and marathoners showed positive pacing strategies. Moreover, two-way analysis of variance showed that women were less likely to slow in the marathon than men (9.85% in comparison to 12.70%) however, not in the 10-km race (3.99% in comparison to 3.38%). Finally, pace changing is more prominent in youngest and oldest marathoners comparing to the other age groups (12.55% in comparison to 10.96%).Conclusions: Based on these ndings, practitioners should adopt different training programmes for marathoners in comparison to shorter long-distance runners. Keywords:running; endurance; health; marathoners; recreation 1. Introduction Pacing can be de ned as the distribution of exercise intensity during a prolonged time [1]. Optimal pacing is one of the most important contributors to achieving the best results in long-distance running [1,2], while signi cantly decreasing the risk of muscu- loskeletal injuries [3]. Although a pacing strategy that can classify as optimal depends on many factors (e.g., race length and pro le, altitude, or weather conditions [4,5]) an even pacing strategy with an end-spurt has often been the best choice for long-distance events [6]. This strategy was best seen in the recent successful sub-2-h marathon challenge by Eliud Kipchoge, where the pacing was arti cially controlled to be even throughout the race, with the spontaneous end-spurt by Kipchoge in the last several hundred meters [7]. Different pacing strategies in long-distance running were extensively investigated by sev- eral in uential studies, both on track [4] and road racing [8,9]. However, those studies only involved a small sample size of professional athletes. On the other hand, the recent availability of mass-participation events data has allowed researchers to examine pacing from the perspective of a wider range of athletes including recreational distance runners of all ages [10,11]. The rst studies using mass-participation events data were focused on independent long-distance events, primarily marathons and half-marathons. Contrary to elite runners, when a wider range of athletes was investigated, previous research reported positive pacing Medicina2021,57, 389.

ages [10,11]. The rst studies using mass-participation events data were focused on independent long-distance events, primarily marathons and half-marathons. Contrary to elite runners, when a wider range of athletes was investigated, previous research reported positive pacing Medicina2021,57, 389.

Medicina2021,57, 389 2 of 11 in endurance running races, where pace (time/distance) increased after approximately 3/4of the race [12,13]. This decline in running speed was more prominent in men than in women due to several physiological [12] and psychological factors [14,15], such as a riskier and faster start by men or women's better fat utilization to obtain energy. Moreover, studies investigating pacing in age group endurance runners proved to be somewhat inconsistent in their ndings, with either no differences between age groups [11,12] or with more even pacing in older age groups [13]. Regarding the 10-km race, several studies examined pacing in this long-distance event, mainly in a small samples of track runners [1,4], time trial runners on either track [16] or treadmill [17], as well as in triathletes [18]. Pacing in the aforementioned elite 10-km runners proved to be rather even with an end-spurt. However, in recent years, 10-km races became increasingly popular in recreational runners, especially among women and beginners of both sexes [19,20]. Recreational athletes would participate in a 10-km race in the context of their preparation for a subsequent longer race [19], as well as a training tool to ameliorate pace time [21]. Therefore, further investigation of pacing in recreational 10-km runners is crucial to better understand the mechanisms controlling the pacing in long-distance events. This might help runners to enjoy running more as well as to achieve better results. In recent years, several studies using new methodological approaches attempted to compare pacing in mass-participation events [10,20]. Nevertheless, the performance of different distances and events was not adjusted in the abovementioned studies; e.g., some events might be under different environmental conditions, opposition elds, or race pro les. On the other hand, a new methodological approach allowed researchers to directly compare pacing between half-marathon and marathon on the same race and track, with rather similar weather conditions [10,22], thus providing more detailed and comparable results. For example, a novel nding was that women's pacing was similar to men's in half-marathon, whereas in a marathon women had more even pacing compared to men. Speci cally, physiological rather than

researchers to directly compare pacing between half-marathon and marathon on the same race and track, with rather similar weather conditions [10,22], thus providing more detailed and comparable results. For example, a novel nding was that women's pacing was similar to men's in half-marathon, whereas in a marathon women had more even pacing compared to men. Speci cally, physiological rather than psychological factors can in uence the additional lack of speed in marathoners (and not half-marathoners), such as better utilization of fat by women or men's muscle glycogen depletion [10,22]. Accordingly, women did not differ by age group in pace variability, whereas youngest and oldest men, showed larger variability in pace [10]. However, further proof is needed that the observed sex and age differences are not speci c to only a few races (i.e., Vienna and Ljubljana) and only two long-distance running events (i.e., half-marathon and marathon). This can be achieved by investigating and comparing pacing strategies in other long-distance races, such as longer ultra-races, or shorter and increasingly popular 10km races with already popular and investigated half-marathon or marathon. Based on an extensive analysis of the literature, we concluded that no study utilizing mass-participation events data has ever directly compared pacing in a 10-km race, with other long-distance races. Such a comparison could shed additional light on the importance of the mechanisms underlying pacing behaviour of the long distance runners, as well as to better understand potential training requirements for both recreational and pro cient runners. Therefore, the main aim of this study was to assess and compare pacing between the increasingly popular 10-km race and the most popular long-distance race—marathon, in regards to their sex and age. We hypothesized that 10-km runners will show more even pacing than marathoners, particularly women and middle age runners. 2. Materials and Methods For this study, of cial results from the Oslo marathon and Oslo 10-km race held from 2015 to 2018 were included [23]. Split times from the middle of the race were also included (i.e., 5 km and 21.0975 km for the 10-km race and marathon respectively). The Oslo marathon was

particularly women and middle age runners. 2. Materials and Methods For this study, of cial results from the Oslo marathon and Oslo 10-km race held from 2015 to 2018 were included [23]. Split times from the middle of the race were also included (i.e., 5 km and 21.0975 km for the 10-km race and marathon respectively). The Oslo marathon was chosen as an of cially certi ed race because it was held on a rather at track (elevation difference 60 m). For reference, the Berlin Marathon considered “the fastest marathon”, has an elevation difference of 21 m [24]. Moreover, both the10-km race and marathon were held on the same day, whereas the 10-km race was entirely

Medicina2021,57, 389 3 of 11 contained within the marathon race. Finally, note that hyperthermia can signi cantly affect pacing in both elite and recreational runners [5,12]. The Oslo Marathon is traditionally held in Norway at the end of September, usually in colder weather conditions, which can reduce the chances of hyperthermia in runners. 2.1. Participants In total, 25,143 participants of all performance levels were considered for this study (10-km race,n= 16,315; Marathon,n= 8828), however, most of them were recreational runners. Participants who did not nish any of the races, or did not have recorded any of the split times were excluded from the initial sample. The present research was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the par- ticipants as the research concerned the study of publicly available data (Ethical Committee St. Gallen 1 June 2010). This research was conducted in accordance with ethical standards derived from the Declaration of Helsinki adopted in 1964 and revised in 2013. 2.2. Data Acquisition All data was acquired from the of cial Oslo Marathon results page [23]. First, overall times, athlete details and a link to each athlete's split times were copied from the results page and pasted into an Excel document. This was done separately per year, distance and gender. The split times were then added in a second step using custom Python scripts that followed the of cial link to each athlete's split times and extracted all available split times. 2.3. Procedures In the rst step of data analysis, the average running speed of all runners was cal- culated for the rst and second half of both the 10-km race and marathon. A particular novelty of this study was the use of time (i.e., minutes and seconds) per kilometre as a unit of speed. This “runners friendly” measurement of speed was chosen as a very practical tool for both professional and recreational runners as well as their coaches. For example, GPS watches, often utilized by runners to monitor running speed, presents minutes per kilometre by default. Moreover,

study was the use of time (i.e., minutes and seconds) per kilometre as a unit of speed. This “runners friendly” measurement of speed was chosen as a very practical tool for both professional and recreational runners as well as their coaches. For example, GPS watches, often utilized by runners to monitor running speed, presents minutes per kilometre by default. Moreover, in long-distance races, each kilometre is usually marked. Therefore, participants can see the time they consumed running between each kilometre. As a result, runners and running coaches often rely on the time needed to run one kilometre when assessing and comparing someone's running speed or pace maintenance. Considering that even pacing is the best choice for long-distance running [6], pacing assessment from the aspect of speed maintenance was selected for this study. Thereafter, speed variation was calculated based on the percentage difference in speed observed between the second and the rst half of the race (i.e., % change = (second half time rst half time)/ rst half time). Percentage change was considered as a continuous variable [14]. Applying this method, it was possible to normalize pace and compare it between different race distances [10]. Criteria for inclusion in the nal data set were having timing data for the halfway mark and the full race in proper sequence (e.g., nishing time greater than split time); a net time less than the gun time; and a slowing less than 400% [14]. 2.4. Statistical Analysis Prior to all statistical tests, descriptive statistics were calculated as mean and standard deviation. Since the Kolmogorov-Smirnov or similar data normality tests are not sensi- tive when using a large sample size, data distribution normality was veri ed by visual inspection of histograms and QQ plots [10,22]. To assess pacing differences between the rst and second half of the 10-km race and marathon, two 2-way between-within ANOVAs were performed (separately for women and men). The main effect of pace ( rst half and second half), race (10-km race and marathon), and the interaction pace x race were observed.

assess pacing differences between the rst and second half of the 10-km race and marathon, two 2-way between-within ANOVAs were performed (separately for women and men). The main effect of pace ( rst half and second half), race (10-km race and marathon), and the interaction pace x race were observed.

Medicina2021,57, 389 4 of 11 To assess pace change between women and men in 10-km race and marathon, two-way ANOVA with between factors was performed. The main effect of sex (women and men), race (10-km race and marathon), and the interaction sex race were observed. Finally, to assess pace change between age groups in 10-km race and marathon, two 2-way between-within ANOVAs were performed (separately for women and men). The main effect of age group (18–23; 24–34; 34–39; 40–44; 45–49; 50–54; 55–59; 60–64; 65+), race (10-km race and marathon) and the interaction age race were observed. In addition, for all ANOVAs, Bonferroni post-hoc test was performed. The effect size was calculated as eta squared ( 2 ), where the values of 0.01, 0.06, and above 0.14 were considered small, medium, and large, respectively [25]. Alpha level was set atp 0.05. All statistical tests were performed using Microsoft Of ce Excel 2007 (Microsoft Corporation, Redmond, WA, USA) and SPSS 20 (IBM, Armonk, NY, USA). 3. Results The rst and second half pacing of participants is presented in Table. Regardless of their sex and age, both 10-km runners and marathoners showed a positive pacing strategy (i.e., second half of the race was slower than the rst half). Further examination of pacing between 10-km runners and marathoners, in regards to their sex and age, is presented in Figures–3. Table 1.Speed indicators (in min/km) of 10-km and marathon runners showed as mean standard deviation. Women (n 10-km= 9932;n marathon= 2048) Men ( n 10-km= 6383;n marathon= 6780) 10-km Race (min/km) Marathon (min/km) 10-km Race (min/km) Marathon (min/km) Mean SD Mean SD Mean SD Mean SD Age: 18–23 n= 1589 First half 6:03.2 1:05.9 5:50.0 0:38.9 5:03.1 1:06.3 5:17.8 0:42.4 Second half 6:16.0 1:12.9 6:27.7 1:04.9 5:12.7 1:10.0 6:06.0 1:11.6 Total 6:09.6 1:08.5 6:08.9 0:49.4 5:07.9 1:07.1 5:41.9 0:54.3 Age: 24–34 n= 7777 First half 6:07.7 1:02.7 5:41.2 0:40.7 5:13.2 1:06.0 5:10.0 0:42.5 Second half 6:19.1 1:09.6 6:15.0 1:02.9 5:22.9 1:10.7 5:49.1 1:07.1 Total 6:13.4 1:05.2 5:58.1 0:49.8 5:18.1 1:07.4 5:29.6 0:52.4 Age: 34–39 n= 3695 First half 6:14.7 1:02.2 5:36.1 0:37.5 5:17.7 1:06.3 5:09.9

1:04.9 5:12.7 1:10.0 6:06.0 1:11.6 Total 6:09.6 1:08.5 6:08.9 0:49.4 5:07.9 1:07.1 5:41.9 0:54.3 Age: 24–34 n= 7777 First half 6:07.7 1:02.7 5:41.2 0:40.7 5:13.2 1:06.0 5:10.0 0:42.5 Second half 6:19.1 1:09.6 6:15.0 1:02.9 5:22.9 1:10.7 5:49.1 1:07.1 Total 6:13.4 1:05.2 5:58.1 0:49.8 5:18.1 1:07.4 5:29.6 0:52.4 Age: 34–39 n= 3695 First half 6:14.7 1:02.2 5:36.1 0:37.5 5:17.7 1:06.3 5:09.9 0:43.7 Second half 6:27.7 1:08.2 6:04.4 0:52.4 5:28.4 1:10.1 5:47.5 1:04.0 Total 6:21.2 1:04.4 5:50.2 0:43.4 5:23.1 1:07.4 5:28.7 0:51.9 Age: 40–44 n= 3784 First half 6:17.9 1:01.1 5:46.3 0:42.6 5:19.3 1:05.2 5:12.5 0:39.4 Second half 6:32.3 1:07.1 6:17.4 0:59.5 5:29.3 1:09.5 5:51.4 1:00.4 Total 6:25.1 1:03.3 6:01.8 0:49.6 5:24.3 1:06.6 5:32.0 0:47.6 Age: 45–49 n= 3514 First half 6:18.7 1:01.4 5:47.7 0:37.4 5:29.0 1:06.4 5:14.6 0:40.1 Second half 6:34.0 1:08.5 6:21.5 0:55.4 5:41.5 1:11.9 5:51.0 0:59.8 Total 6:26.4 1:04.2 6:04.6 0:44.6 5:35.3 1:08.4 5:32.8 0:47.9 Age: 50–54 n= 2342 First half 6:23.4 1:04.8 5:52.8 0:43.1 5:34.1 1:07.2 5:23.2 0:41.6 Second half 6:41.4 1:11.5 6:29.3 0:58.5 5:48.3 1:13.0 6:05.0 1:02.4 Total 6:32.4 1:07.4 6:11.0 0:49.2 5:41.2 1:09.3 5:44.1 0:49.9 Age: 55–59 n= 1256 First half 6:45.1 1:06.2 6:00.3 0:39.7 5:43.7 1:06.4 5:23.6 0:39.1 Second half 7:05.0 1:12.7 6:40.1 0:51.9 5:58.0 1:13.7 6:03.0 0:58.0 Total 6:55.1 1:08.5 6:20.2 0:44.5 5:50.8 1:09.4 5:43.3 0:46.7 Age: 60–64 n= 646 First half 6:52.6 1:09.2 6:29.6 0:50.1 5:53.3 1:17.4 5:37.0 0:47.1 Second half 7:10.9 1:11.8 7:26.8 1:07.5 6:09.4 1:22.8 6:17.3 1:05.3 Total 7:01.7 1:09.7 6:58.2 0:56.8 6:01.3 1:19.0 5:57.2 0:53.5 Age: 65+ n= 540 First half 7:03.6 1:03.8 6:44.0 0:41.5 6:18.7 1:09.1 6:02.3 0:44.0 Second half 7:28.2 1:11.2 7:27.9 1:02.7 6:35.4 1:16.1 6:54.7 1:06.9 Total 7:15.9 1:06.7 7:06.0 0:50.5 6:27.1 1:11.6 6:28.5 0:53.1 n= number of participants, SD = standard deviation of data, min/km = minutes per kilometer.

Medicina2021,57, 389 5 of 11Medicina 2021, 57, x FOR PEER REVIEW 5 of 11 Figure 1. Women’s (upper panel) and men’s (lower panel) running time in the first and second half of 10-km race and marathon. Data showed as mean ± standard deviation. **—Significant dif- ferences at p< 0.01. 3.1. Pacing in 10-km and Marathon For women (Figure 1, upper panel), the two-way ANOVA showed significant main effects of pace(F (3,11978) = 6513.1, ŋ 2 = 0.02, p< 0.01), race(F (3,11978) = 187.7, ŋ 2 = 0.01, p< 0.01) as well as pace × raceinteraction (F (3,11978) = 1086.1, ŋ 2 < 0.01, p< 0.01), whereas for men (Figure Figure 1.Women's (upper panel) and men's (lower panel) running time in the rst and second half of 10-km race and marathon. Data showed as mean standard deviation. **—Signi cant differences atp< 0.01. 3.1. Pacing in 10-km and Marathon For women (Figure, upper panel), the two-way ANOVA showed signi cant main effects ofpace(F (3,11978)= 6513.1, 2 = 0.02,p< 0.01),race(F (3,11978)= 187.7, 2 = 0.01,p< 0.01)

This research analyzes pacing strategies in long-distance running events.