Predicting Marathon Time Using Exhaustive Graded Exercise Test in Marathon Runners

Rosemann

et al. 2019

IJERPH

Ultra-marathon races are increasing in popularity. Women are now 20% of all finishers, and this number is growing. Predictors of performance have been examined rarely for women in ultra-marathon running. This study aimed to examine the predictors of performance for women and men in the 62 km Wellington Urban Ultramarathon 2018 (WUU2K) and create an equation to predict ultra-marathon race time. For women, volume of running during training per week (km) and personal best time (PBT) in 5 km, 10 km, and half-marathon (min) were all associated with race time. For men, age, body mass index (BMI), years running, running speed during training (min/km), marathon PBT, and 5 km PBT (min) were all associated with race time. For men, ultra-marathon race time might be predicted by the following equation: (r² = 0.44, adjusted r² = 0.35, SE = 78.15, degrees of freedom (df) = 18) ultra-marathon race time (min) = −30.85 ± 0.2352 × marathon PBT + 25.37 × 5 km PBT + 17.20 × running speed of training (min/km). For women, ultra-marathon race time might be predicted by the following equation: (r² = 0.83, adjusted r2 = 0.75, SE = 42.53, df = 6) ultra-marathon race time (min) = −148.83 + 3.824 × (half-marathon PBT) + 9.76 × (10 km PBT) − 6.899 × (5 km PBT). This study should help women in their preparation for performance in ultra-marathon and adds to the bulk of knowledge for ultra-marathon preparation available to men.

Section: Discussionmentioning

confidence: 99%

Different Predictor Variables for Women and Men in Ultra-Marathon Running—The Wellington Urban Ultramarathon 2018

O’Loughlin

Rosemann

et al. 2019

IJERPH

“…Considering the increased popularity of marathon running, a large number of studies have examined determinants of race time [1][2][3][4]. Nowadays, it is well known that marathon race time might be predicted by performance indices (e.g., 10 km race time, number of previously finished marathon races) [2], training volume and intensity [1,3], and physiological [5] and anthropometric characteristics (e.g., body mass index) [4]. In addition, marathon race time has been related to body composition [6], where a higher body fat (BF) percentage was associated with slower race time.…”

Section: Introductionmentioning

confidence: 99%

Skinfold Thickness Distribution in Recreational Marathon Runners

Rosemann

Knechtle

2020

IJERPH

The relationship of body fat (BF) percentage with performance of elite marathon runners has been well studied; however, less information is available about the variation of skinfold thickness by sex and performance in non-elite marathon runners. The aim of the present study was to examine the variation of skinfold thickness by sex and performance in recreational marathon runners. Participants included 32 female (age 40.1 ± 9.0 years, BF 19.6 ± 4.7%, and training volume 47.7 ± 22.6 km) and 134 male marathon runners (44.3 ± 8.8 years, 17.6 ± 4.0%, and 53.0 ± 21.2 km, respectively). The largest skinfold thickness was the abdomen in both sexes, whereas the smallest was biceps in men, and chins in women (p < 0.001). The largest sex difference in skinfold thickness was observed in triceps being the fattest in women (p < 0.001). The largest difference in skinfold thickness among men’s performance groups was observed in the iliac crest, and the smallest in the patella and proximal calf (p < 0.001). In summary, skinfold measurements indicated that women had more fat in both their upper and lower limbs, while men had more fat in their trunk. With regards to the role of performance level, the slowest runners presented relatively more fat in the upper limbs and trunk anatomical sites, i.e., away from the active muscles of legs.

“…It has been shown that half of the variance in pacing was accounted for by the variance in race time ( Haney and Mercer, 2011 ). In turn, race time could be predicted by aerobic capacity ( Till et al, 2016 ; Salinero et al, 2017 ). As shown in the present study, aerobic capacity was related inversely with variation in pacing in men, i.e., the higher the aerobic capacity the less variable the pacing.…”

Section: Discussionmentioning

confidence: 99%

Pacing Strategies in the ‘Athens Classic Marathon’: Physiological and Psychological Aspects

Knechtle

2018

Front. Physiol.

Despite the increased scientific interest in the relationship between pacing and performance in marathon running, little information is available about the association of pacing with physiological and psychological parameters. Therefore, the aim of the present study was to examine the role physical fitness and training characteristics on pacing in the ‘Athens Classic Marathon.’ Finishers in this race in 2017 (women, n = 26, age 40.8 ± 9.4 years; men, n = 130, age 44.1 ± 8.6 years) were analyzed for their pacing during the race, completed the Motivation of Marathon Scale (MOMS) and performed a series of physiological tests. Women and faster recreational runners adopted a more even pacing. A more even pacing was related with a higher aerobic capacity and lower muscle strength in men, but not in women. Men with more even pacing scored higher in psychological coping, self-esteem, life meaning, recognition and competition than their counterparts with less even pacing. Considering the increasing number of participants in marathon races, these findings might help a wide range of professionals (fitness trainers, physiologists, and psychologists) working with runners to optimize the pacing of their athletes.