The aim of this study was to investigate the differences and long-term reliability in perceptual, metabolic, and neuromuscular responses to velocity loss resistance training protocols. Using a repeated, counterbalanced, crossover design, twelve team-sport athletes completed 5-sets of barbell back-squats at a load corresponding to a mean concentric velocity of ~0.70 m·s⁻¹. On different days, repetitions were performed until a 10%, 20% or 30% velocity loss was attained, with outcome measures collected after each set. Sessions were repeated after four-weeks. There were substantial between-protocol differences in post-set differential ratings of perceived exertion (dRPE, i.e., breathlessness and leg muscles, AU) and blood lactate concentration (B[La], mmol·L⁻¹), such that 30%>20%>10% by small to large magnitudes. Differences in post-set countermovement jump (CMJ) variables were small for most variables, such that 30%<20%<10%. Standard deviations representing four-week variability of post-set responses to each protocol were: dRPE, 8–11; B[La], 0.8–1.0; CMJ height, 1.6–2.0; CMJ PPO, 1.0–1.8; CMJ PCV, 0.04–0.06; CMJ 100ms-Impulse, 5.7–11.9. Velocity loss thresholds control the magnitude of perceptual, metabolic, and neuromuscular responses to resistance training. For practitioners wanting to reliably prescribe training that can induce a given perceptual, metabolic, or neuromuscular response, it is strongly advised that velocity-based thresholds are implemented.

Purpose: Prescribing resistance training using velocity loss thresholds can enhance exercise quality by mitigating neuromuscular fatigue. As little is known regarding performance during these protocols, we aimed to assess the effects of 10%, 20%, and 30% velocity loss thresholds on kinetic, kinematic, and repetition characteristics in the free-weight back squat. Methods: Using a randomized crossover design, 16 resistance-trained men were recruited to complete 5 sets of the barbell back squat. Lifting load corresponded to a mean concentric velocity (MV) of ∼0.70 m·s⁻¹ (115 [22] kg). Repetitions were performed until a 10%, 20%, or 30% MV loss was attained. Results: Set MV and power output were substantially higher in the 10% protocol (0.66 m·s⁻¹ and 1341 W, respectively), followed by the 20% (0.62 m·s⁻¹ and 1246 W) and 30% protocols (0.59 m·s⁻¹ and 1179 W). There were no substantial changes in MV (−0.01 to −0.02 m·s⁻¹) or power output (−14 to −55 W) across the 5 sets for all protocols, and individual differences in these changes were typically trivial to small. Mean set repetitions were substantially higher in the 30% protocol (7.8), followed by the 20% (6.4) and 10% protocols (4.2). There were small to moderate reductions in repetitions across the 5 sets during all protocols (−39%, −31%, −19%, respectively), and individual differences in these changes were small to very large. Conclusions: Velocity training prescription maintains kinetic and kinematic output across multiple sets of the back squat, with repetition ranges being highly variable. Our findings, therefore, challenge traditional resistance training paradigms (repetition based) and add support to a velocity-based approach.

Student-athletes are subject to significant demands due to their concurrent sporting and academic commitments, which may affect their sleep. This study aimed to compare the self-reported sleep quality, quantity, and intraindividual variability (IIV) of students and student-athletes through an online survey. Student-athletes will have a poorer sleep quality and quantity and experience more IIV. Case-control study. Level 4. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while sleep quantity and IIV were assessed using the Consensus Sleep Diary. Initially, the PSQI and additional questions regarding sport participation habits were completed by 138 participants (65 students, 73 student-athletes). From within this sample, 44 participants were recruited to complete the sleep diary for a period of 14 days. The mean PSQI score was 6.89 ± 3.03, with 65% of the sample identified as poor sleepers, but no difference was observed between students and student-athletes. Analysis of sleep patterns showed only possibly to likely small differences in sleep schedule, sleep onset latency, and subjective sleep quality between groups. IIV analysis showed likely moderate to possibly small differences between groups, suggesting more variable sleep patterns among student-athletes. This study highlights that sleep issues are prevalent within the university student population and that student-athletes may be at greater risk due to more variable sleep patterns. University coaches should consider these results to optimize sleep habits of their student-athletes.

Weakley, J, Fernández-Valdés, B, Thomas, L, Ramirez-Lopez, C, and Jones, B. Criterion validity of force and power outputs for a commonly used flywheel resistance training device and bluetooth app. J Strength Cond Res 33(5): 1180–1184, 2019—Flywheels are a resistance training device that can increase lean body mass, strength, and power. However, because of their unique design and the inertia from the concentric portion directly relating to the force that is applied during the eccentric portion, monitoring the training stimulus can be difficult. Consequently, the aim of this study was to assess the validity of the kMeter app for quantifying force and power at a range of different isoinertial loads from a flywheel training device when compared against a criterion measure. Eleven subjects volunteered to take part in this study, with subjects completing between 5 and 35 repetitions of the harness squat with 0.05, 0.10, 0.15 kg·m⁻² isoinertial load. A synchronized dual force plate and tricamera optoelectronic setup was used as the criterion measure to calculate force and power output, while the kMeter app was used as the practical measure. Very large to nearly perfect relationships were observed between the 2 measures, with trivial to moderate bias reported. In addition, typical error of the estimate (TEE) was found to be <10% at all isoinertial loads. These findings suggest that the kMeter app, when used in conjunction with the kBox flywheel device, demonstrates acceptable levels of validity. However, because of the TEE, the kMeter app may not be able to accurately detect small differences and therefore be suitable for research purposes. These findings suggest that the kMeter app is an acceptable method of monitoring flywheel resistance training. Furthermore, it is advised that practitioners use mean power rather than mean force.

Scientific innovation can be crucial for maximising athletes’ health and performance, which has resulted in a growing interest in sports science and medicine (SSM). Innovation through research helps develop training models, medical treatments and recovery methods3 but the practical relevance of some fundamental studies is often hindered by their poor ecological validity. Tightly controlled conditions can create an artificial sample of athletes and circumstances which are not truly representative of daily practice, allowing for the gap between research and practice to persist. Applied research aims to bridge that gap by developing ecologically valid evidence and can be further enhanced by embedding ‘off-field’ …

Exercise velocity and relative velocity loss thresholds (VLTs) are commonly used in velocity-based resistance training. This study aims to quantify the between-day reliability of 10%, 20%, and 30% VLTs on kinetic and kinematic outputs, changes in external load, and repetition characteristics in well-trained athletes. Using a repeated, counter-balanced crossover design, twelve semi-professional athletes completed five sets of the back squat with an external load corresponding to a mean concentric velocity of ~0.70 m·s⁻¹ and a VLT applied. The testing sessions were repeated after four weeks of unstructured training to assess the long-term reliability of each VLT. A coefficient of variation (CV) 10% (CV: 11.14–14.92%). Alternatively, the repetitions completed within each set showed large variation (CV: 18.92–67.49%). These findings demonstrate that by utilizing VLTs, kinetic and kinematic outputs can be prescribed and replicated across training mesocycles. Thus, for practitioners wishing to reliably control the kinetic and kinematic stimulus that is being applied to their athletes, it is advised that a velocity-based approach is used.