Predictor Variables for 100 Km Race Time in Female Ultra-Marathoners

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

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


2017 ◽  
Vol 2 (2) ◽  
pp. 155-168 ◽  
Author(s):  
David Wong

This research aims at analyzing (1) the effect of vendor’s ability, benevolence, and integrity variables toward e-commerce customers’ trust in UBM; (2) the effect of vendor’s ability, benevolence, and integrity variables toward the level of e-commerce customers’ participation in Indonesia; and (3) the effect of trust variable toward level of e-commerce customers participation in UBM. This research makes use of UBM e-commerce users as research samples while using Likert scale questionnaire for data collection. Furthermore, the questionnaires are sent to as many as 200 respondents. For data analysis method, Structural Equation Model was used. Out of three predictor variables (ability, benevolence, and integrity), it is only vendor’s integrity that has a positive and significant effect on customers’ trust. On the other hand, it is only vendor’s integrity and customer’s trust that have a positive and significant effect on e-commerce customers’ participation in UBM. Keywords: e-commerce customers’ participation, ability, benevolence, integrity


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