Velocity-based training (VBT) is a contemporary method of resistance training that enables accurate and objective prescription of resistance training intensities and volumes. This review provides an applied framework for the theory and application of VBT. Specifically, this review gives detail on how to: use velocity to provide objective feedback, estimate strength, develop load-velocity profiles for accurate load prescription, and how to use statistics to monitor velocity. Furthermore, a discussion on the use of velocity loss thresholds, different methods of VBT prescription, and how VBT can be implemented within traditional programming models and microcycles is provided.

Pérez-Castilla, A, Piepoli, A, Delgado-García, G, Garrido-Blanca, G, and García-Ramos, A. Reliability and concurrent validity of seven commercially available devices for the assessment of movement velocity at different intensities during the bench press. J Strength Cond Res 33(5): 1258–1265, 2019—The aim of this study was to compare the reliability and validity of 7 commercially available devices to measure movement velocity during the bench press exercise. Fourteen men completed 2 testing sessions. One-repetition maximum (1RM) in the bench press exercise was determined in the first session. The second testing session consisted of performing 3 repetitions against 5 loads (45, 55, 65, 75, and 85% of 1RM). The mean velocity was simultaneously measured using an optical motion sensing system (Trio-OptiTrack; “gold-standard”) and 7 commercially available devices: 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 1 smartphone application (PowerLift), and 2 inertial measurement units (IMUs) (PUSH band and Beast sensor). The devices were ranked from the most to the least reliable as follows: (a) Speed4Lift (coefficient of variation [CV] = 2.61%); (b) Velowin (CV = 3.99%), PowerLift (3.97%), Trio-OptiTrack (CV = 4.04%), T-Force (CV = 4.35%), and Chronojump (CV = 4.53%); (c) PUSH band (CV = 9.34%); and (d) Beast sensor (CV = 35.0%). A practically perfect association between the Trio-OptiTrack system and the different devices was observed (Pearson's product-moment correlation coefficient (r) range = 0.947–0.995; p < 0.001) with the only exception of the Beast sensor (r = 0.765; p < 0.001). These results suggest that linear velocity/position transducers, camera-based optoelectronic systems, and the smartphone application could be used to obtain accurate velocity measurements for restricted linear movements, whereas the IMUs used in this study were less reliable and valid.

Background: Monitoring resistance training has a range of unique difficulties due to differences in physical characteristics and capacity between athletes, and the indoor environment in which it often occurs. Traditionally, methods such as volume load have been used, but these have inherent flaws. In recent times, numerous portable and affordable devices have been made available that purport to accurately and reliably measure kinetic and kinematic outputs, potentially offering practitioners a means of measuring resistance training loads with confidence. However, a thorough and systematic review of the literature describing the reliability and validity of these devices has yet to be undertaken, which may lead to uncertainty from practitioners on the utility of these devices. Objective: A systematic review of studies that investigate the validity and/or reliability of commercially available devices that quantify kinetic and kinematic outputs during resistance training. Methods: Following PRISMA guidelines, a systematic search of SPORTDiscus, Web of Science, and Medline was performed; studies included were (1) original research investigations; (2) full-text articles written in English; (3) published in a peer-reviewed academic journal; and (4) assessed the validity and/or reliability of commercially available portable devices that quantify resistance training exercises. Results: A total of 129 studies were retrieved, of which 47 were duplicates. The titles and abstracts of 82 studies were screened and the full text of 40 manuscripts were assessed. A total of 31 studies met the inclusion criteria. Additional 13 studies, identified via reference list assessment, were included. Therefore, a total of 44 studies were included in this review. Conclusion: Most of the studies within this review did not utilise a gold-standard criterion measure when assessing validity. This has likely led to under or overreporting of error for certain devices. Furthermore, studies that have quantified intra-device reliability have often failed to distinguish between technological and biological variability which has likely altered the true precision of each device. However, it appears linear transducers which have greater accuracy and reliability compared to other forms of device. Future research should endeavour to utilise gold-standard criterion measures across a broader range of exercises (including weightlifting movements) and relative loads.

García-Ramos, A, Pestaña-Melero, FL, Pérez-Castilla, A, Rojas, FJ, and Haff, GG. Mean velocity vs. mean propulsive velocity vs. peak velocity: which variable determines bench press relative load with higher reliability? J Strength Cond Res 32(5): 1273–1279, 2018—This study aimed to compare between 3 velocity variables (mean velocity [MV], mean propulsive velocity [MPV], and peak velocity [PV]): (a) the linearity of the load-velocity relationship, (b) the accuracy of general regression equations to predict relative load (%1RM), and (c) the between-session reliability of the velocity attained at each percentage of the 1-repetition maximum (%1RM). The full load-velocity relationship of 30 men was evaluated by means of linear regression models in the concentric-only and eccentric-concentric bench press throw (BPT) variants performed with a Smith machine. The 2 sessions of each BPT variant were performed within the same week separated by 48–72 hours. The main findings were as follows: (a) the MV showed the strongest linearity of the load-velocity relationship (median r ² = 0.989 for concentric-only BPT and 0.993 for eccentric-concentric BPT), followed by MPV (median r ² = 0.983 for concentric-only BPT and 0.980 for eccentric-concentric BPT), and finally PV (median r ² = 0.974 for concentric-only BPT and 0.969 for eccentric-concentric BPT); (b) the accuracy of the general regression equations to predict relative load (%1RM) from movement velocity was higher for MV (SEE = 3.80–4.76%1RM) than for MPV (SEE = 4.91–5.56%1RM) and PV (SEE = 5.36–5.77%1RM); and (c) the PV showed the lowest within-subjects coefficient of variation (3.50%–3.87%), followed by MV (4.05%–4.93%), and finally MPV (5.11%–6.03%). Taken together, these results suggest that the MV could be the most appropriate variable for monitoring the relative load (%1RM) in the BPT exercise performed in a Smith machine.

Background: Velocity loss (VL) experienced in a set during resistance training is often monitored to control training volume and quantify acute fatigue responses. Accordingly, various VL thresholds are used to prescribe resistance training and target different training adaptations. However, there are inconsistencies in the current body of evidence regarding the magnitude of the acute and chronic responses to the amount of VL experienced during resistance training. Objective: The aim of this systematic review was to (1) evaluate the acute training volume, neuromuscular, metabolic, and perceptual responses to the amount of VL experienced during resistance training; (2) synthesize the available evidence on the chronic effects of different VL thresholds on training adaptations; and (3) provide an overview of the factors that might differentially influence the magnitude of specific acute and chronic responses to VL during resistance training. Methods: This review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Five databases were searched, and studies were included if they were written in English, prescribed resistance training using VL, and evaluated at least one (1) acute training volume, neuromuscular, metabolic, or perceptual response or (2) training adaptation. Risk of bias was assessed using a modified Cochrane Collaboration’s tool for assessing the risk of bias in randomized trials. Multilevel and multivariate meta-regressions were performed where possible. Results: Eighteen acute and 19 longitudinal studies met the inclusion criteria, of which only one had more than one risk of bias item assessed as high risk. Based on the included acute studies, it seems that the number of repetitions per set, blood lactate concentration, and rating of perceived exertion generally increase, while countermovement jump height, running sprint times, and velocity against fixed loads generally decrease as VL increases. However, the magnitude of these effects seems to be influenced, among other factors, by the exercise and load used. Regarding training adaptations, VL experienced during resistance training did not influence muscle strength and endurance gains. Increases in VL were associated with increases in hypertrophy (b = 0.006; 95% confidence interval [CI] 0.001, 0.012), but negatively affected countermovement jump (b = − 0.040; 95% CI − 0.079, − 0.001), sprint (b = 0.001; 95% CI 0.001, 0.002), and velocity against submaximal load performance (b = − 0.018; 95% CI − 0.029, − 0.006). Conclusions: A graded relationship exists between VL experienced during a set and acute training volume, neuromuscular, metabolic, and perceptual responses to resistance training. However, choice of exercise, load, and individual trainee characteristics (e.g., training history) seem to modulate these relationships. The choice of VL threshold does not seem to affect strength and muscle endurance gains whereas higher VL thresholds are superior for enhancing hypertrophy, and lower VL thresholds are superior for jumping, sprinting, and velocity against submaximal loads performance. Clinical Trial Registration: The original protocol was prospectively registered (https://osf.io/q4acs/) with the Open Science Framework.

This study aimed (i) to explore the relationship between vertical (jumping) and horizontal (sprinting) force–velocity–power (FVP) mechanical profiles in a large range of sports and levels of practice, and (ii) to provide a large database to serve as a reference of the FVP profile for all sports and levels tested. A total of 553 participants (333 men, 220 women) from 14 sport disciplines and all levels of practice participated in this study. Participants performed squat jumps (SJ) against multiple external loads (vertical) and linear 30–40 m sprints (horizontal). The vertical and horizontal FVP profile (i.e., theoretical maximal values of force (F₀), velocity (v₀), and power (P_max)) as well as main performance variables (unloaded SJ height in jumping and 20-m sprint time) were measured. Correlations coefficient between the same mechanical variables obtained from the vertical and horizontal modalities ranged from −0.12 to 0.58 for F₀, −0.31 to 0.71 for v₀, −0.10 to 0.67 for P_max, and −0.92 to −0.23 for the performance variables (i.e, SJ height and sprint time). Overall, results showed a decrease in the magnitude of the correlations for higher-level athletes. The low correlations generally observed between jumping and sprinting mechanical outputs suggest that both tasks provide distinctive information regarding the FVP profile of lower-body muscles. Therefore, we recommend the assessment of the FVP profile both in jumping and sprinting to gain a deeper insight into the maximal mechanical capacities of lower-body muscles, especially at high and elite levels.