OBJECTIVE: To compare the accuracy of different devices to predict the bench-press 1-repetition maximum (1RM) from the individual load-velocity relationship modeled through the multiple- and 2-point methods. METHODS: Eleven men performed an incremental test on a Smith machine against 5 loads (45-55-65-75-85%1RM), followed by 1RM attempts. The mean velocity was simultaneously measured by 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 2 inertial measurement units (PUSH Band and Beast Sensor), and 1 smartphone application (My Lift). The velocity recorded at the 5 loads (45-55-65-75-85%1RM), or only at the 2 most distant loads (45-85%1RM), was considered for the multiple- and 2-point methods, respectively. RESULTS: An acceptable and comparable accuracy in the estimation of the 1RM was observed for the T-Force, Chronojump, Speed4Lift, Velowin, and My Lift when using both the multiple- and 2-point methods (effect size ≤ 0.40; Pearson correlation coefficient [r] ≥ .94; standard error of the estimate [SEE] ≤ 4.46 kg), whereas the accuracy of the PUSH (effect size = 0.70-0.83; r = .93-.94; SEE = 4.45-4.80 kg), and especially the Beast Sensor (effect size = 0.36-0.84; r = .50-.68; SEE = 9.44-11.2 kg), was lower. CONCLUSIONS: These results highlight that the accuracy of 1RM prediction methods based on movement velocity is device dependent, with the inertial measurement units providing the least accurate estimate of the 1RM.
OBJECTIVE: To compare the accuracy of different devices to predict the bench-press 1-repetition maximum (1RM) from the individual load-velocity relationship modeled through the multiple- and 2-point methods. METHODS: Eleven men performed an incremental test on a Smith machine against 5 loads (45-55-65-75-85%1RM), followed by 1RM attempts. The mean velocity was simultaneously measured by 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 2 inertial measurement units (PUSH Band and Beast Sensor), and 1 smartphone application (My Lift). The velocity recorded at the 5 loads (45-55-65-75-85%1RM), or only at the 2 most distant loads (45-85%1RM), was considered for the multiple- and 2-point methods, respectively. RESULTS: An acceptable and comparable accuracy in the estimation of the 1RM was observed for the T-Force, Chronojump, Speed4Lift, Velowin, and My Lift when using both the multiple- and 2-point methods (effect size ≤ 0.40; Pearson correlation coefficient [r] ≥ .94; standard error of the estimate [SEE] ≤ 4.46 kg), whereas the accuracy of the PUSH (effect size = 0.70-0.83; r = .93-.94; SEE = 4.45-4.80 kg), and especially the Beast Sensor (effect size = 0.36-0.84; r = .50-.68; SEE = 9.44-11.2 kg), was lower. CONCLUSIONS: These results highlight that the accuracy of 1RM prediction methods based on movement velocity is device dependent, with the inertial measurement units providing the least accurate estimate of the 1RM.
Entities:
Keywords:
camera-based optoelectronic system; inertial measurement units; linear position transducer; maximum dynamic strength; smartphone application
Authors: Basilio Pueo; Jose J Lopez; Jose M Mossi; Adrian Colomer; Jose M Jimenez-Olmedo Journal: Sensors (Basel) Date: 2021-01-30 Impact factor: 3.576
Authors: Pedro Jiménez-Reyes; Adrian Castaño-Zambudio; Víctor Cuadrado-Peñafiel; Jorge M González-Hernández; Fernando Capelo-Ramírez; Luis M Martínez-Aranda; Juan J González-Badillo Journal: PeerJ Date: 2021-03-23 Impact factor: 2.984
Authors: Elias J G Caven; Tom J E Bryan; Amelia F Dingley; Benjamin Drury; Amador Garcia-Ramos; Alejandro Perez-Castilla; Jorge Arede; John F T Fernandes Journal: Int J Environ Res Public Health Date: 2020-10-26 Impact factor: 3.390
Authors: John F T Fernandes; Amelia F Dingley; Amador Garcia-Ramos; Alejandro Perez-Castilla; James J Tufano; Craig Twist Journal: Behav Sci (Basel) Date: 2021-05-07