A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models.

The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30-60% 1RM) + back squat (70-100% 1RM)) or back squat only (0 load, 30-100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/ηp2), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; ηp2 = 0.90) and back squat (p = 0.004, ηp2 = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ηp2 = 0.67-0.80), but not quadratic (p = 0.632-0.929, ηp2 = 0.001-0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.

The Reliability and Validity of Current Technologies for Measuring Barbell Velocity in the Free-Weight Back Squat and Power Clean.

This study investigated the inter-day and intra-device reliability, and criterion validity of six devices for measuring barbell velocity in the free-weight back squat and power clean. In total, 10 competitive weightlifters completed an initial one repetition maximum (1RM) assessment followed by three load-velocity profiles (40-100% 1RM) in both exercises on four separate occasions. Mean and peak velocity was measured simultaneously on each device and compared to 3D motion capture for all repetitions. Reliability was assessed via coefficient of variation (CV) and typical error (TE). Least products regression (LPR) (R2) and limits of agreement (LOA) assessed the validity of the devices. The Gymaware was the most reliable for both exercises (CV < 10%; TE < 0.11 m·s-1, except 100% 1RM (mean velocity) and 90‒100% 1RM (peak velocity)), with MyLift and PUSH following a similar trend. Poorer reliability was observed for Beast Sensor and Bar Sensei (CV = 5.1%‒119.9%; TE = 0.08‒0.48 m·s-1). The Gymaware was the most valid device, with small systematic bias and no proportional or fixed bias evident across both exercises (R2 > 0.42-0.99 LOA = -0.03-0.03 m·s-1). Comparable validity data was observed for MyLift in the back squat. Both PUSH devices produced some fixed and proportional bias, with Beast Sensor and Bar Sensei being the least valid devices across both exercises (R2 > 0.00-0.96, LOA = -0.36‒0.46 m·s-1). Linear position transducers and smartphone applications could be used to obtain velocity-based data, with inertial measurement units demonstrating poorer reliability and validity.

Comparison of individual and group-based load-velocity profiling as a means to dictate training load over a 6-week strength and power intervention.

This study compared the effects of dictating load using individual (ILVP) or group (GLVP) load-velocity profiles on lower-body strength and power. Nineteen trained males (23.6 ± 3.7 years) completed a back squat one-repetition maximum (1-RM), load-velocity profiling (LVP), and countermovement (CMJ), static-squat (SSJ) and standing-broad (SBJ) jump tests before and after 6 weeks of resistance training. Participants were randomly assigned to an ILVP, or GLVP intervention with intra-session load dictated through real-time velocity monitoring and prediction of current relative performance using either the participant's LVP (ILVP) or a LVP based on all participant data (GLVP). Training resulted in significant increases in back squat 1-RM for the ILVP and GLVP group (p < 0.01; 9.7% and 7.2%, respectively), with no group-by-time interaction identified between training groups (p = 0.06). All jump performance significantly increased for the ILVP group (p < 0.01; CMJ: 6.6%; SSJ: 4.6%; SBJ: 6.7%), with only CMJ and SSJ improving for the GLVP group (p < 0.05; 4.3%). Despite no significant group-by-time interaction across all variables, the ILVP intervention induced a greater magnitude of adaptation when compared to a GLVP approach. Additionally, an individualised approach may lead to greater positive transfer to power-based movements, specifically vertical and horizontal jumps.

Comparison of Velocity-Based and Traditional Percentage-Based Loading Methods on Maximal Strength and Power Adaptations.

Dorrell, HF, Smith, MF, and Gee, TI. Comparison of velocity-based and traditional percentage-based loading methods on maximal strength and power adaptations. J Strength Cond Res 34(1): 46-53, 2020-This study explored the effects of velocity-based training (VBT) on maximal strength and jump height. Sixteen trained men (22.8 ± 4.5 years) completed a countermovement jump (CMJ) test and 1 repetition maximum (1RM) assessment on back squat, bench press, strict overhead press, and deadlift, before and after 6 weeks of resistance training. Participants were assigned to VBT or percentage-based training (PBT) groups. The VBT group's load was dictated through real-time velocity monitoring, as opposed to pretesting 1RM data (PBT). No significant differences were present between groups for pretesting data (p > 0.05). Training resulted in significant increases (p < 0.05) in maximal strength for back squat (VBT 9%, PBT 8%), bench press (VBT 8%, PBT 4%), strict overhead press (VBT 6%, PBT 6%), and deadlift (VBT 6%). Significant increases in CMJ were witnessed for the VBT group only (5%). A significant interaction effect was witnessed between training groups for bench press (p = 0.004) and CMJ (p = 0.018). Furthermore, for back squat (9%), bench press (6%), and strict overhead press (6%), a significant difference was present between the total volume lifted. The VBT intervention induced favorable adaptations in maximal strength and jump height in trained men when compared with a traditional PBT approach. Interestingly, the VBT group achieved these positive outcomes despite a significant reduction in total training volume compared with the PBT group. This has potentially positive implications for the management of fatigue during resistance training.

Validity and reliability of a linear positional transducer across commonly practised resistance training exercises.

This study investigated the validity and reliability of the GymAware PowerTool (GPT). Thirteen resistance trained participants completed three visits, consisting of three repetitions of free-weight back squat, bench press, deadlift (80% one repetition maximum), and countermovement jump. Bar displacement, peak and mean velocity, peak and mean force, and jump height were calculated using the GPT, a three-dimensional motion capture system (Motion Analysis Corporation; 150 Hz), and a force plate (Kistler; 1500 Hz). Least products regression were used to compare agreeability between devices. A within-trial one-way ANOVA, typical error (TE; %), and smallest worthwhile change (SWC) were used to assess reliability. Regression analysis resulted in R2 values of >0.85 for all variables excluding deadlift mean velocity (R2 = 0.54-0.69). Significant differences were observed between visits 3-2 for bench press bar displacement (0.395 ± 0.055 m; 0.383 ± 0.053 m), and deadlift bar displacement (0.557 ± 0.034 m; 0.568 ± 0.034 m). No other significant differences were found. Low to moderate TE (0.6-8.8%) were found for all variables, with SWC ranging 1.7-7.4%. The data provides evidence that the GPT can be used to measure kinetic and kinematic outputs, however, care should be taken when monitoring deadlift performance.