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.

Agility is a significant determinant of success in soccer; however, studies have rarely presented and evaluated soccer-specific tests of reactive agility (S_RAG) and non-reactive agility (change of direction speed – S_CODS) or their applicability in this sport. The aim of this study was to define the reliability and validity of newly developed tests of the S_RAG and S_CODS to discriminate between the performance levels of junior soccer players. The study consisted of 20 players who were involved at the highest national competitive rank (all males; age: 17.0 ± 0.9 years), divided into three playing positions (defenders, midfielders, and forwards) and two performance levels (U17 and U19). Variables included body mass (BM), body height, body fat percentage, 20-m sprint, squat jump, countermovement jump, reactive-strength-index, unilateral jump, 1RM-back-squat, S_CODS, and three protocols of S_RAG. The reliabilities of the S_RAG and S_CODS were appropriate to high (ICC: 0.70 to 0.92), with the strongest reliability evidenced for the S_CODS. The S_CODS and S_RAG shared 25–40% of the common variance. Playing positions significantly differed in BM (large effect-size differences [ES]; midfielders were lightest) and 1RM-back-squat (large ES; lowest results in midfielders). The performance levels significantly differed in age and experience in soccer; U19 achieved better results in the S_CODS (t-test: 3.61, p < 0.05, large ES) and two S_RAG protocols (t-test: 2.14 and 2.41, p < 0.05, moderate ES). Newly developed tests of soccer-specific agility are applicable to differentiate U17 and U19 players. Coaches who work with young soccer athletes should be informed that the development of soccer-specific CODS and RAG in this age is mostly dependent on training of the specific motor proficiency.

Background: Running biomechanics is considered an important determinant of running economy (RE). However, studies examining associations between running biomechanics and RE report inconsistent findings. Objective: The aim of this systematic review was to determine associations between running biomechanics and RE and explore potential causes of inconsistency. Methods: Three databases were searched and monitored up to April 2023. Observational studies were included if they (i) examined associations between running biomechanics and RE, or (ii) compared running biomechanics between groups differing in RE, or (iii) compared RE between groups differing in running biomechanics during level, constant-speed, and submaximal running in healthy humans (18–65 years). Risk of bias was assessed using a modified tool for observational studies and considered in the results interpretation using GRADE. Meta-analyses were performed when two or more studies reported on the same outcome. Meta-regressions were used to explore heterogeneity with speed, coefficient of variation of height, mass, and age as continuous outcomes, and standardization of running shoes, oxygen versus energetic cost, and correction for resting oxygen or energy cost as categorical outcomes. Results: Fifty-one studies (n = 1115 participants) were included. Most spatiotemporal outcomes showed trivial and non-significant associations with RE: contact time r = − 0.02 (95% confidence interval [CI] − 0.15 to 0.12); flight time r = 0.11 (− 0.09 to 0.32); stride time r = 0.01 (− 0.8 to 0.50); duty factor r = − 0.06 (− 0.18 to 0.06); stride length r = 0.12 (− 0.15 to 0.38), and swing time r = 0.12 (− 0.13 to 0.36). A higher cadence showed a small significant association with a lower oxygen/energy cost (r = − 0.20 [− 0.35 to − 0.05]). A smaller vertical displacement and higher vertical and leg stiffness showed significant moderate associations with lower oxygen/energy cost (r = 0.35, − 0.31, − 0.28, respectively). Ankle, knee, and hip angles at initial contact, midstance or toe-off as well as their range of motion, peak vertical ground reaction force, mechanical work variables, and electromyographic activation were not significantly associated with RE, although potentially relevant trends were observed for some outcomes. Conclusions: Running biomechanics can explain 4–12% of the between-individual variation in RE when considered in isolation, with this magnitude potentially increasing when combining different variables. Implications for athletes, coaches, wearable technology, and researchers are discussed in the review. Protocol registration: https://doi.org/10.17605/OSF.IO/293ND (OpenScience Framework).

Jukic, I, García-Ramos, A, Malecek, J, Omcirk, D, and Tufano, JJ. Validity of load–velocity relationship to predict 1 repetition maximum during deadlifts performed with and without lifting straps: The accuracy of six prediction models. J Strength Cond Res 36(4): 902–910, 2022—This study aimed to compare the accuracy of six 1 repetition maximum (1RM) prediction models during deadlifts performed with (DLw) and without (DLn) lifting straps. In a counterbalanced order, 18 resistance-trained men performed 2 sessions that consisted of an incremental loading test (20-40-60-80-90% of 1RM) followed by 1RM attempts during the DLn (1RM = 162.0 ± 26.9 kg) and DLw (1RM = 179.0 ± 29.9 kg). Predicted 1RMs were calculated by entering both group and individualized mean concentric velocity of the 1RM (V1RM) into an individualized linear and polynomial regression equations, which were derived from the load–velocity relationship of 5 ([20-40-60-80-90% of 1RM], i.e., multiple-point method) or 2 ([40 and 90% of 1RM] i.e., 2-point method) incremental warm-up sets. The predicted 1RMs were deemed highly valid if the following criteria were met: trivial to small effect size, practically perfect r, and low absolute errors (<5 kg). The main findings revealed that although prediction models were more accurate during the DLn than DLw, none of the models provided an accurate estimation of the 1RM during both DLn (r = 0.92–0.98; absolute errors: 6.6–8.1 kg) and DLw (r = 0.80–0.93; absolute errors: 12.4–16.3 kg) according to our criteria. Therefore, these results suggest that the 1RM for both DLn and DLw should not be estimated through the recording of movement velocity if sport professionals are not willing to accept more than 5 kg of absolute errors.