Reliability of the One Repetition-Maximum Power Clean Test in Trained Adolescent Athletes

2011 ◽  
Vol 43 (Suppl 1) ◽  
pp. 665
Author(s):  
Jie Kang ◽  
James E. McFarland ◽  
Avery D. Faigenbaum ◽  
Fernando Naclerio ◽  
Gregory D. Myer ◽  
...  
Keyword(s):  
Maximum Power ◽  
Adolescent Athletes ◽  
Repetition Maximum ◽  
The One ◽  
Power Clean

scholarly journals Reliability of the One-Repetition-Maximum Power Clean Test in Adolescent Athletes

2012 ◽  
Vol 26 (2) ◽  
pp. 432-437 ◽  
Author(s):  
Avery D Faigenbaum ◽  
James E McFarland ◽  
Robert E Herman ◽  
Fernando Naclerio ◽  
Nicholas A Ratamess ◽  
...  
Keyword(s):  
Maximum Power ◽  
Adolescent Athletes ◽  
Repetition Maximum ◽  
The One ◽  
Power Clean

scholarly journals Concurrent Validity and Reliability of the Load-Velocity Relationship to Predict the One-Repetition Maximum during Three Weightlifting Derivatives

Kinesiology ◽  
2021 ◽  
Vol 53 (2) ◽  
pp. 215-225
Author(s):  
Ricardo Berton ◽  
Marcos Soriano ◽  
Demostenys David da Silva ◽  
Marcel Lopes dos Santos ◽  
Gustavo Teixeira ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Concurrent Validity ◽  
Direct Test ◽  
Validity And Reliability ◽  
Repetition Maximum ◽  
Typical Error ◽  
Velocity Relationship ◽  
The One ◽  
Power Clean ◽  
Adequate Reliability

The study investigated the concurrent validity and reliability of the load-velocity relationship to predict the one-repetition maximum (1RM) of the power clean from the knee (PCK), high pull from the knee (HPK), and mid-thigh clean pull (MTCP). For each exercise, 12 participants performed two 1RM sessions tests and two sessions to measure the barbell’s load-velocity relationship at 30, 45, 60, 75, and 90% of 1RM. The velocity recorded at each load was used to establish the linear regression equation and, consequently, to predict 1RM value. A low validity between the 1RM direct test and predicted 1RM was observed for PCK (typical error [TE]=3.96 to 4.50 kg, coefficient of variation [CV]=4.68 to 5.27%, effect size [ES]=-0.76 to -0.58, Bland-Altman bias [BAB]=9.83 to 11.19 kg), HPK (TE=4.58 to 5.82 kg, CV=6.44 to 8.14%, ES=-0.40 to -0.39, BAB=3.52 to 4.17 kg), and MTCP (TE=6.33 to 8.08 kg, CV=4.78 to 6.16%, ES=-0.29 to -0.19, BAB=3.98 to 6.17 kg). Adequate reliability was observed for the 1RM direct test and for the predicted 1RM. However, based on Bland-Altman limits of agreement, lower measurement errors were obtained for the 1RM direct test in comparison to the predicted 1RM for all the exercises. In conclusion, the load-velocity relationship was not able to predict 1RM values with high accuracy in the PCK, HPK, and MTCP. Moreover, the 1RM direct test was the most reliable for PCK, HPK and MTCP.

scholarly journals Differences in Kinetics during One- and Two-Leg Hang Power Clean

Sports ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 45
Author(s):  
Ryohei Hayashi ◽  
Takuya Yoshida ◽  
Yasushi Kariyama
Keyword(s):  
Peak Power ◽  
Peak Force ◽  
Time Data ◽  
Repetition Maximum ◽  
Force Development ◽  
Power Peak ◽  
Explosive Force ◽  
Force Time ◽  
The One ◽  
Power Clean

The purpose of this study was to quantify the kinetics per leg during the one- and two-leg hang power clean using various loads. Nine male track and field athletes performed the one- and two-leg hang power clean on a force platform. The estimated one-repetition maximum was used for the one-leg hang power clean (OHPC), and the one-repetition maximum was used for the two-leg hang power clean (THPC). The loads used were 30%, 60%, and 90% during both trials. We calculated peak power, peak force, and peak rate of force development during the pull phase from the force-time data. The peak power and the peak force for all loads during the OHPC were statistically greater than during the THPC. The peak rates of force development at 60% and 90% during the OHPC were statistically greater than during the THPC. Additionally, the peak power at 90% was significantly less than at 60% during the THPC. These findings suggest that the OHPC at loads of 60% and 90% is a weightlifting exercise that exhibits greater explosive force and power development characteristics than the THPC.

Differences in the one-repetition maximum and load-velocity profile between the flat and arched bench press in competitive powerlifters

2018 ◽  
pp. 1-13 ◽  
Author(s):  
Amador García-Ramos ◽  
Alejandro Pérez-Castilla ◽  
Francisco Javier Villar Macias ◽  
Pedro Á. Latorre-Román ◽  
Juan A. Párraga ◽  
...  
Keyword(s):  
Velocity Profile ◽  
Bench Press ◽  
Repetition Maximum ◽  
The One

Reliability and validity of different methods of estimating the one-repetition maximum during the free-weight prone bench pull exercise

2019 ◽  
Vol 37 (19) ◽  
pp. 2205-2212 ◽  
Author(s):  
Amador García-Ramos ◽  
Paola Barboza-González ◽  
David Ulloa-Díaz ◽  
Angela Rodriguez-Perea ◽  
Darío Martinez-Garcia ◽  
...  
Keyword(s):  
Reliability And Validity ◽  
Repetition Maximum ◽  
Free Weight ◽  
The One

scholarly journals Neural Network Approach to MPPT Control and Irradiance Estimation

2020 ◽  
Vol 10 (15) ◽  
pp. 5051
Author(s):  
Žarko Zečević ◽  
Maja Rolevski
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Low Complexity ◽  
Maximum Power ◽  
Photovoltaic Module ◽  
Neural Network Approach ◽  
Point Tracking ◽  
The Neural Network ◽  
Power Point ◽  
The One

Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.

scholarly journals The effect of low-intensity resistance training after heat stress on muscle size and strength of triceps brachii: a randomized controlled trial

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Masatoshi Nakamura ◽  
Tomoichi Yoshida ◽  
Ryosuke Kiyono ◽  
Shigeru Sato ◽  
Nobushige Takahashi
Keyword(s):  
Heat Stress ◽  
Resistance Training ◽  
Muscle Strength ◽  
Muscle Thickness ◽  
Control Group ◽  
Triceps Brachii ◽  
Stress Group ◽  
Repetition Maximum ◽  
Low Intensity ◽  
The One

Abstract Background The purpose of this study was to clarify whether there is a synergistic effect on muscular strength and hypertrophy when low-intensity resistance training is performed after heat stress. Methods Thirty healthy young male volunteers were randomly allocated to either the low-intensity resistance training with heat stress group or the control group. The control group performed low-intensity resistance training alone. In the low-intensity resistance training with heat stress group, a hot pack was applied to cover the muscle belly of the triceps brachii for 20 min before the training. The duration of the intervention was 6 weeks. In both groups, the training resistance was 30% of the one repetition maximum, applied in three sets with eight repetitions each and 60-s intervals. The one repetition maximum of elbow extension and muscle thickness of triceps brachii were measured before and after 6 weeks of low intensity resistance training. Results There was no significant change in the one-repetition maximum and muscle thickness in the control group, whereas there was a significant increase in the muscle strength and thickness in the low-intensity resistance training with heat stress group. Conclusion The combination of heat stress and low-intensity resistance training was an effective method for increasing muscle strength and volume. Trial registration University Hospital Medical Information Network Clinical Trials Registry (UMIN000036167; March 11, 2019).

Jump Shrug Height and Landing Forces Across Various Loads

2016 ◽  
Vol 11 (1) ◽  
pp. 61-65 ◽  
Author(s):  
Timothy J. Suchomel ◽  
Christopher B. Taber ◽  
Glenn A. Wright
Keyword(s):  
Potential Energy ◽  
Body Mass ◽  
Repeated Measures ◽  
External Load ◽  
Track And Field ◽  
Pairwise Comparisons ◽  
Jump Height ◽  
Repetition Maximum ◽  
Power Clean

The purpose of this study was to examine the effect that load has on the mechanics of the jump shrug. Fifteen track and field and club/intramural athletes (age 21.7 ± 1.3 y, height 180.9 ± 6.6 cm, body mass 84.7 ± 13.2 kg, 1-repetition-maximum (1RM) hang power clean 109.1 ± 17.2 kg) performed repetitions of the jump shrug at 30%, 45%, 65%, and 80% of their 1RM hang power clean. Jump height, peak landing force, and potential energy of the system at jump-shrug apex were compared between loads using a series of 1-way repeated-measures ANOVAs. Statistical differences in jump height (P < .001), peak landing force (P = .012), and potential energy of the system (P < .001) existed; however, there were no statistically significant pairwise comparisons in peak landing force between loads (P > .05). The greatest magnitudes of jump height, peak landing force, and potential energy of the system at the apex of the jump shrug occurred at 30% 1RM hang power clean and decreased as the external load increased from 45% to 80% 1RM hang power clean. Relationships between peak landing force and potential energy of the system at jump-shrug apex indicate that the landing forces produced during the jump shrug may be due to the landing strategy used by the athletes, especially at lighter loads. Practitioners may prescribe heavier loads during the jump-shrug exercise without viewing landing force as a potential limitation.

scholarly journals A Novel Current Sensor less MPPT based Approach for PV Applications Using PSO Technique

2021 ◽  
Vol 2070 (1) ◽  
pp. 012123
Author(s):  
Vinay Kumar ◽  
T Naveen Kumar ◽  
K T Prajwal
Keyword(s):  
Solar Energy ◽  
Renewable Energy Sources ◽  
Maximum Power ◽  
Current Sensor ◽  
Pv System ◽  
Pv Systems ◽  
Power Resources ◽  
Point Tracking ◽  
Power Point ◽  
The One

Abstract As an increased demand in power resources and to reduce global warming, Renewable Energy Sources (RES) are preferred over the conventional sources. Among various available RES, solar energy is the effective and efficient one. The solar energy is also clean and free energy. The use of Maximum Power Point Tracking (MPPT) is the one of the techniques to get maximized output power from the Photo Voltaic (PV) system. The proposed method uses a voltage sensor by eliminating the need of current sensor based on selected technique using Partial Swarm Optimization (PSO) technique interfaced with DC-DC boost converter. PSO technique is one of the methods which has high conflux speed, to precisely track the maximum power. The result of the planned methodology is studied with the assistance of an acceptable simulation applied in MATLAB/Simulink setting for experiment to valid of microcontroller which is employed. The result obtained from the simulations studies showed that current sensor less methodology using PSO technique can extract the maximize power from PV systems.

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