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THE JOURNAL OF SPORTS MEDICINE AND PHYSICAL FITNESS
A Journal on Applied Physiology, Biomechanics, Preventive Medicine,
Sports Medicine and Traumatology, Sports Psychology
Indexed/Abstracted in: Chemical Abstracts, CINAHL, Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 1,111
The Journal of Sports Medicine and Physical Fitness 2016 Jul 06
Predicting race time in male amateur marathon runners
Juan J. SALINERO, María L. SORIANO, Beatriz LARA, César GALLO-SALAZAR, Francisco ARECES, Diana RUIZ-VICENTE, Javier ABIAN-VICEN, Cristina GONZÁLEZ-MILLÁN, Juan DEL COSO ✉
Camilo José Cela University, Exercise Physiology Laboratory, Madrid, Spain
BACKGROUND: The aim of the study was to analyze the relationship between anthropometry, training characteristics, muscular strength and effort-related cardiovascular response and marathon race time in male amateur runners.
METHODS: A total of 84 male amateur marathon runners aged between 23 and 70 years took part in this study (41.0±9.5 years). All of them competed in the 2013 edition of the Madrid Marathon with a finish time between 169.8 and 316 min (226.0±28.5 min). Age, running experience, number of marathon races finished, mean kilometres run weekly in the last three months, and previous personal best time in the 10 km, half marathon and marathon were recorded. Moreover, anthropometric characteristics, and the results from the Ruffier test and a whole-body isometric force test were measured. After the marathon, the race time was registered.
RESULTS: Training volume (r=-0.479; P=0.001), previous running milestones (marathon r=0.756; half-marathon r=0.812; 10-km r= 0.732; P<0.001), cardiovascular fitness (r=0.371; P=0.001) and anthropometric variables (body mass, body mass index, body fat percentage, skinfolds and lower leg volume) were correlated to marathon performance (P<0.05). Two regression models appeared from the data with r2>0.50. The best, including body fat percentage, heart rate change during the recovery after the Ruffier test and the half-marathon race time, was strongly correlated with real marathon performance (r=0.77; P<0.001). A second regression model was proposed replacing the half-marathon performance with the 10-km race time, reducing the correlation to 0.73 (P<0.001).
CONCLUSION: Marathon performance could be partially predicted by two different equations, including body fat percentage, recovery heart rate in the Ruffier test and a half- marathon or 10-km performance.