Predictors Of Performance During A 161 Km Mountain Footrace

Training volume and cardiovascular dynamics influence endurance performance. However, there is limited information on the interplay between training volume, cardiovascular dynamics, and performance in ultra-marathon athletes. PURPOSE: We aimed to determine predictors of performance in finishers of the 2023 Western States Endurance Run (WSER). METHODS: Sixty participants who finished the race (49 males/11 females; mean age: 44.7 ± 9.6 y, range: 26-66 y; BMI: 22.7 ± 2.2 kg/m2) completed pre-race surveys including average training volume (AV) and peak training volume (PV), as well as resting cardiovascular measures including resting heart rate (RHR) and augmentation index (AIx), a measure of wave reflection characteristics. Based on WSER completion time, we calculated average running velocity (RV). We assessed associations among 22 variables using bivariate correlation analysis (Pearson’s Correlation for normally distributed data and Spearman’s Rank Correlation if normality was not met). Within our listed variables, normality was met in age and AV. Additionally, we completed multiple regression analyses for predictors. We present descriptive data as mean ± SD. RESULTS: Participants had an average RV of 6.33 ± 0.97 km/h (3.93 ± 0.6 mph), and reported an AV of 91.9 ± 24.5 km/wk (57.1 ± 15.2 miles/wk) and a PV of 141.0 ± 47.2 km/wk (87.6 ± 29.3 miles/wk). We observed significant associations between RV and age (r(58) = -0.57, p < .001); AV (r(58) = 0.41, p < .001); and PV (r(58) = 0.34, p < .001). Our regression model inclusive of age, AV, and PV was associated with RV (R2 = 0.37; F(3,56) = 12.4, p < .001). Moreover, AV was a significant unique predictor in the model (b1 = 0.013; t(56) = 2.57, p = 0.013), resulting in a 0.33 km/h increase in RV for every 25-km increase in AV. Last, significant relations existed between RV and AIx (r(58) = -0.30, p = 0.022); and RHR (r(58) = -0.26, p = 0.046). CONCLUSION: We found that (1) average weekly training volume is a significant predictor of performance in elite ultra-marathon athletes and (2) race performance was inversely associated with resting arterial wave reflection characteristics and heart rate. Western States Endurance Run Research Committee

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Journal
Medicine & Science in Sports & Exercise
Published
2024-09-16
DOI
https://doi.org/10.1249/01.mss.0001055440.92705.7e
Primary Topic
Sports Performance and Training
Type
article
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article

Predictors Of Performance During A 161 Km Mountain Footrace

Patrick Bialy, Braxton A. Linder, Nina L. Stute, Matthew C. Babcock et al.
Medicine & Science in Sports & Exercise
Sports Performance and Training
article

Predictors Of Performance During A 161 Km Mountain Footrace

Patrick Bialy, Braxton A. Linder, Nina L. Stute, Matthew C. Babcock, Omar B. El‐Kurd, Soolim Jeong, Kent A. Lorenz, James R. Bagley, Austin T. Robinson, Joseph D. Vondrasek, Joseph C. Watso, Gregory J. Grosicki, Christine Lee
article en

Abstract

Training volume and cardiovascular dynamics influence endurance performance. However, there is limited information on the interplay between training volume, cardiovascular dynamics, and performance in ultra-marathon athletes. PURPOSE: We aimed to determine predictors of performance in finishers of the 2023 Western States Endurance Run (WSER). METHODS: Sixty participants who finished the race (49 males/11 females; mean age: 44.7 ± 9.6 y, range: 26-66 y; BMI: 22.7 ± 2.2 kg/m2) completed pre-race surveys including average training volume (AV) and peak training volume (PV), as well as resting cardiovascular measures including resting heart rate (RHR) and augmentation index (AIx), a measure of wave reflection characteristics. Based on WSER completion time, we calculated average running velocity (RV). We assessed associations among 22 variables using bivariate correlation analysis (Pearson’s Correlation for normally distributed data and Spearman’s Rank Correlation if normality was not met). Within our listed variables, normality was met in age and AV. Additionally, we completed multiple regression analyses for predictors. We present descriptive data as mean ± SD. RESULTS: Participants had an average RV of 6.33 ± 0.97 km/h (3.93 ± 0.6 mph), and reported an AV of 91.9 ± 24.5 km/wk (57.1 ± 15.2 miles/wk) and a PV of 141.0 ± 47.2 km/wk (87.6 ± 29.3 miles/wk). We observed significant associations between RV and age (r(58) = -0.57, p < .001); AV (r(58) = 0.41, p < .001); and PV (r(58) = 0.34, p < .001). Our regression model inclusive of age, AV, and PV was associated with RV (R2 = 0.37; F(3,56) = 12.4, p < .001). Moreover, AV was a significant unique predictor in the model (b1 = 0.013; t(56) = 2.57, p = 0.013), resulting in a 0.33 km/h increase in RV for every 25-km increase in AV. Last, significant relations existed between RV and AIx (r(58) = -0.30, p = 0.022); and RHR (r(58) = -0.26, p = 0.046). CONCLUSION: We found that (1) average weekly training volume is a significant predictor of performance in elite ultra-marathon athletes and (2) race performance was inversely associated with resting arterial wave reflection characteristics and heart rate. Western States Endurance Run Research Committee

Medicine & Science in Sports & ExerciseVol. 56(10S)
Florida State University (US), Georgia Southern University (US), University of Colorado Anschutz Medical Campus (US), San Francisco State University (US), Auburn University (US), University of Colorado Denver (US)
Climate action
Openalex Percentile: Top 79%
Sports Performance and Training
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