Steve W. Thompson

Despite the popularity of knee sleeves in strength sport participation, little is known about how athletes perceive to benefit from them during training or competition. Therefore, this study investigated current practices and beliefs of knee sleeves employed by competitive and recreational athletes from various strength sports. An open, anonymous survey was distributed via social media and industry experts to a cross-sectional convenience sample of competitive and recreational strength sport athletes. The 19-item survey was distributed between May and July 2025. A total of 225 respondents completed the survey (males =103 [45.8%], females = 122 [54.2%]; overall age = 36.1 ± 11.9 years; competition experience 5.5 ± 4.6 years). Respondents represented a range of strength sports (e.g., powerlifting, weightlifting, strong(wo)man, etc.) at various competitive levels (amateur to Olympic), as well as non-competitive recreational trainees. The most popular material for knee sleeves was neoprene (188; 83.6%), and the majority of respondents reported that their knee sleeves were 7 mm thick (143; 63.6%). More respondents favored manufacturer-recommended fitting knee sleeves 156 (69.3%) than tighter fitting (69; 30.7%). Perceived advantages and benefits were analyzed using content analysis principles, resulting in two categories: psychophysiological and social. Strength athletes use knee sleeves for several reasons, with the most frequently reported perceived benefits being increased stability, joint warmth, a rebound effect, and augmented maximal strength. Additional findings note that several of the perceived benefits have not been empirically substantiated, and further research is required to fully elucidate the potential benefits that knee sleeves have on strength performance.

Background: The aim of this study was to characterise the performance, perceptual, and wellness responses to a barbell back squat overreaching training protocol. Methods: Eight trained male participants (age = 24.6 ± 2.8 years; relative to body mass back squat one repetition maximum (1-RM) = 1.9 ± 0.4; training experience = 7.0 ± 3.2 years) participated in a 5-day squat OR protocol (SqOR), followed by a 14-day taper. SqOR consisted of five sets of barbell back squats using 80% of daily adjusted 1-RM. A 40% velocity loss threshold was used to determine the set end point. For performance, isometric mid-thigh pull (IMTP) peak force (PF), and countermovement jump (CMJ) PF and jump height; for perceptual, perceived recovery scale (PRS); and for wellness, Hooper Wellness Index (HWI), were recorded at baseline, each day of SqOR, and at select intervals during the taper (POST 1 d, 2 d, 7 d, and 14 d). Follow-up back squat 1-RM testing was conducted at POST 7 d and POST 14 d to determine strength-performance changes relative to baseline. Results: Back squat 1-RM increased by 4.8% at POST 7 d and 5.2% at POST 14 d. IMTP PF increased by 10.3% at POST 7 d and 11.4% at POST 14 d relative to the baseline. CMJ PF and jump height decreased during SqOR but returned to baseline by POST 7 d. PRS and HWI worsened during SqOR, with the greatest impairment occurring on day 3 (PRS = −41.5%; HWI = 34.4%), and did not return to baseline until POST 14 d and POST 2 d, respectively. Conclusions: These findings demonstrate that a short-term period of planned OR improves muscular strength performance, but the duration of the taper influences when peak strength improvements are observed.

Velocity-based training (VBT) uses the measurement of velocity to help inform and influence testing, monitoring, programming, autoregulation, and feedback strategies within strength and conditioning (S&C) practice. This review focuses specifically on the definitions and underpinning principles of VBT, as well as load-velocity profiling, while undertaking an in-depth evaluation of the literature and translating this information into practical recommendations for S&C coaches. There is a detailed evaluation of the different ways to construct a load-velocity profile (LVP), providing examples of simplicity, time efficiency, increased accuracy, and optimizing training prescriptions and manipulations. New approaches, such as combining ballistic and nonballistic exercises, and methods of extrapolation have been explored. In addition, this review highlights practical considerations regarding data analysis, specific exercises, statistical modeling, number of loads, and extrapolation methods. A worked example of using LVP data within Excel has also been presented. Finally, there is a focus on 1 repetition maximum prediction, the benefits and challenges of implementing such an approach, and the most appropriate ways of collecting reliable and valid data.

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.

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.

Background: Advances in smartphone technology have facilitated an increase in the number of commercially available smartphone and tablet apps that enable the collection of physiological and biomechanical variables typically monitored in sport and exercise settings. Currently, it is not fully understood whether individuals collect data using mobile devices and tablets, independent of additional hardware, in their practice. Objective: This study aims to explore the use of smartphone and tablet software apps to collect data by individuals working in various sport and exercise settings, such as sports coaching, strength and conditioning, and personal training. Methods: A total of 335 practitioners completed an electronic questionnaire that surveyed their current training practices, with a focus on 2 areas: type of data collection and perceptions of reliability and validity regarding app use. An 18-item questionnaire, using a 5-point Likert scale, evaluated the perception of app use. Results: A total of 204 respondents reported using apps to directly collect data, with most of them (196/335, 58.5%) collecting biomechanical data, and 41.2% (138/335) respondents reported using at least one evidence-based app. A binomial general linear model determined that evidence accessibility (β=.35, 95% CI 0.04-0.67; P=.03) was significantly related to evidence-based app use. Age (β=−.03, 95% CI −0.06 to 0.00; P=.03) had a significant negative effect on evidence-based app use. Conclusions: This study demonstrates that practitioners show a greater preference for using smartphones and tablet devices to collect biomechanical data such as sprint velocity and jump performance variables. When it is easier to access information on the quality of apps, practitioners are more likely to use evidence-based apps. App developers should seek independent research to validate their apps. In addition, app developers should seek to provide clear signposting to the scientific support of their software in alternative ways.

This study determined the effects of a single sled push at different loads on sprint performance in competitive male soccer players. Twenty male competitive outfield soccer players (age 19.6±1.3y, body mass 73.6±8.2kg) were split into experimental (n=10) and control groups. In the experimental group, 20m linear sprint time was measured immediately before and 5, 6 and 7minutes after the sled push with either 50 or 100% body mass. The control group performed the 20m sprints only. A repeated measures ANOVA comparing control and experimental groups revealed no effects of time, group or time by group interaction for either experimental condition (all P>0.05). The repeated measures ANOVA compared the experimental conditions revealed effects of time (P=0.034) and group (P=0.002), but not time by group (P>0.05). The effects sizes demonstrated within group effects on sprint time that were small to moderate (-0.26 to 0.71) and trivial to small (-0.31 to 0.09) for the 50% and 100% body mass condition, respectively. These findings demonstrate that a sled push has no significant effect on 20 m sprint time in competitive footballers. If coaches continue to prescribed a sled push before sprinting, a single 15 m push with 50% body mass could have positive benefits.

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.