Tempo de provas curtas e volume de treinamento como preditores do desempenho de maratona

Melo, Onécimo Ubiratã Medina; Bernardo, Rodrigo Torma; Silva, Edson Soares da; Rosa, Karen Przybysz da Silva; Coimbra, Renan dos Santos; Peyré‐Tartaruga, Leonardo Alexandre

doi:10.1016/j.rbce.2017.06.004

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…In addition to running pace calculations, various anthropometric components [ 15 ] and training variables [ 16 ] have been used to determine an athlete’s competitive level. For example, Thuany et al [ 10 ] found, in a study with Brazilian runners, that amateur runners who completed the highest volume and running frequency/week were four times more likely to produce a higher performance compared to recreational runners.…”

Section: Introductionmentioning

confidence: 99%

Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels

Thuany

Souza

Hill

et al. 2021

IJERPH

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The purpose of this study was to investigate the multivariate profile of different types of Brazilian runners and to identify the discriminant pattern of the distinct types of runners, as a runners’ ability to self-classify well. The sample comprised 1235 Brazilian runners of both sexes (492 women; 743 men), with a mean age of 37.94 ± 9.46 years. Individual characteristics were obtained through an online questionnaire: Sex, age, body height (m) and body mass (kg), socioeconomic status, and training information (i.e., self-classification, practice time, practice motivation, running pace, frequency and training volume/week). Multivariate analysis of variance was conducted by sex and the discriminant analysis was used to identify which among running pace, practice time, body mass index and volume/training could differentiate groups such as “professional athletes”, “amateur athletes” and “recreational athletes”. For both sexes, running pace was the variable that better discriminated the groups, followed by BMI and volume/week. The practice time is not a good indicator to differentiate runner’s types. In both sexes, semi-professional runners were those that better self-classify themselves, with amateur runners presenting the highest classification error. This information can be used to guide the long-term training, athlete’s selection programs, and to identify the strengths and weaknesses of athletes.

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Section: Introductionmentioning

confidence: 99%

Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels

Thuany

Souza

Hill

et al. 2021

IJERPH

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“…However, many studies have been limited to either trained or elite athletes, thus limiting the generalizability of findings regarding the specific variables determining V MAX in recreationally active runners through low-cost tests with simple instrumentation ( Abe et al, 1999 ; Gómez-Molina et al, 2017 ; Lanferdini et al, 2020 ; McLaughlin et al, 2010 ). Further, very simple measures as body mass, fat mass, previous performance data of training and races may give important clues on performance prediction in distance runners ( Melo et al, 2018 , 2022 ). Allometric models have also been applied to understand the relationship between biomechanics versus running economy and performance ( Detoni et al, 2015 ; Tartaruga et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Biomechanical, physiological and anthropometrical predictors of performance in recreational runners

Peyré-Tartaruga,

Machado,

Guimarães

et al. 2024

PeerJ

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Background The maximal running speed (VMAX) determined on a graded treadmill test is well-recognized as a running performance predictor. However, few studies have assessed the variables that predict VMAX in recreationally active runners. Methods We used a mathematical procedure combining Fick’s law and metabolic cost analysis to verify the relation between (1) VMAX versus anthropometric and physiological determinants of running performance and, (2) theoretical metabolic cost versus running biomechanical parameters. Linear multiple regression and bivariate correlation were applied. We aimed to verify the biomechanical, physiological, and anthropometrical determinants of VMAX in recreationally active runners. Fifteen recreationally active runners participated in this observational study. A Conconi and a stead-steady running test were applied using a heart rate monitor and a simple video camera to register the physiological and mechanical variables, respectively. Results Statistical analysis revealed that the speed at the second ventilatory threshold, theoretical metabolic cost, and fat-mass percentage confidently estimated the individual running performance as follows: VMAX = 58.632 + (−0.183 * fat percentage) + (−0.507 * heart rate percentage at second ventilatory threshold) + (7.959 * theoretical metabolic cost) (R2 = 0.62, p = 0.011, RMSE = 1.50 km.h−1). Likewise, the theoretical metabolic cost was significantly explained (R2 = 0.91, p = 0.004, RMSE = 0.013 a.u.) by the running spatiotemporal and elastic-related parameters (contact and aerial times, stride length and frequency, and vertical oscillation) as follows: theoretical metabolic cost = 10.421 + (4.282 * contact time) + (−3.795 * aerial time) + (−2.422 * stride length) + (−1.711 * stride frequency) + (0.107 * vertical oscillation). Conclusion Critical determinants of elastic mechanism, such as maximal vertical force and vertical and leg stiffness were unrelated to the metabolic economy. VMAX, a valuable marker of running performance, and its physiological and biomechanical determinants can be effectively evaluated using a heart rate monitor, treadmill, and a digital camera, which can be used in the design of training programs to recreationally active runners.

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Tempo de provas curtas e volume de treinamento como preditores do desempenho de maratona

Cited by 2 publications

References 19 publications

Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels

Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels

Biomechanical, physiological and anthropometrical predictors of performance in recreational runners

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