Beyond the border of the athlete-centered approach: a model to understand runners' performance

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

Is there stability in the performance of elite half-marathoners?

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. Keywords: Elite athletes; Endurance; Running; Trackin

The Matthew Effect in Running: An Analysis of Elite Endurance Athletes Over 23 Years

Purpose The purpose of this study was to investigate the frequency of countries represented in the TOP20 long-distance elite runners ranking during 1997–2020, taking into account the countries’ Human Development Index (HDI), and to verify if the Matthew effect can be observed regarding countries’ representativeness in the raking alongside the years. Methods The sample comprised 1852 professional runner athletes, ranked in the Senior World TOP20 half-marathon (403 female and 487 male) and marathon (480 female and 482 male) races, between the years 1997–2020. Information about the countries’ HDI was included, and categorized as “low HDI”, “medium HDI”, “high HDI”, and “very-high HDI”. Athletes were categorized according to their ranking positions (1st–3rd; 4th–10th; > 10th), and the number of athletes per country/year was summed and categorized as “total number of athletes 1997–2000”; “total number of athletes 2001–2010”; and “total number of athletes 2011–2020”. The Chi-square test and Spearman correlation were used to verify potential associations and relationships between variables. Results Most of the athletes were from countries with medium HDI, followed by low HDI and very-high HDI. Chi-square test results showed significant differences among females (χ2 = 15.52; P = 0.017) and males (χ2 = 9.03; P = 0.014), in half-marathon and marathon, respectively. No significant association was verified between HDI and the total number of athletes, but the association was found for the number of athletes alongside the years (1997–2000 to 2001–2010: r = 0.60; P < 0.001; 2001–2010 to –2011–2020: r = 0.29; P < 0.001). Conclusion Most of the athletes were from countries with medium HDI, followed by those with low HDI and very-high HDI. The Matthew effect was observed, but a generalization of the results should not be done

Local de nascimento dos atletas brasileiros de Tóquio 2020 e variáveis dos estados relacionados a chance de ser medalhista

The purpose of this study was to describe the birthplace of Brazilian Tokyo 2020 Olympic athletes, and to investigate the states-related variables associated with winning an Olympic medal. Data were collected based on the Brazilian Olympic Committee official list of athletes competing at the Tokyo 2020 Olympic Games and from ESTADIC research (2016) and Brazilian Institute of Geography and Statistics (IBGE). Binary Logistic Regression was used with the purpose to estimate the chances of the states have athletes who won an Olympic medal. All the analysis was performed in JAMOVA, considering p&lt;0.05. Sample comprised 303 athletes, from the five Brazilian regions. The model comparisons indicated that the second model was the best to explain the chances of the state have an athlete who won an Olympic medal (Model 2 versus Model 1: c² = 21.41; p&lt;0.001; Model 3 versus Model 2: c² = 2.77; p = 0.429). So, age (OR= 0.91; CI95% = -0.15 – -0.2) and to be part in team sports (OR= 4.36; CI95% = 0.80 – 2.15) are associated with chances to winning an Olympic medal. The most of Brazilian Olympic athletes in Tokyo 2020 were born in Southeast region, especially in São Paulo and Rio de Janeiro states. The best model to explain associations with chances to winning a medal was composed to meso-level variables, and it was not found significant association between the macro-level characteristics investigated with the chances to be a medalist.El objetivo de este estudio fue describir el lugar de nacimiento de los atletas olímpicos brasileños en Tokio 2020 e investigar las variables de los estados asociados con la obtención de medalla olímpica. Los datos se recopilaron con base en la lista oficial del Comité Olímpico Brasileño, la encuesta ESTADIC (2016) y del Instituto Brasileño de Geografía y Estadística (IBGE). Regresión logística binaria fue utilizado para estimar las posibilidades de que los estados tengan atletas medallista olímpico. Los análisis se realizaron con el software JAMOVA, considerando p&lt;0.05. La muestra estuvo compuesta por 303 deportistas, de las cinco regiones brasileñas, distribuidos en 21 estados y el Distrito Federal. El segundo modelo fue lo que mejor explicó las posibilidades de que el estado tenga medallista olímpico (Modelo 2 versus Modelo 1: c²=21,41; p&lt;0,001; Modelo 3 versus Modelo 2: c²=2,77; p=0,429). Así, la edad (OR=0,91; IC del 95%=-0,15 - -0,2) y la práctica de deportes de equipo (OR=4,36; IC del 95%=0,80 - 2,15) se asocian con las posibilidades de ganar medalla olímpica. La mayoría de los deportistas brasileños en Tokio 2020 nacieron en la región Sudeste, principalmente en los estados de São Paulo y Río de Janeiro. El mejor modelo para explicar las asociaciones con las posibilidades de ser medallista se fue composto de variables de nivel meso, y no se encontró asociación significativa entre las características de nivel macro investigadas y las posibilidades de ser medallista.O objetivo deste estudo foi descrever o local de nascimento dos atletas olímpicos brasileiros de Tóquio 2020 e investigar as variáveis ??dos estados associadas à conquista de medalha olímpica. Os dados foram obtidos a partir da lista oficial do Comitê Olímpico Brasileiro de atletas selecionados para os Jogos Olímpicos de Tóquio 2020, através da pesquisa ESTADIC (2016) e Instituto Brasileiro de Geografia e Estatística (IBGE). Regressão Logística Binária foi utilizada para estimar as chances de os estados terem atletas que conquistaram medalha olímpica. As análises foram realizadas no Software JAMOVA, considerando p&lt;0,05. A amostra foi composta por 303 atletas, das cinco regiões brasileiras. O segundo modelo foi o que melhor explicou as chances de o estado ter um atleta medalhista olímpico (Modelo 2 versus Modelo 1: c² = 21,41; p &lt;0,001; Modelo 3 versus Modelo 2: c² = 2,77; p = 0,429). Assim, idade (OR = 0,91; IC95% = -0,15 - -0,2) e prática de esportes coletivos (OR = 4,36; IC95% = 0,80 – 2,15) foram associados às chances de conquista de medalha olímpica. A maioria dos atletas brasileiros em Tóquio 2020 nasceram na região Sudeste, principalmente nos estados de São Paulo e Rio de Janeiro. O melhor modelo para explicar as associações com chances de ser medalhista foi composto por variáveis ??de nível meso, e não foi encontrada associação significativa entre características de nível macro investigadas com chances de ser medalhista olímpico

Physical (in)activity, and its predictors, among Brazilian adolescents: a multilevel analysis

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. Keywords: Adolescents; Guidelines compliance; Physical activity; School

The subject-environment interplay between runners from different Brazilian macro-regions

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 &lt; 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

A macro to micro analysis to understand performance in 100-mile ultra-marathons worldwide

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

The subject-environment interplay between runners from different Brazilian macro-regions

PURPOSE Our purpose was to investigate the interplay between runners and their environment using a network approach. METHODS This 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. RESULTS At 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. CONCLUSION Public 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