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.

Background: Optimal prescription of resistance exercise load (kg) is essential for the development of maximal strength. Two methods are commonly used in practice with no clear consensus on the most effective approach for the improvement of maximal strength.Objective: The primary aim of this review was to compare the effectiveness of percentage 1RM (% 1RM) and repetition maximum targets (RM) as load prescription methods for the development of maximal strength.Methods: Electronic database searches of MEDLINE, SPORTDiscus, Scopus, and CINAHL Complete were conducted in accordance with PRISMA guidelines. Studies were eligible for inclusion if a direct measure of maximal strength was used, a non-training control group was a comparator, the training intervention was > 4 weeks in duration and was replicable, and participants were defined as healthy and between the ages of 18–40. Methodological quality of the studies was evaluated using a modified Downs and Black checklist. Percentage change (%) and 95% confidence intervals (CI) for all strength-based training groups were calculated. Statistical significance (p < 0.05) was reported from each study.Results: Twenty-two studies comprising a total of 761 participants (585 males and 176 females) were found to meet the inclusion criteria. 12 studies were returned for % 1RM, with 10 for RM. All studies showed statistically significant improvements in maximal strength in the training groups (31.3 ± 21.9%; 95% CI 33.1–29.5%). The mean quality rating for all studies was 17.7 ± 2.3. Four studies achieved a good methodological rating, with the remainder classified as moderate.Conclusions: Both % 1RM and RM are effective tools for improving maximal strength. % 1RM appears to be a better prescriptive method than RM potentially due to a more sophisticated management of residual fatigue. However, large heterogeneity was present within this data. Lower body and multi-joint exercises appear to be more appropriate for developing maximal strength. Greater consensus is required in defining optimal training prescriptions, physiological adaptations, and training status.

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/${\eta }_p^2$), 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; ${\eta }_p^2$ = 0.90) and back squat (p = 0.004, ${\eta }_p^2$ = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ${\eta }_p^2$ = 0.67–0.80), but not quadratic (p = 0.632–0.929, ${\eta }_p^2$= 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.

Purpose: This study compared pooled against individualized load–velocity profiles (LVPs) in the free-weight back squat and power clean. Methods: A total of 10 competitive weightlifters completed baseline 1-repetition maximum assessments in the back squat and power clean. Three incremental LVPs were completed, separated by 48 to 72 hours. Mean and peak velocity were measured via a linear-position transducer (GymAware). Linear and nonlinear (second-order polynomial) regression models were applied to all pooled and individualized LVP data. A combination of coefficient of variation (CV), intraclass correlation coefficient, typical error of measurement, and limits of agreement assessed between-subject variability and within-subject reliability. Acceptable reliability was defined a priori as intraclass correlation coefficient > .7 and CV < 10%. Results: Very high to practically perfect inverse relationships were evident in the back squat (r = .83–.96) and power clean (r = .83–.89) for both regression models; however, stronger correlations were observed in the individualized LVPs for both exercises (r = .85–.99). Between-subject variability was moderate to large across all relative loads in the back squat (CV = 8.2%–27.8%) but smaller in the power clean (CV = 4.6%–8.5%). The power clean met our criteria for acceptable reliability across all relative loads; however, the back squat revealed large CVs in loads ≥90% of 1-repetition maximum (13.1%–20.5%). Conclusions: Evidently, load–velocity characteristics are highly individualized, with acceptable levels of reliability observed in the power clean but not in the back squat (≥90% of 1-repetition maximum). If practitioners want to adopt load–velocity profiling as part of their testing and monitoring procedures, an individualized LVP should be utilized over pooled LVPs.

Velocity-based training (VBT) is a contemporary prescriptive, programming, and testing tool commonly utilised in strength and conditioning (S&C). Over recent years, there has been an influx of peer-reviewed literature investigating several different applications (e.g. load-velocity profiling, velocity loss, load manipulation, and reliability of technology) of VBT. The procedures implemented in research, however, do not always reflect the practices within applied environments. The aim of this study, therefore, was to investigate the perceptions and applications of VBT within elite S&C to enhance contextual understanding and develop appropriate avenues of practitioner-focused research. Fourteen high-performance S&C coaches participated in semi-structured interviews to discuss their experiences of implementing VBT into their practices. Reflexive thematic analysis was adopted, following an inductive and realist approach. Three central organising themes emerged: Technology, applications, and reflections. Within these central themes, higher order themes consisting of drivers for buying technology; programming, testing, monitoring, and feedback; and benefits, drawbacks, and future uses also emerged. Practitioners reported varied drivers and applications of VBT, often being dictated by simplicity, environmental context, and personal preferences. Coaches perceived VBT to be a beneficial tool yet were cognizant of the drawbacks and challenges in certain settings. VBT is a flexible tool that can support and aid several aspects of S&C planning and delivery, with coaches valuing the impact it can have on training environments, objective prescriptions, tracking player readiness, and programme success.

Thompson, SW, Lake, JP, Rogerson, D, Ruddock, A, and Barnes, A. Kinetics and kinematics of the free-weight back squat and loaded jump squat. J Strength Cond Res 37(1): 1–8, 2023—The aim of this study was to compare kinetics and kinematics of 2 lower-body free-weight exercises, calculated from concentric and propulsion subphases, across multiple loads. Sixteen strength-trained men performed back squat 1 repetition maximum (1RM) tests (visit 1), followed by 2 incremental back squat and jump squat protocols (visit 2) (loads = 0% and 30–60%, back squat 1RM). Concentric phase and propulsion phase force-time-displacement characteristics were derived from force plate data and compared using analysis of variance and Hedges' g effect sizes. Intrasession reliability was calculated using intraclass correlation coefficient (ICC) and coefficient of variation (CV). All dependent variables met acceptable reliability (ICC >0.7; CV < 10%). Statistically significant 3-way interactions (load × phase × exercise) and 2-way main effects (phase × exercise) were observed for mean force, velocity (30–60% 1RM), power, work, displacement, and duration (0%, 30–50% 1RM) ( p < 0.05). A significant 2-way interaction (load × exercise) was observed for impulse ( p < 0.001). Jump squat velocity ( g = 0.94–3.80), impulse ( g = 1.98–3.21), power ( g = 0.84–2.93), and work ( g = 1.09–3.56) were significantly larger across concentric and propulsion phases, as well as mean propulsion force ( g = 0.30–1.06) performed over all loads ( p < 0.001). No statistically significant differences were observed for mean concentric force. Statistically longer durations ( g = 0.38–1.54) and larger displacements ( g = 2.03–4.40) were evident for all loads and both subphases ( p < 0.05). Ballistic, lower-body exercise produces greater kinetic and kinematic outputs than nonballistic equivalents, irrespective of phase determination. Practitioners should therefore use ballistic methods when prescribing or testing lower-body exercises to maximize athlete's force-time-displacement characteristics.