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- W4247446052 abstract "PURPOSE The purpose of this study was to develop and validate three prediction formulas (male, M; female, F; and combined, C) for marathon race performance based on anthropometric data and self-reported demographics including training history and actual race performance times (FT). METHODS A total of 102 long distance runners, 65 males (age 37.1+/−10.8yr) and 37 females (age 35.0+/−9.8yr) underwent the experimental procedures prior to participating in the San Diego Marathon at Carlsbad, CA. Self-reported training and performance data were collected via questionnaire within 36 hrs of the race. Height, body mass and body mass index (BMI; kg/m2) were determined at the event. Body composition was assessed using skinfold assessment (SF) and bioelectrical impedance analysis (BIA). RESULTS Skinfold and BIA percent fat were significant predictors for C. In addition, BIA percent fat as well as number of speed workouts/wk contributed to the prediction equation for F, but not M. However, fat mass contributed to the prediction equation for M. Weekly training mileage held the greatest relation (p<0.01) to marathon race performance in all groups. Years of running also contributed to M and C. These variables were used in multiple regression analyses to develop equations to predict marathon time. Separate M, F and C equations were generated (Table). A subset of subjects (29 male and 17 female) not used in generating the prediction equations was used to validate the M, F and C prediction models. Body mass, fat mass, and fat free mass were weakly and non-significantly correlated with FT under all three conditions. Body composition assessed by SF was found to be weakly and non-significantly correlated with FT for M (r = 0.28 p=0.14), and F (r = 0.09 p=0.71).TableCONCLUSIONS Prediction equations were generated and validated for marathon race performance without using laboratory data or previous race times. Although not applicable for all subjects, the inclusion of personal best marathon time improved the three prediction equations." @default.
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- W4247446052 date "2005-05-01" @default.
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- W4247446052 title "Predicting Marathon Performance Using Body Mass Variables And Training Data" @default.
- W4247446052 doi "https://doi.org/10.1249/00005768-200505001-00473" @default.
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