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Purpose This study aimed to verify participation and performance in all road and trail-based races in Switzerland over two decades (1999–2019). Methods This is a secondary data-based study. We used 1,149,182 race records (788,818 from men and 360,364 from women) from 407,944 unique finishers of the 5 km, 10 km, half-marathon, marathon, and ultra-marathon races in Switzerland, between 1999 and 2019. Data included the athletes’ genders, ages, race distances, and types of races (road or trail). The mento- women ratio was calculated and box plots were used to present differences according to gender, age groups, and race distances in both terrains. Differences between the groups were calculated. Results The men-to-women ratio has generally decreased in all race distances over the years. Runners competing in roadbased races were faster than those in a trail run in all distances. Men had faster finish times than women independent of age, except those competing in 10 km trail races. In road-based races, the men-to-women ratio was higher among older marathoners, while for trail runs, a higher men-to-women ratio was shown for older half-marathoners and participants in 10 km races. For 5 km, 10 km, half-marathon, and ultra-marathon races, a decrease in running speed differences was shown until the age of 50 years. Conclusions In Switzerland, most of the runners competed in road-based races, but the interest in trail running has increased. The men-to-women ratio declined over time, which could show an increase in the interest of women to participate in endurance activities.

Abstract The majority of participants in Ironman triathlon races are age group athletes. We have extensive knowledge about recreational athletes’ training and competition participation. Nonetheless, Ironman age group triathletes must achieve fast race times to qualify for the Ironman World Championship in Hawaii. They can, therefore, benefit from knowing where the fastest Ironman racecourses in the world are. The aim of the present study was to investigate where the fastest Ironman racecourses for age group triathletes are located in the world. Data from 677,702 Ironman age group finishers’ records (544,963 from men and 132,739 from women) originating from 228 countries and participating in 444 events across 66 different Ironman race locations between 2002 and 2022 were analyzed. Data was analyzed through traditional descriptive statistics and with machine learning regression models. Four algorithms were tested (Random Forest Regressor, XG Boost Regressor, Cat Boot Regressor, and Decision Tree Regressor). The models used gender, age group, country of origin, environmental factors (average air and water temperatures), and the event location as independent variables to predict the final overall race time. Despite the majority of successful Ironman age group triathletes originating from the USA (274,553), followed by athletes from the United Kingdom (55,410) and Canada (38,264), these countries exhibited average overall race times that were significantly slower compared to the fastest countries. Most of the triathletes competed in Ironman Wisconsin (38,545), followed by Ironman Florida (38,157) and Ironman Lake Placid (34,341). The fastest overall race times were achieved in Ironman Copenhagen (11.68 ± 1.38 h), followed by Ironman Hawaii (11.72 ± 1.86 h), Ironman Barcelona (11.78 ± 1.43 h), Ironman Florianópolis (11.80 ± 1.52 h), Ironman Frankfurt (12.03 ± 1.38 h) and Ironman Kalmar (12.08 ± 1.47 h). The fastest athletes originated from Belgium (11.48 ± 1.47 h), followed by athletes from Denmark (11.59 ± 1.40 h), Switzerland (11.62 ± 1.49 h), Austria (11.68 ± 1.50), Finland (11.68 ± 1.40 h) and Germany (11.74 ± 15.1 h). Flat running and cycling courses were associated with faster overall race times. Three of the predictive models identified the ‘country’ and ‘age group’ variables as the most important predictors. Environmental characteristics showed the lowest influence regarding the other variables. The origin of the athlete was the most predictive variable whereas environmental characteristics showed the lowest influence. Flat cycling and flat running courses were associated with faster overall race times. The fastest overall race times were achieved mainly in European races such as Ironman Copenhagen, Ironman Hawaii, Ironman Barcelona, Ironman Florianópolis, Ironman Frankfurt and Ironman Kalmar. The fastest triathletes originated from European countries such as Belgium, Denmark, Switzerland, Austria, Finland, and Germany.

Purpose The IRONMAN® triathlon is an endurance multisport discipline of high popularity. Professional IRONMAN® triathletes need to qualify for the IRONMAN® World Championships and, therefore, would benefit from identifying the fastest race courses. Our purpose was to identify the fastest races held for professional IRONMAN® triathletes competing from 2002 to 2022. Methods This was an observational study, sampling 7,078 race records (380 different events in 55 different event locations) of professional IRONMAN® triathletes of both sexes (4,235 males and 2,843 females). We downloaded information about sex, nationality, both split (swimming, cycling, and running) and overall race times, the event location, and the year. Information about race course characteristics, water temperatures, and air temperatures was obtained. Descriptive statistics were calculated for each event location, and factorial ANOVA tests were used to explore the statistical significance of the results. A confidence interval of 95% was adopted. Results The fastest overall IRONMAN® average race times were achieved during the IRONMAN® Tallinn, IRONMAN® Switzerland held in Thun, and IRONMAN® Des Moines. A lake for the swim split characterized the first five courses, while the bike split featured rolling or hilly terrain. For the run split, most of the courses were characterized as flat. For environmental characteristics, lower water and air temperatures also added time to the average finish time. Conclusions Understanding the fastest race courses and their characteristics would assist professional IRONMAN® triathletes in selecting races that offer the optimal conditions for their competition. Fast IRONMAN® race courses typically feature a lakeside swim course, a rolling or hilly bike course, and a flat run course.

BackgroundTriathletes are pushing their limits in multi-stage Ironman-distance triathlons. In the present case study, we investigated the pacing during 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, of one professional IRONMAN® triathlete.Case studyEvent 1 (20 Ironman-distance triathlons in 20 days), Event 2 (40 Ironman-distance triathlons in 40 days), and Event 3 (60 Ironman-distance triathlons in 60 days) were analyzed by discipline (swimming, cycling, running, and overall event time), by Deca intervals (10 days of consecutive Ironman-distance triathlons) and additional data (sleep duration, body mass, heart rate in cycling and running). To test differences between Events and Deca intervals within the same discipline, T-tests (2 groups) or one-way ANOVAs (3 or more groups) were used.ResultsSwimming splits were fastest in Event 1, (ii) cycling and running splits were fastest in both Event 2 and 3, (iii) overall speed was fastest in Event 3, (iv) sleep duration increased during Event 2 but decreased in Event 3, (v) body mass decreased in Event 2, but increased in Event 3 and (vi) heart rate during cycling was similar in both Event 2 and 3. In contrast, heart rate during running was greater in Event 3.ConclusionIn a professional IRONMAN® triathlete finishing 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, split performances and both anthropometrical and physiological changes such as body mass and heart rate differed depending upon the duration of the events.

Abstract Background Ultra-endurance events are gaining popularity in multiple exercise disciplines, including cycling. With increasing numbers of ultra-cycling events, aspects influencing participation and performance are of interest to the cycling community. Main body The aim of this narrative review was, therefore, to assess the types of races offered, the characteristics of the cyclists, the fluid and energy balance during the race, the body mass changes after the race, and the parameters that may enhance performance based on existing literature. A literature search was conducted in PubMed, Scopus, and Google Scholar using the search terms ‘ultracycling’, ‘ultra cycling’, ‘ultra-cycling’, ‘ultra-endurance biking’, ‘ultra-bikers’ and ‘prolonged cycling’. The search yielded 948 results, of which 111 were relevant for this review. The studies were classified according to their research focus and the results were summarized. The results demonstrated changes in physiological parameters, immunological and oxidative processes, as well as in fluid and energy balance. While the individual race with the most published studies was the Race Across America, most races were conducted in Europe, and a trend for an increase in European participants in international races was observed. Performance seems to be affected by characteristics such as age and sex but not by anthropometric parameters such as skin fold thickness. The optimum age for the top performance was around 40 years. Most participants in ultra-cycling events were male, but the number of female athletes has been increasing over the past years. Female athletes are understudied due to their later entry and less prominent participation in ultra-cycling races. A post-race energy deficit after ultra-cycling events was observed. Conclusion Future studies need to investigate the causes for the observed optimum race age around 40 years of age as well as the optimum nutritional supply to close the observed energy gap under consideration of the individual race lengths and conditions. Another research gap to be filled by future studies is the development of strategies to tackle inflammatory processes during the race that may persist in the post-race period.

Abstract The 50-km ultra-marathon is a popular race distance, slightly longer than the classic marathon distance. However, little is known about the country of affiliation and age of the fastest 50-km ultra-marathon runners and where the fastest races are typically held. Therefore, this study aimed to investigate a large dataset of race records for the 50-km distance race to identify the country of affiliation and the age of the fastest runners as well as the locations of the fastest races. A total of 1,398,845 50-km race records (men, n = 1,026,546; women, n = 372,299) were analyzed using both descriptive statistics and advanced regression techniques. This study revealed significant trends in the performance of 50-km ultra-marathoners. The fastest 50-km runners came from African countries, while the fastest races were found to occur in Europe and the Middle East. Runners from Ethiopia, Lesotho, Malawi, and Kenya were the fastest in this race distance. The fastest 50-km racecourses, providing ideal conditions for faster race times, are in Europe (Luxembourg, Belarus, and Lithuania) and the Middle East (Qatar and Jordan). Surprisingly, the fastest ultra-marathoners in the 50-km distance were found to fall into the age group of 20–24 years, challenging the conventional belief that peak ultra-marathon performance comes in older age groups. These findings contribute to a better understanding of the performance models in 50-km ultra-marathons and can serve as valuable insights for runners, coaches, and race organizers in optimizing training strategies and racecourse selection.

Participation in marathons has dramatically increased over the last few years. Marathon running has many proven beneficial effects, especially on cardiovascular health and fitness. Most research has focused on physiologic and pathophysiologic adaptations in connection with endurance exercise. Nevertheless, marathon running also has a major impact on psychological aspects and positively influences mental health, which has only recently attracted research interest. The present narrative review aimed to review the personality traits of marathon runners with an emphasis on recent literature. Marathon runners show a distinct personality and highly characteristic personality traits needed to successfully finish such a demanding race, i.e., a strong sense of vigor, self-sufficiency, and intelligence as well as low scores in anger, fatigue, tension, and depression. Furthermore, personality differences are detectable between runners of different sexes, ages, and performance level groups. This has significant clinical implications for athletes, coaches and competition organizers, as these groups show different patterns of personality traits. Future studies should focus on changes in cognition and mood states pre-, during, and post-endurance events, as well as during training periods. Large-scale studies comparing personality differences by sex, age, and performance are also important for better clinical guidance.

This study aimed to investigate the trends of finishers in the São Silvestre race in Brazil, taking into account sex, age, and performance levels. A total of 31 ​775 runners (women, n ​= ​13 ​847; men, n ​= ​17 ​928), aged (45.2 ​± ​16.8) years, finishers in the São Silvestre race between 2007 and 2021, were considered in the present analysis. Data (event year, date of birth, sex, and race times) were downloaded from the official race website. The man-Whitney U test, Spearman correlation, and robust regression model were computed. Participation increased over time for both sexes. Regarding age groups, “31–40 years” (women) and “> 60 years” (men) were those with the highest number of finishers. We found a decrease in performance across the years (β ​= ​2.45; p ​

Abstract Objective The current study aimed to explore the association of individual characteristics, social and environmental factors - school and region - in the intention to be physically active in Brazilian adolescents. Methods This is a cross sectional study based on the third edition of the National School Health Survey. The study included a total of 53,937 adolescents. To assess the intention to be physically active, only who engaged in less than 300 min of physical activity per week were included. Participants were asked: “If you had the opportunity to practice physical activity most days of the week, what would your attitude be?” Individual characteristics, physical activity domains, social factors, school, and regional environments were used as exposures. Network analysis was utilized to evaluate the associations. Results We observed that boys had higher intentions to be physically active compared to their peers, as did adolescents who perceived themselves as fat. In addition, students from private schools show a higher intention to regularly engage in physical activities, and in general, private schools offer more extracurricular physical activities. Conclusion In conclusion, individual factors such as sex and body image perception, and environmental factors such as school administrative dependency and availability of extracurricular activities had a significant contribution to the intention to be physically active among Brazilian adolescents.

Abstract The present systematic review aimed to discuss the impacts of different triathlon protocols on the level of pro and anti-inflammatory cytokines, as well as biomarkers related to the performance of healthy individuals. Four databases [PubMed (28 articles), Scopus (24 articles), Science Direct (200 articles), and SPORT Discus (1101 articles) were assessed. The eligibility criteria were applied, and the selected articles were used in the peer review, independently, as they were identified by March 2022. Of the 1359 articles found, 10 were included in this systematic review. Despite the difference in triathlon protocols, it was observed an increase in pro and anti-inflammatory cytokines including IL-4 and IL-10, and chemokines, such as IL-8 and MCP-1. Moreover, the anti-inflammatory serum levels increase after triathlon. Overall, the studies also reported enhancement in the serum levels of cortisol, creatine kinase, C reactive protein, Endothelial Growth Factor, Vascular Endothelial Growth Factor, Myostatin, Lactate dehydrogenase, free fatty acids, and lactate dehydrogenase in triathlon athletes. This systematic review indicates that different triathlon race promotes an acute elevation of circulating cytokines and chemokines levels which return to standard levels after triathlon races. The findings of this systematic review demonstrate that the modulation of inflammatory parameters may be associated with an increase in metabolic indicators (CK, Cortisol, and LDH) after the end of different types of triathlon races.

Abstract Background Although several studies have shown that the Coronavirus Disease 2019 (COVID-19) lockdown has had negative impacts on mental health and eating behaviors among the general population and athletes, few studies have examined the long-term effects on elite and sub-elite athletes. The present study aimed to investigate the long-term impact of COVID-19 lockdown on mental health and eating behaviors in elite versus sub-elite athletes two years into the pandemic. A cross-sectional comparative study was conducted between March and April 2022, involving athletes from 14 countries, using a convenient non-probabilistic and snowball sampling method. A total of 1420 athletes (24.5 ± 7.9 years old, 569 elites, 35% women, and 851 sub-elites, 45% women) completed an online survey-based questionnaire. The questionnaire included a sociodemographic survey, information about the COVID-19 pandemic, the Depression, Anxiety and Stress Scale—21 Items (DASS-21) for mental health assessment, and the Rapid Eating Assessment for Participants (REAP-S) for assessing eating behavior. Results The results showed that compared to sub-elite athletes, elite athletes had lower scores on the DASS-21 (p = .001) and its subscales of depression (p = .003), anxiety (p = .007), and stress (p

Background: For Ironman® triathlon, it has been reported that most of the fnishers and the fastest women and men in Ironman® Hawaii originated from the United States of America (USA). We have, however, no knowledge of where the fastest race courses in the Ironman® 70.3 triathlon took place. We aim to analyse where the Ironman® 70.3 races were held and where the fastest split and overall race times were achieved. Methods: The athletes’ sex, age group, country of origin, and split times for swimming, running, cycling, and transitioning were obtained from the official Ironman® website. To investigate the locations of the fastest Ironman® 70.3 competitions between 2004 and 2020, a full sample of 852,721 qualifying records throughout 197 different event locations was processed. These race records were aggregated by location, and each location’s split and full finish times were calculated. Data analysis was performed first for the full sample (all race records), and then for an elite sub-sample consisting of the top 100 males and top 100 females records in each location. Results: For the full sample, the fastest overall race times were achieved in Ironman® 70.3 Zell am See (Austria). For the top 100 athletes sub-sample, the Ironman® 70.3 European Championship Elsinore and Ironman® 70.3 World Championship were the fastest courses. Conclusion: These results are useful for athletes’ strategic planning and inform event organisers about the strengths of different courses, aiding in the optimisation and promotion of future Ironman® 70.3 races worldwide. Keywords: endurance, performance, half-distance Ironman®, multi-sport, running.

IntroductionOur study examined 16,611 records of professional triathletes from 163 Ironman® 70.3 races across 97 countries (2004-2020). The aim was to identify the most predictive discipline—swim, bike, or run—for overall race time.MethodsWe used correlation matrices to compare the dependent variable “finish time” with independent variables “swim time,” “bike time,” and “run time.” This analysis was conducted separately for male and female athletes. Additionally, univariate and multiple linear regression models assessed the strength of these associations.ResultsThe results indicated that “bike time” had the strongest correlation with finish time (0.85), followed by “run time” (0.75 for females, 0.82 for males) and “swim time” (0.46 for females, 0.63 for males). Regression models confirmed “bike time” as the strongest predictor of overall race time (R² = 0.8), with “run time” and “swim time” being less predictive.DiscussionThe study concludes that in Ironman 70.3 races, “bike time” is the most significant predictor of overall race performance for both sexes, suggesting a focus on cycling in training and competition strategies. It also highlights a smaller performance gap between genders in swimming than in cycling or running.

Abstract Few studies were developed to understand the relationship between running characteristics and motivation. The purpose of this study was to assess the relationship between running event history, running experience, and best race performances in recreational distance runners. We used a web survey to obtain information regarding running experience, racing history, and periodization training routines/exercise habits, including weekly volumes and daily mileage and duration across periods and conditions. Associations between variables were conducted with the Chi-square test (χ2; nominal scale) and Wilcoxon test. Multiple linear regression analysis and multivariate linear regression were performed. Concerning the participants’ motive for exercising, a significant difference was identified between the race distance subgroups (p

Abstract Pacing during cycling and running in an IRONMAN triathlon has been investigated in only one study with elite IRONMAN triathletes. We have, however, no knowledge of how professional triathletes pace during an IRONMAN World Championship. To investigate the split-by-split speed, pacing strategies and pacing variability in professional female and male IRONMAN World Championship participants in the fastest IRONMAN World Championship ever in IRONMAN Hawaii 2022. For both cycling and running, 25 specific split times were recorded in each discipline. The best 30 men and 30 women overall were chosen from the official IRONMAN website database for further analysis. They were divided into three performance groups: Top 10, 11–20th place, and 21st–30th place. Mean speed, individual linear regressions with the corresponding correlation coefficients, and coefficient of variation were calculated to assess split-by-split speed, pacing strategies, and pacing variability, respectively. In both men’s and women’s cycling and running segments, the top ten participants exhibited faster split times compared to the slower performance groups. Notably, no discernible differences existed between the 11–20th and 21st–30th place in men’s cycling and women’s running times. Conversely, in men’s running and women’s cycling segments, those in the 11–20th place displayed quicker times than those in the 21st–30th place. In the cycling segment across all groups, men demonstrated a more negative pacing pattern (indicating an increase in speed), whereas women exhibited more consistent pacing. In the running segment, the top 10 men and all women’s groups showcased relatively similar slightly positive pacing profiles. However, men ranking 11–20th and 21st–30th displayed more pronounced positive pacing strategies, implying a more significant decline in speed over time. In terms of cycling, the variability in pacing remained relatively consistent across the three performance groups. Conversely, during the running segment, the top ten male triathletes and those in the 11–20th place displayed lower pacing variability than their counterparts in the 21st–30th position place and all women’s groups. In summary, performance and pacing were examined in professional male and female IRONMAN World Championship participants during IRONMAN Hawaii 2022. Top performers showed faster cycling and running split times, with differences in pacing strategies between sexes. The pacing was more consistent in cycling, while running pacing varied more, particularly among male triathletes in different performance groups.

Abstract Background The sex difference in the three split disciplines (swimming, cycling, and running) and overall race times in triathlon races has mainly been investigated for the Olympic distance and IRONMAN® triathlon formats, but not for the half IRONMAN® distance, i.e., the IRONMAN® 70.3. The aim of the present study was to investigate the sex differences in IRONMAN® 70.3 by age group in 5-year intervals for the split disciplines of this race. Data from 823,459 records (625,393 males and 198,066 females) of all age group finishers (in 5-year intervals) competing in all official IRONMAN® 70.3 races held worldwide between 2004 and 2020 were analyzed, and sex differences by age group and split disciplines were evaluated. Results Males were faster than females in all split disciplines and all age groups. The sex difference was lower in swimming than in cycling and running and less pronounced for triathletes between 20 and 50 years of age. After the age of 60 years, females were able to reduce the sex difference to males in swimming and cycling, but not in running, where the reduction in the sex difference started after the age of 70 years. The lowest sex difference was in the age group 75 + years for swimming and cycling and in the age group 30–34 years for running. Across age groups, the sex difference was U-shaped in swimming and running, with an increase after 18–24 years in swimming and after 40–44 years in running. In contrast, the sex difference decreased continuously with the increasing age for cycling. Conclusions In conclusion, the study found that the sex difference in performance decreases with age in the IRONMAN® 70.3 race distance. However, females did not outperform males at older ages. Notably, sex differences were observed across different disciplines, with swimming displaying lower differences compared to cycling and running. These findings underscore the complex interplay between age, sex, and performance in endurance sports, emphasizing the need for additional research to understand the factors influencing these differences.

Este estudo analisoua relação entre indicadores de risco à saúde e as componentes do somatótipo consoante os níveis de atividade física em crianças. Trata-se de uma pesquisa epidemiológica de base escolar, composta por 168 crianças (6 a11 anos) de ambos os sexos. Informações demográficas foram obtidas. Avaliações antropométricas (estatura; massa corporal; pregas de adiposidade subcutânea; perímetros do braço contraído, cintura, quadril e panturrilha), pressão arterial sistólica e diastólica, e atividade física (ativo; inativo) foram mensuradas. O somatótipo, razão cintura-quadril e razão cintura-estatura foram calculados. Análise de comparação, correlação e regressão linear múltipla foram realizadas, considerando intervalo de confiança de 95%. Meninos apresentaram maior número de passos, razão cintura-quadril e cintura-estatura. Crianças ativas eram mais velhas, possuíam maior perímetro de cintura, quadril e pressão arterial sistólica. No grupo fisicamente ativo, o sexo esteve associado diretamente ao perímetro de cintura e razãocintura-estatura, enquanto a idade associou-se com a perímetro de quadril e pressão arterial diastólica. Nas crianças inativas, todas as variáveis preditoras (exceto para a variável sexo em relação ao perímetro de quadril) mostraram-se significativas parao perímetro da cintura e quadril, explicando, conjuntamente, ~83% e 85% da expressão destas variáveis, respectivamente. A relação entre indicadores de risco à saúde e as componentes do somatótipo foi maior em crianças inativas. Independentemente do nível de atividade física, a endomorfia mostrou relação significativa com os componentes de saúde, com maior efeito no grupo inativo, tendo em vista a associação com a composição corporal dos sujeitos.

Abstract Recent studies showed that female runners reduced the performance gap to male runners in endurance running with increasing age and race distance. However, the investigated samples were generally small. To investigate this further, the present study examined sex differences by age across various race distances (5, 10 km, half-marathon, marathon, and ultra-marathon) using a large dataset of over 1,100,000 race records from Switzerland over two decades (1999–2019). The study explored performance and participation disparities between male and female runners by employing diverse methods, such as descriptive statistics, histograms, scatter and line plots, correlations, and a predictive machine learning model. The results showed that female runners were more prevalent in shorter races (5, 10 km, half-marathon) and outnumbered male runners in 5 km races. However, as the race distance increased, the male-to-female ratio declined. Notably, the performance gap between sexes reduced with age until 70 years, after which it varied depending on the race distance. Among participants over 75 years old, ultra-marathon running exhibited the smallest sex difference in performance. Elderly female ultra-marathoners (75 years and older) displayed a performance difference of less than 4% compared to male ultra-marathoners, which may be attributed to the presence of highly selected outstanding female performers.

Abstract Knowing which discipline contributes most to a triathlon performance is important to plan race pacing properly. To date, we know that the running split is the most decisive discipline in the Olympic distance triathlon, and the cycling split is the most important discipline in the full-distance Ironman® triathlon. However, we have no knowledge of the Ironman® 70.3. This study intended to determine the most crucial discipline in age group athletes competing from 2004 to 2020 in a total of 787 Ironman® 70.3 races. A total of 823,459 athletes (198,066 women and 625,393 men) from 240 different countries were analyzed and recorded in 5-year age groups, from 18 to 75 + years. Correlation analysis, multiple linear regression, and two-way ANOVA were applied, considering p

Abstract Participation and performance trends of male and female athletes have been thoroughly analyzed in various endurance sports. Knowing these trends can help coaches and athletes prepare for competitions and may influence their training strategy and career planning. However, duathlon events—consisted of two splits of running (Run1 and Run2) interspersed by a split of cycling (Bike)—have not been thoroughly studied, unlike other endurance sports. The present study aimed to compare participation and performance trends in duathletes who competed in duathlon races hosted by World Triathlon or affiliated National Federations between 1990 and 2021. A total of 25,130 results of age group finishers who competed in run-bike-run duathlon races of varying distances were analyzed with different general linear models. Races were divided into three distances: short-distance (up to 5.5 km run, 21 km bike, 5 km run), medium-distance (5–10 km run, 30–42 km bike, 7–11 km run) and long-distance (at least 14 km run, 60 km bike, 25 km run). On average, women represented 45.6% of all finishers in short-distance, 39.6% in medium-distance and 24.9% in long-distance duathlon races. Throughout the years, men were consistently faster than women in all three race legs (Run 1, Bike, and Run 2) in all three distances across all age groups, and women could not reduce the performance gap. Concerning the age of peak performance, duathletes of the age group 30–34 finished most often in the top three in short- and medium-distance duathlons, whereas male duathletes of the age group 25–29 and female duathletes of the age group 30–34 finished most often in the top three in long-distance duathlons. Women participated less, especially in longer distances, and were constantly slower than men. Duathletes of the age group 30–34 finished most often in the top three. Future studies should analyze participation and performance trends in further subgroups (e.g., elite athletes) and pacing behaviours.

Abstract Our purpose was to evaluate the effect of physically active lessons (PAL) on the cognitive performance of children during two years of follow-up. Four classes (second grade of elementary school) were divided into two intervention classes (n = 34) and two control classes (n = 27). Evaluations were performed before the intervention (M1), after 3 (M2) and 9 (M3) months in the 1st year, and 14 (M4) and 18 (M5) months in the 2nd year. The intervention was based on PAL integrated with the curricular components, which stimulated the children to stand or move in the classroom. Cognitive performance was evaluated using three computerized tests for response inhibition, selective attention, and cognitive flexibility. The children in the intervention classes presented improved cognitive performance in the execution of all tasks along the two years follow-up, in both correct answers and time reactions, with exception of correct answers of visual search. For the intervention classes, in most of the tasks, the mean differences confidence interval of 95% did not include the 0 on the two last moments of evaluation, and in all cases, the mean differences of them between M1 versus M5 were significantly different with high values of effect size (cohen -d > 1). PAL promotes modest improvements in diverse cognitive functions in children.

The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the running speed from the athlete's age (Age group), gender (Gender), country of origin (Athlete country) and where the race occurred (Event country). Model explainability tools were then used to investigate how each independent variable influenced the predicted running speed. A total of 172,110 race records from 65,392 unique runners from 68 different countries participating in races held in 44 different countries were used for analyses. The model rates Event country (0.53) as the most important predictor (based on data entropy reduction), followed by Athlete country (0.21), Age group (0.14), and Gender (0.13). In terms of participation, the United States leads by far, followed by Great Britain, Canada, South Africa, and Japan, in both athlete and event counts. The fastest 100-mile races are held in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. The fastest athletes come mostly from Eastern European countries (Lithuania, Latvia, Ukraine, Finland, Russia, Hungary, Slovakia) and also Israel. In contrast, the slowest athletes come from Asian countries like China, Thailand, Vietnam, Indonesia, Malaysia, and Brunei. The difference among male and female predictions is relatively small at about 0.25 km/h. The fastest age group is 25-29 years, but the average speeds of groups 20-24 and 30-34 years are close. Participation, however, peaks for the age group 40-44 years. The model predicts the event location (country of event) as the most important predictor for a fast 100-mile race time. The fastest race courses were occurred in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. Athletes and coaches can use these findings for their race preparation to find the most appropriate racecourse for a fast 100-mile race time.

BackgroundAlthough runner’s profiles were previously investigated, information on the training frequency and training distance for short (5 km, 10 km) and long-distance (>21 km) running is absent. The present study aimed to investigate the associations between training routines and exercise habits of recreational endurance runners considering self-reported preferred race distance [10 km, half-marathon (HM), and marathon/ultra-marathon (M/UM)] subgroups.MethodsThis is a cross-sectional study, sampling 154 recreational runners of both sexes. A web survey was used for data collection regarding age, sex, preferred distance (10-km, HM, M/UM), training routines, exercise habits, and periodized training routines. The Chi-square test (Cramer’s V) and Kruskal-Wallis test (Eta-Squared η2) with effect sizes were used for comparisons between race distances.ResultsSignificant differences were shown for anthropometric, training, and periodization characteristics. Highly significant differences were found between subgroups for the number of sessions, running kilometers, and training hours at all periods and within all four preparation conditions. M/UM runners were training more frequently, for longer durations, and ran greater distances each week.ConclusionThis finding supports the notion that training habits and periodization characteristics are different for different race distances (10-km, half marathon, marathon, and ultramarathon).

Abstract Despite the increasing scientific interest in the relationship between pacing and performance in endurance sports, little information is available about pacing and pacing variation in ultra-endurance events such as ultra-triathlons. Therefore, we aimed to investigate the trends of pacing, pacing variation, the influence of age, sex, and performance level in ultra-triathlons of different distances. We analysed 969 finishers (849 men, 120 women) in 46 ultra-triathlons longer than the original Ironman® distance (e.g., Double-, Triple-, Quintuple- and Deca Iron ultra-triathlons) held from 2004 to 2015. Pacing speed was calculated for every cycling and running lap. Pacing variation was calculated as the coefficient of variation (%) between the average speed of each lap. Performance level (i.e., fast, moderate, slow) was defined according to the 33.3 and 66.6 percentile of the overall race time. A multivariate analysis (two-way ANOVA) was applied for the overall race time as the dependent variable with ‘sex’ and ‘age group’ as independent factors. Another multivariate model with ‘age’ and ‘sex’ as covariates (two-way ANCOVA) was applied with pacing variation (cycling and running) as the dependent variable with ‘race’ and ‘performance level’ as independent factors. Different pacing patterns were observed by event and performance level. The general pacing strategy applied was a positive pacing. In Double and Triple Iron ultra-triathlon, faster athletes paced more evenly with less variation than moderate or slower athletes. The variation in pacing speed increased with the length of the race. There was no significant difference in pacing variation between faster, moderate, and slower athletes in Quintuple and Deca Iron ultra-triathlon. Women had a slower overall performance than men. The best overall times were achieved at the age of 30–39 years. Successful ultra-triathlon athletes adapted a positive pacing strategy in all race distances. The variation in pacing speed increased with the length of the race. In shorter ultra-triathlon distances (i.e., Double and Triple Iron ultra-triathlon), faster athletes paced more evenly with less variation than moderate or slower athletes. In longer ultra-triathlon distances (i.e., Quintuple and Deca Iron ultra-triathlon), there was no significant difference in pacing variation between faster, moderate, and slower athletes.

Key points Sports participation during childhood was the most important social variable in female runners’ performance; Running practice in the family was related to male runners’ performance; Among social and training variables, volume for the week presented the strongest expected influence for runners of both sexes.

PurposeOur purpose was to investigate the interplay between runners and their environment using a network approach.MethodsThis cross-sectional study sampled Brazilian runners of both sexes, from the five macro-regions of the country. An electronic questionnaire was used to obtain information regarding age, sex, training volume, socio-economic level, place of residence, and running pace. Environmental indicators (public illumination, pavement, sidewalk, and green areas) were collected from available public information. Descriptive statistics were presented in mean (SD), and frequency (%). A network analysis was performed to evaluate the association between individual and environmental characteristics. Statistical analyses were performed in the JASP, considering p < 0.05.ResultsAt North and Mid-West regions, public illumination presents the highest values for the expected influence (1.74 and 1.56), while in Northeast and Southeast, sidewalks present the highest values (2.13; 0.91). For betweenness centrality, in North, Northeast, and Mid-West regions, residency in the capital of a state presented a hub. In contrast, pavement, and training volume present higher values in the South and Southeast. Network topologies are different.ConclusionPublic illumination (North and Mid-West) and sidewalk (Northeast, Southeast) were the most important variables for runners. Continental size countries need specific approaches to improve physical activity levels and health outcomes that consider the cultural, historical, and environmental background.

Our purpose is to move beyond the borders of the athlete-centered approach by examining the runners' environment interplay as a key factor for performance. Based on the ecological systems theory, the micro-level (intrapersonal, interpersonal, and training characteristics), meso-level (a direct association with athletes is not observed, but the environment plays an influence on the relationships built at the micro-level), and macro-level (contextual features that influence athletic systems) were theorized and contextualized as important factors for the expression of different outcomes, including performance and participation. We also conceptualized the microtime, mesotime, and macrotime as a constraint. Through this model, we aimed to provide applications and conclusions about how this conceptual model provides advances in the scientific research field. By understanding how environmental factors influence their performance, runners can make informed decisions about where and how to train and compete. Furthermore, by recognizing the role of culture and social context in shaping runners' experiences and outcomes, we can work toward creating a more equitable and supportive running culture for all.

BackgroundUltramarathon running is the most popular ultraendurance competition in terms of the number of races and runners competing annually worldwide; however, no study has compared pacing and performance over a long period. ObjectiveThis study analyzes the pacing of successful finishers and nonfinishers in multistage ultramarathons worldwide. MethodsA total of 4079 athletes (men=3288; women=791) competing in 99 multistage ultramarathon events from 1983 to 2021 were analyzed, including the number of participants, age, gender, rank, and running speed of successful finishers. ResultsThe results showed a significant increase in the number of events (n=338) and a significant increase in the number of finishers and nonfinishers (n=5575) in the ultramarathons worldwide during this period. The general linear models (GLMs) of pacing variation showed nonsignificant effects for gender (F1,36.2=2.5; P=.127; ηp2=0.063) and age group (F10,10=0.6; P=.798; ηp2=0.367), but it showed a significant interaction (gender × age) effect (F10,2689=2.3; P=.008; ηp2=0.009). Post hoc analyses showed that men have a higher pacing variation than women in the under 30 years (U30), U35, U45, and U50 groups. Additionally, the fastest women’s age group (U35) had the lowest pacing variation. The GLM of pacing variation by gender and event distance showed significant effects for both gender (F1,3=18.5; P

Abstract The purposes of this study were (i) to describe differences in participation in 100-mile ultra-marathons by continent; (ii) to investigate differences in performance between continents; and (iii) to identify the fastest runners by continent and country. Data from 148,169 athletes (119,408 men), aged 18–81 years, and finishers in a 100-miles ultra-marathon during 1870–2020 were investigated. Information about age, gender, origin, performance level (top three, top 10, top 100) was obtained. Kruskal–Wallis tests and linear regressions were performed. Athletes were mostly from America and Europe. A macro-analysis showed that the fastest men runners were from Africa, while the fastest women runners were from Europe and Africa. Women from Sweden, Hungary and Russia presented the best performances in the top three, top 10 and top 100. Men from Brazil, Russia and Lithuania were the fastest. The lowest performance and participation were observed for runners from Asia. In summary, in 100-miles ultra-marathon running, the majority of athletes were from America, but for both sexes and performance levels, the fastest runners were from Africa. On a country level, the fastest women were from Sweden, Hungary and Russia, while the fastest men were from Brazil, Russia and Lithuania.

Abstract It is well known that weather and pacing have an influence on elite marathon performance. However, there is limited knowledge about the effect of weather on running speed in elite marathoners. The aim of the present cross-sectional study was to investigate potential associations between running speed and weather variables in elite runners competing in the ‘New York City Marathon’ between 1999 and 2019. Data from all official female and male finishers with name, sex, age, calendar year, split times at 5 km, 10 km, 15 km, 20 km, 25 km, 30 km, 35 km, 40 km and finish and hourly values for temperature (°Celsius), barometric pressure (hPa), humidity (%) and sunshine duration (min) between 09:00 a.m. and 04:00 p.m. were obtained from official websites. A total of 560,731 marathon runners' records were available for analysis (342,799 men and 217,932 women). Pearson and Spearman correlation analyses were performed between the average running speed and the weather variables (temperature, pressure, humidity and sunshine). Ordinary Least Squares (OLS) regressions were also performed. The runner´s records were classified into four performance groups (all runners, top 100, top 10 and top 3) for comparison. Differences in running speed between the four performance groups were statistically significant (p

PurposeOur purpose was to verify the existence of birthplace and relative age effect (RAE), as well as the association between birthplace effect and RAE among Brazilian athletes competing in the Olympic Games.MethodsData included information about Brazilian Summer Olympic athletes from 1920 to 2021. To investigate RAE, athletes’ birthdate was distributed into birth quartiles (Q1: Jan–Mar; Q2: Apr–Jun; Q3: Jul–Sep; Q4: Oct–Dec), while birthplace effect was assessed considering the state and the region (Southeast; Northeast; South; North; and Midwest) of birth. The Chi-square test (χ2) was used to verify differences between the birthplace effect and RAE.ResultsThe sample consisted of 388 Brazilian athletes of both sexes, distributed in 38 sports modalities from 23 Brazilian states (Southeast = 66.5%; South = 14.4%; Northeast = 12.1%; North = 1.5%; Midwest = 5.4%). For both sexes, most of the athletes were from the São Paulo state (37.4%), followed by Rio de Janeiro (18.3%), both from the Southeast region. For birthdate distribution according to birthplace, it was observed that the North region presented the highest frequency of athletes born in Q1 (50%), followed by the Northeast and Southeast regions. No significant differences were found for the birthplace effect (χ2 = 5.69, value of p = 0.128) and RAE between sexes (χ2 = 0.530, value of p = 0.912), nor was shown an association between the birthplace effect and RAE.ConclusionMost Brazilian Olympic athletes are from the Southeast region, but no RAE was established regarding their birthplace. Results from the present study can guide sports public policies in Brazilian regions, especially in the Midwest, North, and Northeast regions, which are underrepresented in Brazilian high-performance sports.

The purpose of this study was to investigate the performance stability of elite half-marathoners of both sexes. The study was composed of 24 elite athletes (12 female and 12 male), ranked among the Senior World TOP20 half-marathoners, who completed a half-marathon race for at least three consecutive years. Tracking was tested using autocorrelations and Kappa Cohen. Autocorrelation revealed a significant association, but a decrease in correlation among the years in both sexes. The overall weighted kappa showed lower stability in performance for both sexes (K ​= ​0.191 and K ​= ​−0.245) than for males. These findings suggest that both female and male elite half-marathoners showed low stability in performance during three events. Besides that, athletes with a better performance tended to present the highest performance stability. It is recommended that coaches track the developmental trajectories of the athletes to comprised the changes in performance across the years, as to provide environmental characteristics that can influence performance.

Abstract Background Physical activity is a multifactorial trait, determined by both individual and environmental characteristics, it seems relevant to understand the determinants related to youth guidelines accomplishment. The present study aimed to verify the differences between the Brazilian federative units regarding to the prevalence of youth physical activity guidelines accomplishment and to investigate the determinants related to the inter-individual differences in this accomplishment. Methods Sample comes from the 2015 Brazilian National School Health Survey (PeNSE), comprising 99,570 adolescents (51,527 girls, 13-17y), enrolled in 3039 schools. Adolescents reported the time they spend in moderate-to-vigorous physical activity daily, and they were categorized as active or inactive, if the guidelines were achieved, or not, respectively, and multilevel statistical analyses were used, including both child and school-level variables. Multilevel Binomial model was computed in the SuperMix software. Results The majority of the adolescents did not comply with the physical activity guidelines daily, where Bahian children complied the least, while those from Amazonas, Tocantins, and Mato Grosso do Sul complied the most. Boys (OR: 2.305; 95%CI: 2.277-2.334), older adolescents (OR: 1.044; 95%CI: 1.036-1.051), and those who spent more time in active travelling to/from school (OR: 1.001; 95%CI: 1.001-1.001) complied more the physical activity guidelines. At the school level, adolescents from larger schools (OR: 0.957; 95%CI: 0.928-0.986) tended to comply less with the guidelines. Conclusion Significant differences between Brazilian federative units in youth daily physical activity guidelines compliance were observed, highlighting the role of individual but also environmental constraints in the Brazilian adolescents’ engagement in physical activity.

Sports performance is the result of a complex interaction between individual and environmental factors. The purpose of this paper is to explain the methods used in the InTrack Project, a cross-sectional and cross-cultural project developed to investigate the variance in the performance of runners from different countries and to understand whether the differences in the performance can be explained by micro-level (athletes characteristics and proximal environment), meso-level (the distal environment that plays a relevant role on the relationships established at micro-level), and the macro-level (environmental features that shape countries characteristics). The sample will be comprised of runners, of both sexes, from four countries. Data collection will be performed in two steps: i) Individual information and ii) Country-level information. At the individual level, data will be obtained from an online survey. At the country level, characteristics data will be obtained from the secondary data available (demographic, social, and economic variables). Statistical procedures expected to be used include multilevel analysis, latent class analysis, addictive and multiplicative interaction in regression models. This wealth of information is of relevance to fill gaps regarding the existence of variables to connect different levels of information, and to provide scientific support about environmental characteristics important to predict runners' performance within and between countries.

Sports performance is the result of a complex interaction between individual and environmental factors. The purpose of this paper is to explain the methods used in the InTrack Project, a cross-sectional and cross-cultural project developed to investigate the variance in the performance of runners from different countries and to understand whether the differences in the performance can be explained by micro-level (athletes characteristics and proximal environment), meso-level (the distal environment that plays a relevant role on the relationships established at micro-level), and the macro-level (environmental features that shape countries characteristics). The sample will be comprised of runners, of both sexes, from four countries. Data collection will be performed in two steps: i) Individual information and ii) Country-level information. At the individual level, data will be obtained from an online survey. At the country level, characteristics data will be obtained from the secondary data available (demographic, social, and economic variables). Statistical procedures expected to be used include multilevel analysis, latent class analysis, addictive and multiplicative interaction in regression models. This wealth of information is of relevance to fill gaps regarding the existence of variables to connect different levels of information, and to provide scientific support about environmental characteristics important to predict runners’ performance within and between countries.

A plethora of factors determine elite cycling performance. Those include training characteristics, pacing strategy, aerodynamics, nutritional habits, psychological traits, physical fitness level, body mass composition, and contextual features; even the slightest changes in any of these factors can be associated with performance improvement or deterioration. The aim of the present case report is to compare the performances of the same ultra-cyclist in achieving two world records (WR) in 24 h cycling. We have analyzed and compared the distance covered and speed for each WR. The 24 h period was split into four-time intervals (0–6 h; > 6–12 h; > 12–18 h; > 18–24 h), and we compared the differences in the distance covered and speed between the two WRs. For both WRs, a strong negative correlation between distance and speed was confirmed (r = –0.85; r = –0.89, for old and new WR, respectively). Differences in speed (km/h) were shown between the two WRs, with the most significant differences in 12–18 h (Δ = 6.50 km/h). For the covered distance in each block, the most significant differences were observed in the last part of the cycling (Δ = 38.54 km). The cyclist effective surface area (ACd) was 0.25 m2 less and 20% more drag in the new WR. Additionally, the mechanical power was 8%, the power to overcome drag was 31%, and the power-weight ratio was 8% higher in the new WR. The mechanical efficiency of the cyclist was 1% higher in the new WR. Finally, the heart rate (HR) presented significant differences for the first 6 h (Old WR: 145.80 ± 5.88 bpm; New WR: 139.45 ± 5.82 bpm) and between the 12 and 18 h time interval (Old WR: 133.19 ± 3.53 bpm; New WR: 137.63 ± 2.80 bpm). The marginal gains concept can explain the performance improvement in the new WR, given that the athlete made some improvements in technical specifications after the old WR.

This study aims to analyze the specialized literature on the protocols, instruments, and techniques used to train and assess strength in gymnastics practitioners or athletes who are represented by the International Gymnastics Federation (Fédération Internationale de Gymnastique – FIG). The systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Systematic searches on the PubMed, Web of Science, Scopus, LILACS, SciELO, and SPORTDiscus databases were conducted using the following keywords: “assessment,” OR “measurement,” OR “evaluation,” AND “child*,” OR “young,” OR “adolesc*,” AND “athlete*,” OR “practitioner,” OR “gymnast*,” AND “gymnastic*,” OR “trampoline,” AND “muscle strength,” OR “muscle power,” OR “strength,” OR “power,” OR “concentric,” OR “performance,” OR “explosive strength,” OR “motor tests”. Studies included in this review address the assessment and training of strength in gymnastics or athletes. Fourteen studies assessing the physical capacity strength in practitioners or gymnastic athletes were the basis of this systematic review. These articles describe low-cost, easy-to-apply protocols and instruments performed in the training gym. The included articles focused on assessing the strength level of adolescent male and female practitioners or athletes for talent selection and detection, as well as lesion reduction. Despite the benefits of strength training, such as better performance and fewer injuries, in general, strength is not regularly assessed. Only studies encompassing artistic and rhythmic gymnastics were found.

Background: The two aspects of the influence of environmental conditions on marathon running performance and pacing during a marathon have been separately and widely investigated. The influence of environmental conditions on the pacing of age group marathoners has, however, not been considered yet.Objective: The aim of the present study was to investigate the association between environmental conditions (i.e., temperature, barometric pressure, humidity, precipitation, sunshine, and cloud cover), gender and pacing of age group marathoners in the “New York City Marathon”.Methodology: Between 1999 and 2019, a total of 830,255 finishes (526,500 males and 303,755 females) were recorded. Time-adjusted averages of weather conditions for temperature, barometric pressure, humidity, and sunshine duration during the race were correlated with running speed in 5 km-intervals for age group runners in 10 years-intervals.Results: The running speed decreased with increasing temperatures in athletes of age groups 20–59 with a pronounced negative effect for men aged 30–64 years and women aged 40–64 years. Higher levels of humidity were associated with faster running speeds for both sexes. Sunshine duration and barometric pressure showed no association with running speed.Conclusion: In summary, temperature and humidity affect pacing in age group marathoners differently. Specifically, increasing temperature slowed down runners of both sexes aged between 20 and 59 years, whereas increasing humidity slowed down runners of 80 years old.

There is evidence for the effectiveness of youth combined plyometric and sprint with change of direction (COD) training. However, the evidence is not well-known regarding the in-season effects of biological age (peak height velocity—PHV) on the motor adaptive processes following combined plyometric and COD with ball training (P-CODBT) in youth soccer players. This study aimed to examine the in-season effects of P-CODBT (8 weeks and twice a week) on the athletic performances of male youth soccer players, circa- and post-PHV. In a randomized controlled training study with pre-to-post measurements, forty-eight male players were assigned into two experimental (performing P-CODBT; n = 12 × circa-PHV and n = 12 × post-PHV) and two control groups (CONG; n = 12 × circa-PHV and n = 12 × post-PHV). The pre- and post-training participants were assessed for their anthropometric, linear sprinting with and without a ball, COD speed with and without a ball, vertical jump, dynamic balance, and endurance-intensive performances. After the intervention, the experimental condition induced significant (all p < 0.0001) and small to large effect size (ES = 0.263–3.471) additional gains only on explosive measures compared to CONG. Both the experimental (all p < 0.0001; ES = 0.338–1.908) and control (p = 0.011–0.0001; ES = 0.2–1.8) groups improved their athletic performances over the training period. The improvements generated by p-CODBT were not affected by biological age. In-season short-term P-CODBT (twice a week) could be safe way to generate benefits in explosive performances in youth soccer players, which are relevant components of match-winning actions in soccer.

Short- to middle-term plyometric training has been shown to be an effective method to promote youth fitness and health. However, there is no knowledge of previous studies that investigated the sex and age effects on physical fitness following different PT surfaces (i.e., firm vs. sand) in schoolchildren. This study examined the effects of age and sex on explosive and high-intensity responses following plyometric training (4 weeks, twice/week) performed on firm vs. sand surfaces in untrained schoolchildren. Ninety girls and ninety boys (under 8: age = 7.1 ± 0.5 and 7.1 ± 0.4 years; under 10: age = 9.0 ± 0.4 and 9.0 ± 0.5 years; under 12: age = 11.0 ± 0.5 and 11.0 ± 0.5 years, respectively) participated in a randomized and parallel training design with pre-to-post testing. Participants were allocated (i.e., 30 boys and 30 girls for each group) into either two experimental groups (firm group: performing plyometrics on a clay surface and sand group: performing plyometrics on a dry surface of 20 cm deep sand) or a control group (CG, habitual physical education classes) within their corresponding age groups. Children were tested for sprint, jumping and change of direction speed performances before and after 4 weeks of plyometric training. Both experimental groups induced more significant improvements in all assessed variables than CG (p < 0.0001; effect size > 0.80), whereas both surfaces induced similar improvements (p > 0.05). Older boys achieved better performances than their younger counterparts (p < 0.05) and older girls (p < 0.0001), respectively. This finding showed that age and sex could affect explosive and high-intensity performances during childhood after a short-term plyometric training. In contrast, the training-induced fitness changes were not influenced by the type of surface.

The purpose of this study was to identify the individual and environmental predictors associated with performance in female runners of different ages. The sample comprised 440 female Brazilian runners, who answered an online questionnaire, that provided information regarding height, weight, age (categories: “young adult”, “adult”, “early middle-age”, and “older adults”), socioeconomic status, and training characteristics (frequency and volume per week, running pace, race event, and running club participation). Information about environmental variables was obtained from the official institutes and comprised the human development index (HDI), athletics events, athletic tracks, and female homicide. A linear regression model, clustered by state and performed by age groups, was computed. The sample presented a mean running pace of 5:57min/km, and a mean BMI of 23.51kg/m−2. An increase in running pace and volume/week was observed with increasing age. In “young adults”, any of the variables were significantly associated with the performance. In “adult” group, only individual characteristics were statistically significantly related with the performance. In “early middle-age”, besides BMI (β=5.72; 95%CI=3.65–7.79) and training volume (β=−0.67; 95%CI=−1.07 − −0.27), the HDI was associated with the performance (β=−23.30; 95%CI=−44.11 − −2.49). In older adults, it was found an association between socioeconomic status (β=−19.47; 95%CI=−32.29 − −6.65), practice time (β=142.92; 95%CI=89.34–196.50), running event participation (β=−80.12; 95%CI=−114.35− −45.88), athletic events (β=33.44; 95%CI=15.16–51.72), and female homicide (β=−0.11; 95%CI=−0.17 − −0.05) with the performance, highlighting the influence of both individual and environmental characteristics. Information about the role of these constraints, and their relationships, in female runners’ performance, can be used to guide the development of projects/strategies aiming to increase their involvement in physical activities and sports practice, through the promotion of a more “friendly environment” to women, and providing support for decision-makers when suggesting/implementing public policies.

Abstract Aims: to identify and describe the clustering of characteristics related to running amongst Brazilian runners using latent class analysis and to verify if there is a profile associated with better performance. Material and Methods: a sample of 1149 Brazilian runners answered an online questionnaire, that provided information about biological (sex, age, height, weight), training (running pace, frequency and volume/week, motivation), and socioeconomic factors, as well as a multidimensional questionnaire of fear of failure. Latent Class Analysis was used to identify subgroups of Brazilian runners, based on BMI, training volume and frequency/week, motivation, socioeconomic factors, and the fear of failure. Further, a χ2 test was computed to verify statistical differences in the frequency of the descriptive variables between classes. Finally, binary logistic regression analysis estimated factors associated with running performance, with running pace as the dependent variable. Results: It was possible to identify two different classes among Brazilian runners, which were called “amateur runners” and “recreational runners”. Variables that highlighted classes’ differences were: volume and frequency training/week, motivation for the practice, and BMI. Regarding the running performance, logistic regression analysis showed that men (OR=5.39; 95%CI=4.00-7.25), young runners (OR=0.38; 95%CI=0.28-0.51), and “amateur runners” (OR=4.19; 95%CI=2.95-5.94) were more prone to have higher performance. Conclusion: Two distinct classes were found among Brazilian runners that were linked to their performance, highlighting that even among non-professional runners, differences can be observed regarding their classification. Hence, future studies should consider using these classes to properly stratify or identify non-professional runners.

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 (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.

Background and Objectives: The 6-h ultra-marathon is the shortest time-limited ultra-marathon race, but little has been investigated regarding this race format. Previously, only the age of peak performance in the context of longer time-limited ultra-marathons was determined. The purpose of this study was to investigate the trends in 6-h ultra-marathon races from 1982 to 2020 for female and male ultra-runners, the participation and performance by countries, the age of peak performance, and the differences in performance regarding countries. Materials and Methods: The sample included 23,203 female ultra-runners, aged 18–83 years, and 87,264 male ultra-runners, aged 18–85 years, who were finishers in a 6-h ultra-marathon held between 1982 and 2020. The age of peak performance was tested using the Kruskal–Wallis test, followed by the Bonferroni Correction. The difference in performance by countries was verified using a linear regression model with the fastest runners from Russia in women, and Tunisia in men, used as reference. Results: Over the years, the men-to-women ratio decreased. The mean age was 43.20 ± 9.30 years for female and 46.09 ± 10.17 years for male runners. Athletes in younger age groups were faster than athletes in older age groups. Most female and male participants originated from Germany. Women from Russia (10.01 ± 1.28 km/h) and men from Tunisia (12.16 ± 1.46 km/h) were the fastest. Conclusions: In summary, in 6-h ultra-marathons held between 1982 and 2020, the participation for both women and men increased, while the men-to-women ratio decreased. The mean age was higher in men compared to women. Most female and male runners originated from Germany, but the fastest women were from Russia, and the fastest men from Tunisia. Future studies need to investigate whether Russian women and Tunisian men are also the best in other distance-limited ultra-marathon races, such as 12-h and 24-h.

Sports performance is a multifactorial trait that can be associated with individual and environmental characteristics. In this study, the sample comprised 35 male runners, enrolled in the “InTrack” project. Information regarding variables related to runners’ training was obtained via an online questionnaire, while anthropometric and body composition variables, as well as physical fitness components (muscular power, isometric strength, local muscular endurance, agility, and aerobic capacity) were measured, and a global physical fitness score (based on physical fitness components measured) was computed. The Weltman test (3200 m) was used to estimate runners’ pace and their stride frequency. Linear regression was used, taking the running pace as dependent variable. The final model, comprising biological, physical fitness, spatiotemporal, and training variables, explained 86% of the running performance variance. Muscular power (β = −1.02; 95% CI = (−1.69)–(−0.35)), abdominal muscle endurance (β = −4.81; 95% CI = (−7.52)–(−2.10)), isometric strength (β = −422.95; 95% CI = (−689.65)–(−156.25)), global physical fitness (β = 27.14; 95% CI = 9.52–45.03), and stride frequency (β = −2.99; 95% CI = (−4.29)–(−1.69)) were significantly associated with performance, meaning that better results in tests and increasing the stride frequency leads to better performance. Individual characteristics and physical fitness components were demonstrated to be significant predictors for running performance.

Background: The purpose of this study was to establish sex-specific percentile curves and values for the running pace of Brazilian non-professional runners. Methods: The sample comprised 1152 amateur runners aged 18–72 (61.8% males), from the five Brazilian regions. The runners answered an online questionnaire providing information about their biological (sex, age, height, weight) and training (volume and frequency/week, running pace) characteristics. Using 25th, 50th, and 75th percentile, the running pace was computed for women and men by age groups and by running distances (5 km, 10 km, 21 km, and 42 km). Sex- and age-specific percentile curves (10th, 25th, 50th, 75th, and 90th) were created through the Lambda Mu Sigma method. Results: For all ages and distance, men performed better than women, and a decrease in the performance was observed across age groups. Among male runners, the beginning of their thirties and the end of their forties seem to be the moments where they observed substantial improvements in running pace; among female runners, this improvement phase was observed to be more pronounced toward the end of their forties. Conclusions: Percentile values of running pace could help coaches during training programs and runners to better understand “how well” they are comparing against their peers.

Background and Objectives: We examined the possible trend in the age of peak performance in elite endurance athletes according to sex, continent of athletes’ national citizenship, and ranking position. Since performance is a multifactorial trait, this information can be used to guide the long-term training and to plan the strategies related to the selection process of athletes. Materials and methods: Information of 1852 professional athletes, classified as top 20 performance of each year in marathon and half-marathon events between 1997 and 2020 were considered. Analysis of variance was computed to test differences in age between sex, continent, and rank position. Results: A significant difference between groups in the mean age of peak performance was observed (F (3, 1884) = 42,31; p < 0.001). For both sexes, half-marathoners were younger than marathoners (male, 25.6 ± 3.6 years vs. 28.0 ± 3.9 years; female, 27.5 ± 4.4 years vs. 28.4 ± 4.1). Female half-marathoners in 2004 presented the highest mean age (31.1 ± 4.8 years) compared to their peers in the years 1997, 2001, 2018 and 2019; among male half-marathoners, those in 1999 presented the highest mean age when compared to 2011, 2018, and 2019. Differences between the continents of athletes’ national citizenship were observed (F (4, 1884) = 62,85,601; p < 0,001). Asian runners presented the lowest mean age (26.5 ± 3.7 years), while their European peers presented the highest (31.1 ± 3.9 years). No significant interaction between sex and ranking position was verified. Differences were observed between sexes for categories “4th–10th positions” and “11th–20th” (F (1, 1879) = 23,114; p < 0.001). Conclusions: Over the last two decades, no clear trend was observed in the changes in the age of peak performance among endurance athletes of both sexes, but, in general, female half-marathoners tended to be significantly older than their male peers.