Skinfold Thickness Distribution in Recreational Marathon Runners

Skinfold Thickness as a Cardiometabolic Risk Predictor in Sedentary and Active Adult Populations

Studies report that increased body fat can lead to health risks for individuals. However, some methods used for analyzing adiposity did not identify its distribution in the human body because they are typically measured using bioimpedance scales. This study aims to associate the presence of cardiometabolic risk factors in sedentary and active adult populations through anthropometric methods based on skinfold thickness measurements. A cross-sectional study was conducted on 946 adults aged between 18 and 79 years with prior informed consent. Clinical, anthropometric, and biochemical parameters, as well as some cardiometabolic risk factors, were evaluated. Almost half of the population (45.1%; n = 427) is sedentary. A significant association was found between the sum of the skinfolds (bicipital, tricipital, subscapular, and suprailiac) and the cardiometabolic risk factors evaluated, highlighting the cardiovascular risk associated with abdominal obesity, risk of insulin resistance, as well as the development of hyperglycemia, and hypertriglyceridemia. The bicipital fold was thicker (19.67 mm) in the population with a sedentary lifestyle than in the physically active population (18.30 mm). Furthermore, the skinfolds that predict higher metabolic risks were suprailiac and subscapular in sedentary and active populations. Thus, these skinfold measurements could be considered in assessing the adult population for early cardiometabolic risk detection, even in healthy and physically active people.

Sex Differences in Racing History of Recreational 10 km to Ultra Runners (Part B)-Results from the NURMI Study (Step 2)

Sex differences in anatomy and physiology are the primary underlying factor for distinctions in running performance. Overall participation in recreational running events has been dominated by males, although increasing female participation has been reported in recent years. The NURMI study participants filled in a survey following the cross-sectional study design with questions on sociodemographic data, running and racing motivations, training behaviors, and racing history and experience. Data analysis included 141 female and 104 male participants aged 39 (IQR 17) with a healthy median BMI (21.7 kg/m²; IQR 3.5). Statistical analyses revealed sex differences with the males performing faster at half-marathon (p < 0.001) and marathon (p < 0.001) events but no difference at ultra-marathons (p = 0.760). Mediation analyses revealed no significant sex differences in the performance of half-marathon and marathon when considering training behaviors (p > 0.05), racing history (p > 0.05), or racing experience (p > 0.05). Differences in recreational performance may be more closely related to social constraints and expectations of females rather than the physiological advantages of the male athlete. Health professionals who guide and support recreational runners as well as the runners themselves and their coaches may benefit from this study's results in order to improve the best time performance through a deeper understanding of the areas that mediate sex differences.

Relationship between Biological, Training, and Physical Fitness Variables in the Expression of Performance in Non-Professional Runners

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.

Running Performance Variability among Runners from Different Brazilian States: A Multilevel Approach

The ecological model theory highlights that human development (or a given behavior) is the result of the interaction of variables derived from different levels, comprising those directly related to the subjects and those related to the environment. Given that, the purpose of this study is to establish whether runners' performance may vary among different Brazilian states, as the factors associated with this difference. The sample comprised 1151 Brazilian runners (61.8% men) that completed an online questionnaire, providing information about biological (sex, age, height, and weight), training (running pace, frequency and volume/week, and motivation), sociodemographic (place of residence and wage) aspects, and perceptions about the environmental influences on the practice. Information about state variables was obtained from official institutes, and comprised the human development index (HDI), athletics events, and violence index. Multilevel analysis was conducted in HLM software. State-level characteristics explained ≈3% of the total variance in running performance. Of the total variance explained for the individual level, 56.4% was associated with male sex (β = -54.98; p < 0.001), age (β = 1.09; p < 0.001), body mass index (β = 6.86; p < 0.001), economic status (β = 6.23; p = 0.003), the perception of the natural environment (β = 7.58; p = 0.02), training frequency (β = -16.64; p < 0.001), and weekly volume (β = -0.30; p < 0.001). At the state level, only athletics events presented a positive and significant influence on performance. There is a significant role of the environment on the explanation of running performance variability, and given the diversity across states, environmental variables should not be neglected, as they are relevant to the exploration of other variables possibly related to running performance.