(untitled) (ACTIVIDAD FÍSICA Y DESARROLLO HUMANO, 2013)
The purpose of this research was to develop a biometric-functional predictive model for calculating 1RM in bench press, which can be used to prescribe training programs for different populations. To this end, 47 participants, male physical education students (age 22.55 ± 1.81 years; height 176.39 ± 5.5 cm; weight 72.47 ± 6.5 kg; fat percentage 14.11 ± 3.5; muscle percentage 60.88 ± 8.5) were evaluated in the horizontal bench press on a guided portico using a progressive load test until reaching their 1RM in two grip situations, 90º and 105º. The biometric variables measured were skinfolds (biceps, triceps, pectoral), circumferences (chest, relaxed arm, contracted arm, and wrist), diameters (biacromial, bicondylar humerus, and biestiloid), and fat and muscle percentages. After multiple linear regression analysis, four predictive models were obtained. Two of the models accepted the biometric and mechanical variables related to grip distance, R2 = 0.73 for the 90° situation and R2 = 0.75 for the 105° situation. The other two models were constructed using only biometric variables, R2 = 0.69 for the 90º position and R2 = 0.70 for the 105º. The other two models were constructed using only biometric variables, with R2 = 0.69 for the 90º position and R2 = 0.70 for the 105º position. The main conclusion is that the biometric-functional predictive model obtained is useful for prescribing strength training for the horizontal bench press in a sample of young athletes with different levels of physical fitness.