Pacing in World-Class Age Group Swimmers in 100 and 200 m Freestyle, Backstroke, Breaststroke, and Butterfly

swimming has been investigated in pool swimming for elite-standard and age group freestyle swimmers, but little is known about pacing in age group swimmers competing at world class level in backstroke, breaststroke, and butter y. The aim of this study was to investigate pacing for age group swimmers competing at world class level in 100 and 200 m in the four single disciplines (freestyle, backstroke, breaststroke and butter y). Data on 18,187 unique nishers competing in four FINA Master World Championships between 2014 and 2019 were analyzed. The sample included 3334 women and 14,853 men. Swimming speed decreased with increasing age (p<0.05). Freestyle was the fastest and breaststroke the slowest (p<0.05) stroke. Women and men were faster in 100 m (p<0.05) than in 200 m. Backstroke was the stroke with the lowest and butter y with the highest coe cient of variation in swimming speed. One hundred meters had a higher coe cient of variation in swimming speed than breaststroke (p<0.05). For 100 m, swimming speed decreased for all strokes and all age groups during the second lap. For 200 m, swimming speed was the fastest for all strokes and all age groups during the rst lap. In summary, the FINA World

in swimming speed. One hundred meters had a higher coe cient of variation in swimming speed than breaststroke (p<0.05). For 100 m, swimming speed decreased for all strokes and all age groups during the second lap. For 200 m, swimming speed was the fastest for all strokes and all age groups during the rst lap. In summary, the FINA World Masters Championships presented the unique characteristic that, when all competitors were considered, (i) swimming speed decreased with increasing age, (ii) women and men were faster in 100 m than in 200 m, (iii) freestyle was the fastest stroke and (iv) the largest increase in swimming time for 100 m all strokes and all age groups occurred during the second (out of two) lap and for 200 m, swimming speed was the fastest for all strokes and age groups during the rst lap. These ndings should help coaches to develop age- and event-tailored pacing strategies. Keywords:world class; age-group; pacing; performance; sex di erence 1. Introduction Master athletes can be considered as a model of successful ageing because they provide a unique opportunity to study human physical performance potential; therefore, there is an increasing scienti c interest in them [1,2]. One of the most popular sports disciplines is swimming with many elderly athletes practicing it at a recreational level and a large number of master athletes participating in o cial sport events such as the FINA (F²d²ration Internationale de Natation) World Masters Championship [3]. Int. J. Environ. Res. Public Health2020,17, 3875; doi:10.3390 /ijerph17113875 /journal/ijerph

Int. J. Environ. Res. Public Health2020,17, 3875 2 of 10 Participation and performance trends for master swimmers have been investigated for all four single disciplines of freestyle [4], butter y [5], breaststroke [6] and backstroke [7]. Participation in master freestyle swimmers increased from 1986 to 2014 in women and men in older age groups. Moreover, both women and men improved their performance in all distances across time, and women were not slower compared to men in age groups 80–84 to 85–89 years. Pacing—in the most simpli ed terms—can be de ned as the distribution of exercise intensity during any kind of a race [8,9]. Abbiss and Laursen [10] described six pacing strategies in athletic performance such as negative pacing (i.e., increase in speed over time), positive pacing (i.e., continuous slowing over time), all-out pacing (i.e., maximal speed possible), even pacing (i.e., same speed over time), parabolic-shaped pacing (i.e., positive and negative pacing in di erent segments of the race) and variable pacing (i.e., pacing with multiple uctuations). In masters swimming, pacing has been investigated for master freestyle swimmers for all distances [11] but not for master swimmers of other disciplines and distances. For age group freestyle swimmers in the events of 100, 200, 400 and 800 m, the largest increase in swimming time occurred during the second lap, and the largest decreases in swimming time occurred during the last lap, except in the event of 100m [11]. However, no data exist about pacing in age group swimmers in backstroke, breaststroke and butter y. Age has been reported as an important variable in pacing of endurance athletes such as marathon runners [12]. For age group freestyle swimmers, it has been shown that the e ect of age group was greater than the e ect of participants' sex, and women were not slower compared to men in age groups 80–84 to 85–89 years in the FINA World Masters Championships [11]. However, no study investigated the aspect of age in pacing in age group swimmers for other disciplines such as backstroke, breaststroke and butter y. The knowledge of pacing in age group swimmers

the e ect of participants' sex, and women were not slower compared to men in age groups 80–84 to 85–89 years in the FINA World Masters Championships [11]. However, no study investigated the aspect of age in pacing in age group swimmers for other disciplines such as backstroke, breaststroke and butter y. The knowledge of pacing in age group swimmers for backstroke, breaststroke and butter y might have practical implications for both sports scientists and coaches working with age group swimmers competing in other strokes than butter y at world class level. Information about changes in swimming time by laps in age group swimmers would help to design speci c age-tailored training programs. The present study investigated changes in swimming time by laps in age group swimmers competing in the FINA World Masters Championships 2014, 2015, 2017 and 2019 in 100 and 200 m freestyle, backstroke, breaststroke and butter y. The hypothesis was that older swimmers would slow down with increasing age and with increasing distance without an impact of sex and discipline. 2. Materials and Methods 2.1. Ethics Approval This study was approved by the Institutional Review Board of St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the participants as the study involved the analysis of publicly available data (EKSG 01-06-2010). 2.2. Data Sampling and Data Analysis All data were obtained from the o cial and publicly accessible website of the FINA [13] on 1 February 2020. Any swimmer older than 25 years ful lling the quali cation time and a liated to an o cial swimming club can start in a FINA World Masters Championship [14]. Trial times for 100 m and 200 m distances, in 50 m increments, were recorded in the XV FINA World Masters Championships held in Montreal, Canada, in 2014; in the XVI FINA World Masters Championships held in Kazan, Russia, in 2015; in the XVII FINA World Masters Championships held in Budapest, Hungary, in 2017 and in the XVIII FINA World Masters Championships held in Gwangju, South Korea, in 2019. A total of 4857 women and 6239 men

XV FINA World Masters Championships held in Montreal, Canada, in 2014; in the XVI FINA World Masters Championships held in Kazan, Russia, in 2015; in the XVII FINA World Masters Championships held in Budapest, Hungary, in 2017 and in the XVIII FINA World Masters Championships held in Gwangju, South Korea, in 2019. A total of 4857 women and 6239 men swimmers who competed in 100 m and 3334 women and 3753 men who competed in 200 m freestyle, backstroke, breaststroke and butter y were considered. We included all women and all men from every 5-year age groups from 25–29 years to 95–99 years to avoid a selection bias by analyzing only a limited sample of top athletes such as the top 10 or top 100 of each age group.

Int. J. Environ. Res. Public Health2020,17, 3875 3 of 10 2.3. Statistical Analyses All statistical procedures were carried out using the Statistical Package for the Social Sciences (SPSS version 26. IBM, NY, USA) and GraphPad Prism (version 8.4.2. GraphPad Software LLC, CA, USA). Based on lap time, the coe cient of variance (CV) in lap times was calculated for each participant as CV=(standard deviation/mean) 100. Lap times and CV were the dependent variables, whereas sex, age group, stroke and distance were de ned as independent variables. The Shapiro–Wilk and Levene's tests were applied for normality and homogeneity, respectively. General linear models were applied with Distance Sex Age group Stroke as factors. Sex was always included as xed factor while all other variables were analyzed as random factors. When interactions were found (p<0.05), pairwise comparisons were applied to identify the di erences more accurately. The hypothesis of sphericity was veri ed by Mauchly test, and when violated, the degrees of freedom were corrected by the Greenhouse–Geisser estimates. The level of signi cance utilized wasp 0.05. A positive pacing strategy is observed when the speed gradually declines throughout the duration of the event whereas a negative pacing strategy is observed when there is an increase in speed over the duration of the event. In an all-out pacing strategy, speed tends to gradually decrease, possibly resulting in suboptimal performance times after an athlete has reached peak velocity [10]. 3. Results Participants from 100 m (n=11,100) and 200 m (n=7087) were included, for a total sample size of 18,187 age group athletes with 3334 women and 14,853 men. The four strokes comprised data for freestyle (n=6729), butter y (n=2606), backstroke (n=3547) and breaststroke (n=5332) (Table). Finally, the age groups were separated by sex, distance and stroke; the age group with the lowest number of participants was women in the age group 70 years competing in the 200 m butter y, with only 36 athletes. The age group with the highest number of participants was 100 m freestyle of men 50–54 years old (M55) with 358 athletes. Table 1. Number of athletes adopting a positive

and stroke; the age group with the lowest number of participants was women in the age group 70 years competing in the 200 m butter y, with only 36 athletes. The age group with the highest number of participants was 100 m freestyle of men 50–54 years old (M55) with 358 athletes. Table 1. Number of athletes adopting a positive or negative pacing in 100m and 200m swim races in the four strokes. Distance Stroke Positive Pacing Negative Pacing 100 m Freestyle 4221 13 Butter y 1635 1 Backstroke 1888 26 Breaststroke 3243 5 200 m Freestyle 2443 1 Butter y 940 0 Backstroke 1569 2 Breaststroke 2074 0 The multifactorial model for average swimming speed showed a trend towards signi cance (p=0.059) for sex, and signi cant e ects (p<0.05) for age group, stroke, distance and the interactions sex age group, sex distance, age group stroke, age group distance, stroke distance, sex age group stroke and sex stroke distance (Table). Pairwise comparisons showed that swimming speed decreased with increasing age, where the 25–29 and 30–34 were the fastest age groups for both women and men among all strokes, except 200 m butter y, where 30–34 was the fastest age group in both men and women. Freestyle was the fastest stroke and breaststroke the slowest. Women and men were faster in 100 m than in 200 m (Figure).

Int. J. Environ. Res. Public Health2020,17, 3875 4 of 10 Table 2. Multifactorial model with dependent variables being average swimming speed and coe cient of variance (CV). Factor Average Speed CV F p-Value F p-Value Sex 114.2 0.059 9.3 0.202 Age group 446.2 <0.001 1.3 0.359 Stroke 61.5 0.003 17.1 0.022 Distance 26.4 0.014 2.5 0.209 Sex Age group 5.4 0.010 3.1 0.054 Sex Stroke 3.5 0.164 11.2 0.039 Sex Distance 14.1 0.026 0.8 0.429 Age group Stroke 13.9 <0.001 4.0 <0.001 Age group Distance 4.0 0.015 1.8 0.175 Stroke Distance 14.3 0.017 15.4 0.013 Sex Age group Stroke 2.3 0.015 1.8 0.070 Sex Age group Distance 1.3 0.258 0.9 0.513 Sex Stroke Distance 5.9 0.003 4.6 0.009 Distance Age group Stroke 1.7 0.096 1.6 0.101 Distance Sex Age group Stroke 1.0 0.458 1.3 0.136Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 4 of 10 Table 2. Multifactorial model with dependent variables being average swimming speed and coefficient of variance (CV). Factor Average Speed CV F P-value F P-Value Sex 114.2 0.059 9.3 0.202 Age group 446.2 <0.001 1.3 0.359 Stroke 61.5 0.003 17.1 0.022 Distance 26.4 0.014 2.5 0.209 Sex × Age group 5.4 0.010 3.1 0.054 Sex × Stroke 3.5 0.164 11.2 0.039 Sex × Distance 14.1 0.026 0.8 0.429 Age group × Stroke 13.9 <0.001 4.0 <0.001 Age group × Distance 4.0 0.015 1.8 0.175 Stroke × Distance 14.3 0.017 15.4 0.013 Sex × Age group × Stroke 2.3 0.015 1.8 0.070 Sex × Age group × Distance 1.3 0.258 0.9 0.513 Sex × Stroke × Distance 5.9 0.003 4.6 0.009 Distance × Age group × Stroke 1.7 0.096 1.6 0.101 Distance × Sex × Age group × Stroke 1.0 0.458 1.3 0.136 Figure 1. Swimming speed of women (a–d) and men (e–h) in 100 m and 200 m of the four strokes across age groups. The multifactorial model for the individual CV showed a significant effect (p < 0.05) for stroke, a trend (p = 0.054) for the interaction sex × age group and significant effect for the interactions sex × stroke,

Figure 1. Swimming speed of women (a–d) and men (e–h) in 100 m and 200 m of the four strokes across age groups. The multifactorial model for the individual CV showed a significant effect (p < 0.05) for stroke, a trend (p = 0.054) for the interaction sex × age group and significant effect for the interactions sex × stroke, age group × stroke, stroke × distance and sex × stroke × distance (Table 2). Pairwise comparisons showed that backstroke was the stroke with the lowest CV and butterfly the stroke with the highest CV. One hundred meters showed the highest CV in breaststroke (Figure 2). The multifactorial model for pacing in 100 m races (Figure 3a–d) showed a significant effect for swim stroke (F = 1385.4; p < 0.001), sex (F = 2215.7; p < 0.001), pace (F = 67,268.7; p < 0.001) and for the interactions sex × stroke (F = 14.9; p < 0.001) and pace × stroke (F = 1258.5; p < 0.001). The multifactorial model for pacing in 200 m races (Figure 3e–h) showed a significant effect for swim stroke (F = 527.7; p < 0.001), sex (F = 851.1; p < 0.001), pace (F = 18,474.1; p < 0.001) and for the interactions sex × stroke (F = 9.3; p < 0.001) and pace × stroke (F = 382.4; p < 0.001). Figure 1. Swimming speed of women (a–d) and men (e–h) in 100 m and 200 m of the four strokes across age groups. The multifactorial model for the individual CV showed a signi cant e ect (p<0.05) for stroke, a trend (p=0.054) for the interaction sex age group and signi cant e ect for the interactions sex stroke, age group stroke, stroke distance and sex stroke distance (Table). Pairwise comparisons showed that backstroke was the stroke with the lowest CV and butter y the stroke with the highest CV. One hundred meters showed the highest CV in breaststroke (Figure). The multifactorial model for pacing in 100 m races (Figurea–d) showed a signi cant e ect for swim stroke (F=1385.4;p<0.001), sex (F=2215.7;p<0.001), pace

distance and sex stroke distance (Table). Pairwise comparisons showed that backstroke was the stroke with the lowest CV and butter y the stroke with the highest CV. One hundred meters showed the highest CV in breaststroke (Figure). The multifactorial model for pacing in 100 m races (Figurea–d) showed a signi cant e ect for swim stroke (F=1385.4;p<0.001), sex (F=2215.7;p<0.001), pace (F=67,268.7;p<0.001) and for the interactions sex stroke (F=14.9;p<0.001) and pace stroke (F=1258.5;p<0.001). The multifactorial model for pacing in 200 m races (Figuree–h) showed a signi cant e ect for swim stroke (F=527.7;p<0.001), sex (F=851.1;p<0.001), pace (F=18,474.1;p<0.001) and for the interactions sex stroke (F=9.3;p<0.001) and pace stroke (F=382.4;p<0.001).

Int. J. Environ. Res. Public Health2020,17, 3875 5 of 10Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 5 of 10 Figure 2. Individual coefficient of variance of swimming speed of women (a–d) and men (e–h) in 100 m and 200 m swim races of the four strokes across age groups. 4. Discussion This study investigated for the first time pacing in world-class age group swimmers in 100 and 200 m freestyle, backstroke, breaststroke and butterfly with the hypothesis that older swimmers would slow down with increasing age and with increasing distance without an impact of sex and discipline. Based on the current results for 400 and 800 m freestyle, master swimmers adopted a parabolic pacing [10], and the first and the last lap were the fastest. The same pacing strategy was also observed in elite-standard swimmers competing in 400 m freestyle swimming where a fast-start- even-and-parabolic pacing strategy was used [15]. Based on our results, the main findings were: (i) average swimming speed decreased with increasing age, where M30 and F30 were the fastest for both women and men among all styles, except 200 m butterfly, where M35 and F35 were the fastest age groups in both men and women; (ii) freestyle was the fastest stroke and breaststroke the slowest; (iii) women and men are faster in 100 m races than in 200 m, (iv) the first lap was the fastest one for 100 m and 200 m, and (v) for 200, m the largest increase during swimming time for all age groups and for all strokes occurred during the second lap (out of four). A first finding was that swimming speed decreased with increasing age, but the youngest age group (25–29 years) was not the fastest age group. It might be possible that the world class elite swimmers do not swim anymore after their career has been finished, and in the masters swimmers, the second age group swimmers begin by competing. This study found that the age group 30–34 years was the fastest for both women and men in all strokes except 200 m butterfly,

fastest age group. It might be possible that the world class elite swimmers do not swim anymore after their career has been finished, and in the masters swimmers, the second age group swimmers begin by competing. This study found that the age group 30–34 years was the fastest for both women and men in all strokes except 200 m butterfly, where the age group 35–39 years was the fastest in both men and women. Similar findings have been reported for other sports disciplines such as running for example in triathlon races [16]. Figure 2. Individual coe cient of variance of swimming speed of women (a–d) and men (e–h) in 100 m and 200 m swim races of the four strokes across age groups.Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 6 of 10 Figure 3. Race time for each 50 m split in 100 m (a–d) and 200 m (e–h) of men and women in the four strokes. Regarding the marathon races in Oslo from 2008 to 2018, the fastest men and women runners were also in the age group 35–39 years, whereas the oldest runners were the slowest [17]. In pacing of Ironman triathletes, it has previously been shown that the younger age groups were relatively faster in swimming than their older counterparts [18]. However, the youngest age group is not the fastest in achieving peak performances for top athletes. The age of peak performance for athletes specializing in specific events can be obtained from the equations of the linear trends [19]. Apart from age, sex is another important variable regarding pacing in athletes. To date, we have knowledge about the sex differences in pacing during half-marathon and marathon running [20]. Men marathon runners showed greater speed fluctuations than women, whereas in half-marathon, both men and women had rather similar pacing profiles. In addition, success does not only result from age but highlights also the importance for example of the oxygen uptake, maximal heart rate, stroke volume, arteriovenous oxygen difference, active muscle mass, type II muscle fibre size and blood volume. In masters endurance athletes,

This study investigates pacing in world-class age group swimmers in 100 and 200 m across four strokes.