Open AccessThe reliability of Berger’s table in estimating 1-RM and 10-RM of the elbow flexor muscles in normal young adults
Author(s) -
Aderonke O. Akinpelu,
J. O. Iyaniwura,
B. O. Ajagbe
Publication year - 2001
Publication title -
south african journal of physiotherapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.166
H-Index - 3
eISSN - 2410-8219
pISSN - 0379-6175
DOI - 10.4102/sajp.v57i2.499
Subject(s) - isotonic , mathematics , elbow , elbow flexion , table (database) , linear regression , one repetition maximum , muscle strength , isotonic regression , combinatorics , statistics , medicine , physical therapy , physical medicine and rehabilitation , anatomy , computer science , data mining , estimator
Isotonic strength training remains one of the methods of strengthening muscles, as it requires inexpensive materials. The method requires the determination of maximal isotonic strength or one repetition maximum [1-RM] and/or sub-maximal isotonic strength (2-RM - 10-RM) of a muscle group in order to know the appropriate resistance to subject the muscle group to, and to monitor strength gains by the muscle group. Berger’s table contains percentages for predicting 1-RM - 10-RM, and it is intended to improve the efficiency of determining isotonic muscle strength. The aim of this study was to investigate the reliability of Berger’s table in predicting 1-RM and 10-RM of the right elbow flexor muscles.1-RM and 10-RM were estimated using Berger’s table in 100 young adults following the determination of the number of repetitions that could be carried out against randomly selected weights. Weight adjustments were made to obtain the actual 1-RM and 10-RM. Data were subjected to regression analysis. A significant linear relationship exists between the estimated and actual values of 1-RM and 10-RM. Results also showed that Berger’s table explains 93% of 1-RM and 98% of 10-RM. It was concluded that Berger’s table is reliable in determining 1-RM and 10-RM of the elbow flexors in young healthy adults. Regression equations that may improve the accuracy of estimation were derived.