scholarly journals Predictor variables for half marathon race time in recreational female runners

Clinics ◽  
2011 ◽  
Vol 66 (2) ◽  
pp. 287-291 ◽  
Author(s):  
Beat Knechtle ◽  
Patrizia Knechtle ◽  
Ursula Barandun ◽  
Thomas Rosemann ◽  
Romuald Lepers
2012 ◽  
Vol 3 (2) ◽  
Author(s):  
Wiebke Schmid ◽  
Beat Knechtle ◽  
Patrizia Knechtle ◽  
Ursula Barandun ◽  
Christoph Alexander Rüst ◽  
...  

Author(s):  
Beat Knechtle ◽  
Rüst ◽  
Knechtle ◽  
Barandun ◽  
Romuald Lepers ◽  
...  

Author(s):  
Emma O’Loughlin ◽  
Pantelis T. Nikolaidis ◽  
Thomas Rosemann ◽  
Beat Knechtle

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.


2010 ◽  
Vol 14 (4) ◽  
pp. 214-220 ◽  
Author(s):  
Knechtle Beat ◽  
Knechtle Patrizia ◽  
Rosemann Thomas ◽  
Lepers Romuald

2018 ◽  
Vol 26 (4) ◽  
pp. 629-636 ◽  
Author(s):  
Pantelis T. Nikolaidis ◽  
Stefania Di Gangi ◽  
Beat Knechtle

The relationship between age and elite marathon race times is well investigated, but little is known for half-marathon running. This study investigated the relationship between half-marathon race times and age in 1-year intervals by using the world single age records in half-marathon running and the sex difference in performance from 5 to 91 years in men and 5 to 93 years in women. We found a fourth-order polynomial relationship between age and race time for both women and men. Women achieve their best half-marathon race time earlier in life than men, 23.89 years compared with 28.13 years, but when using a nonlinear regression analysis, the age of the fastest race time does not differ between men and women, with 26.62 years in women and 26.80 years in men. Moreover, the sex difference in half-marathon running performance increased with advancing age.


2021 ◽  
Vol 12 ◽  
Author(s):  
Pantelis T. Nikolaidis ◽  
Thomas Rosemann ◽  
Beat Knechtle

AimDespite the increasing popularity of outdoor endurance running races of different distances, little information exists about the role of training and physiological characteristics of recreational runners. The aim of the present study was (a) to examine the role of training and physiological characteristics on the performance of recreational marathon runners and (b) to develop a prediction equation of men’s race time in the “Athens Authentic Marathon.”MethodsRecreational male marathon runners (n = 130, age 44.1 ± 8.6 years)—who finished the “Athens Authentic Marathon” 2017—performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO2max), anaerobic power, squat, and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, n = 65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, n = 65).ResultsIn the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF, and VO2max (p ≤ 0.003, η2 ≥ 0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (r = −0.36, p < 0.001) and largely with the number of weekly training days (r = 0.52, p < 0.001) and weekly running distance (r = 0.58, p < 0.001), but not with the number of previously finished marathons (r = 0.08, p = 0.369). With regard to physiological characteristics, running speed correlated largely with body mass (r = −0.52, p < 0.001), BMI (r = −0.60, p < 0.001), BF (r = −0.65, p < 0.001), VO2max (r = 0.67, p < 0.001), moderately with isometric muscle strength (r = 0.42, p < 0.001), and small with anaerobic muscle power (r = 0.20, p = 0.021). In EXP, race speed could be predicted (R2 = 0.61, standard error of the estimate = 1.19) using the formula “8.804 + 0.111 × VO2max + 0.029 × weekly training distance in km −0.218 × BMI.” Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12 ± 1.09 km/h, 95% confidence intervals −0.15, 0.40, p = 0.122).ConclusionThese findings highlighted the role of aerobic capacity, training, and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.


2010 ◽  
Vol 111 (3) ◽  
pp. 681-693 ◽  
Author(s):  
Beat Knechtle ◽  
Thomas Rosemann ◽  
Patrizia Knechtle ◽  
Romuald Lepers

Author(s):  
Barry Smyth ◽  
Padraig Cunningham

We describe and evaluate a novel application of case-based reasoning to help marathon runners to achieve a personal best by: (a) predicting a challenging, but realistic race-time; and (b) recommending a race-plan to achieve this time.


Sign in / Sign up

Export Citation Format

Share Document