The Interaction of Aging and 10 Years of Racing on Ultraendurance Running Performance

Rae, Dale E.; Bosch, Andrew; Collins, Malcolm; Lambert, Michael

doi:10.1123/japa.13.2.210

Cited by 9 publications

(10 citation statements)

References 27 publications

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“…Knechtle and Nikolaidis [ 13 ] support these findings, with data for performances ranging from 6 h to 10 days’ duration, with the average age for peak performers at 6- and 10-day events being 44.8 years (43.9–45.7) and 44.6 years (42.9–46.3), respectively. SG was 47 years old when she set this WR and it is highly likely that experience and physiological changes related to her continuous training helped her complete this challenge [ 69 ]. Even with continuous training, ageing leads to reductions in VO 2max , velocity at lactate threshold, maximal heart rate, and muscle strength [ 70 ].…”

Section: Discussionmentioning

confidence: 99%

Physiological Responses and Nutritional Intake during a 7-Day Treadmill Running World Record

Berger

Cooley

Graham

et al. 2020

IJERPH

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Ultra-running comprises running events longer than a marathon (>42.2 km). The prolonged duration of ultra-running leads to decrements in most or all physiological parameters and considerable energy expenditure (EE) and energy deficits. SG, 47 years, 162.5 cm, 49 kg, VO2max 4 mL/kg/min−1/2.37 L/min−1, ran continuously for 7 days on a treadmill in 3 h blocks followed by 30 min breaks and slept from 1–5 a.m. Heart rate (HR) oxygen uptake (VO2), rating of perceived exertion, weight, blood lactate (mmol·L−1), haemoglobin (g·dL), haematocrit (%) and glucose (mmol·L−1), and nutrition and hydration were recorded. SG ran for 17.5 h/day, covering ~120 km/day at ~7 km/h. Energy expenditure for each 24 h period was 6878 kcal/day and energy intake (EI) was 2701 kcal/day. EE was 382 kcal/h, with 66.6% from fat and 33.4% from carbohydrate oxidation. 7 day EI was 26,989 kcal and EE was 48,147 kcal, with a total energy deficit (ED) of 21,158 kcal. Average VO2 was 1.2 L·min−1/24.7 mL·kg·min−1, Respriatory echange ratio (RER) 0.80 ± 0.03, HR 120–125 b·min−1. Weight increased from 48.6 to 49.5 kg. Haemoglobin decreased from 13.7 to 11 g·dL and haematocrit decreased from 40% to 33%. SG ran 833.05 km. SG exhibits an enhanced fat metabolism through which she had a large daily ED. Her success can be attributed to a combination of physiological and psychological factors.

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Section: Discussionmentioning

confidence: 99%

Physiological Responses and Nutritional Intake during a 7-Day Treadmill Running World Record

Berger

Cooley

Graham

et al. 2020

IJERPH

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“…We corroborate the results of Knechtle et al [12], as the variable years running was not associated with race time for women in this 62 km race. Rae et al [28] examined the interaction of aging and racing on ultra-endurance running performance. Rae et al [28] found that that overall athletes (18 women, 176 men) took approximately four years to reach peak running speed for a 56 km ultra-endurance race.…”

Section: Discussionmentioning

confidence: 99%

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

O’Loughlin

Nikolaidis

Rosemann

et al. 2019

IJERPH

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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.

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“…For the race-experience category, the skiers were divided into inexperienced skiers comprising subjects who had completed the 90 km course less than four times and experienced skiers, that is, skiers with race experience from four or more races on the 90 km course. The differentiation between inexperienced and experienced skiers was based on the results showing that approximately four races in a 56 km ultramarathon were required to optimize performance, 20 and that athletes with less than 4 years of experience had a larger decrease in their pace during the course of a marathon compared with the decrease of the experienced athletes. 19 To investigate a specific category’s influence on the pacing profile during the VSR, the subjects were matched based on finish time, start group, and the other two categories to minimize their influence on the results of the statistical analysis.…”

Section: Methodsmentioning

confidence: 99%

The influence of sex, age, and race experience on pacing profiles during the 90 km Vasaloppet ski race

Carlsson¹,

Assarsson²,

Carlsson³

2016

OAJSM

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The purpose of this study was to investigate pacing-profile differences during the 90 km Vasaloppet ski race related to the categories of sex, age, and race experience. Skiing times from eight sections (S1 to S8) were analyzed. For each of the three categories, 400 pairs of skiers were matched to have a finish time within 60 seconds, the same start group, and an assignment to the same group for the other two categories. Paired-samples Student’s t-tests were used to investigate sectional pacing-profile differences between the subgroups. Results showed that males skied faster in S2 (P=0.0042), S3 (P=0.0049), S4 (P=0.010), and S1–S4 (P<0.001), whereas females skied faster in S6 (P<0.001), S7 (P<0.001), S8 (P=0.0088), and S5–S8 (P<0.001). For the age category, old subjects (40 to 59 years) skied faster than young subjects (19 to 39 years) in S3 (P=0.0029), and for the other sections, there were no differences. Experienced subjects (≥4 Vasaloppet ski race completions) skied faster in S1 (P<0.001) and S1–S4 (P=0.0054); inexperienced skiers (<4 Vasaloppet ski race completions) had a shorter mean skiing time in S5–S8 (P=0.0063). In conclusion, females had a more even pacing profile than that of males with the same finish time, start group, age, and race experience. No clear age-related pacing-profile difference was identified for the matched subgroups. Moreover, experienced skiers skied faster in the first half whereas inexperienced skiers had higher skiing speeds during the second half of the race.

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The Interaction of Aging and 10 Years of Racing on Ultraendurance Running Performance

Cited by 9 publications

References 27 publications

Physiological Responses and Nutritional Intake during a 7-Day Treadmill Running World Record

Physiological Responses and Nutritional Intake during a 7-Day Treadmill Running World Record

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

The influence of sex, age, and race experience on pacing profiles during the 90 km Vasaloppet ski race

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