Fatores determinantes da estratégia de ritmo em esportes de endurance

Franco‐Alvarenga, Paulo Estevão; Brietzke, Cayque; Lima, Felipe De Russi de; Pinheiro, Fabiano Aparecido; Santos, Tony Meireles; Asano, Ricardo Yukio

doi:10.1016/j.rbce.2018.04.004

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Among training variables, the running pace has received the most attention, mainly during training [ 28 ], and for pacing strategy during competition [ 29 ]. Pacing strategy refers to the energy distribution during a workout session [ 29 ], and is associated with the athlete competitive level, experience, and also the previous knowledge regarding the race (e.g., race distance and route, and mean time to complete), besides the athlete’s physiological capacity [ 30 ]. In addition, an inverse relationship can be observed between aerobic capacity and pace variation [ 31 ], and previous studies indicated that the oldest and fastest marathoners presented a more consistent pace than the youngest and slowest ones [ 31 , 32 ], and these factors can explain the result found in the present study, where it was observed that running pace was the best discriminant variable, and that professional athletes were those with the highest accuracy in their self-classification (75% among women and 70% among men).…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

Thuany

Souza

Hill

et al. 2021

IJERPH

View full text Add to dashboard Cite

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Pacing Variation in Multistage Ultramarathons: Internet-Based Cross-Sectional Study

Fariod¹,

Olher²,

Sousa³

et al. 2023

JMIR Form Res

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Background Ultramarathon 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. Objective This study analyzes the pacing of successful finishers and nonfinishers in multistage ultramarathons worldwide. Methods A 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. Results The 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<.001; ηp2=0.007) and distance (F2,3=20.1; P<.001; ηp2=0.015). Post hoc analyses showed a growing pacing variation with increasing race distance for both men and women. In addition, men had a higher variation in long events. Furthermore, there was a significant main effect for both genders (F1,3=33.7; P<.001; ηp2=0.012) and rank (F1,3=136.6; P<.001; ηp2=0.048) on performance, with men being faster than women. Pacing varied greatly due to gender (F1,3=4.0; P=.047; ηp2=0.001), with a lower (ie, more even) pacing variation for male athletes in the top 3 finishers. Male nonfinishers showed a higher performance than female nonfinishers (F1,1340=25.6; P<.001), and no difference was identified for pacing variation (F1,789=1.5; P=.228) based on gender. In addition, a weak but significant correlation (r=–0.130; P<.001) was identified between the average running speed and pacing variation for both female and male nonfinishers. Conclusions In summary, multistage ultramarathon competitions showed an increasing number of competitors and a higher performance challenge. Men have a higher pacing (ie, less even) variation than women, especially observed in longer events. A higher pacing variation was associated with lower performance for men, women, and nonfinishers.

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Fatores determinantes da estratégia de ritmo em esportes de endurance

Cited by 2 publications

References 38 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

Pacing Variation in Multistage Ultramarathons: Internet-Based Cross-Sectional Study

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