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Abstract

Several investigations have demonstrated that running performance gradually decreases with age by using runners >25 years grouped in 5-year age brackets. The aim of this study was to determine the relationship between race time in marathon and age in elite marathoners by including all ages and 1-year intervals. Running times of the top ten men and women at 1-year intervals (from 18 to 75 years) in the New York City marathon were analyzed for the 2010 and 2011 races. Gender differences in performance times were analyzed between 18 and 70 years of age. The relationship between running time and runner's age was U-shaped: the lowest race time was obtained at 27 years (149 ± 14 min) in men and at 29 years (169 ± 17 min) in women. Before this age (e.g., 27 years for men and 29 years for women), running time increased by 4.4 ± 4.0 % per year in men and 4.4 ± 4.3 % per year in women. From this age on, running time increased by 2.4 ± 8.1 % per year in men and 2.5 ± 9.9 % per year in women. The sex difference in running time remained stable at ~18.7 ± 3.1 % from 18 to 57 years of age. After this, sex difference progressively increased with advancing age. In summary, endurance runners obtained their best performance in the marathon at 27 years in men and 29 in women. Thus, elite marathon runners should program their long-term training to obtain maximal performance during their late 20s.

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... Therefore, segmental mass distribution may be the main cause for the larger range of RE in endurance (Shaw, Ingham, Atkinson & Folland, 2015). The outcomes from these researchers show that the aerobic needs of transporting added weight develop drastically when the weight is situated more distally (Lara, Salinero & Del Coso, 2014). Moore, Jones, and Dixon (2013) observed that the aerobic needs of transporting additional mass on the body were increased by 1%, though when the same mass was transported on the leg, running economy increased by 4%. ...
... The BF% was higher in the current sample compared to other endurance athletes because their body weight is high. Seasonal changes have been described in BF% specifically -BF% decreases by 1% to 3% during the competitive season (Del Coso et al., 2014). Pronounced changes in BF% of highly trained athletes take place if, during the off-season, the distance run is approximately 120 km/week (Wood et al., 2016). ...
... Pronounced changes in BF% of highly trained athletes take place if, during the off-season, the distance run is approximately 120 km/week (Wood et al., 2016). Cross-country athletes of equal BF% could have different competition results depending on the additional morphological factors and biomechanical efficiency (Lara et al., 2014). It is well known that the performance of higher BF% runners is generally poorer than of their lower BF% counterparts (Giovanelli et al., 2017). ...
Thesis
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In recent years, there has been an increasing interest in the morphological and physiological characteristics for many sporting codes. Morphological and physiological testing is an important tool for cross-country athletes and coaches and assists in the training intensity prescription, monitoring of training adaptation and profiling athletes for specific competitions. So far, however, there has been few reports on senior male cross-country athletes. The aim of this research was to determine the relationship between morphological and physiological characteristics of senior male cross-country athletes in Gauteng province, South Africa. Forty males (age: 20-35 years; height: 173.09 cm; weight: 63.05 kg) who competed in the Central Gauteng Senior Cross-Country Championships competition were invited to participate in this study. Parameters tested included stature, body weight, seven skinfolds, body fat percentage, lean body mass, somatotype and 10km time measured. The maximal oxygen consumption, running economy and two ventilatory thresholds (VT1 and VT2) were calculated using online assessments of each participant as explained in the methods of this study. Data were analysed using descriptive statistics (SPSS, v.21) and Pearson coefficient of correlation procedures. A significant difference was observed between athletes who trained for <45 minutes and those who trained for >45 minutes per day by an independent t-test. An independent t-test was used to determine significant differences between the two groups. The data were collected experimentally by using a self-administered questionnaire for the medical and sporting status of the runners. The results of this study indicated mean values of body weight (63.05 kg), body fat percentage (8.04 %), sum of seven skinfolds (34.12 mm), lean body mass (59.24 kg) and somatotype (i.e., endomorph, mesomorph, and ectomorph ratios) (1.80, 1.40. and 2.80) respectively. The mean values for maximum oxygen consumption (V̇ O2max) (63.50 mlO2 . kg˗1.min-1 ), running economy (at 12 km·hr -1 32.8 L/min, 14.5 km·hr -1 41.70 L/min, 16 km·hr -1 56 L/min, 19.2 km·hr -1 30.60 L/min), ventilatory threshold (2.95 L/min-1 ), maximum heart rate (191.00 bpm), respiratory exchange ratio (1.23) and average 10 km running speed (16.24 km·hr -1 ) were also determined. The VT1 and VT2 were calculated and at the intensities corresponding to the last point before a first non-linear increase in both VT1 and VT2. The senior male cross-country athletes showed higher values for O2 expressed relative to morphological and physiological factor. The above measurements were captured in Johannesburg at the following altitude (1753 m), barometric pressure (82.54 kPa), air density (0.98 kg/m2 at 20 ºC/ (293 k). These characteristics are generally associated with cross-country iii runners, suggesting that senior male cross-country athletes in Gauteng province, South Africa, are professional athletes. There were no significant V̇ O2max, RE and personal best 10 km time differences between participants who trained <45 minutes and those who trained >45 minutes per day during training sessions (p > 0.05). However, there were significant body weight (p = 0.028) and BF% (p = 0.030) differences between the two groups. It can thus suggest that the duration of the daily training session has a direct effect on some morphological characteristics of athletes, but no effect on others. The analysis showed that athletes of various endurance events statistically differ in morphological measures, especially in dimensions of BW and BF%. Further, highlight the importance of morphological and physiological factors in cross�country running. This research will serve as a basis for future studies and will provide information on senior male cross-country athletes, which can be referred to by coaches and sports scientists who train athletes during the competition preparation phase. KEY WORDS: anthropometry, V̇ O2max, running economy, ventilatory threshold
... Therefore, segmental mass distribution may be the main cause for the larger range of RE in endurance (Shaw, Ingham, Atkinson & Folland, 2015). The outcomes from these researchers show that the aerobic needs of transporting added weight develop drastically when the weight is situated more distally (Lara, Salinero & Del Coso, 2014). Moore, Jones, and Dixon (2013) observed that the aerobic needs of transporting additional mass on the body were increased by 1%, though when the same mass was transported on the leg, running economy increased by 4%. ...
... The BF% was higher in the current sample compared to other endurance athletes because their body weight is high. Seasonal changes have been described in BF% specifically -BF% decreases by 1% to 3% during the competitive season (Del Coso et al., 2014). Pronounced changes in BF% of highly trained athletes take place if, during the off-season, the distance run is approximately 120 km/week (Wood et al., 2016). ...
... Pronounced changes in BF% of highly trained athletes take place if, during the off-season, the distance run is approximately 120 km/week (Wood et al., 2016). Cross-country athletes of equal BF% could have different competition results depending on the additional morphological factors and biomechanical efficiency (Lara et al., 2014). It is well known that the performance of higher BF% runners is generally poorer than of their lower BF% counterparts (Giovanelli et al., 2017). ...
Article
Full-text available
In recent years, there has been an increasing interest in the morphological and physiological characteristics for many sporting codes. Morphological and physiological testing is an important tool for cross-country athletes and coaches and assists in the training intensity prescription, monitoring of training adaptation and profiling athletes for specific competitions. So far, however, there has been few reports on senior male cross-country athletes. The aim of this research was to determine the relationship between morphological and physiological characteristics of senior male cross-country athletes in Gauteng province, South Africa. Forty males (age: 20-35 years; height: 173.09 cm; weight: 63.05 kg) who competed in the Central Gauteng Senior Cross-Country Championships competition were invited to participate in this study. Parameters tested included stature, body weight, seven skinfolds, body fat percentage, lean body mass, somatotype and 10km time measured. The maximal oxygen consumption, running economy and two ventilatory thresholds (VT1 and VT2) were calculated using online assessments of each participant as explained in the methods of this study. Data were analysed using descriptive statistics (SPSS, v.21) and Pearson coefficient of correlation procedures. A significant difference was observed between athletes who trained for <45 minutes and those who trained for >45 minutes per day by an independent t-test. An independent t-test was used to determine significant differences between the two groups. The data were collected experimentally by using a self-administered questionnaire for the medical and sporting status of the runners. The results of this study indicated mean values of body weight (63.05 kg), body fat percentage (8.04 %), sum of seven skinfolds (34.12 mm), lean body mass (59.24 kg) and somatotype (i.e., endomorph, mesomorph, and ectomorph ratios) (1.80, 1.40. and 2.80) respectively. The mean values for maximum oxygen consumption (V̇ O2max) (63.50 mlO2 . kg˗1.min-1 ), running economy (at 12 km·hr -1 32.8 L/min, 14.5 km·hr -1 41.70 L/min, 16 km·hr -1 56 L/min, 19.2 km·hr -1 30.60 L/min), ventilatory threshold (2.95 L/min-1 ), maximum heart rate (191.00 bpm), respiratory exchange ratio (1.23) and average 10 km running speed (16.24 km·hr -1 ) were also determined. The VT1 and VT2 were calculated and at the intensities corresponding to the last point before a first non-linear increase in both VT1 and VT2. The senior male cross-country athletes showed higher values for O2 expressed relative to morphological and physiological factor. The above measurements were captured in Johannesburg at the following altitude (1753 m), barometric pressure (82.54 kPa), air density (0.98 kg/m2 at 20 ºC/ (293 k). These characteristics are generally associated with cross-country iii runners, suggesting that senior male cross-country athletes in Gauteng province, South Africa, are professional athletes. There were no significant V̇ O2max, RE and personal best 10 km time differences between participants who trained <45 minutes and those who trained >45 minutes per day during training sessions (p > 0.05). However, there were significant body weight (p = 0.028) and BF% (p = 0.030) differences between the two groups. It can thus suggest that the duration of the daily training session has a direct effect on some morphological characteristics of athletes, but no effect on others. The analysis showed that athletes of various endurance events statistically differ in morphological measures, especially in dimensions of BW and BF%. Further, highlight the importance of morphological and physiological factors in cross�country running. This research will serve as a basis for future studies and will provide information on senior male cross-country athletes, which can be referred to by coaches and sports scientists who train athletes during the competition preparation phase. KEY WORDS: anthropometry, V̇ O2max, running economy, ventilatory threshold.
... Comparing marathon and ultra-marathon running, differences in the age of peak performance have been very recently reported. In marathon running, the best performances of women and men are achieved between 25 and 35 years of age [4,11,[14][15][16]. In above-marathon distances, the age of peak performance is higher [4]. ...
... In contrast to such expectations, some results seem to indicate a larger sex gap in ultra-marathons compared to marathons [20], although there might be a potential bias underlying these results. Most studies either did not consider all participants and only focused on the top athletes [15,22,23], or had a limited sample size of athletes, only investigating a small number of races and/or a limited period [11,16]. Comparing only the top ten world record performances carries the risk of the results being affected by athletes with the highest performance level. ...
... Most previous studies suggested that the age of peak performance in ultra-marathons lies between 30 to 49 years for men and between 30 to 54 years for women [3,4,13,17,45]. However, our result is more in the suggested range of marathon peak performance age of 25 to 35 years for men and women [11,[14][15][16]. ...
Article
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Age and sex are well-known factors influencing ultra-marathon race performance. The fact that women in older age groups are able to achieve a similar performance as men has been documented in swimming. In ultra-marathon running, knowledge is still limited. The aim of this study was to analyze sex-specific performance in ultra-marathon running according to age and distance. All ultra-marathon races documented in the online database of the German Society for Ultra-Marathon from 1964 to 2017 for 50-miles races (i.e. 231,980 records from 91,665 finishers) and from 1953 to 2017 for 100-miles races (i.e. 107,445 records from 39,870 finishers) were analyzed. In 50-miles, races times were 11.74±1.95 h for men and 12.31±1.69 h for women. In 100-miles, race times were 26.6±3.49 h for men and 27.47±3.6 h for women. The sex differences decreased with older age and were smaller in 100-miles (4.41%) than in 50-miles races (9.13%). The overall age of peak performance was 33 years in both distances. In summary, women reduced the performance difference to men with advancing age, the relative difference being smaller in 100-miles compared to 50-miles. These findings might aid coaches and ultra-marathon runners setting long-term training goals considering their sex and age. Keywords: age of peak performance; athlete; sex difference; ultra-endurance
... L'équation permet une bonne estimation du pic de performance. Certes, un polynôme du second degré en est aussi capable, toutefois sa symétrie rend médiocre l'ajustement des données sur la durée de vie totale (136,287). De même, l'équation de Moore présente des avantages considérables sur les équations linéaires. (290). ...
... Le vieillissement est alors perçu comme un phénomène constant au cours du temps. Certes le vieillissement est progressif, mais nombreux sont les exemples qui montrent que la dynamique d'ensemble n'est pas linéaire (12,44,66,72,116,132,130,136,287,292). ...
... D'abord, l'équation est uniquement descriptive. En dehors de ses qualités d'ajustement, elle ne repose, pour l'instant, [91] sur aucune hypothèse biologique, comme beaucoup d'autres équations et modèles proposés (72,83,132,130,136,287,293). Même si quelques études récentes proposent, comme Rittweger et al., de relier le déclin de la performance physique à la dégradation de certains grandes fonctions physiologiques (131). ...
Thesis
L’organisation biologique, du niveau moléculaire jusqu’au niveau des performances de l’organisme. La locomotion est une fonction neurophysiologique hautement intégrée illustrant un tel processus multi-échelle. Le déclin des performances de locomotion avec l’âge, comme la vitesse maximale, a été observé pour de nombreuses espèces, aussi bien en captivité qu’en milieu naturel. Cependant, ces descriptions restent souvent succinctes, sans précision sur la progression de ces performances au cours du vieillissement. Dans ces travaux, nous utilisons une équation bi-phasique pour décrire la relation entre performance de locomotion et âge sur l’ensemble de la durée de la vie pour Caenorhabditis elegans, Mus domesticus, Canis familiaris, Equus caballus et Homo sapiens. Les performances maximales de locomotion se révèlent être des bio-marqueurs robustes pour suivre la progression des performances sur l’ensemble de la durée de vie des animaux, permettant ainsi d’estimer le pic physiologique et le début du déclin des performances. De plus, dans tous les cas, nous remarquons que la forme de progression des performances maximales selon l’âge est similaire et conservée d’une espèce à l’autre ; seule varie la pente dans le temps, dépendant de l’espèce et la performance mesurée. L’observation des performances selon le genre ne montre pas de différence dans la forme de l’enveloppe. Néanmoins, elle révèle des écarts variables dans les performances maximales entre femelles et mâles selon les espèces. Enfin, les conditions thermiques affectent les performances maximales de locomotion, mais la forme de l’enveloppe reste aussi préservée. Nous avons ensuite étudié le développement et l’expansion de cette dynamique au cours du siècle dernier pour les performances athlétiques maximales d’Homo sapiens. Cette étude révèle que la forme s’est progressivement précisée au cours du temps en s’étendant à tous les âges et suivant homothétiquement la progression des records du monde. Néanmoins, la progression semble ralentir au cours des dernières décennies, laissant présager l’atteinte possible des limites biologiques d’Homo sapiens. Ces travaux offrent de nouvelles perspectives sur l’utilité des approches comparatives et l’utilisation d’un bio-marqueur comme les performances de locomotion pour suivre les dynamiques sur l’ensemble de la durée de vie à différentes échelles. Elles apportent aussi un regard novateur sur la progression des performances avec l’âge, en intégrant à la fois les processus de développement et de vieillissement, permettant ainsi de préciser les pics physiologiques et la forme des progressions des performances sur toute la durée de la vie.
... 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][8][9][10][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][8][9][10][11]. ...
... 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][8][9][10][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][8][9][10][11]. With regards to the role of sex, it has been suggested that APP was older in women than in men [7,[9][10][11], with an exception [8] that showed the opposite trend. ...
... 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][8][9][10][11]. With regards to the role of sex, it has been suggested that APP was older in women than in men [7,[9][10][11], with an exception [8] that showed the opposite trend. ...
Article
Full-text available
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) mostly from USA have been examined so far. Therefore, the aim of the present study was to examine the age of peak performance (APP) in a European race, the ‘Berlin Marathon’. 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
... The largest participation numbers so far were reached in 2016, with approximately nine million runners crossing finish lines all over the world (The State of Running, 2019). A total of 12% of those finishers were marathoners (The State of Running, 2019), and most of them were age group athletes Lara et al., 2014). ...
... There is no consensus about the precise age of peak performance and the dynamics of the age-related performance decline in endurance sport in the current scientific literature (Lara et al., 2014;Zavorsky et al., 2017;Nikolaidis et al., 2018Nikolaidis et al., , 2019aJäckel et al., 2020). Depending on the discipline ("locomotion models") (Jäckel et al., 2020), the study population (recreational athletes versus top age group athletes (Lepers and Cattagni, 2012;Zavorsky et al., 2017) versus top professional athletes (Knechtle et al., , 2019 and other factors like research period (Leyk et al., 2007;Lara et al., 2014;, the outcomes are different. ...
... There is no consensus about the precise age of peak performance and the dynamics of the age-related performance decline in endurance sport in the current scientific literature (Lara et al., 2014;Zavorsky et al., 2017;Nikolaidis et al., 2018Nikolaidis et al., , 2019aJäckel et al., 2020). Depending on the discipline ("locomotion models") (Jäckel et al., 2020), the study population (recreational athletes versus top age group athletes (Lepers and Cattagni, 2012;Zavorsky et al., 2017) versus top professional athletes (Knechtle et al., , 2019 and other factors like research period (Leyk et al., 2007;Lara et al., 2014;, the outcomes are different. For example, Jäckel et al. (2020) stated a progressive running performance decline for recreational half-ironman triathletes after the age of 50 years. ...
Article
Full-text available
The aspect of participation and performance trends in marathon running has been investigated mainly in marathons held in the United States of America (e.g., “New York City Marathon,” “Boston Marathon”), but not for the fastest course in the world, the “Berlin Marathon” held in Berlin, Germany. This study aimed to examine trends in participation and performance in the “Berlin Marathon” on all its previous 46 editions from 1974 to 2019, the largest dataset ever studied in this event with 696,225 finishers (after data cleaning). Athletes in all age groups increased their participation, except for male athletes aged 20–49 years and athletes of both sexes above 79 years of age. This overall increase in participation was more pronounced in women, but still, there are more men than women participating in “Berlin Marathon” nowadays. All age group athletes decreased their performance across years overall, whereas the top ten recreational athletes improved their performance over the years. Our findings improved the knowledge about the evolution of male and female marathoners across calendar years, especially for the fastest marathon race in the world, the “Berlin Marathon.”
... For running, the age of peak athletic performance has mainly been investigated for half-marathon , marathon (Lara et al., 2014;Nikolaidis and Knechtle, 2018b,c;, and ultra-marathon running. However, the age of peak athletic performance in shorter endurance running distances has not been studied previously to the best of our knowledge. ...
... For example, in half-marathon Nikolaidis and Knechtle, 2018a; and in marathon running (Nikolaidis and Knechtle, 2018b;, women achieved their best race times earlier in life than men in studies where all runners by age group were considered. These findings were in contrast to earlier findings where the age of peak marathon performance was assumed to appear earlier in life in men compared to women in a research where top 10 runners by age group were considered (Lara et al., 2014). When world class track-and-field athletes were investigated, the mean age of peak performance was older for marathon and male throwers whereas women reached an older age of peak performance than men in the hurdles, middle and long-distance running events. ...
Article
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This study investigated 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 km, 10 km, 12 km, 15 km, 10 miles, 20 km, half-marathon, 25 km, 30 km, and marathon). For each race, a regression model was fitted. Effects of sex, alone and in interaction with age, and the effect of country of origin on performance were examined in a multi-variable model. The relationship between age and race time was modelled through a 4th order-polynomial function. Women achieved their best half-marathon and marathon race time, respectively, one year and three years earlier in life than men. On the contrary, in the other races, the best women performances were achieved later in life than men (i.e. 4 miles and 30 km: 2 years later, 8km: 3 years later, 15-20-25 km: 1 year later, 10 miles : 4 years) or at the same age (i.e. 5 km, 10 km, 12 km). Moreover, age of peak performance did not change monotonically with the distance of race. For all races, except 12 km, sex differences had an absolute maximum at old ages and a relative maximum near the age of peak performance. From 8 km onward, estimated sex differences were increasing with increasing race distance. Regarding country, runners from Canada were slower than runners from the United States of America in 5 km by 00:10:05 h:min:s (p<0.001) and in half-marathon by 00:18:43 h:min:s (p<0.01). On the contrary, in marathon, they were 00:18:43 h:min faster (p<0.05). Moreover, in 10 miles, runners from Great Britain were 00:02:53 h:min:s faster (p<0.05) than runners from the United States of America. In summary, differences seem to exist in the age of peak performance between women and men and for nearly all distances sex differences showed an absolute maximum at old ages and relative maximum near the age of peak performance. Thus, these findings highlight the need for sex-specific training programs, especially near the age of peak performance and for elder runners. Key words: female, male, aging, youth, performance
... The age of peak performance has been mainly investigated in athletics showing different characteristics between male and female athletes [2][3][4][5] . For instance, Lara et al. 5 showed that the best women's performance was obtained at 29 years and in men and at 27 years. ...
... The age of peak performance has been mainly investigated in athletics showing different characteristics between male and female athletes [2][3][4][5] . For instance, Lara et al. 5 showed that the best women's performance was obtained at 29 years and in men and at 27 years. Nikolaidis et al. 4 has also shown that women were older than the men in the 10-km, half-marathon, and marathon events. ...
Article
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Abstract Aim: The purpose of this study was to investigate the relative age effect and the age of peak performance of women's football players who participated in the Olympic Games from 1996 to 2016. Methods: Birth dates, playing positions, and nationality of all players registered in women's football competition in the Olympic Games (1996 to 2016) were collected. All data used in this study were obtained from the official website of the Federation Internationale de Football Association (www.fifa.com). The sample size of the study comprised 1,203 players. Results: We found an average age of 25.1 ± 4.0 years old and a significant increase of 1.4 years in the average age from 1996 (25.0 ± 3.9 years old) to 2016 (26.4 ± 3.7 years old) (p < 0.001). The comparison of the players’ age between playing positions reveals that the goalkeepers are the oldest players (26.2 ± 4.4 years) and the forwards are the youngest players (24.4 ± 3.8 years) (p < 0.001). The RAE for women's football players showed neither effect over the years nor in different playing positions. Conclusion: We found an aging trend in women's football in the past two decades and different ages of peak performance among the playing positions. The current findings provide valuable information to coaches and professionals to program long-term training and to promote athletes’ progression towards their performance targets.
... Despite the fact that some of the details are still up for debate, there is general consensus that until a certain age, performance increases and eventually as one gets older, performance decreases, such that there must be an age-related optimum for physiological functional capacity. [3][4][5][6] For running, it seems that the optimal age is positively correlated with distance: the longer the distance, the older the optimal age. 7 This may be further substantiated by the findings of Wiswell et al. 8 for shorter distances (5 and 10 km). ...
... The typical profile for the MSE as a function of the degree of the polynomial of the model is shown in b F2 Figure 2. In this figure, we observe that the MSE is parabolically shaped with a large range where the MSE barely changes. 6 DE LEEUW ET AL. ...
Article
This article focuses on the performance of runners in official races. Based on extensive public data from participants of races organized by the Boston Athletic Association, we demonstrate how different pacing profiles can affect the performance in a race. An athlete's pacing profile refers to the running speed at various stages of the race. We aim to provide practical, data-driven advice for professional as well as recreational runners. Our data collection covers 3 years of data made public by the race organizers, and primarily concerns the times at various intermediate points, giving an indication of the speed profile of the individual runner. We consider the 10 km, half marathon, and full marathon, leading to a data set of 120,472 race results. Although these data were not primarily recorded for scientific analysis, we demonstrate that valuable information can be gleaned from these substantial data about the right way to approach a running challenge. In this article, we focus on the role of race distance, gender, age, and the pacing profile. Since age is a crucial but complex determinant of performance, we first model the age effect in a gender- and distance-specific manner. We consider polynomials of high degree and use cross-validation to select models that are both accurate and of sufficient generalizability. After that, we perform clustering of the race profiles to identify the dominant pacing profiles that runners select. Finally, after having compensated for age influences, we apply a descriptive pattern mining approach to select reliable and informative aspects of pacing that most determine an optimal performance. The mining paradigm produces relatively simple and readable patterns, such that both professionals and amateurs can use the results to their benefit.
... However, this relationship may be different according to age category and sex. Previous studies have reported that older athletes presented a decrease in endurance performance [7,39], and this fact can be associated with central and peripheral changes, as well as changes in training habits [18,41]. In addition, regarding sex differences, female athletes tended to perform lower than their male peers, which can be partially explained by physiological and anthropometric differences, in association with differences in training commitment [15,21]. ...
Article
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Purpose: The relationships between anthropometric and training variables with running performance were previously investigated. However, it is possible that through the moderating role of anthropometric variables, the magnitude of the relationship between training and performance may be changed. The purpose of this study was to estimate the mediation role of body mass index (BMI) in the relationship between training volume and running performance among non-professional runners, taking into account sex and age category. Methods: The sample comprised 1151 non-professional road runners (61.8% male), aged 18-72 years. Information about sex, age, body mass (kg), body height (cm), running pace, motivation for running, training volume and frequency were obtained through an online questionnaire. Taking into account athletes' age, they were split into two age categories: "until 34 years" (adult runners) and "≥ 35 years" (master athletes). A mediation analysis was computed in Macro Process (SPSS 26), considering sex and age category. BMI was the mediating variable, while training volume/week was used as independent variable, and running pace was considered as dependent variable. Results: For both sexes and age categories, a significant association between training volume on running pace was observed [male adult: β = − 0.67; 95% CI (− 1.04 to − 0.53); male master: β = − 0.241; 95% CI (− 0.44 to − 0.26); female adult: −0.83; 95% CI (− 1.25 to − 0.41); female master: − 0.76; 95% CI (− 1.09 to − 0.44)], as well the association between training volume and BMI; and running BMI and running pace. Except for female adult runners, a mediation effect of BMI was observed. Conclusion: The present study showed that BMI mediated the association between training volume and running pace in non-professional runners of different sexes and age categories. On the other hand, a small influence of training volume on the expression of BMI was found.
... A number of studies have drawn attention to agerelated changes in the musculoskeletal system and other organ systems that lead to a decline in performance with age. 22,23 The age of runners in the present study did not correlate with their performance (p>0.05; Table 3). ...
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Introduction: The key elements of success in a given sports competition have become an area of interest for researchers. The reason for the success of Ethiopian runners was not proved scientifically. This study aimed at documenting the anthropometric parameters of 10,000 meter runners and to find out the association between such parameters and performances. Methods: A descriptive field study was conducted. 32 elite 10,000 meter runners participated. The data were collected while the athletics team was preparing for the world athletics championship. The procedure was repeated three times for each individual. Statistical analysis was performed using SPSS version 18. All the data were presented as mean ± S.D. The Pearson product-moment test was used to determine the correlation between the variables and finishing time. The level of significance for all statistical tests was set at p < 0.05. Results: The experience of male and female athletes showed a negative association with finishing time. However, there was no statistically significant correlation between the age and running time in both sexes. A significant positive association of body weight to running time was observed in both sexes. Body height correlates positively to running time in males (p<0.05), but not in females. The length of the arm, the forearm, the leg in both sexes and length of the thigh in women had no significant association with finishing time. A smaller arm and calf circumferences have a positive effect on the performance of both sexes. Smaller thigh circumference showed a positive association with the performance of men. Conclusion: The age of the runners did not correlate with their performance. The anthropometric variables displayed significantly higher values in men than in women. Experienced athletes performed better in both sexes. Anthropometric parameters may be useful for selection, prediction, improving running performance besides for preventing injuries and health risk assessment.
... Multiple methods have been developed to estimate the age of peak performance, including typical polynomial curve fitting, mixed models, rolling means and other regression analyses [32]. Quadratic and other second-order polynomial fittings, such as in [33] and in [31], provide a poor estimate of the age of peak performance as the is consistently reported to be asymmetrical, with an early (i.e. before mid-life) age of peak performance [14,17,18,31,34,35]. ...
Article
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The age-performance relationship describes changes in the organism’s structural and functional capabilities over the course of the lifespan. The typical, empirical pattern is an asymmetrical inverted-U shape association with peak capacity occurring early in life. This process is well described in the literature, with an increasing interest in features that characterize this pattern, such as the rate of growth, age of peak performance, and rate of decline with aging. This is usually examined in cohorts of individuals followed over time with repeat assessments of physical or cognitive abilities. This framework ought to be integrated into public health programs, embedding the beneficial (such as physical or cognitive training) or adverse effects (such as chronic diseases or injuries) that respectively sustain or limit capabilities. The maintenance of physical or cognitive performances at older ages would result in both optimal health and promote resistance to disabling conditions and chronic diseases, such as obesity and type 2 diabetes. The causes of accelerated degeneration of health optima are mainly: sedentary and unhealthy lifestyles -including poor nutrition-, exposure to environmental pollutants, and heterogeneity in aging. Better knowledge of optima, compatible with or required for good health, should also allow for establishing ideal conditions for longevity.
... These sudden results ("here and now") are maybe one of the main reasons why these sports have received a boost in popularity during the last decade (Knechtle et al., 2014;Lara et al., 2014). We live in a system that hardly enables us enough time to meet our fitness goals, and we try to compensate by signing into shorter but more intense training programs with the rationale of achieving visible benefits in the short/mid-term. ...
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The social relevance of endurance sports has increased motivation to engage in these particular physical activities, associating their practice with a particular lifestyle (e.g., feeling victorious, self-improvement). Therefore, the dark personality traits (not because they are negative, but because they are more hidden), understood as a personal and adaptive response to the psychosocial relationships that athletes establish while practicing these sports. Following these arguments, Grit has been used to trace the response of athletes in their quest to improve performance and endurance in the face of common setbacks suffered as a result of long hours of training. Empirical studies should help to discover how these personality traits can pose real challenges to their adaptation, and what the impact of their psychological response may be in a functional or dysfunctional way (e.g., exercise addiction), in order to classify them as risk or protective factors. Through transversal design, the present study seeks to explore the relationship between Grit and Dark Traits of Personality regarding the appear of Exercise Addiction (EA), in a sample (N=241) of amateur endurance sport athletes (Mage=31.80; SD=9.87). The results show that men not only score higher for addiction levels, but also for narcissism (grandiosity feelings) and psychopathy (coldness) factors. If signs of narcissism and Machiavellianism increase, perseverance efforts grow too, and the likelihood of exercise addiction increases considerably. The conclusions thrown by the results allow us to place consistency in the interest as a protective factor for the EA, whereas dark traits of personality– especially Machiavellianism–constitute a risk factor.
... Although for the full sample of full-marathon runners (column 1), as well as for the male and female fullmarathon runners (columns 2-3), old runners generally run faster than do young runners, this pattern is reversed for top runners. Columns 6-8 show that among top 30 runners, finish time generally increases with age, consistent with the findings in the sports medicine and physiology literature (Lara, Salinero, & Del Coso, 2014;Trappe, 2007;Zavorsky, Tomko, & Smoliga, 2017). In general, elite marathon runners achieve their best performance between age 25 and 35 and their performance declines with aging as the cardiovascular capacity declines with aging (Trappe, 2007;Zavorsky et al., 2017). ...
Article
Using a sample of more than 0.3 million marathon runners of 56 race events in China in 2014 and 2015, we estimate the air pollution elasticity of finish time to be 0.041. Our causal identification comes from the exogeneity of air pollution on the race day because runners are required to register for a race a few months in advance and we control for confounding factors. Including individual fixed effects also provides consistent evidence. Our study contributes to the emerging literature on the effect of air pollution on short-run productivity, particularly on the performance of athletes engaging in outdoor sports.
... The control variables are age, gender, nationality, number of previous participations, the defined clusters, and start corral dummy (since 2008). An alternative model inspecting a quadratic age effect (cf., Lara et al., 2014;Nikolaidis et al., 2019a) is also carried out but both terms -first and second order -shows insignificant effect and does not influence the results. Start corral was established in the New York Marathon in 2008 with the aim to offer safer conditions for runners by an ordered and smooth flow. ...
Article
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Heat exposure affects human performance in many ways. Both physiological (i.e., glycogen sparing, oxygen uptake, thermoregulation) and biomechanical mechanisms (i.e., contact time, knee flexion, muscle activity) are affected, hence reducing performance. However, the exposure affects persons differently. Not all athletes necessarily experience an identical thermal condition similarly, and this point has been overlooked to date. We analyzed endurance performances of the top 1000 runners for every year during the last 12 New York City Marathons. Thermal conditions were estimated with wet-bulb globe temperature (WBGT) and universal thermal climate index (UTCI). Under identical thermal exposure, the fastest runners experienced a larger decline in performance than the slower ones. The empirical evidence offered here not only shows that thermal conditions affect runners differently, but also that some groups might consistently suffer more than others. Further research may inspect other factors that could be affected by thermal conditions, as pacing and race strategy.
... Their primary findings were there are virtually no relevant running time differences (P<.01) in marathon finishers from 20 to 55 years and the majority of middle-aged and elderly athletes have training histories of less than 7 years of running. Lara et al [33] explored the correlations of gender and age with completion time in marathon runners. The results showed that there was a significant positive correlation between the men's age and completion time (Pearson correlation coefficient r=.92, P<.05). ...
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Background: Long-distance running can be a form of stress to the heart. Technological improvements combined with the public's gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. Objective: We investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. Methods: This study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. Results: The results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = -2.702 - 0.096 × [waist circumference] - 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner's CFR varies with changes in activity. Since the runner's acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner's explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. Conclusions: In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.
... Although the effect of age-related reduction in reproductive hormones on human performance was not clear [21], the decrease of sex difference in race time with aging might be partially attributed to the women's superiority in survival and longevity that might be enhanced when the capacity to reproduce would be inhibited (hormonal adaptations after menopause) [20]. On the other hand, when the top 5 runners were considered, the sex difference in performance did not differ by age group, which confirmed the trend in top 10 marathon runners [37], where men were faster by ~19 % than women in all age groups. Sex difference in sport performance has been partially attributed to a higher level of circulating testosterone concentration, muscle strength and circulating hemoglobin in men than in women [38]. ...
Article
Participation and performance trends have been analyzed for different ultramarathons for limited time periods. This study examined trends in participation and performance in the oldest ultramarathon in the world, the ‘Comrades Marathon’ (South Africa), during a century (1921-2019). Data from www.ultra-marathon.org on 100,000 unique finishers were analysed using different general linear models. Women represented 4.2% of the total sample (n=4,152), and the first women ran this race in 1978. Before the year 1965, the number of participants in the race ranged between 5 and 35 athletes, then started to grow exponentially until mid 90’s. An increase in finishers in the 70s mainly due to an increase in male athletes in age groups 30-39, 40-49 and 50-59 years was observed (p<0.001). A stable running speed for overall women and men but an improvement in performance for the annual top five women and men were shown (p<0.001). Male runners were faster than female runners for all age groups (p<0.001). While overall performance was not improved across years, the annual top five women and men were able to improve their performance over years. Keywords: age group, performance analysis, race time, sex difference, ultra-endurance
... In contrast, athletes running shorter distances, such as the New York City Marathon, show a significant decline in performance among elite marathon runners aged 36 years and older [26]. Similarly, the analysis of several marathon races showed a significant performance gap between the age groups of athletes over 56 years in recreational runners [27]. ...
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This study aimed to analyze the number of successful finishers and the performance of the athletes in the 100-km ultra-marathons worldwide. A total of 2,067 100-km ultra-marathon races with 369,969 men and 69,668 women competing between 1960 and 2019 were analyzed, including the number of successful finishers, age, sex, and running speed. The data was obtained from the publicly accessible website of the DUV (German Ultramarathon Association) with the help of a Python script from their database. General linear models were as follows: average speed (three-way ANOVA) and sex × decade × age group; average running speed considering only the top three athletes per event (two-way ANOVA) sex × decade. Sex was always included as a fixed factor, decade, and age group were included as random factors. The results showed a strong increase in the number of running events as well as a strong increase in the number of participants in the 100-km ultra-marathons worldwide. The performance gap disappeared in older age groups and from the age of 60 years onwards where participants no longer showed a significant performance gap in a 100-km ultra-marathon. Nevertheless, the running speed of athletes over 70 years has improved every decade. In contrast to the top three athletes, for whom the performance gap has also narrowed somewhat in recent decades, the performance gap between men and women is significant over all decades (F = 83.4, p<0.001; p2 = 0.039). The performance gap between the sexes is not significant in the youngest age groups (20-29 years) and the oldest age groups (>90 years) among recreational athletes and among top-three athletes over the age of 70 years. In summary, especially for older athletes, a 100-km ultra-marathon competition shows an increasing number of opponents and a stronger performance challenge. This will certainly be of interest for coaches and athletes in the future, both from a scientific and a sporting point of view.
... Shorter events were characterised by fast starts, followed by progressive slowing, while 5,000m and 10,000m events were associated with fast starts and fast finishes, with a period of slower running during the middle of the race. March et al. [42] conclude that more even pacing tends to be associated with faster finish-times in the marathon, with females associated with more consistent pacing than males, even when the effects of ability and age were controlled for [43][44][45][46]. Tucker & Noakes [47] emphasise how pacing can be impacted by many different factors. ...
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Introduction In the marathon, how runners pace and fuel their race can have a major impact on race outcome. The phenomenon known as hitting the wall (HTW) refers to the iconic hazard of the marathon distance, in which runners experience a significant slowing of pace late in the race, typically after the 20-mile mark, and usually because of a depletion of the body’s energy stores. Aim This work investigates the occurrence of significant late-race slowing among recreational marathoners, as a proxy for runners hitting the wall, to better understand the likelihood and nature of such slowdowns, and their effect on race performance. Methods Using pacing data from more than 4 million race records, we develop a pacing-based definition of hitting the wall, by identifying runners who experience a sustained period of slowing during the latter stages of the marathon. We calculate the cost of these slowdowns relative to estimates of the recent personal-best times of runners and compare slowdowns according to runner sex, age, and ability. Results We find male runners more likely to slow significantly (hit the wall) than female runners; 28% of male runners hit the wall compared with 17% of female runners, χ ² (1, N = 1, 928, 813) = 27, 693.35, p < 0.01, OR = 1.43. Such slowdowns are more frequent in the 3 years immediately before and after a recent personal-best (PB) time; for example, 36% of all runners hit the wall in the 3 years before a recent PB compared with just 23% in earlier years, χ ² (1, N = 509, 444) = 8, 120.74, p < 0.01, OR = 1.31. When runners hit the wall, males slow more than females: a relative slowdown of 0.40 vs. 0.37 is noted, for male and female runners, when comparing their pace when they hit the wall to their earlier race (5km-20km) pace, with t (475, 199) = 60.19, p < 0.01, d = 0.15. And male runners slow over longer distances than female runners: 10.7km vs. 9.6km, respectively, t (475, 199) = 68.44, p < 0.01, d = 0.17. Although, notably the effect size of these differences is small. We also find the finish-time costs of hitting the wall (lost minutes) to increase with ability; r ² (7) = 0.91, p < 0.01 r ² (7) = 0.81, p < 0.01 for male and female runners, respectively. Conclusions While the findings from this study are consistent with qualitative results from earlier single-race or smaller-scale studies, the new insights into the risk and nature of slowdowns, based on the runner sex, age, and ability, have the potential to help runners and coaches to better understand and calibrate the risk/reward trade-offs that exist as they plan for future races.
... Still, only a few studies have focused on diversity among the group of recreational runners [300][301][302] . Within the group of recreational runners, anthropometrics, including age and gender, have been found to relate to performance 299,303,304 . Also, body height and body mass were lower in more economical runners 88,305 . ...
Thesis
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The large-scale usage of smartphone applications and sports watches in running provides the potential to lower injury risk and improve performance. To achieve these common goals, contextual factors need to be taken into account to provide users with accurate and personal feedback. This thesis aims to develop methods to improve the quality of wearable feedback and its interpretation. Within the data process from parameter detection to feedback to the user, the are several ways to improve the quality of the feedback. The studies in this thesis demonstrate various possibilities. The thesis projects concern an improved algorithm for cycle detection; a method to cross-validate speed; an approach to determine an energetic optimal running technique; highlight the importance of individual differences; and a concise, yet comprehensive description of the full spectrum of running styles. It is concluded that to further improve the quality of wearable feedback, cross-validation, self-optimization, biomechanical dependencies, and individual differences should be considered as demonstrated in the thesis.
... Age has been determined to be one of the main factors influencing sports performance [1], with physical exercise and training being influential aspects in the delay of the muscular-tendinous deterioration caused by ageing [2]. The last few decades have seen an increase in age regarding high-level athletes [3][4][5][6]. ...
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The purpose of this study was to determine the evolution of the age of badminton players in the top 100 of the World Ranking for men and women from 1994 to 2020. Data were collected from badminton players participating in the top 100 World Rankings (4800 entries: 1233 players; 595 men and 638 women) from 1994 to 2020. The mean age of the top 100 and the average highest ranking of the players were analysed for both genders. The mean age of the male players in the World Ranking increased from 23.7 ± 3.2 years in 1994 to 26.3 ± 4.4 years in 2020 (p < 0.001) and in female players, from 22.8 ± 3.8 years in 1994 to 24.7 ± 3.3 years in 2020 (p < 0.001). In addition, women recorded a younger age at entry into the top 100 and when reaching their best ranking. Additionally, there has been a clear increase in Asian players in the top 100 of the World Ranking in recent years, reaching over 60%. These data could be used to develop and organise training plans in this sport, optimising and maximising players’ performance.
... In general, the best ultra-marathon performance is achieved at an older age than the best performance over half-marathon and marathon (Figure 2; Knechtle et al., 2012dKnechtle et al., , 2014bRomer et al., 2014;Rüst et al., 2014;Zingg et al., 2014b;Nikolaidis and Knechtle, 2018a,b). The best marathon race time is achieved at the age of ∼30 years, where performance level and nationality are important predictor variables of the age of peak performance (Hunter et al., 2011;Knechtle et al., 2014aKnechtle et al., , 2016aLara et al., 2014;Nikolaidis et al., 2016). In ultra-marathons, the age of best performance is often ∼35 years or older Rüst et al., 2014;Knechtle and Nikolaidis, 2017;Nikolaidis and Knechtle, 2018a,b). ...
Article
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In this overview, we summarize the findings of the literature with regards to physiology and pathophysiology of ultra-marathon running. The number of ultra-marathon races and the number of official finishers considerably increased in the last decades especially due to the increased number of female and age-group runners. A typical ultra-marathoner is male, married, well-educated, and ~45 years old. Female ultra-marathoners account for ~20% of the total number of finishers. Ultra-marathoners are older and have a larger weekly training volume, but run more slowly during training compared to marathoners. Previous experience (e.g. number of finishes in ultra-marathon races and personal best marathon time) is the most important predictor variable for a successful ultra-marathon performance followed by specific anthropometric (e.g. low body mass index, BMI, and low body fat) and training (e.g. high volume and running speed during training) characteristics. Women are slower than men, but the sex difference in performance decreased in recent years to ~10-20% depending upon the length of the ultra-marathon. The fastest ultra-marathon race times are generally achieved at the age of 35-45 years or older for both women and men, and the age of peak performance increases with increasing race distance or duration. An ultra-marathon leads to an energy deficit resulting in a reduction of both body fat and skeletal muscle mass. An ultra-marathon in combination with other risk factors, such as extreme weather conditions (either heat or cold) or the country where the race is held, can lead to exercise-associated hyponatremia. An ultra-marathon can also lead to changes in biomarkers indicating a pathological process in specific organs or organ systems such as skeletal muscles, heart, liver, kidney, immune and endocrine system. These changes are usually temporary, depending on intensity and duration of the performance, and usually normalize after the race. In longer ultra-marathons, ~50-60% of the participants experience musculoskeletal problems. The most common injuries in ultra-marathoners involve the lower limb, such as the ankle and the knee. An ultra-marathon can lead to an increase in creatine-kinase to values of 100,000-200,000 U/l depending upon the fitness level of the athlete and the length of the race. Furthermore, an ultra-marathon can lead to changes in the heart as shown by changes in cardiac biomarkers, electro- and echocardiography. Ultra-marathoners often suffer from digestive problems and gastrointestinal bleeding after an ultra-marathon is not uncommon. Liver enzymes can also considerably increase during an ultra-marathon. An ultra-marathon often leads to a temporary reduction in renal function. Ultra-marathoners often suffer from upper respiratory infections after an ultra-marathon. Considering the increased number of participants in ultra-marathons, the findings of the present review would have practical applications for a large number of sports scientists and sports medicine practitioners working in this field. Keywords: extreme endurance, pathophysiology, performance, injury, gender
... In summary, among non-athletes, running events have become a leisure/social activity, with a reduction of the competitive perspective generally observed in the past [24][25][26]. In addition, the increase in the number of women, as well as younger and older athletes' participation in recent years, can also be associated with this observed decrease in performance [27,28]. The present results are different from those shown by Teutsch et al. [17], in a study with athletes who completed the 12 h and 24 h races in Basel (Switzerland). ...
Article
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Background and Objectives: Increases in the number of participants in time-limited ultra-marathons have been reported. However, no information is available regarding the trends in participation, performance and age in 12 h and 24 h time-limited events. The aim of the study was to describe the trends in runners’ participation, performance and age in 12 h and 24 h ultra-marathons for both sexes and to identify the age of peak performance, taking into account the ranking position and age categories. Materials and Methods: The sample comprised 210,455 runners in time-limited ultra-marathons (female 12 h = 23,706; female 24 h = 28,585; male 12 h = 61,594; male 24 h = 96,570) competing between 1876 and 2020 and aged 18 to 86 years. The age of peak performance was tested according to their ranking position (first–third; fourth–tenth and >tenth position) and taking into account their running speed in different age categories (<30 years; 31–40 years; 41–50 years; 51–60 years; >60 years), using the Kruskal–Wallis test, followed by the Bonferroni adjustment. Results: An increase in the number of participants and a decrease in running speed were observed across the years. For both events, the sex differences in performance decreased over time. The sex differences showed that male runners performed better than female runners, but the lowest differences in recent years were observed in the 24 h ultra-marathons. A positive trend in age across the years was found with an increase in mean age (“before 1989” = 40.33 ± 10.07 years; “1990–1999” = 44.16 ± 10.37 years; “2000–2009” = 45.99 ± 10.33 years; “2010–2020” = 45.62 ± 10.80 years). Male runners in 24 h races were the oldest (46.13 ± 10.83 years), while female runners in 12 h races were the youngest (43.46 ± 10.16 years). Athletes ranked first–third position were the youngest (female 12 h = 41.19 ± 8.87 years; female 24 h = 42.19 ± 8.50 years; male 12 h = 42.03 ± 9.40 years; male 24 h = 43.55 ± 9.03 years). When age categories were considered, the best performance was found for athletes aged between 41 and 50 years (female 12 h 6.48 ± 1.74 km/h; female 24 h 5.64 ± 1.68 km/h; male 12 h 7.19 ± 1.90 km/h; male 24 h 6.03 ± 1.78 km/h). Conclusion: A positive trend in participation in 12 h and 24 h ultra-marathons was shown across the years; however, athletes were becoming slower and older. The fastest athletes were the youngest ones, but when age intervals were considered, the age of peak performance was between 41 and 50 years.
... Finally, age seemed to have a different impact on the determinants of performance. Due to the relationship with age, the maximum heart rate is reduced, so the highest values of maximum oxygen consumption, in elite marathoners, are reached around 27 years for males and 29 for females (22). However, the best performance is registered around the age of 35 due to the improvement in RE which offsets the drop in VO 2 max (23). ...
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Research has outlined that elite marathon runners possess excellent running economy among other wellknown physiological and biomechanical determinants (2). Not only is whole body dynamic exercise metabolically costly, but neural processing effort, requiring the brain’s limited metabolic resources, continually occurs during prolonged exercise (4), notably for self-paced exercise like running a marathon. Under the umbrella of energy saving, executive functioning capacity resting on goal-oriented behavior may also explain differences in endurance performance even at top levels. First, executive function may be predictive of endurance performance (1): faster runners would have better inhibitory control, not only over motor responses but also over interfering, distracting information. Further, the elite athletes through deliberate practice over the years may have developed the ability to execute their patterns free of much frontal cortex participation. Neuroimaging studies corroborate this idea, as prefrontal cortex activity is seen to decrease in elite Kenyan runners (5). Second, effective pacing involving cognitive control and decision-making process is crucial to endurance performance. As highlighted (2), optimal pacing was an important factor in the exhibition event to break the 2-h barrier. Given that marathon might be seen as an effortful cognitive task that places high demands on several brain areas related to emotional, motivational, interoception, and executive processing, pacing assistance would be valuable in reaching an automatic mode to divert resources effortlessly and when needed. Thus, we can assume that this strategic conservation of mental effort resources through pacing aid may lead to hypofrontality phenomenon (4) and the so-called neural efficiency.
Preprint
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Background . The time distribution of biological phenomena (phenology) is a subject of wide interest, but a general statistical distribution to describe and quantify its essential properties is lacking. Existing distributions are limiting, if not entirely inappropriate, because their parameters do not in general correlate with biologically relevant attributes of the organism and the conditions under which they find themselves. Methods . A distribution function that allows quantification of three essential properties of a biological dynamic process occurring over a continuous timescale was derived from first principles. The distribution turned out to have three parameters with clear meanings and units: (i) a scaled rate of completion (dimensionless), (ii) a measure of temporal concentration of the process (units: time ⁻¹ ), and (iii) an overall measure of temporal delay (units: time). Its performance as an accurate description of the process was tested with completion data for the London Marathon employing non-linear regression. Results . The parameters of the distribution correlated with biological attributes of the runners (gender and age) and with the maximum temperature on the day of the race. These relationships mirrored known differences in morphology and physiology of participants and the deterioration of these biological attributes with age (senescence), as well as the known effects of hypo- and hyperthermia. Discussion . By relating the variation in parameter values to possible biological and environmental variables, the marathon example demonstrates the ability of the distribution to help identify possible triggers and drivers of the duration, shape and temporal shift of its temporal distribution. This more detailed account of the effect of biological and environmental variables would provide a deeper insight into the drivers of a wide variety of phenological phenomena of high current interest, such as the shifting patterns of leafing, flowering, growth, migration, etc. of many organisms worldwide.
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Elite athletes have been exercise regularly for several years with heavy training loads. The former elite athletes commonly decreased or stop their exercise that might decrease their anaerobic capacity. Eighteen male former elite athletes and nineteen non elite athletes participated on this research. The running based anaerobic test (RAST) was done to investigate anaerobic capacity included Maximal Power, Minimal Power, Velocity and Fatigue index. The blood lactate levels were measured two minute after RAST. The rate of maximal power of former elite athlete 387.3 watts, minimal power 242.2 watts, fatigue index 3.67 and blood lactate level 7.20. The rate of maximal power of non elite athlete was 445.8 watts, minimal power 282.4 watts, fatigue index 4.57 and blood lactate level 7.01. There were no differences of maximal power (p = 0.251), minimal power (p = 0.166), fatique index (p = 0.203) and blood lactate level (p = 0,878). The velocity and body mass index were difference between former elite athlete and non elite athlete (p = 0.000). There was no different of the anaerobic capacity and blood lactate level between former elite athlete and non elite athletes. The age, mass body index and less exercise may influence the decreasing of anaerobic capacity of former elite athlete.
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Physical activity is one of the most important resources used to promote health habits and well-being through a controlled and regular practice. Nevertheless, it is increasingly clear that in the area of sports, cases of excessive practice are becoming more prevalent, therefore normalizing the appearance of addictive behaviors. Previous studies on this topic highlight the importance of personality and the presence of different traits in identifying the appearance of this behavioral pattern. Taking into account all this information and the meaning of grit (perseverance and passion), one of the most emerging traits in the field of personality, we selected a sample of CrossFit and endurance sports practitioners (133 athletes; 34.59% women and 65.41% men) to understand the possible association between exercise addiction and grit, which could be affected by some indicators such as ambition and satisfaction in this relationship. A t-test, correlation analysis (Pearson), and linear regression (backward method) showed that the factor of Perseverance is positively correlated with addiction, and the other factor of grit, Consistency of Interest, did not present any kind of relationship. This seems to indicate that Perseverance is a trigger for addiction, while Consistency may help to self-regulate this behavior. In addition, younger athletes showed higher indicators of ambition to achieve their goals, and a higher risk of exercise addiction, whereas gaining more experience with sports could facilitate the development of grit.
Chapter
All people want to age "successfully," maintaining functional capacity and quality of life as they reach advanced age. Achieving this goal depends on preserving optimal cognitive and brain functioning. Yet, significant individual differences exist in this regard. Some older adults continue to retain most cognitive abilities throughout their lifetime. Others experience declines in cognitive and functional capacity that range from mild decrements in certain cognitive functions over time to severe dementia among those with neurodegenerative diseases. Even among relatively healthy "successful agers," certain cognitive functions are reduced from earlier levels. This is particularly true for cognitive functions that are dependent on cognitive processing speed and efficiency. Working memory and executive and attentional functions tend to be most vulnerable. Learning and memory functions are also usually reduced, although in the absence of neurodegenerative disease learning and retrieval efficiency rather than memory storage are affected. Other functions, such as visual perception, language, semantics, and knowledge, are often well preserved. Structural, functional, and physiologic/metabolic brain changes correspond with age-associated cognitive decline. Physiologic and metabolic mechanisms, such as oxidative stress and neuroinflammation, may contribute to these changes, along with the contribution of comorbidities that secondarily affect the brain of older adults. Cognitive frailty often corresponds with physical frailty, both affected by multiple exogenous and endogenous factors. Neuropsychologic assessment provides a way of measuring the cognitive and functional status of older adults, which is useful for monitoring changes that may be occurring. Neuroimaging is also useful for characterizing age-associated structural, functional, physiologic, and metabolic brain changes, including alterations in cerebral blood flow and metabolite concentrations. Some interventions that may enhance cognitive function, such as cognitive training, neuromodulation, and pharmacologic approaches, exist or are being developed. Yet, preventing, slowing, and reversing the adverse effects of cognitive aging remains a challenge.
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TO THE EDITOR: We would like to comment on the recent Viewpoint by Joyner et al. (2). Recently, the search for breaking two hours in the men’s marathon has increased the discussion of what to do to achieve this goal (1–3). Determination and prediction factors of endurance performance such as maximal oxygen consumption (V˙ O2max), velocity corresponding to V˙ O2max sustained for the maximal time, running economy, and anaerobic threshold are elucidated by the literature (2). As much as the combination of neural (4), metabolic, and mechanical mechanisms (5) are the main adaptations for performance, technology must also be added in this process. The evolution of running shoes and their relationship with performance are based mainly on sports biomechanics. Models that combine high midsoles, rigid carbon fiber plates, and low weight have been used even by athletes sponsored by other sports brands. Foams are highly compliant and resilient, cushioning, storing and returning energy in mechanical response. Carbon plates, on the other hand, can increase longitudinal flexural stiffness (1), providing modifications in the lever systems and consequently a possible improvement of the stretch-shortening cycle. For these reasons, World Athletics banned the use of a shoes prototype that had already been used in street competitions, further increasing the possible mechanisms related to shoes technology. Thus, in the current scenario, can technology be considered the main variable in fast marathons? We suggest vigorous discussions and studies on the topic.
Article
In their Viewpoint, Joyner et al. (2) proposed that a convergence of factors (physiology, training, technology, and logistics) may explain the recent swift improvement in marathon times. While we agree on the importance of these factors and we acknowledge previous research in elite marathon runners, we believe that masters athletes can add to the discussion for reaching fast marathons. The analysis of recent exceptional performances in masters runners (2:27:52 and 2:54:23 at 59 and 70 yr of age, respectively) reveals a common characteristic among these athletes, which is a very high fraction (91–93%) of VO2max at marathon pace (4, 5). In comparison, elite runners generally sustain 80–85%VO2max on the marathon with a quite similar running economy (1, 2). These data show new limits to human physiological capacities during endurance exercise and raise questions about the determinants of performance in the marathon. We may first wonder if the best marathon runners could sustain >90%VO2max on the marathon, and by how much the current record could be improved. We may also wonder if the higher fractional utilization of VO2max observed in masters could derive from the reduction of VO2max with aging or could result from specific long-term training adaptation. Finally, it reopens the debate about the optimization of training for the marathon; should the fractional utilization of VO2max become a priority with advancing age? Within this context, masters athletes require the continued attention of exercise physiologists, and a better knowledge of their training practices could be valuable for improving performance after 40 yr of age (3).
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The main determinants of performance during the marathon are 1) maximal oxygen uptake (VO2max), 2) ability to sustain high percentages of VO2max during long periods of time, and 3) running economy (RE). The fractional use of VO2max is related to the ability to sustain high workloads before lactate begins to accumulate in the blood, i.e., the so-called lactate threshold (LT). Another important concept is the critical speed (CS) considered the boundary between fatigue and performance during endurance exercises. Typically, LT occurs at 75–90% VO2max while CS occurs at higher absolute and relative intensities. Thus, physiologically, LT demarcates the transition between moderate- and heavy-intensity domains while CS demarcates the transition between heavy- and severe-intensity domains. Consequently, workloads above CS promote an increase in oxygen consumption, blood lactate accumulation, and a worsening in RE, causing a decrease in performance. In a literature review, Jones and Vanhatalo showed that elite long-distance runners complete the marathon distance, on average, at 96% of their CS. In this way, considering that currently, CS is the main landmark for separating the physiological limit at which physiological homeostasis can be maintained during prolonged exercises, we believe that CS can be an attractive tool to guide the prescription of training intensity, as well as the race-pace strategy for the marathon. Furthermore, future studies should verify CS as a method to quantify the training intensity distribution, similar to other studies that used blood lactate accumulation as a reference.
Article
TO THE EDITOR: The Viewpoint by Joyner and colleagues (4) on the physiology of fast marathons comes at a timely crossroads in athletics. The authors discuss the physiological limitations pertaining to two of the primary aerobic performance outcome factors, VO2max and lactate threshold. While athletes like Eliud Kipchoge and Brigid Kosgei are arguably near the limits of these physiological parameters, the athletic world has been remarkably naive regarding technological considerations to improve running economy (RE), until very recently. Improvements in RE via footwear have been claimed by athletic companies for quite some time. In 1980, claims of 2.85% improvement in RE were demonstrated with an air cushion in the midsole of marathon shoes versus still-utilized ethylenevinyl acetate (EVA) foams (2). The minimalist footwear trend also distracted the running media, which were hypersensitized to data supporting the improvement of RE with reductions in shoe mass (1). Eventually, the ergogenic effects of cushioning outweighed the once-prevailing thoughts (5), and the search for novel lightweight foams with high rebound had begun. With new applications of polyether block amide (PEBA) foam with carbon fiber plates reported to exhibit resilience of up to 87% (3), it was only a matter of time before athletic performances caught up to the polymer science. Still, there remains a gap in the true effect of high-cushion, high-energy return marathon shoes. Studies typically measure running economy in short duration circumstances; while these data are useful, it may underestimate the true improvements in running economy over the late stages of the marathon distance. REFERENCES 1. Frederick EC. Physiological and ergonomics factors in running shoe design. Appl Ergon 15: 281–287, 1984. doi:10.1016/0003-6870(84) 90199-6. 2. Frederick EC, Howley ET, Powers SK. Lower O2 cost while running in air-cushion type shoe. Med Sci Sports Exerc 12: 81–82, 1980. 3. Hoogkamer W, Kipp S, Frank JH, Farina EM, Luo G, Kram R. A comparison of the energetic cost of running in marathon racing shoes. Sports Med 48: 1009–1019, 2018. [An Erratum for this article appears in Sports Med 48: 1521–1522, 2018.] doi:10.1007/s40279-017-0811-2. 4. Joyner MJ, Hunter SK, Lucia A, Jones AM. Physiology and fast marathons. J Appl Physiol (1985). doi:10.1152/japplphysiol.00793.2019. 5. Tung KD, Franz JR, Kram R. A test of the metabolic cost of cushioning hypothesis during unshod and shod running. Med Sci Sports Exerc 46: 324–329, 2014. doi:10.1249/MSS.0b013e3182a63b81.
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The NIKE Vaporfly shoe was introduced in May 2017 as part of the original #Breaking2 Project (an event aimed to run the first marathon under 2 h). This new advanced shoe technology (NAST) changed the footwear design conception. The aim of this study was (i) to analyse the effect of NAST in men’s marathon performance, (ii) to analyse whether the changes in the environmental constraints (temperature and wind) and orography of the marathons, age and birthplace of the runners has changed from 2015 to 2019 and (iii) to analyse the impact of NAST on the historical 50 best performances. Data from top-100 men's marathon performances were collected in that timeframe. The shoes used by the athletes were identified (in 91.8% of the cases) by publicly available photographs. External and environmental conditions of each marathon and age and birthplace of the runners were also analysed. Marathon performances improved from 2017 onwards between 0.75 and 1.50% compared to 2015 and 2016 (p < 0.05). In addition, the improvement was greater in the upper deciles than in the lower ones (p < 0.001). Runners wearing NAST ran ~ 1% faster in marathon compared to runners that did not use it (p < 0.001). When conducting an individual analysis of athletes who ran with and without NAST, 72.5% of the athletes who completed a marathon wearing NAST improved their performance by 0.68% (p < 0.01). External and environmental conditions, age or birthplace of runners seems not to have influenced this performance improvement. NAST has had a clear impact on marathon performance unchanged in the environmental constraints (temperature and wind), orography, age, and birthplace of the runners but with differences between venues.
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Background With increasing age physical performance decreases. However, especially in orienteering, with the risk of running too fast in relation to map reading skills and thus of not being able to process important map information correctly a lower running speed can be an advantage. Further, with increasing age and the often-greater experience, the susceptibility to errors should decrease yielding to the aim of the study to analyze the relationship between average speed and age in senior orienteering. Material & methods All national Competitions of Middle and Long Distance as well as the Swiss Championships in the Middle and Long Distance as well as in night orienteering were analyzed for the season 2018 and 2019 for all senior categories. Average running speed was calculated per category with the concept of performance km, whereby in 2018 ten competitions and in 2019 nine competitions yielding to a total sample size of 855 female and 950 male orienteers analyzed. Furthermore, polynomial analyses of second degree between average running speed and age were conducted with calculation of coefficient of determination and in addition multilinear regression analysis between average course time and age respectively distance absolved. Results Average age of peak performance was identified with 50.0 ± 2.3 years for men and 47.3 ± 3.1 for women in 2018 respectively 51.1 ± 3.1 years for men and 47.0 ± 3.0 for women in 2019. Furthermore, high coefficients of determination were identified with average values of 0.916 ± 0.061 in men and 0.910 ± 0.051 in women 2018 respectively 0.891 ± 0.102 in men and 0.913 ± 0.079 in women in 2019. Discussion The relatively high ages are probably due to the fact that Classic beginner mistakes such as running too fast compared to map reading skills become less common, which is reflected in an absolutely lower error rate and correspondingly leads to a less severe decrease in age-related performance.
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Although endurance running (ER) seems to be a simple repetitive exercise, good ER performance also requires and relies on multiple cognitive and motor control processes. Most of previous neuroimaging studies on ER were conducted by using a single MRI modality, yet no multimodal study to our knowledge has been performed in this regard. In this study, we used multimodal MRI data to investigate the brain structural and functional differences between endurance runners (n=22; age = 26.27 ± 6.07 years; endurance training = 6.23 ± 2.41 years) and healthy controls (HCs; n =20; age = 24.60 ± 4.14 years). Compared with the HCs, the endurance runners showed greater gray matter volume (GMV) and cortical surface area in the left precentral gyrus, which at the same time had higher functional connectivity (FC) with the right postcentral and precentral gyrus. Subcortically, the endurance runners showed greater GMV in the left hippocampus and regional inflation in the right hippocampus. Using the bilateral hippocampi as seeds, further seed-based FC analyses showed higher hippocampal FC with the supplementary motor area, middle cingulate cortex, and left posterior lobe of the cerebellum. Moreover, compared with the HCs, the endurance runners also showed higher fractional anisotropy in several white matter regions, involving the corpus callosum, left internal capsule, left corona radiata, left external capsule, left posterior lobe of cerebellum and bilateral precuneus. Taken together, our findings provide several lines of evidence for the brain structural and functional differences between endurance runners and HCs. The current data suggest that these brain characteristics may have arisen as a result of regular ER training, however, whether they represent the neural correlates underlying the good ER performances of the endurance runners requires further investigations.
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TO THE EDITOR: First, we would like to commend the authors of the Viewpoint (2) for this comprehensive summary of factors of importance for running fast marathons. Then, we would like to comment on their discussion of fast marathon physiology (2). We follow closely the debate concerning 1) footwear de- signed to improve marathon performance; and 2) nonofficial optimization of the course arrangement, ambient conditions, including headwind, individualized starting times, possibilities for hydration, pacing, etc., that influence running performance. Although marathon performance has improved more than middle-distance running (4 –5% versus 1–2%), does this reflect optimization of such factors and/or improvements in long-term preparation for fast marathons during the last 30 years? De- scriptions of long-, middle- and short-term preparation by current elite marathon runners (1, 2) lack comprehensive anal- ysis of macro- and mesocycles of exercise intensity, volume, frequency, and sequence and individual monitoring and control of internal and external loads. Our understanding, in particular, of the distribution of train- ing intensity (5) and technology-assisted monitoring among elite athletes has improved (3), and researchers should describe in detail the preparation for and monitoring of fast marathons. This will advance our knowledge concerning intra-individual variations in the fundamental determinants of fast marathons (i.e., maximal oxygen uptake, running economy, etc.). This reporting should provide a holistic overview (4) of the distri- bution of training intensity and volume, frequency of sessions, recovery procedures, the type and characteristics of strength training, environmental conditions (heat and altitude) and poten- tial nutritional strategies associated with the different macro- and mesocycles and tapering utilized by elite male and female mara- thon runners.
Article
TO THE EDITOR: Limited evidence is currently available on the influence that genetics exert on athletic performance (1), which may be due to the multifactorial nature of the latter. A recent systematic review including 10,442 participants, of whom 2,984 were elite marathoners, identified 16 singlenucleotide polymorphisms associated with marathon performance (3). There is, however, a lack of replication studies of most of these genes, and thus it is not possible to identify yet the optimum genotype for endurance running performance (1, 3). Further, about half of world-class endurance athletes do not possess the supposedly “optimum” genetic pool (5), which suggests that having the right genetics might favor but not determine the odds of achieving elite-level performance, possibly due to the key influence of epigenetics. Although genetics are commonly considered an important factor to break the 2-h marathon barrier, we still do not possess any genetic tool to identify those runners with greater chances of achieving this feat (4). Future multicenter research involving whole genome sequencing, especially in top level marathoners, is needed to identify the performance-enhancing polymorphisms that would allow athletes to break the limits of human performance.
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TO THE EDITOR: Joyner et al. (5) in their Viewpoint left no stone unturned in their search for determinants of Kipchoge’s world record. However, they poorly defined the “mechanical efficiency,” which should be clarified since it is a key parameter of running performance. The minimum, inevitable, work that Kipchoge et al. did to cross the finish line is given by the external frictional drag times the 42.195 km. The overall efficiency can thus be expressed as the ratio between this minimum work and the chemical energy transformed by the muscles (2). It can be also defined as the product of the “muscular efficiency,” indicating the “propulsive efficiency,” indicating the ability to utilize the muscle work to move the body against the wind resistance. While Kipchoge’s recent performance may be partly explained by lower drag due to his body shape and drafting, the recent improvements of running performances are certainly closely related to an enhancement of muscular efficiency. For instance, trained subjects can exploit better the dynamic coupling between segments to save mechanical energy than untrained (1). Additionally, smaller muscle-tendons (and shoes!) hysteresis in athletes (3) reduces the imbalance between energy dissipation and generation, a major determinant of the running cost (4). Scientific contributions on fatigue resistance, muscle strengthening, and training intensity have potentially led to biochemical and neuromechanical adaptations, improving efficiency. Even a small enhancement of the role played by elasticity may especially impact long-distance performances, by reducing muscular fatigue over a huge number of steps. REFERENCES 1. Bianchi L, Angelini D, Lacquaniti F. Individual characteristics of human walking mechanics. Pflugers Arch 436: 343–356, 1998. doi:10.1007/ s004240050642. 2. Cavagna GA. Symmetry and asymmetry in bouncing gaits. Symmetry (Basel) 2: 1270–1321, 2010. doi:10.3390/sym2031270. 3. da Rosa RG, Oliveira HB, Gomeñuka NA, Masiero MPB, da Silva ES, Zanardi APJ, de Carvalho AR, Schons P, Peyré-Tartaruga LA. Landing- takeoff asymmetries applied to running mechanics: a new perspective for performance. Front Physiol 10: 415, 2019. doi:10.3389/fphys.2019. 00415. 4. Dewolf AH, Willems PA. Running on a slope: A collision-based analysis to assess the optimal slope. J Biomech 83: 298–304, 2019. doi:10.1016/j. jbiomech.2018.12.024. 5. Joyner MJ, Hunter SK, Lucia A, Jones AM. Physiology and fast marathons. J Appl Physiol (1985). doi:10.1152/japplphysiol.00793.2019.
Article
Accumulating evidence indicates that the brain can play a role to determine endurance performance, in addition to classical aerobic parameters (2) . For instance, induction of positive expectations regarding an intervention can improve endurance performance of well-trained runners without modifying maximal oxygen consumption, lactate threshold and running economy (5) . Moreover, application of transcranial direct current stimulation on the left dorsolateral prefrontal cortex enhanced Stroop task performance (i.e., a measure of inhibitory control) at rest, as well as reduced perceived effort and improved endurance performance in healthy individuals (1) . Such findings are possibly explained by a complex brain regulation of endurance performance. Signals derived from the brain itself (e.g., corollary discharges) and the periphery (e.g., muscle afferents) are involved in the formation of exercise-related sensations (e.g., pain, dyspnea, thermal discomfort, perceived effort) (4) . Thus, the ability to cope with such sensations, which is known as inhibitory control, likely contribute to determine endurance performance. In this sense, professional cyclists have been shown to present better inhibitory control at rest as compared to recreational cyclist (3) . However, few studies have investigated the brain regulation of endurance performance in elite athletes. Therefore, many questions remain unanswered. For example, does inhibitory control during exercise indeed play a role to performance regulation? Do African runners present better inhibitory control than other runners? Is it possible to improve elite runners’ inhibitory control to further improve performance? Thus, better understanding and manipulation of brain physiology may give an extra push to elite marathoners continue improving their marks. Doi: 10.1152/japplphysiol.00167.2020.
Chapter
In recent years, more women competed in shorter running races (10 km to marathon) than in longer races (ultra-marathons longer than a marathon). Generally, men are running faster than women with a sex difference of 10–20% regarding the length of a running race. Although earlier studies assumed that would be able to outrun men in ultra-marathon running, the fastest men are ~17–20% faster than the fastest women for all ultra-marathon distances up to 3100 miles. Women can, however, reduce the gap to men in older age groups (i.e., 40–99 years). The sex difference depends in ultra-marathon running on the participation of women where the sex difference in running speed was largest when there were fewer women than men finishers in a race. The age of peak running performance is similar for women and men for shorter running races (10 km to marathon) but seems to be higher for women in ultra-marathons. The better performance in male runners compared to female runners can be explained by physiological differences (i.e., maximum oxygen uptake, running economy). Women will never outrun men in the future.
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TO THE EDITOR: Over the last three decades, the improvement in the marathon world record (WR) has been ~4–5% for elite runners (1). During the same time period, marathon performances of the best master runners have improved at a much greater rate, especially for the older age groups (� 60 yr old) (2, 3). When changes in marathon world record performances are considered with advancing age, the decline in performance is ~10% per decade. For example, the marathon WR for a 60-yr-old male is 02:36:30, which represents a running velocity 22% slower than that of the world’s fastest time, set by Eliud Kipchoge (age 34 yr old). However, this trend of agerelated decline in marathon performance is based on WRs that belong to different runners and thus induces bias in the analysis. Previous studies showed that the age-related decline could be limited to 5–7% per decade at least until 60 yr of age for the same well-trained individual (4). Imagine therefore that Kipchoge remains competitive until 60 yr old. If so, we could predict a 6% decline in velocity per decade which would result in a marathon time of 02:18:15 at 60 yr old i.e., 18 min faster than the current WR for a 60-yr-old. This simulation suggests that marathon WRs in master categories will probably continue to improve in the future if ex-elite runners preserve their motivation to compete as they age. These super master runners will therefore offer valuable information about how lifelong endurance exercise can counteract the age-related decline in integrative physiological function (3, 5).
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TO THE EDITOR: While the viewpoint (3) superbly summarizes key factors underlining marathon running physiology and potential reasons for recent records surge, the inherently dynamic physiological nature of marathon running might have been understated. To comprehensively interpret marathon performance, one also needs to consider the time-dependent physiological alterations during both, the actual marathon run and the preceding training. In particular, the average elite marathon running velocities can be explained by regression calculations using “static” values of maximal oxygen uptake, lactate threshold (LT) and running economy (RE) (2). However, given the dynamic nature of long-distance running, the contribution of these determinants to subsequent physiological responses and actual running performance significantly varies and cannot be precisely predicted by static values modeling. The variation can relate to both, the relative contribution/importance of each factor and the duration-related dynamic differences. Indeed, LT can be altered due to potential glycogen-depletion related reduction in lactate production while RE is known to decrease as a function of running duration (4). Training also represents a complex dynamical system comprised of numerous fluctuating determinants (i.e. intensity/duration/frequency, hypoxic/heat training, tapering) further complicated by the distinct individual (5) and daily (1) variability in training-induced responses. It, thus, seems crucial to constantly monitor the corresponding training-related physiological fluctuations. Given our currently scarce understanding, further exploration of time-dependent dynamics of physiological determinants during both, the marathon running and training seems warranted. It will provide important insight into the often omitted “dynamic” aspect of the marathon performance puzzle and, ultimately, limits of marathon running. References 1. Cappaert TA. Time of Day Effect on Athletic Performance: An Update. The Journal of Strength & Conditioning Research 13: 412-421, 1999. 2. Joyner MJ. Modeling: optimal marathon performance on the basis of physiological factors. J Appl Physiol (1985) 70: 683-687, 1991. 3. Joyner MJ, Hunter SK, Lucia A, and Jones AM. Physiology and Fast Marathons. J Appl Physiol (1985) 2020. 4. Lazzer S, Salvadego D, Rejc E, Buglione A, Antonutto G, and di Prampero PE. The energetics of ultra-endurance running. Eur J Appl Physiol 112: 1709-1715, 2012. 5. Ross R, Goodpaster BH, Koch LG, Sarzynski MA, Kohrt WM, Johannsen NM, Skinner JS, Castro A, Irving BA, Noland RC, Sparks LM, Spielmann G, Day AG, Pitsch W, Hopkins WG, and Bouchard C. Precision exercise medicine: understanding exercise response variability. Br J Sports Med 53: 1141-1153, 2019.
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TO THE EDITOR: With interest we read the Viewpoint by Joyner et al. (2) addressing the physiology of fast marathons. In addition to the prerequisite of a high VO2max, the ability to sustain a high % of VO2max, and excellent running economy (2), we consider a role for cerebral oxygenation. A reduction in cerebral oxygenation has been implicated in the development of central fatigue as a limitation for exercise performance (4). Among elite Kenyan (Kalenjin) runners (mean half-marathon time 62.2 1.0 min), the top performers in a 5-km trial are those who best maintain their cerebral oxygenation (3). Although a reduced ventilatory drive during exercise would attenuate reduction in PaCO2 and in turn cerebral blood flow and oxygenation, Hansen et al. (1) found, by clamping PETCO2 during high-intensity exercise (~90% VO2max), that despite preventing the hyperventilation-induced reduction in PaCO2 and the concomitant decrease in cerebral flow velocity, cerebral oxygenation was reduced at exhaustion. We take reduction in cerebral oxygenation to indicate that during maximal exercise the cerebral demand exceeds the O2 delivery even under conditions of maintained cerebral blood flow (1), suggesting that not only O2 delivery but also the magnitude of cerebral O2 demand is important for exercise tolerance. It may be that Kenyan runners due to both excellent genetically endowed mechanical efficiency (2) and training (5) are better in attenuating the cerebral O2 demand for running and thus maintain cerebral oxygenation that contributes to the astonishing middle- and long-distance performances in this population (2)
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Thousands of endurance running events are held each year in the United States, and most of them use age and sex categories to account for documented effects of those factors on running performance. However, most running events do not provide categories of body mass, despite abundant evidence that it, too, dramatically influences endurance running performance. The purposes of this article are to (1) discuss how body mass affects endurance running performance, (2) explain several mechanisms through which body mass influences endurance running performance, and (3) suggest possible ways in which body mass might be categorized in endurance running events.
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The aims of this study were (1) to investigate the participation and performance trends at the '100 km Lauf Biel' in Switzerland from 1998 to 2010, and (2) to compare the age-related changes in 100-km running performance between males and females. For both sexes, the percent of finishers significantly (P < 0.01) decreased for the 18-29 and the 30-39-year age groups, while it significantly (P < 0.01) increased for the 40-49 and the 50-59-year age groups over the studied period. From 1998 to 2010, the mean age of the top ten finishers increased by 0.4 years per annum for both females (P = 0.02) and males (P = 0.003). The running time for the top ten finishers remained stable for females, while it significantly (P = 0.001) increased by 2.4 min per annum for males. There was a significant (P < 0.001) age effect on running times for both sexes. The best 100-km running times was observed for the age comprised between 30 and 49 years for males, and between 30 and 54 years for females, respectively. The age-related decline in running performance was similar until 60-64 years between males and females, but was greater for females compared to males after 65 years. Future studies should investigate the lifespan from 65 to 75 years to better understand the performance difference between male and female master ultra-marathoners.
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In the last decades, the participation of elderly trained people in endurance events such as marathon running has dramatically increased. Previous studies suggested that the performance of master runners (>40 years) during marathon running has improved. The aims of the study were (1) to analyze the changes in participation and performance trends of master marathon runners between 1980 and 2009, and (2) to compare the gender differences in performance as a function of age across the years. Running times of the best male and female runners between 20 and 79 years of age who competed in the New York City Marathon were analyzed. Gender differences in performance times were analyzed for the top 10 male and female runners between 20 and 65 years of age. The participation of master runners increased during the 1980-2009 period, to a greater extent for females compared to males. During that period, running times of master runners significantly (P < 0.01) decreased for males older than 64 years and for females older than 44 years, respectively. Gender differences in running times decreased over the last three decades but remained relatively stable across the ages during the last decade. These data suggest that male (≥65 years) and female (≥45 years) master runners have probably not yet reached their limits in marathon performance. The relative stability of gender differences in marathon running times across the different age groups over the last decade also suggests that age-related declines in physiological function do not differ between male and female marathoners.
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Whoever breaks 2 h will likely have outstanding running economy and small body size along with exposure to high altitude and significant physical activity early in life. However, neither of these factors nor any specific suite of genotypes appear to be obligatory for a time this fast. Current trends suggest that an East African will be the first to break 2 h. However periods of regional dominance in distance running are not unique to the East Africans: athletes from Finland, Eastern Europe, Australia, and New Zealand have all had extended periods of success at a range of distances. From a physiological perspective, more information is clearly needed on the relationship between VO(2max) and running economy and the influence of running economy and body size on thermoregulation and fuel use.
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Successful finishing of marathon requires regular endurance training and appropriate lifestyle. Thus, marathon running times and training data from large samples of physically active and fit elderly are ideal for the assessment of age-related performance. In the present study we analyzed 439 278 running times from result lists of 108 marathon competitions and data from a survey via internet questionnaire about training and behavioural factors of marathon finishers. Marathon times and 6 992 data sets from the internet questionnaire were separated into groups based on age and sex and analyzed by two-way ANOVA. Our main findings are that 1) there are virtually no relevant running time differences (p<0.01) in marathon finishers from 20 to 55 years and 2) the majority of middle-aged and elderly athletes have training histories of less than seven years of running. With the exception of marathon running times we did not encounter any significant gender related differences (p>0.01). The present findings strengthen the concept that considers aging as a biological process that can be considerably speeded up or slowed down by multiple lifestyle related factors.
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Using a meta-analytic approach, we recently reported that the rate of decline in maximal oxygen uptake (VO2 max) with age in healthy women is greatest in the most physically active and smallest in the least active when expressed in milliliters per kilogram per minute per decade. We tested this hypothesis prospectively under well-controlled laboratory conditions by studying 156 healthy, nonobese women (age 20-75 yr): 84 endurance-trained runners (ET) and 72 sedentary subjects (S). ET were matched across the age range for age-adjusted 10-km running performance. Body mass was positively related with age in S but not in ET. Fat-free mass was not different with age in ET or S. Maximal respiratory exchange ratio and rating of perceived exertion were similar across age in ET and S, suggesting equivalent voluntary maximal efforts. There was a significant but modest decline in running mileage, frequency, and speed with advancing age in ET. VO2 max (ml . kg-1 . min-1) was inversely related to age (P < 0.001) in ET (r = -0.82) and S (r = -0.71) and was higher at any age in ET. Consistent with our meta-analysic findings, the absolute rate of decline in VO2 max was greater in ET (-5.7 ml . kg-1 . min-1 . decade-1) compared with S (-3.2 ml . kg-1 . min-1 . decade-1; P < 0. 01), but the relative (%) rate of decline was similar (-9.7 vs -9. 1%/decade; not significant). The greater absolute rate of decline in VO2 max in ET compared with S was not associated with a greater rate of decline in maximal heart rate (-5.6 vs. -6.2 beats . min-1 . decade-1), nor was it related to training factors. The present cross-sectional findings provide additional evidence that the absolute, but not the relative, rate of decline in maximal aerobic capacity with age may be greater in highly physically active women compared with their sedentary healthy peers. This difference does not appear to be related to age-associated changes in maximal heart rate, body composition, or training factors.
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Older ('Masters') athletes strive to maintain or even improve upon the performance they achieved at younger ages, but declines in athletic performance are inevitable with ageing. In this review, we describe changes in peak endurance exercise performance with advancing age as well as physiological factors responsible for those changes. Peak endurance performance is maintained until approximately 35 years of age, followed by modest decreases until 50-60 years of age, with progressively steeper declines thereafter. Among the three main physiological determinants of endurance exercise performance (i.e. maximal oxygen consumption , lactate threshold and exercise economy), a progressive reduction in appears to be the primary mechanism associated with declines in endurance performance with age. A reduction in lactate threshold, i.e. the exercise intensity at which blood lactate concentration increases significantly above baseline, also contributes to the reduction in endurance performance with ageing, although this may be secondary to decreases in . In contrast, exercise economy (i.e. metabolic cost of sustained submaximal exercise) does not change with age in endurance-trained adults. Decreases in maximal stroke volume, heart rate and arterio-venous O(2) difference all appear to contribute to the age-related reductions in in endurance-trained athletes. Declines in endurance exercise performance and its physiological determinants with ageing appear to be mediated in large part by a reduction in the intensity (velocity) and volume of the exercise that can be performed during training sessions. Given their impressive peak performance capability and physiological function capacity, Masters athletes remain a fascinating model of 'exceptionally successful ageing' and therefore are highly deserving of our continued scientific attention as physiologists.
Article
Acclimatization to moderate high altitude accompanied by training at low altitude (living high-training low) has been shown to improve sea level endurance performance in accomplished, but not elite, runners. Whether elite athletes, who may be closer to the maximal structural and functional adaptive capacity of the respiratory (i.e., oxygen transport from environment to mitochondria) system, may achieve similar performance gains is unclear. To answer this question, we studied 14 elite men and 8 elite women before and after 27 days of living at 2,500 m while performing high-intensity training at 1,250 m. The altitude sojourn began 1 wk after the USA Track and Field National Championships, when the athletes were close to their season's fitness peak. Sea level 3,000-m time trial performance was significantly improved by 1.1% (95% confidence limits 0.3–1.9%). One-third of the athletes achieved personal best times for the distance after the altitude training camp. The improvement in running performance was accompanied by a 3% improvement in maximal oxygen uptake (72.1 ± 1.5 to 74.4 ± 1.5 ml · kg ⁻¹ · min ⁻¹ ). Circulating erythropoietin levels were near double initial sea level values 20 h after ascent (8.5 ± 0.5 to 16.2 ± 1.0 IU/ml). Soluble transferrin receptor levels were significantly elevated on the 19th day at altitude, confirming a stimulation of erythropoiesis (2.1 ± 0.7 to 2.5 ± 0.6 μg/ml). Hb concentration measured at sea level increased 1 g/dl over the course of the camp (13.3 ± 0.2 to 14.3 ± 0.2 g/dl). We conclude that 4 wk of acclimatization to moderate altitude, accompanied by high-intensity training at low altitude, improves sea level endurance performance even in elite runners. Both the mechanism and magnitude of the effect appear similar to that observed in less accomplished runners, even for athletes who may have achieved near maximal oxygen transport capacity for humans.
Article
More than a decade ago it was reported in the journal Natures that the slope of improvement in the men’s and women’s running records, extrapolated from mean running velocity plotted against historical time, would eventually result in a performance intersection of the sexes across a variety of running distances. The first of these intersections was to occur for 42 000m before the 21st century. Most of the error in this prediction is probably explained by the linear mathematical treatment and extrapolation of limited performance data, since including world record-setting running performances for women before and after 1985 results in a non-linear data fit. The reality of early, disproportionate improvements in women’s running that gave the appearance of an impending convergence with men is best explained by an historical social sports bias. Women’s times have now reached a plateau similar to that observed for men at comparative performance milestones in the marathon. Sex differences at distances from 100 to 10 000m show similar trends. The remaining sex gaps in performance appear biological in origin. Success in distance running and sprinting is determined largely by aerobic capacity and muscular strength, respectively. Because men possess a larger aerobic capacity and greater muscular strength, the gap in running performances between men and women is unlikely to narrow naturally.
Article
Unlabelled: The sex difference in marathon performance increases with age and place of the finisher, even at the elite level. Sociological factors may explain the increased sex gap, but there is limited empirical evidence for specific factors. Purpose: The purposes of this study were to determine the sex difference in velocity for the marathon across the place of finisher (1st-10th place) with advanced age and (2) to determine the association between the sex difference in participation (ratio of men-to-women finishers) and the sex difference in running velocity. Methods: Running times of the first 10 placed men and women in the 5-yr age brackets between 20 and 79 yr and the number of men and women who finished the New York City marathon were analyzed for a 31-yr period (1980-2010). Results: The sex difference in running velocity increased between the 1st and the 10th place because of a greater relative drop in velocity of women than men (P < 0.001). The sex difference increased with advanced age and decreased across the 31 yr, but more for the older age groups (P < 0.001). The number of women finishers also increased relative to men for the 31 yr, but more in the older age groups (P < 0.001). Importantly, approximately 34% of the sex difference in velocity among the first-place finishers was associated with the ratio of men-to-women finishers (r = 0.58, r² = 0.34, P < 0.001). Conclusions: The greater sex difference in velocity that occurs with age and with increased place was primarily explained by the lower number of women finishers than men. These data provide evidence that lower participation rates and less depth among women competitors can amplify the sex difference in running velocity above that due to physiological sex differences alone.
Article
The purpose of this study was to investigate the relationship that age has on factors affecting running economy (RE) in competitive distance runners. Fifty-one male and female subelite distance runners (Young [Y]: 18-39 years [n = 18]; Master [M]: 40-59 years [n = 22]; and Older [O]: 60-older [n = 11]) were measured for RE, step rate, lactate threshold (LT), VO2max, muscle strength and endurance, flexibility, power, and body composition. An RE test was conducted at 4 different velocities (161, 188, 215, and 241 m·min(-1)), with subjects running for 5 minutes at each velocity. The steady-state VO2max during the last minute of each stage was recorded and plotted vs. speed, and a regression equation was formulated. A 1 × 3 analysis of variance revealed no differences in the slopes of the RE regression lines among age groups (y = 0.1827x - 0.2974; R2 = 0.9511 [Y]; y = 0.1988x - 1.0416; R2 = 0.9697 [M]; y = 0.1727x + 3.0252; R2 = 0.9618 [O]). The VO2max was significantly lower in the O group compared to in the Y and M groups (Y = 64.1 ± 3.2; M = 56.8 ± 2.7; O = 44.4 ± 1.7 mlO2·kg(-1)·min(-1)). The maximal heart rate and velocity @ LT were significantly different among all age groups (Y = 197 ± 4; M = 183 ± 2; O = 170 ± 6 b·min(-1) and Y = 289.7 ± 27.0; M = 251.5 ± 32.9; O = 212.3 ± 24.6 m·min(-1), respectively). The VO2max @ LT was significantly lower in the O group compared to in the Y and M groups (Y = 50.3 ± 2.0; M = 48.8 ± 2.9; O = 34.9 ± 3.2 mlO2·kg(-1)·min(-1)). The O group was significantly lower than in the Y and M groups in flexibility, power, and upper body strength. Multiple regression analyses showed that strength and power were significantly related to running velocity. The results from this cross-sectional analysis suggest that age-related declines in running performance are associated with declines in maximal and submaximal cardiorespiratory variables and declines in strength and power, not because of declines in running economy.
Article
The purposes of this study were (i) to investigate the effect of age on gender difference in Hawaii Ironman triathlon performance time and (ii) to compare the gender difference among swimming (3.8 km), cycling (180 km), and running (42 km) performances as a function of age. Gender difference in performance times and estimated power output in the three modes of locomotion were analyzed for the top 10 men and women amateur triathletes between the ages of 18 and 64 yr for three consecutive years (2006-2008). The gender difference in total performance time was stable until 55 yr and then significantly increased. Mean gender difference in performance time was significantly (P < 0.01) smaller for swimming (mean ± 95% confidence interval = 12.1% ± 1.9%) compared with cycling (15.4% ± 0.7%) and running (18.2% ± 1.3%). In contrast, mean gender difference in cycling estimated power output (38.6% ± 1.1%) was significantly (P < 0.01) greater compared with swimming (27.5% ± 3.8%) and running (32.6% ± 0.7%). This cross-sectional study provides evidence that gender difference in ultraendurance performance such as an Ironman triathlon was stable until 55 yr and then increased thereafter and differed between the locomotion modes. Further studies examining the changes in training volume and physiological characteristics with advanced age for men and women are required to better understand the age-associated changes in ultraendurance performance.
Article
The purposes of this study were to determine i) if there is a sex difference in the age of the elite marathon runners and ii) if the sex difference in performance altered across the years that women have participated in the marathon. Age at time of competition and running times of the first five placed male and female runners who competed in the seven marathons of the World Marathon Majors Series were analyzed. Data from as many years as was available online were retrieved so that 410 men and 410 women were included in the analysis. The marathons and years included the Berlin (1999-2009), Boston (2000-2009), Chicago (1997-2009), London (2001-2009), New York City (1990-2009), International Athletic Association Federation World Championship (1983, 1987, and every 2 yr from 1991), and Olympic (every 4 yr since 1984) marathons. Women were older than men (mean ± SD = 29.8 ± 4.2 vs 28.9 ± 3.8 yr), but for only two of the seven marathons, the Chicago and the London marathons (P < 0.05): the sex difference in age was not consistent across the years. There was no sex difference in age for the Berlin, Boston, New York City, World Championship, and Olympic marathons. Men were faster than women (11.6% ± 1.8%). The sex difference in running velocity varied across marathons (least for the World Championships, 10.2%) and also across years, but not systematically. This sex difference in running velocity increased from first to fifth place across all marathons. These data indicate that men and women physiologically peak at a similar age in marathon running performance. The sex difference in performance of elite marathon runners varied across years but has not systemically decreased or varied since the 1980s.
Article
Previous researchers have suggested that faster marathoners tend to run at a more consistent pace compared with slower runners. None has examined the influence of sex and age on pacing. Therefore, the purpose of this study was to determine the simultaneous influences of age, sex, and run time on marathon pacing. Pacing was defined as the mean velocity of the last 9.7 km divided by that of the first 32.5 km (closer to 1.0 indicates better pacing). Subjects were 186 men and 133 women marathoners from the 2005, 2006, and 2007 races of a midwestern U.S. marathon. The course was a 1.6 km (1 mile) loop with pace markers throughout, thus facilitating pacing strategy. Each 1.6-km split time was measured electronically by way of shoe chip. The ambient temperature (never above 5°C) ensured that hyperthermia, a condition known to substantially slow marathon times and affect pacing, was not likely a factor. Multiple regression analysis indicated that age, sex, and run time (p < 0.01 for each) were simultaneously independent determinants of pacing. The lack of any 2- or 3-way interactions (p > 0.05 for each) suggests that the effects of 1 independent variable is not dependent upon the levels of others. We conclude that older, women, and faster are better pacers than younger, men, and slower marathoners, respectively. Coaches can use these findings to overcome such tendencies and increase the odds of more optimal pacing.
Article
This study sought to determine how lactate threshold (LT) is related to running performance in older male and female runners, if LT changes significantly with age, and if gender alters the relationship between LT and performance in older runners. Subjects were 168 master runners (111 men, 57 women) selected from a longitudinal study, who ran at least 10 miles x wk(-1) for 5 yr or more. VO2max was measured on a treadmill and body composition by hydrostatic weighing. Blood samples taken each minute of exercise were analyzed for lactate concentration and LT determined as the breakpoint in lactate accumulation. Performance times and training histories were self-reported by questionnaire. Men had significantly greater body mass, fat-free mass (FFM), and VO2max (L x min(-1); mL x kg(-1) x min(-1)) than women. FFM and VO2max (L x min(-1); mL x kg(-1) x min(-1)) declined with age in both men and women. Running performance was significantly different between men and women and declined with age in both. LT (L x min(-1); mL x kg(-1) x min(-1)) was significantly different between men and women, and declined significantly with age in men, whereas LT (%VO2max) did not differ between men and women and increased significantly with age in both. VO2max (mL x kg(-1) x min(-1)) was the most significant predictor of performance in both men and women, whereas LT (L x min(-1)) added to the prediction of 5-km and 10-km performance in women. The results of this study demonstrate that VO2max (mL x kg(-1) x min(-1)) is a better predictor of performance than LT in older male and female runners. Additionally, LT as a percentage of VO2max increases significantly with age.
Article
Acclimatization to moderate high altitude accompanied by training at low altitude (living high-training low) has been shown to improve sea level endurance performance in accomplished, but not elite, runners. Whether elite athletes, who may be closer to the maximal structural and functional adaptive capacity of the respiratory (i.e., oxygen transport from environment to mitochondria) system, may achieve similar performance gains is unclear. To answer this question, we studied 14 elite men and 8 elite women before and after 27 days of living at 2,500 m while performing high-intensity training at 1,250 m. The altitude sojourn began 1 wk after the USA Track and Field National Championships, when the athletes were close to their season's fitness peak. Sea level 3,000-m time trial performance was significantly improved by 1.1% (95% confidence limits 0.3-1.9%). One-third of the athletes achieved personal best times for the distance after the altitude training camp. The improvement in running performance was accompanied by a 3% improvement in maximal oxygen uptake (72.1 +/- 1.5 to 74.4 +/- 1.5 ml x kg(-1) x min(-1)). Circulating erythropoietin levels were near double initial sea level values 20 h after ascent (8.5 +/- 0.5 to 16.2 +/- 1.0 IU/ml). Soluble transferrin receptor levels were significantly elevated on the 19th day at altitude, confirming a stimulation of erythropoiesis (2.1 +/- 0.7 to 2.5 +/- 0.6 microg/ml). Hb concentration measured at sea level increased 1 g/dl over the course of the camp (13.3 +/- 0.2 to 14.3 +/- 0.2 g/dl). We conclude that 4 wk of acclimatization to moderate altitude, accompanied by high-intensity training at low altitude, improves sea level endurance performance even in elite runners. Both the mechanism and magnitude of the effect appear similar to that observed in less accomplished runners, even for athletes who may have achieved near maximal oxygen transport capacity for humans.
Article
Marathon running performance among men and women is generally fastest, as indicated by world record performances, when individuals are 25-35 years old. The time to complete a marathon gradually increases with age, with substantial losses in performance after the age of 70 years. A decline in cardiovascular capacity of 0.5% per decade occurs in highly trained distance runners, while a 1.0% and 1.5% decline per decade occurs in moderately trained and untrained individuals, respectively. In middle-aged veteran runners, skeletal muscle continues to have high aerobic potential, while a decline in muscle cell size and contractile performance are apparent. These changes in the skeletal muscle profile may contribute to distance running performance with age. The changes in physiological function and running performance with age are closely related to the level of distance run training. Current research supports the concept that continued running late into life attenuates a decline in physiological function with age and is beneficial for overall health.
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