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

Abstract

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.

The Influence of Environmental Conditions on Pacing in Age Group Marathoners Competing in the “New York City Marathon”

“…In this sense, runners with the highest body mass can be performance-limited regarding heat retention. Considering that, slower runners tend to present the highest values for body mass (especially, body fat), these morphological characteristics can be related to performance decrease among the non-professional runners ( Thuany et al, 2021 ). An analysis from the “Berlin Marathon” with 882,540 finishers from 1974–2019 showed that the general mass of runners slowed down with increasing temperature and sunshine duration ( Knechtle et al, 2021 ).…”

Review of Neural Networks in the EEG Signal Recognition

“…• The indicator of fitness has a direct effect on the fatigue performance. [39]. Relationships between biological, training, and physical variables in the expression of performance in lay runners were considered in the paper [40].…”