Deviation from goal pace, body temperature and body mass loss as predictors of road race performance (original) (raw)
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Influence of temperature and performance level on pacing a 161 km trail ultramarathon
International journal of sports physiology and performance, 2011
Even pacing has been recommended for optimal performances in running distances up to 100 km. Trail ultramarathons traverse varied terrain, which does not allow for even pacing. This study examined differences in how runners of various abilities paced their efforts in the Western States Endurance Run (WSER), a 161 km trail ultramarathon in North America, under hot vs cooler temperatures. Temperatures in 2006 (hot) and 2007 (cooler) ranged from 7-38°C and 2-30°C, respectively. Arrival times at 13 checkpoints were recorded for 50 runners who finished the race in both years. After stratification into three groups based on finish time in 2007 (<22, 22-24, 24-30 h), paired t tests were used to compare the difference in pace across checkpoints between the years within each group. The χ2 test was used to compare differences between the groups on the number of segments run slower in the hot vs cooler years. For all groups, mean pace across the entire 161 km race was slower in 2006 than in...
Physiological Determinants of Ultramarathon Trail Running Performance
2020
The physiological determinants of ultramarathon success have rarely been assessed, and likely differ in their contributions to performance as race distance increases. The aim of this study was to examine predictors of performance in athletes who completed either a 50km (n:23, F:11), 80km (n:14, F:4), or 160km (n:14, F:2) trail race over a 20km-loop course on the same day. Measures of training history, aerobic fitness, running economy, in-race dehydration, and cardiovascular health were examined in relation to race-day performance. Performance was defined as the percent difference from the winning time at a given race distance, with 0% representing the fastest possible time. In the 50km race, higher training volumes, cardiovascular health, aerobic fitness, and a greater loss of body mass during the race were all related to better performance (all P<0.05). Using multiple linear regression, peak velocity achieved in the VO2max test (β=-11.7, P=0.002) and baseline blood pressure (β=3...
The present study aims to investigate the association between the half 10.000 m race time, body composition characteristics and physiological responses at half race (i.e., 5.000 m); and to determine the predictor variables of 10.000 m road race time, in recreational female runners. Observational field study of a '10.000 m road race' in Brazil. A sample of 11 recreational runners (age, 45.1 ± 6.9 years), participated in the study. Before road race, body mass and stature were measured, body composition was determined by multi-frequency bioelectrical impedance analysis and blood lactate concentration ([Lac]) was collected. After the first 5.000 m (half road race distance), the rate of perceived exertion (RPE) and [Lac] were collected. The race time, in both tests race were also collected. In the bivariate analysis, absolute values of protein (r = -0.63), soft lean mass (r = -0.61) and fat free mass (r = -0.61), correlated to half race time (all, p < 0.05). Stepwise multiple regressions revealed that absolute fat free mass (r2a = 0.981, p < 0.001) and rate of perceived exertion in half race (r2a = 0.973, p < 0.001) were the best variables to predict 10.000 m road race time for recreational female runners. It seems that recreational female endurance performance is negatively related to absolute fat free mass, and the 10.000 m road race time might be predicted by the following equation (r2a = 0.983, p < 0.001): Total race time (10.000 m; minutes) = 0.742 x (fat free mass, kg) + 1.805 x (half race RPE; Borg6-20).
2020
Objectives: The 2018 Virgin Money London Marathon (2018VMLM) was the hottest in the race's 37 year history, the major aims of this research were to 1) survey novice-mass participation marathoners to examine the thermal demands of the extreme weather event; and 3) investigate the effect of the air temperature on finish times. Methods: A mixed methods design involving the collection of survey data (n = 364; male = 63, female = 294) and secondary analysis of environmental and marathon performance (676,456 finishers) between 2009 and 2018 was used. Results: The 2018VMLM was hotter than the mean of all other marathons (P < 0.05); mean finishing time was slower than the mean of all other London Marathons (P < 0.05); there were positive correlations between maximum race-day temperature and finish time for mass-start participants and championship runners, and the difference in maximum race day temperature and mean maximum daily temperature for the 60 days prior to the London Marat...
Defining the determinants of endurance running performance in the heat
Temperature
In cool conditions, physiologic markers accurately predict endurance performance, but it is unclear whether thermal strain and perceived thermal strain modify the strength of these relationships. This study examined the relationships between traditional determinants of endurance performance and time to complete a 5-km time trial in the heat. Seventeen club runners completed graded exercise tests (GXT) in hot (GXTHOT; 32 C, 60% RH, 27.2 C WBGT) and cool conditions (GXTCOOL; 13 C, 50% RH, 9.3 C WBGT) to determine maximal oxygen uptake (V̇O 2max), running economy (RE), velocity at V̇O 2max (vV̇O 2max), and running speeds corresponding to the lactate threshold (LT, 2 mmol.l ¡1) and lactate turnpoint (LTP, 4 mmol.l ¡1). Simultaneous multiple linear regression was used to predict 5 km time, using these determinants, indicating neither GXTHOT (R 2 D 0.72) nor GXTCOOL (R 2 D 0.86) predicted performance in the heat as strongly has previously been reported in cool conditions. vV̇O 2max was the strongest individual predictor of performance, both when assessed in GXT HOT (r D ¡0.83) and GXT COOL (r D ¡0.90). The GXTs revealed the following correlations for individual predictors in GXT HOT ; V̇O 2max
International Journal of Sports Science & Coaching
Energy and macronutrient intake of ultra-endurance runners (UER n=74; control (CON) n=12) during a 5-days 225km multi-stage ultra-marathon (MSUM) in the heat (Tmax 32-40˚C), were determined through dietary recall interview and analysed by dietary analysis software. Body mass (BM) and urinary ketones were determined pre- and post-stage. Recovery, appetite and gastrointestinal symptoms were monitored daily. Pre-stage BM, total daily energy (overall mean: 3348kcal/day), protein (1.5g/kgBM/day), carbohydrate (7.5g/kgBM/day) and fat (1.4g/kgBM/day) intakes did not differ between stages in UER. CON presented a daily macronutrient profile closer to benchmark recommendations than UER. Carbohydrate intake pre-stage (102g), during running (24g/h) and immediately post-stage (1.7g/kgBM), and protein intake post-stage (0.3g/kgBM) did not differ between stages, and were below benchmark recommendations in the majority of UER. Post-stage urinary ketones increased in UER as competition progressed (S...
Some physiological demands of a half-marathon race on recreational runners
British Journal of Sports Medicine, 1983
The purpose of this study was to assess the physiological demands of a half-marathon race on a group of ten recreational runners (8 men and 2 women). The average running speed was 223.1 ± 22.7 m.mini1 (mean ± SD) for the group and this represented 79 ± 5% V02 max for these runners. There was a good correlation between V02 max and performance time for the race (r = -0.81; p < 0.01) and an even better correlation between running speed equivalent to a blood lactate concentration of 4 mmol.r1 and performance times (r = -0.877; p < 0.01). The blood lactate concentration of 4 of the runners at the end of the race was 5.65 ± 1.42 mmol.rF (mean ± SD) and the estimated energy expenditure for the group was 6.22 M.J. While there was only a poor correlation between total energy expenditure and performance time for the race, the correlation coefficient was improved when the energy expenditure of each individual was expressed in KJ.kg 1 min1 (r = -0.938; p < 0.01).
Open Access Journal of Sports Medicine, 2012
Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners. Methods: Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times. Results: After multivariate regression, running speed of the training units (β = -0.52, P , 0.0001) and percent body fat (β = 0.27, P , 0.0001) were the two variables most strongly correlated with marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r 2 = 0.44): race time ( minutes) = 326.3 + 2.394 × (percent body fat, %) -12.06 × (speed in training, km/hours). Running speed during training sessions correlated with prerace percent body fat (r = 0.33, P = 0.0002). The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics. Conclusion: The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour) are two key factors for a fast marathon race time in recreational male marathoner runners.
Real-Time Observations of Food and Fluid Timing During a 120 km Ultramarathon
Frontiers in Nutrition, 2018
The aim of the present case study was to use real-time observations to investigate ultramarathon runners' timing of food and fluid intake per 15 km and per hour, and total bodyweight loss due to dehydration. The study included 5 male ultramarathon runners observed during a 120 km race. The research team members followed on a bicycle and continuously observed their dietary intake using action cameras. Hourly carbohydrate intake ranged between 22.1 and 62.6 g/h, and fluid intake varied between 260 and 603 mL/h. These numbers remained relatively stable over the course of the ultra-endurance marathon. Runners consumed food and fluid on average 3-6 times per 15 km. Runners achieved a higher total carbohydrate consumption in the second half of the race (p = 0.043), but no higher fluid intake (p = 0.08). Energy gels contributed the most to the total average carbohydrate intake (40.2 ± 25.7%). Post-race weight was 3.6 ± 2.3% (range 0.3-5.7%) lower than pre-race weight, revealing a non-significant (p = 0.08) but practical relevant difference. In conclusion, runners were able to maintain a constant timing of food and fluid intake during competition but adjusted their food choices in the second half of the race. The large variation in fluid and carbohydrate intake indicate that recommendations need to be individualized to further optimize personal intakes.