Does Muscle Mass Affect Running Times in Male Long-distance Master Runners?
ABSTRACT The aim of the present study was to investigate associations between skeletal muscle mass, body fat and training characteristics with running times in master athletes (age > 35 years) in half-marathon, marathon and ultra-marathon.
We compared skeletal muscle mass, body fat and training characteristics in master half-marathoners (n=103), master marathoners (n=91) and master ultra-marathoners (n=155) and investigated associations between body composition and training characteristics with race times using bi- and multi-variate analyses.
After multi-variate analysis, body fat was related to half-marathon (β=0.9, P=0.0003), marathon (β=2.2, P<0.0001), and ultra-marathon (β=10.5, P<0.0001) race times. In master half-marathoners (β=-4.3, P<0.0001) and master marathoners (β=-11.9, P<0.0001), speed during training was related to race times. In master ultra-marathoners, however, weekly running kilometers (β=-1.6, P<0.0001) were related to running times.
To summarize, body fat and training characteristics, not skeletal muscle mass, were associated with running times in master half-marathoners, master marathoners, and master ultra-marathoners. Master half-marathoners and master marathoners rather rely on a high running speed during training whereas master ultra-marathoners rely on a high running volume during training. The common opinion that skeletal muscle mass affects running performance in master runners needs to be questioned.
- SourceAvailable from: Felipe García PinillosNutricion hospitalaria: organo oficial de la Sociedad Espanola de Nutricion Parenteral y Enteral 01/2015; · 1.25 Impact Factor
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ABSTRACT: Recent findings suggested that the age of peak ultra-marathon performance seemed to increase with increasing race distance. The present study investigated the age of peak ultra-marathon performance for runners competing in time-limited ultra-marathons held from 6 to 240 h (i.e. 10 days) during 1975-2013. Age and running performance in 20,238 (21 %) female and 76,888 (79 %) male finishes (6,863 women and 24,725 men, 22 and 78 %, respectively) were analysed using mixed-effects regression analyses. The annual number of finishes increased for both women and men in all races. About one half of the finishers completed at least one race and the other half completed more than one race. Most of the finishes were achieved in the fourth decade of life. The age of the best ultra-marathon performance increased with increasing race duration, also when only one or at least five successful finishes were considered. The lowest age of peak ultra-marathon performance was in 6 h (33.7 years, 95 % CI 32.5-34.9 years) and the highest in 48 h (46.8 years, 95 % CI 46.1-47.5). With increasing number of finishes, the athletes improved performance. Across years, performance decreased, the age of peak performance increased, and the age of peak ultra-marathon performance increased with increasing number of finishes. In summary, the age of peak ultra-marathon performance increased and performance decreased in time-limited ultra-marathons. The age of peak ultra-marathon performance increased with increasing race duration and with increasing number of finishes. These athletes improved race performance with increasing number of finishes.Journal of the American Aging Association 10/2014; 36(5):9715. DOI:10.1007/s11357-014-9715-3 · 3.45 Impact Factor
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ABSTRACT: A variety of anthropometric and training characteristics have been identified as predictor variables for race performance in endurance and ultra-endurance athletes. Anthropometric characteristics such as skin-fold thicknesses, body fat, circumferences and length of limbs, body mass, body height, and body mass index were bi-variately related to race performance in endurance athletes such as swimmers in pools and in open water, in road and mountain bike cyclists, and in runners and triathletes over different distances. Additionally, training variables such as volume and speed were also bi-variately associated with race performance. Multi-variate regression analyses including anthropometric and training characteristics reduced the predictor variables mainly to body fat and speed during training units. Further multi-variate regression analyses including additionally the aspects of previous experience such as personal best times showed that mainly previous best time in shorter races were the most important predictors for ultra-endurance race times. Ultra-endurance athletes seemed to prepare differently for their races compared to endurance athletes where ultra-endurance athletes invested more time in training and completed more training kilometers at lower speed compared to endurance athletes. In conclusion, the most important predictor variables for ultra-endurance athletes were a fast personal best time in shorter races, a low body fat and a high speed during training units.06/2014; 5(2):73-90.